Singapore is becoming New Patenting Hub for Innovations
Alibaba bags a patent titled "a method for evaluating performance of an object detection model"
Emergence of Blockchain based innovations in fintech, cybersecurity, and robotics is new ball game in 2019. Icing the technology patent drafting with big data analysis based on AI and ML adds the novelty and inventive step criteria of tackling patentability of innovations globally.
One can infer in current schemes of global dialogue more Intellectual property agreements are going to be inked and signed between growing economies. Role play of China and India has to be viewed and reviewed taking into consideration a number of parameters as the growing start-up culture is gaining momentum to create level playing field.
Tagging Along with Concept of Innovation Hubs
On global platform the emphasis is being emphasised on inclusion of Innovation hubs. True definition of Innovation hub encompass creating asset light models starred with portfolio of Intellectual Property Right (IPR) Assets. Economic growth of any country is measured in gaining GDP momentum. Historical facts state that Intellectual Property generated by Apple, Amazon, Alibaba, Google and Microsoft are ruling the global charts which is creating job prospects at the same time for right talent.
3I's of Industry 4.0 Era. Innovation, Intellectual Property and Investment. Recouping energy to build that ecosystem
#Blockchain #PeertoPeerpatent #AI #ML #MLlife #destinationlife #Acceleratelife #Innovationcoach #Designlife
LinkedIN: https://www.linkedin.com/in/patentindiaiplawpritykhastgir/
About Patent Lawyer in India
International Speaker speaking on Strategic Aspects of amalgamating technology, law and business in Industry 4 Spectrum Era. Active speaker at tech global conferences and actively participate in ITU Regulatory Workshops & Initiatives on SDGs. Problem SOLVER & Business Strategist with 14+ yrs exp. Seasoned Patent Strategist with expertise in IP portfolio research, cross-border tech transactions, licensing agreements, product clearance, FTO opinion, patent infringement & invalidity, IPR R&D Consultancy.
Core practice: IP harvesting, patent drafting, patent searches, PCT National phase patent prosecution in India (drafting office action responses for USPTO, EPO, UKIPO) & International trademark registration in India under Madrid Protocol.
Technical expertise: Bitcoin, Blockchain, Bigdata, Internet of Things (IoT), AI, ML, Software, Hardware, Therapeutic biologics, Agri biotech, Biosimilar drug, Plant Variety, Mechanical inventions, Electrical, Medical devices & Healthcare.
Go-to patent strategist for all time zones, be it new product launch in Asia, IP landscape across EU, freedom-to-operate analysis in Japan or patent invalidation for litigation in US.
Senior executive profile with featured publications: BBC World, Nature Group (Nature Reviews Drug Discovery), BusinessWorld, BioSpectrum Asia etc.
This presentation is an introduction to the new GBIF Portal API. It explores the possibilities of using it to connect GBIF mediated data to existing systems.
The Augmented Payment System Patent for ShopSavvyAlexander Muse
Systems and methods for applying a referral credit to an entity account based on a geographic location of a computing device
Disclosed herein are systems and methods for applying a referral credit to an entity based on a geographic location of a computing device. According to an aspect, a method may include receiving a product identifier and a geographic location identifier of a computing device. The method may also include determining an interface with an online retailer server based on the product identifier. The method may also include applying a referral credit to an entity account associated with the geographic location identifier in response to the determined interface.
UCD14 Workshop - John Knight - Radical Innovation UCD UK Ltd
Radical Innovation was presented at UCD14 by Riccie Janus and Leon Bovett of Accenture.
Approaches and methods to innovating at the level of strategic and detailed design and delivery of digital products and services.
Goldman Sachs defines IoT (internet of things) as the third wave of internet revolution: by connecting to the internet billions of devices, IoT opens up a host of new business opportunities and challenges. The basic building blocks of the IoT are devices that can sense/recognize their surrounding environments and communicate with other devices, connecting/communicating network medium/infrastructure that can interconnect devices and connect devices to the internet, back-end IT systems that can process information (data) obtained by the IoT devices (e.g. cloud computing/big data analytics) and provide the value added services exploiting the information. Therefore, a part or whole of data aggregation, data transfer, data correlation, data analysis and services based on the data are the essential elements of the IoT inventions, and thus, the elements of the IoT patent claims. Consequently, many of IoT patents can be identified as abstract ideas because they are the certain methods of organizing human activities/mental process or fundamental economic practices or mathematical relationships/formulas unless the IoT patent claims are drafted carefully to pass the post-Alice 101 patent eligibility test.
Systems and methods for electronic communicationsTal Lavian Ph.D.
Embodiments of the invention provide a system for enhancing user interaction with the Internet of Things. The system includes a processor, and a memory coupled to the processor. The memory includes a database having one or more options corresponding to each of the Internet of Things. The memory further includes instructions executable by the processor to share at least one of the one or more options with one or more users of the things. Further, the instructions receive information corresponding to selection of the at least one option by the one or more users. Additionally, the instructions update the database based on the selection of the at least one option by the one or more users. Further, a device for enhancing interaction with the things is also disclosed.
This presentation is an introduction to the new GBIF Portal API. It explores the possibilities of using it to connect GBIF mediated data to existing systems.
The Augmented Payment System Patent for ShopSavvyAlexander Muse
Systems and methods for applying a referral credit to an entity account based on a geographic location of a computing device
Disclosed herein are systems and methods for applying a referral credit to an entity based on a geographic location of a computing device. According to an aspect, a method may include receiving a product identifier and a geographic location identifier of a computing device. The method may also include determining an interface with an online retailer server based on the product identifier. The method may also include applying a referral credit to an entity account associated with the geographic location identifier in response to the determined interface.
UCD14 Workshop - John Knight - Radical Innovation UCD UK Ltd
Radical Innovation was presented at UCD14 by Riccie Janus and Leon Bovett of Accenture.
Approaches and methods to innovating at the level of strategic and detailed design and delivery of digital products and services.
Goldman Sachs defines IoT (internet of things) as the third wave of internet revolution: by connecting to the internet billions of devices, IoT opens up a host of new business opportunities and challenges. The basic building blocks of the IoT are devices that can sense/recognize their surrounding environments and communicate with other devices, connecting/communicating network medium/infrastructure that can interconnect devices and connect devices to the internet, back-end IT systems that can process information (data) obtained by the IoT devices (e.g. cloud computing/big data analytics) and provide the value added services exploiting the information. Therefore, a part or whole of data aggregation, data transfer, data correlation, data analysis and services based on the data are the essential elements of the IoT inventions, and thus, the elements of the IoT patent claims. Consequently, many of IoT patents can be identified as abstract ideas because they are the certain methods of organizing human activities/mental process or fundamental economic practices or mathematical relationships/formulas unless the IoT patent claims are drafted carefully to pass the post-Alice 101 patent eligibility test.
Systems and methods for electronic communicationsTal Lavian Ph.D.
Embodiments of the invention provide a system for enhancing user interaction with the Internet of Things. The system includes a processor, and a memory coupled to the processor. The memory includes a database having one or more options corresponding to each of the Internet of Things. The memory further includes instructions executable by the processor to share at least one of the one or more options with one or more users of the things. Further, the instructions receive information corresponding to selection of the at least one option by the one or more users. Additionally, the instructions update the database based on the selection of the at least one option by the one or more users. Further, a device for enhancing interaction with the things is also disclosed.
Solution to Help Companies Patent their Inventions, License Technologies, and...Dr. Haxel Consult
With more than one million patent applications filed every year, searching for prior-art has become a daunting task. This constitutes an important challenge for technology companies and their legal representatives, as the value of its assets depends on their ability to demonstrate the novelty of their inventive efforts. Failing to identify prior-art makes it difficult for companies to patent their inventions and exposes them to costly litigation. The breadth and complexity of the IP space makes it all but impossible to search for prior-art without the help of machine-based intelligence that identifies relationships between a new invention and those described in millions of patent documents. Existing solutions fail to take into account that companies often use (and are strongly motivated to) different words to describe similar inventions. This makes search efforts based on the similarities between words prone to miss relevant prior-art. What is more, existing techniques do not account for temporal changes in the terminology used to describe particular inventions. This is not a trivial omission as, by definition, the search for prior-art requires comparing an invention with other produced at different points in time. AIP developed an advance search engine that addresses these shortcomings. AIP uses thousands of examination reports to learn about textual relationship that describe scientific concepts and applies this learning to compare inventions. That is, instead of comparing document on the basis that these contain similar words, our algorithm compares document on the basis that these describe similar ideas. We present a number of cases were AIP’s solution helped companies patent their inventions, license technologies, and address litigation challenges.
Network resources allocated for particular application traffic are aware of the characteristics of L4+ content to be transmitted. One embodiment of the invention realizes network resource allocation in terms of three intelligent modules, gateway, provisioning and classification. A gateway module exerts network control functions in response to application requests for network resources. The network control functions include traffic path setup, bandwidth allocation and so on. Characteristics of the content are also specified in the received application network resource requests. Under request of the gateway module, a provisioning module allocates network resources such as bandwidth in optical networks and edge devices as well. An optical network resource allocation leads to a provisioning optical route. Under request of the gateway module, a classification module differentiates applications traffic according to content specifications, and thus creates and applies content-aware rule data for edge devices to forward content-specified traffic towards respective provisioning optical routes.
https://www.google.com/patents/US7580349?dq=US+7580349&hl=en&sa=X&ei=DXVSVID-EoTc8AW-_IDwBw&ved=0CB8Q6AEwAA
Angelo van der Sijpt - How well do you know your network stack? - Codemotion ...Codemotion
Networking is a core part of computing in the digital world we inhabit. But, how well do you know how it works? Do you understand all the moving parts of the OSI stack inside your computer, and how the network is actually put together? How can this ever work? This guided safari of layers, standards, protocols, and happenstance will bring us close to the copper wire, and up through the layers of CDMA/CD, ARP, routing and HTTP. We will make a few excursions through patchworks that still work forty years later, and cleverly designed mechanisms that show that simplicity is the only way to last.
Multilayer Collection Selection and Search of Topically Organized PatentsMike Salampasis
We present a federated patent search system that explores three issues: (a) topical organization of patents based on their IPC, (b) collection selection of topically organised patent collections and (c) integration of collection selection tools to patent search systems.
Technical workshop, presented by Gerald Martinez, Product Development Manager at Apex Tech, that aim and dive through interactive demonstrations of the building blocks of a Web 2.0 application. Explaining the use of the APEX RESTful API using C# and JQuery.
Presentation about the collaboration between ADAPT and the Ordnance Survey Ireland at Linked Data Seminar -- Culture, Base Registries & Visualisations held in Amsterdam, The Netherlands on the 2nd of December 2016
Outlier Detection in Secure Shell Honeypot using Particle Swarm Optimization ...Eswar Publications
With trends and technologies, developments and deployments, network communication has become vital and in evitable with human beings. On the other side, a network communication without security is powerless. There are so many technologies and developments have been rooted to provide a secure and an efficient means of communication through network. Parallel to this, network threats and attacks are also trendy and much
technologized. In order to detect such a kind of threats and attacks, this research work proposes honeypot technology. Honeypot is a supplemented active defense system for network security. It traps attacks, records intrusion information about tools and activities of the hacking process, and prevents attacks outbound from the compromised system. This research work implements a kind of honeypot called Secure Shell (SSH) honeypot.
SSH honeypot is a secure communication channel which allows users to remotely control computer systems. With the implementation of SSH honeypot, this research work collects the incoming and outgoing traffic data in a network. The collected traffic data can be then analyzed to detect outliers in order to find the abnormal or malicious traffic. This research work detects outliers from the collected SSH honeypot data using Particle Swarm Optimization technique which belongs to the category of cluster-based outlier detection method. With
experiments and results, Particle Swarm Optimization shows best results in detecting outliers and has best cost function when compared to other cluster-based algorithms like Genetic Algorithm and Differential Evolution algorithm.
https://www.blockchainailawyer.com/iprs Oracle Database is a trademark of Oracle Corporation, which is a leading provider of database software and cloud services. Oracle Database trademark applications are the legal documents that seek to register and protect the Oracle Database brand name and logo in various jurisdictions around the world.
Migrating Oracle Database trademark applications to cloud infrastructure means moving the data and processes related to these applications from on-premises servers or other cloud platforms to Oracle Cloud Infrastructure (OCI), which is Oracle’s next-generation cloud platform that offers high performance, security, scalability, and cost-efficiency for various workloads.
There are several advantages of migrating Oracle Database trademark applications to cloud infrastructure, such as:
Reducing the operational and maintenance costs of managing on-premises servers or other cloud platforms
Leveraging the advanced features and services of OCI, such as autonomous database, data management, analytics, integration, security, and identity
Enhancing the availability, reliability, and performance of the trademark applications and data
Simplifying the compliance and governance of the trademark applications and data across different regions and jurisdictions
Accelerating the innovation and development of new trademark applications and services
There are different methods and tools for migrating Oracle Database trademark applications to cloud infrastructure, depending on the source and target environments, the size and complexity of the data, and the migration objectives and requirements. Some of the common methods and tools are:
Oracle Data Pump: A utility that enables the export and import of data and metadata between Oracle databases, either on-premises or on OCI
Oracle GoldenGate: A software solution that enables the replication and synchronization of data and transactions across heterogeneous databases, either on-premises or on OCI
Oracle Zero Downtime Migration: A tool that automates the migration of Oracle databases from on-premises or other cloud platforms to OCI, with minimal or no downtime
Oracle Cloud Infrastructure Database Migration: A fully-managed service that provides a high-performing, self-service experience for migrating Oracle databases from on-premises, Oracle Cloud, or Amazon RDS to OCI
For more information about migrating Oracle Database trademark applications to cloud infrastructure, you can refer to the following resources:
Overview of Oracle Cloud Infrastructure Database Migration
About the Advantages of Migrating Custom Applications to Oracle Cloud
Learn about migrating Oracle Database Appliance workloads to the cloud
Learn About Migrating Application Data to the Cloud
METHOD AND SYSTEM FOR PREDICTING OPTIMAL LOAD FOR WHICH THE YIELD IS
MAXIMUM BY USING MULTIPLE INPUT ELECTROLYZER PARAMETERS filed by RIPIK TECHNOLOGY PRIVATE LIMITED
This patent describes a system to optimize operations in manufacturing facilities that use electrolyzers. Electrolyzers split water into hydrogen and oxygen using electricity. Their electrical load levels impact costs and efficiency.
The present invention uses AI and machine learning to predict the best way to allocate load across multiple electrolyzers. This maximizes total yield for the manufacturing plant while minimizing electricity costs.
It takes into account equipment factors like the capacity of each electrolyzer. It also considers constraints like raw material supply and product storage limits. The system models chlorine evacuation too since that can hinder caustic soda output.
The algorithms are capable of processing the many complex variables at play. No easy spreadsheet could accomplish this modeling. A type of mathematical optimization called "swarm optimization" is applied. Examples are genetic algorithms and particle swarm optimization.
The AI system keeps adapting its load recommendations based on data coming into its monitoring dashboard. Operators can customize certain parameters but the software is configuring most of the decision logic automatically.
The inventors claim results show their AI optimizer significantly cutting power consumption and costs versus alternatives. Smart coordination of multiple electrolyzers is complex. This technology handles the data analysis to efficiently divide load distribution.
In essence, the patent discloses an intelligent electrolyzer load prediction platform. It leverages AI to boost manufacturing performance while reducing electricity usage. Company managers can see optimized operations guidance on easy dashboard interfaces. It aims to save power, costs and effort through algorithmic process coordination.
More Related Content
Similar to Alibaba Bags Patent in Singapore in Three Months for Computational AI Innovation #AIpatent
Solution to Help Companies Patent their Inventions, License Technologies, and...Dr. Haxel Consult
With more than one million patent applications filed every year, searching for prior-art has become a daunting task. This constitutes an important challenge for technology companies and their legal representatives, as the value of its assets depends on their ability to demonstrate the novelty of their inventive efforts. Failing to identify prior-art makes it difficult for companies to patent their inventions and exposes them to costly litigation. The breadth and complexity of the IP space makes it all but impossible to search for prior-art without the help of machine-based intelligence that identifies relationships between a new invention and those described in millions of patent documents. Existing solutions fail to take into account that companies often use (and are strongly motivated to) different words to describe similar inventions. This makes search efforts based on the similarities between words prone to miss relevant prior-art. What is more, existing techniques do not account for temporal changes in the terminology used to describe particular inventions. This is not a trivial omission as, by definition, the search for prior-art requires comparing an invention with other produced at different points in time. AIP developed an advance search engine that addresses these shortcomings. AIP uses thousands of examination reports to learn about textual relationship that describe scientific concepts and applies this learning to compare inventions. That is, instead of comparing document on the basis that these contain similar words, our algorithm compares document on the basis that these describe similar ideas. We present a number of cases were AIP’s solution helped companies patent their inventions, license technologies, and address litigation challenges.
Network resources allocated for particular application traffic are aware of the characteristics of L4+ content to be transmitted. One embodiment of the invention realizes network resource allocation in terms of three intelligent modules, gateway, provisioning and classification. A gateway module exerts network control functions in response to application requests for network resources. The network control functions include traffic path setup, bandwidth allocation and so on. Characteristics of the content are also specified in the received application network resource requests. Under request of the gateway module, a provisioning module allocates network resources such as bandwidth in optical networks and edge devices as well. An optical network resource allocation leads to a provisioning optical route. Under request of the gateway module, a classification module differentiates applications traffic according to content specifications, and thus creates and applies content-aware rule data for edge devices to forward content-specified traffic towards respective provisioning optical routes.
https://www.google.com/patents/US7580349?dq=US+7580349&hl=en&sa=X&ei=DXVSVID-EoTc8AW-_IDwBw&ved=0CB8Q6AEwAA
Angelo van der Sijpt - How well do you know your network stack? - Codemotion ...Codemotion
Networking is a core part of computing in the digital world we inhabit. But, how well do you know how it works? Do you understand all the moving parts of the OSI stack inside your computer, and how the network is actually put together? How can this ever work? This guided safari of layers, standards, protocols, and happenstance will bring us close to the copper wire, and up through the layers of CDMA/CD, ARP, routing and HTTP. We will make a few excursions through patchworks that still work forty years later, and cleverly designed mechanisms that show that simplicity is the only way to last.
Multilayer Collection Selection and Search of Topically Organized PatentsMike Salampasis
We present a federated patent search system that explores three issues: (a) topical organization of patents based on their IPC, (b) collection selection of topically organised patent collections and (c) integration of collection selection tools to patent search systems.
Technical workshop, presented by Gerald Martinez, Product Development Manager at Apex Tech, that aim and dive through interactive demonstrations of the building blocks of a Web 2.0 application. Explaining the use of the APEX RESTful API using C# and JQuery.
Presentation about the collaboration between ADAPT and the Ordnance Survey Ireland at Linked Data Seminar -- Culture, Base Registries & Visualisations held in Amsterdam, The Netherlands on the 2nd of December 2016
Outlier Detection in Secure Shell Honeypot using Particle Swarm Optimization ...Eswar Publications
With trends and technologies, developments and deployments, network communication has become vital and in evitable with human beings. On the other side, a network communication without security is powerless. There are so many technologies and developments have been rooted to provide a secure and an efficient means of communication through network. Parallel to this, network threats and attacks are also trendy and much
technologized. In order to detect such a kind of threats and attacks, this research work proposes honeypot technology. Honeypot is a supplemented active defense system for network security. It traps attacks, records intrusion information about tools and activities of the hacking process, and prevents attacks outbound from the compromised system. This research work implements a kind of honeypot called Secure Shell (SSH) honeypot.
SSH honeypot is a secure communication channel which allows users to remotely control computer systems. With the implementation of SSH honeypot, this research work collects the incoming and outgoing traffic data in a network. The collected traffic data can be then analyzed to detect outliers in order to find the abnormal or malicious traffic. This research work detects outliers from the collected SSH honeypot data using Particle Swarm Optimization technique which belongs to the category of cluster-based outlier detection method. With
experiments and results, Particle Swarm Optimization shows best results in detecting outliers and has best cost function when compared to other cluster-based algorithms like Genetic Algorithm and Differential Evolution algorithm.
https://www.blockchainailawyer.com/iprs Oracle Database is a trademark of Oracle Corporation, which is a leading provider of database software and cloud services. Oracle Database trademark applications are the legal documents that seek to register and protect the Oracle Database brand name and logo in various jurisdictions around the world.
Migrating Oracle Database trademark applications to cloud infrastructure means moving the data and processes related to these applications from on-premises servers or other cloud platforms to Oracle Cloud Infrastructure (OCI), which is Oracle’s next-generation cloud platform that offers high performance, security, scalability, and cost-efficiency for various workloads.
There are several advantages of migrating Oracle Database trademark applications to cloud infrastructure, such as:
Reducing the operational and maintenance costs of managing on-premises servers or other cloud platforms
Leveraging the advanced features and services of OCI, such as autonomous database, data management, analytics, integration, security, and identity
Enhancing the availability, reliability, and performance of the trademark applications and data
Simplifying the compliance and governance of the trademark applications and data across different regions and jurisdictions
Accelerating the innovation and development of new trademark applications and services
There are different methods and tools for migrating Oracle Database trademark applications to cloud infrastructure, depending on the source and target environments, the size and complexity of the data, and the migration objectives and requirements. Some of the common methods and tools are:
Oracle Data Pump: A utility that enables the export and import of data and metadata between Oracle databases, either on-premises or on OCI
Oracle GoldenGate: A software solution that enables the replication and synchronization of data and transactions across heterogeneous databases, either on-premises or on OCI
Oracle Zero Downtime Migration: A tool that automates the migration of Oracle databases from on-premises or other cloud platforms to OCI, with minimal or no downtime
Oracle Cloud Infrastructure Database Migration: A fully-managed service that provides a high-performing, self-service experience for migrating Oracle databases from on-premises, Oracle Cloud, or Amazon RDS to OCI
For more information about migrating Oracle Database trademark applications to cloud infrastructure, you can refer to the following resources:
Overview of Oracle Cloud Infrastructure Database Migration
About the Advantages of Migrating Custom Applications to Oracle Cloud
Learn about migrating Oracle Database Appliance workloads to the cloud
Learn About Migrating Application Data to the Cloud
METHOD AND SYSTEM FOR PREDICTING OPTIMAL LOAD FOR WHICH THE YIELD IS
MAXIMUM BY USING MULTIPLE INPUT ELECTROLYZER PARAMETERS filed by RIPIK TECHNOLOGY PRIVATE LIMITED
This patent describes a system to optimize operations in manufacturing facilities that use electrolyzers. Electrolyzers split water into hydrogen and oxygen using electricity. Their electrical load levels impact costs and efficiency.
The present invention uses AI and machine learning to predict the best way to allocate load across multiple electrolyzers. This maximizes total yield for the manufacturing plant while minimizing electricity costs.
It takes into account equipment factors like the capacity of each electrolyzer. It also considers constraints like raw material supply and product storage limits. The system models chlorine evacuation too since that can hinder caustic soda output.
The algorithms are capable of processing the many complex variables at play. No easy spreadsheet could accomplish this modeling. A type of mathematical optimization called "swarm optimization" is applied. Examples are genetic algorithms and particle swarm optimization.
The AI system keeps adapting its load recommendations based on data coming into its monitoring dashboard. Operators can customize certain parameters but the software is configuring most of the decision logic automatically.
The inventors claim results show their AI optimizer significantly cutting power consumption and costs versus alternatives. Smart coordination of multiple electrolyzers is complex. This technology handles the data analysis to efficiently divide load distribution.
In essence, the patent discloses an intelligent electrolyzer load prediction platform. It leverages AI to boost manufacturing performance while reducing electricity usage. Company managers can see optimized operations guidance on easy dashboard interfaces. It aims to save power, costs and effort through algorithmic process coordination.
The U.S. Food and Drug Administration (USFDA) regulates the use of food additives in the United States. Specifically, food additives are subject to regulation under the Federal Food, Drug, and Cosmetic Act (FD&C Act) and the regulations in Title 21 of the Code of Federal Regulations (21 CFR). These regulations define the conditions under which food additives, including acidity regulators like Orthophosphoric acid (Phosphoric Acid), can be used in food and beverages.
The specific regulations and allowable levels of Orthophosphoric acid in food and beverages, including gold drinks, can be found in the Code of Federal Regulations (21 CFR) Title 21, Part 182, which deals with substances generally recognized as safe (GRAS) for use in food. Orthophosphoric acid is covered in the section "21 CFR 182.1073 - Phosphoric acid" and its use in various food categories is outlined in this regulation.
For precise information on the allowable levels of Orthophosphoric acid in gold drinks or any other food and beverage products in the United States, it is advisable to refer to the specific regulations provided in 21 CFR 182.1073 and consult with the U.S. Food and Drug Administration (USFDA) or their official resources to ensure compliance with the most up-to-date regulations.
https://sciwri.club/archives/4288
Prity Khastgir is a techno-savvy patent attorney in India with 12 yrs of experience working with clients across the globe. Her areas of expertise are IP portfolio research, cross-border technology transactions, licensing agreements, product clearance, freedom-to-operate, patent infringement & invalidity analysis, research & opinions. Currently, she helps startups to raise funds, assists foreign companies to find right business partners in India. She also assists enterprises to enter and find the right angels, and VCs in Malaysia, Singapore, US, UK, Japan and India. Here, she answers questions about IP career prospects in India, in her Face-to-face interaction with Reetu Mehta.
Is Intellectual Property Rights (IPR) a good career option in India?
Before answering this question, I ask myself why I do what I do every morning. Am I passionate about my day? IPR is a fascinating field and apt for people who wants answers to questions related to what, why, when and how. IPR is a field of law protecting creations of mind in form of ideas, inventions, brands, trademarks, copyrights, software codes, industrial design registration and trade secrets. However, if research is not your cup of tea then please do not enter this field. You have to passionate about innovations. Many people enter this field because it is a high paying job in the long run compared to normal 9-5 jobs in India. However, not everyone will be able to sustain in this field in the long run. Therefore, be wise before entering this field. You should be fascinated by technologies around you, have a problem solving approach, and be compassionate towards your peers.
How can a life science PhD begin with career in IPR?
Life science is study of life processes. The scope of PhD in IPR is better as the person is familiar with research. The best way to move forward is to do online IPR courses and get an overview of IPR. One should have interest and passion to solve the queries related to the intellectual creations of mind. One should have problem solving approach as client is looking for solutions.
What are the required skill sets?
First and foremost, the person should have technology and legal acumen to understand innovations and have a research capability to work at least for 10-12 hours. Thinking out of the box to solve a research project is the key to assisting inventors to protect their inventions.
How does a career in IP evolve/grow in India?
The individuals I have come across are passionate about protecting innovations. To evolve in this field you have to be technology savvy. The Intellectual Property law has evolved a lot in last 10 years. Now a trademark can be registered in less than one month in India with proper documentation. The Indian government is giving a number of grants to startups in India to grow by leap and bounds.
What is the kind of work one is expected to do and what are the skill sets one can acquire over the course?
Trade Secret as Intellectual Property Strategic
Tool in Industry 4.0
Prity Khastgir
TCIS India, Level-5, Caddie Commercial Tower, Novotel Hotel, Hospitality District Aerocity,
IGI Airport, New Delhi 110037 India
khasip@khastgir.com
Abstract -- In covid situation, most businesses have reviewed
their existing contracts to understand scope of parties involved
and thereby Intellectual Property clauses and sub-clauses of any
contract or agreement become very important. Private blockchain
implementation in the current scenario is key to the maze to
protect trade secrets in a confidential manner.
Progressive IP strategy and partnering with right partners to
create and maintain sustainable development positions in the
market to serve customers and catering to their needs is the need
of the hour. Proactive strategic signed contracts and agreements
should be mandatory for smooth functioning of any start-up or
enterprise. Negotiation, Mediation and Arbitration are like trinity
in law to resolve any arising conflict.
Keywords: Intellectual Property, Trade secrets protection, Blockchain
implementation, Industry 4.0, Public domain
I. INTRODUCTION
EVERY technology launch contributes to macroeconomic
growth of the country. Contracts and agreements drafted around
technology licensing or assignments play a pivotal role as to
how developed innovation would be deployed in different
verticals. Over the last one decade, we have witnessed a very
strong nonlinear curve and especially with what forms part of
terms and conditions to exert control over under applicable
contract law while collaborating with different parties to
manage successful ventures in Industry 4.0 operating in
multiple jurisdictions and protecting trade secrets.
Primarily, contracts and proper agreements in place sets the
ambit right to protect trade secrets of technology driven startup ventures. Understanding Intellectual property landscape and
conducting IP due-diligence is very important and is essentially
a first step to understand short-term and long-term goals to
prepare rock solid IP strategy with a holistic viewpoint.
Intellect is the creation of the mind and protecting intellectual
capacity by utilizing different IPRs is key to deliver market cap
results. In covid situation, most businesses have reviewed their
existing contracts to understand scope of parties involved and
thereby Intellectual Property clauses and sub-clauses of any
t's quite unfair because laundering is taking place either domestic investors we can even take place by foreign investors. What kind of modification we could have done? Now you case capital investors of foreign investors what they have. Some of you are still asking me how do you calculate here. Fair market value for calculation is by a book value method or discounted cash flow method. In order to see examples of beginning for examination, basically calculate the fair market value. So you can see new budget proposal is applicable to capital raise from both as well as foreign. Taxi. Number 21. Venture capitalists and Angel investors are both safe. Companies considered startups were approved. Five years annual turnover? Not usually. The Startup India scheme is implemented by Ministry of Commerce and Industry. There is indeed implemented by Minister. No. The Angel Tax has been introduced for the first time in amendment to Income Tax Act. For the first time, Angel Tax is applicable to both Mr. and Mr. Companies. And the Angel facts made applicable to capital raised for more domestic and foreign investors kits. And there's investors now it is for both. This quarter. This tax is imposed only when the startup company is issuing shares at a price on the fair market price. It's a startup companies raising capital at a fair market value added taxes. The tax on long term debt. So recently the government has made certain changes to the spec long term capital gains tax on debt. Take this on long term capital gains tax on deposits. Let's look at this more capital gains. What is capital gains? Capital gains tax is the tax which people pay on profits which they make by selling. And whenever I sell an asset begin profit a part of that profit paid to commit tax capital gains, for example. I want to share at least in the year 15. I have shared and made a mistake to comment says if you have made pocket. Is capital gains tax will come into play if you make a loss? Your losses. Understand this concept of what is long term comes against that. Which takes money from investors and after taking money from investors, that money is invested in different different financial shares. So here we have predominantly more types of. Mutual funds are those funds. There mutual funds are investing more than 65% money in checks that mutual funds are. More than 65% of money is invested in bonds, which means it pretty much the funds can make investment in shares. Bonds. But where is the priority investment shares? They can make investment in both shares and bonds. Investment. On the equity mutual funds, we have short term capital gains tax and long term capital gains. Short term capital gains tax comes into play if I buy and sell. Long term capital gains tax will come into play if I buy and sell after. Play. March 2023, just in one year. And sending. You can. And. What is long term Complications? They're 2023. I bought it in March 2024. After more than one year. How much is it? Capital
xtell. Why September the 26th, I don't have a good picture, but I know for the first half the intelligence was saying it's gonna be a long war. He decided then to do it, and he had set it up so he could just say yes and a day later would blow away. You said it and say work by sonars. Frequency, you can blow them up, you can do it. So tough thing to do. And so why at that point, whether he was deciding is this is going to be a long war, it's going to cost a lot of money. I'm going to go all the way. He's now moving a lot of American troops and equipment into Poland. I don't know if he's going to commit native to it. I don't know if NATO wants to go, but he basically gave Chancellor Schultz, took away the option of Schultz to say I'm going to stop giving them arms. Winter's coming. I want that pipeline. I want to keep my people warm and wealthy. So he blew it up. So there's been a lot of news reporting, a lot of interest on this very simple question. Who blew up the North Stream pipelines? Remember in September of last year, the North Stream One and N Stream Two, which are two parallelly running pipelines carrying natural gas from Russia to Germany, were blown up. There were massive explosions which happened 250 feet are below the sea. Now the first report that came out, which was last month by a famous American journalist, which seemed to suggest that this was an inside job, it was carried out by the United States using state actors. now just this past week there's been some follow up reporting
September the 26th last year and. I there was no way. Common sense. You have to deal with common sense. Well, let's see. I knew that no Russia would have done it because of Putin already had responded. There's two major pipelines, N Stream one, which has been alive for since 2011, and Nord Stream two, which is. These are huge. They moved millions of whatever the measurement is of natural gas. There is a different measurement and it's not cubic feet, it's they they they move in, they mouth stream one turn Germany because they supplied cheap natural gas. That was very clear. Then they needed it anyway. I'm just giving you all this because I'm not here. I can answer your question, but I be coming from the blue one. I don't want to come from the blue. I want people to understand I've done this a long time. So the White House, Mr. Hush, they have rejected your story outright. They've called it a figment of your imagination. More fiction than fact is the wording that they used. Germany has also taken a very cautious mind on your story. You referred before in your first answer to Western media like the New York Times, The Washington Post. Had you taken your story to the editors there, would they have published such a consequential story basis, Just one single, unnamed anonymous source? How would you react to that? This source? Would I the way described the story and the intent of how I sourced it? Was to be as as vague as possible. It would have
Play with Data aka Magic
tell. Why September the 26th, I don't have a good picture, but I know for the first half the intelligence was saying it's gonna be a long war. He decided then to do it, and he had set it up so he could just say yes and a day later would blow away. You said it and say work by sonars. Frequency, you can blow them up, you can do it. So tough thing to do. And so why at that point, whether he was deciding is this is going to be a long war, it's going to cost a lot of money. I'm going to go all the way. He's now moving a lot of American troops and equipment into Poland. I don't know if he's going to commit native to it. I don't know if NATO wants to go, but he basically gave Chancellor Schultz, took away the option of Schultz to say I'm going to stop giving them arms. Winter's coming. I want that pipeline. I want to keep my people warm and wealthy. So he blew it up. So there's been a lot of news reporting, a lot of interest on this very simple question. Who blew up the North Stream pipelines? Remember in September of last year, the North Stream One and N Stream Two, which are two parallelly running pipelines carrying natural gas from Russia to Germany, were blown up. There were massive explosions which happened 250 feet are below the sea. Now the first report that came out, which was last month by a famous American journalist, which seemed to suggest that this was an inside job, it was carried out by the United States using state actors. now just this past week there's been some follow up reporting
September the 26th last year and. I there was no way. Common sense. You have to deal with common sense. Well, let's see. I knew that no Russia would have done it because of Putin already had responded. There's two major pipelines, N Stream one, which has been alive for since 2011, and Nord Stream two, which is. These are huge. They moved millions of whatever the measurement is of natural gas. There is a different measurement and it's not cubic feet, it's they they they move in, they mouth stream one turn Germany because they supplied cheap natural gas. That was very clear. Then they needed it anyway. I'm just giving you all this because I'm not here. I can answer your question, but I be coming from the blue one. I don't want to come from the blue. I want people to understand I've done this a long time. So the White House, Mr. Hush, they have rejected your story outright. They've called it a figment of your imagination. More fiction than fact is the wording that they used. Germany has also taken a very cautious mind on your story. You referred before in your first answer to Western media like the New York Times, The Washington Post. Had you taken your story to the editors there, would they have published such a consequential story basis, Just one single, unnamed anonymous source? How would you react to that? This source? Would I the way described the story and the intent of how I sourced it? Was to be as as vague as possible.
I know which you know. What is left? So I can close my eyes and enter into this and reach my office. So on a more serious note, I think. You cannot classify it as a treatment team or a team or you know, limited over 50. See Virat Kohli is good in 20, is good in limited over 20 Cricket is also good in Test cricket. So the RBI has to function and perform those fifty over match as well as in our effort. On effort and endeavor is always to optimize our for us to the best thing to extend that we can we put our best foot forward. Thank you very much. Thank you very much for accepting our condition. Great pleasure talking to you. Thank you very much and thank you very much for your patience. For short the time primarily for my longest answers. I can see your colleagues are very very efficient. But thank you once again. Thank you very much for your time. Thank you so much, Governor, and thank you again. Right for our next session. Welcome everyone And there's been talk about. Exciting transformations over the next several years will be the advent of Fight Jail India and this whole idea that India had. Physical. And it is an absolute delight to welcome for this fireside conversation to the Business Today India 100 Country Summit, one of the legendary. This generation. Quality and it's still standing fighting scale this in this fight can they have a popular as he walked through this Business Today and they have. Thank you so much. i want to start by asking you about the hearing which is about 5G everyone watching want We start at what does this mean? Everyone was listening to this. Fighting. And if you really look at the world fighting at most time to take roots, you know most of the Western world, Southeast Asian countries, Singapore and starts right as well I think we are some people think. Check this the perfect time some use cases. You can see devices coming through Priceline devices trying to find one perfectly from the country they can do so. I think we are ready. Thankfully this time the government put up a lot of spectrum because 5G requires made large chunks of spectrum to be effective. Otherwise they don't really get the joy of our friendship and not only the mid band which the seed bank claims. They're All in all, I think India is now ready from the spectrum point of view, but it's now standard and available. We have been building from this day for the last 24 months and predicted that we would want sometimes some of the preparedness felt that would probably come in 2021. But I think they're just takes time to develop the ecosystem. So we are ready. Expect launches October Believe will be from very early in October. Sometime in October you can start to see on your phones if there are any. We'll start with dictionaries, the key towns and the keep on as we've seen here earlier, keep on with right across and going into deep. What should you do to us, I mean today? Customer. Very important use cases which are developing around which took my mind are
tell. Why September the 26th, I don't have a good picture, but I know for the first half the intelligence was saying it's gonna be a long war. He decided then to do it, and he had set it up so he could just say yes and a day later would blow away. You said it and say work by sonars. Frequency, you can blow them up, you can do it. So tough thing to do. And so why at that point, whether he was deciding is this is going to be a long war, it's going to cost a lot of money. I'm going to go all the way. He's now moving a lot of American troops and equipment into Poland. I don't know if he's going to commit native to it. I don't know if NATO wants to go, but he basically gave Chancellor Schultz, took away the option of Schultz to say I'm going to stop giving them arms. Winter's coming. I want that pipeline. I want to keep my people warm and wealthy. So he blew it up. So there's been a lot of news reporting, a lot of interest on this very simple question. Who blew up the North Stream pipelines? Remember in September of last year, the North Stream One and N Stream Two, which are two parallelly running pipelines carrying natural gas from Russia to Germany, were blown up. There were massive explosions which happened 250 feet are below the sea. Now the first report that came out, which was last month by a famous American journalist, which seemed to suggest that this was an inside job, it was carried out by the United States using state actors. now just this past week there's been some follow up reporting
September the 26th last year and. I there was no way. Common sense. You have to deal with common sense. Well, let's see. I knew that no Russia would have done it because of Putin already had responded. There's two major pipelines, N Stream one, which has been alive for since 2011, and Nord Stream two, which is. These are huge. They moved millions of whatever the measurement is of natural gas. There is a different measurement and it's not cubic feet, it's they they they move in, they mouth stream one turn Germany because they supplied cheap natural gas. That was very clear. Then they needed it anyway. I'm just giving you all this because I'm not here. I can answer your question, but I be coming from the blue one. I don't want to come from the blue. I want people to understand I've done this a long time. So the White House, Mr. Hush, they have rejected your story outright. They've called it a figment of your imagination. More fiction than fact is the wording that they used. Germany has also taken a very cautious mind on your story. You referred before in your first answer to Western media like the New York Times, The Washington Post. Had you taken your story to the editors there, would they have published such a consequential story basis, Just one single, unnamed anonymous source? How would you react to that? This source? Would I the way described the story and the intent of how I sourced it? Was to be as as vague as possible. It would have been very
tell. Why September the 26th, I don't have a good picture, but I know for the first half the intelligence was saying it's gonna be a long war. He decided then to do it, and he had set it up so he could just say yes and a day later would blow away. You said it and say work by sonars. Frequency, you can blow them up, you can do it. So tough thing to do. And so why at that point, whether he was deciding is this is going to be a long war, it's going to cost a lot of money. I'm going to go all the way. He's now moving a lot of American troops and equipment into Poland. I don't know if he's going to commit native to it. I don't know if NATO wants to go, but he basically gave Chancellor Schultz, took away the option of Schultz to say I'm going to stop giving them arms. Winter's coming. I want that pipeline. I want to keep my people warm and wealthy. So he blew it up. So there's been a lot of news reporting, a lot of interest on this very simple question. Who blew up the North Stream pipelines? Remember in September of last year, the North Stream One and N Stream Two, which are two parallelly running pipelines carrying natural gas from Russia to Germany, were blown up. There were massive explosions which happened 250 feet are below the sea. Now the first report that came out, which was last month by a famous American journalist, which seemed to suggest that this was an inside job, it was carried out by the United States using state actors. now just this past week there's been some follow up reporting
September the 26th last year and. I there was no way. Common sense. You have to deal with common sense. Well, let's see. I knew that no Russia would have done it because of Putin already had responded. There's two major pipelines, N Stream one, which has been alive for since 2011, and Nord Stream two, which is. These are huge. They moved millions of whatever the measurement is of natural gas. There is a different measurement and it's not cubic feet, it's they they they move in, they mouth stream one turn Germany because they supplied cheap natural gas. That was very clear. Then they needed it anyway. I'm just giving you all this because I'm not here. I can answer your question, but I be coming from the blue one. I don't want to come from the blue. I want people to understand I've done this a long time. So the White House, Mr. Hush, they have rejected your story outright. They've called it a figment of your imagination. More fiction than fact is the wording that they used. Germany has also taken a very cautious mind on your story. You referred before in your first answer to Western media like the New York Times, The Washington Post. Had you taken your story to the editors there, would they have published such a consequential story basis, Just one single, unnamed anonymous source? How would you react to that? This source? Would I the way described the story and the intent of how I sourced it? Was to be as as vague as possible. It would have been
tell. Why September the 26th, I don't have a good picture, but I know for the first half the intelligence was saying it's gonna be a long war. He decided then to do it, and he had set it up so he could just say yes and a day later would blow away. You said it and say work by sonars. Frequency, you can blow them up, you can do it. So tough thing to do. And so why at that point, whether he was deciding is this is going to be a long war, it's going to cost a lot of money. I'm going to go all the way. He's now moving a lot of American troops and equipment into Poland. I don't know if he's going to commit native to it. I don't know if NATO wants to go, but he basically gave Chancellor Schultz, took away the option of Schultz to say I'm going to stop giving them arms. Winter's coming. I want that pipeline. I want to keep my people warm and wealthy. So he blew it up. So there's been a lot of news reporting, a lot of interest on this very simple question. Who blew up the North Stream pipelines? Remember in September of last year, the North Stream One and N Stream Two, which are two parallelly running pipelines carrying natural gas from Russia to Germany, were blown up. There were massive explosions which happened 250 feet are below the sea. Now the first report that came out, which was last month by a famous American journalist, which seemed to suggest that this was an inside job, it was carried out by the United States using state actors. now just this past week there's been some follow up reporting
September the 26th last year and. I there was no way. Common sense. You have to deal with common sense. Well, let's see. I knew that no Russia would have done it because of Putin already had responded. There's two major pipelines, N Stream one, which has been alive for since 2011, and Nord Stream two, which is. These are huge. They moved millions of whatever the measurement is of natural gas. There is a different measurement and it's not cubic feet, it's they they they move in, they mouth stream one turn Germany because they supplied cheap natural gas. That was very clear. Then they needed it anyway. I'm just giving you all this because I'm not here. I can answer your question, but I be coming from the blue one. I don't want to come from the blue. I want people to understand I've done this a long time. So the White House, Mr. Hush, they have rejected your story outright. They've called it a figment of your imagination. More fiction than fact is the wording that they used. Germany has also taken a very cautious mind on your story. You referred before in your first answer to Western media like the New York Times, The Washington Post. Had you taken your story to the editors there, would they have published such a consequential story basis, Just one single, unnamed anonymous source? How would you react to that? This source? Would I the way described the story and the intent of how I sourced it? Was to be as as vague as possible. It would have been very
AboutAbout
As a Machine Learning Engineer, I am enthusiastic about exploring the fascinating field of machine learning and its potential applications in solving real-world problems. I have a good amount of knowledge in mathematics, statistics, and computer science, which enables me to understand and implement the fundamental concepts and techniques of machine learning.
My experience includes working with various data analytics tools and technologies, such as SQL, Python, Tableau, and Power BI. I have developed expertise in data cleaning and exploratory data analysis, statistical inference, and machine learning techniques such as regression, and clustering, my experience also includes working with a variety of machine learning tools and frameworks, including, Scikit-learn, TensorFlow and Keras. I have also developed expertise in data pre-processing, feature engineering, and model selection, which has enabled me to create robust and accurate machine-learning models.
I am eager to expand my knowledge and expertise in machine learning research and AI-related fields. I am a quick learner, and I am always willing to take on new challenges and stretch myself to develop new skills. Furthermore, I am a good communicator and collaborator, able to work effectively in a team and learn from more experienced colleagues.
I am excited about the potential of machine learning to create positive change and improve people's lives specifically in Health Care industries. I am committed to staying up-to-date with the latest developments in machine learning and related fields, attending conferences and workshops, and engaging in continuous learning. I am eager to apply my skills and experience to solve complex problems and create value for organizations.
https://www.linkedin.com/in/sudharmendra-gv-7214361b2/
Machine Learning EngineerMachine Learning Engineer
Longevity InTime BioTech · Part-timeLongevity InTime BioTech · Part-time
Nov 2022 - Present · 6 mosNov 2022 - Present · 6 mos
Wilmington, Delaware, United StatesWilmington, Delaware, United States
Longevity Intime is a company developing online technology, which enables early diagnosis of diseases using real-time biometrics from the human body.
The Longevity InTime team includes experienced biotechnologists, award-winning scientists, developers, and experienced executives with years of experience. Together we are solving the global problem of life expectancy for each person and for all mankind.
Our goal is to extend a person's life by more than 20 active and happy years without any restrictions to him. We respond to both the personal and public interests of every country on Earth.Longevity Intime is a company developing online technology, which enables early diagnosis of diseases using real-time biometrics from the human body. The Longevity InTime team includes experienced biotechnologists, award-winning scientists, developers, and experienced executives with years of experience.
Patent Number : 411898 Date of Patent : 24/05/2014
Application Number : 10903/DELNP/2015 Date of Grant : 21/11/2022
Type of Application : PCT NATIONAL PHASE APPLICATION Date of Recordal : 21/11/2022
Parent Application Number : --- Appropriate Office : DELHI
PCT International Application Number : PCT/JP2014/063758 PCT International Filing Date : 24/05/2014
Grant Title : VERTICAL AXIS WATER/WIND TURBINE MOTOR USING FLIGHT FEATHER OPENING/CLOSING WING SYSTEM
Sl No Name of Grantee Grantee Type Grantee Address
1 TAMATSU Yoshiji NATURAL PERSON Room16Toyota Apartment1656 5Aza OrokuNaha shi Okinawa 9010152
Sl No Name of Patentee Patentee Type Address of Patentee
1 TAMATSU Yoshiji NATURAL PERSON Room16Toyota Apartment1656 5Aza OrokuNaha shi Okinawa 9010152
Address of Service : Tech Corp. International Strategist TCIS, lndia 5th Floor, Caddie Commercial Tower, Aerocity, New Delhi- 110037 India
Additional Address of Service : --
Priority Date : 25/05/2013
Year Due dates for Renewal CBR No CBR Date Renewal Amount Renewal Certificate No Date of Renewal Renewal Period:
Normal Due Date Due Date with Extension From To
3rd year 21/02/2023 21/08/2023 12919 21/03/2023 800 27207 21/03/2023 24/05/2016 24/05/2017
4th year 21/02/2023 21/08/2023 12919 21/03/2023 800 27208 21/03/2023 24/05/2017 24/05/2018
5th year 21/02/2023 21/08/2023 12919 21/03/2023 800 27209 21/03/2023 24/05/2018 24/05/2019
6th year 21/02/2023 21/08/2023 12919 21/03/2023 800 27210 21/03/2023 24/05/2019 24/05/2020
7th year 21/02/2023 21/08/2023 12919 21/03/2023 2400 27211 21/03/2023 24/05/2020 24/05/2021
8th year 21/02/2023 21/08/2023 12919 21/03/2023 2400 27212 21/03/2023 24/05/2021 24/05/2022
9th year 21/02/2023 21/08/2023 12919 21/03/2023 2400 27213 21/03/2023 24/05/2022 24/05/2023
10th year 24/05/2023 24/11/2023 12919 21/03/2023 2400 27214 21/03/2023 24/05/2023 24/05/2024
11th year 24/05/2024 24/11/2024 12919 21/03/2023 4800 27215 21/03/2023 24/05/2024 24/05/2025
12th year 24/05/2025 24/11/2025 12919 21/03/2023 4800 27216 21/03/2023 24/05/2025 24/05/2026
13th year 24/05/2026 24/11/2026 12919 21/03/2023 4800 27217 21/03/2023 24/05/2026 24/05/2027
14th year 24/05/2027 24/11/2027 12919 21/03/2023 4800 27218 21/03/2023 24/05/2027 24/05/2028
15th year 24/05/2028 24/11/2028 12919 21/03/2023 4800 27219 21/03/2023 24/05/2028 24/05/2029
16th year 24/05/2029 24/11/2029 12919 21/03/2023 8000 27220 21/03/2023 24/05/2029 24/05/2030
17th year -- -- -- -- -- -- -- -- --
18th year -- -- -- -- -- -- -- -- --
19th year -- -- -- -- -- -- -- -- --
20th year -- -- -- -- -- -- -- -- --
Sl No Date of Entry Particulars/Remarks
Information u/s 146 (Working of Patents)
Sl No Patent Number Year
1 411898
TITLE OF INVENTION VERTICAL AXIS WATER/WIND TURBINE MOTOR USING FLIGHT FEATHER OPENING/CLOSING WING SYSTEM
FIELD OF INVENTION MECHANICAL ENGINEERING
E-MAIL (As Per Record) prity.khastgir@gmail.com
ADDITIONAL-EMAIL (As Per Record)
E-MAIL (UPDATED Online) khasip@khastgir.com
PCT INTERNATIONAL APPLICATION NUMBER PCT
Dear Sir/ Madam,
Congratulations, you have been shortlisted for our next phase of audition of Shark Tank India, which is the In-Person Audition! At this stage of the audition our team will be meeting you in-person to further discuss your business/idea. Please note, we will be conducting these “In-Person Auditions” in Mumbai, Bengaluru, Delhi & Kolkata.
As per your form, your audition city is Delhi
Audition Venue: WelcomHotel ITC Dwarka, Plot No 3, District Center, Sector 10 Dwarka, New Delhi, Delhi 110075.
Landline - 01142229222
Location – Welcomhotel Dwarka offers easy access to the Delhi Metro Blue Line near by metro station Dwarka Sector 10. Located 20 minutes away from Indira Gandhi International Airport and the domestic airport in New Delhi.
Audition Date & Time: 15th July'22 Friday at 1pm
Time commitment required: 4 hours on the audition day
Kindly note the following guidelines for the in-person audition -
COVID Mandatory Safety Guidelines:
Applicants and any accompanying persons will need to wear a mask at all times and adhere to all the social distancing guidelines communicated by us, while at the venue
In case, you and any accompanying persons are vaccinated please carry your updated vaccination certificate (first and second dosage) or the Arogya Setu Pass.
In case you and any accompanying persons are not vaccinated, RT PCR Test with a 48 hour validity is mandatory and the status will be checked prior to at the venue.
A mandatory Rapid Antigen Test will be administered at the venue and only if the same turns out to be negative, will the applicant be allowed to proceed further.
Please note, that all applicants and any accompanying person will at all times have to comply with the rules and regulations mandated by the Central or State Government with respect to COVID 19 prevention issued at that time.
Documents for verification required at the time of audition: -
(Please note the documents are required for verification at the time of audition, kindly carry original copies too & also please do email us these documents scanned copies before the audition date and also get it on your pen drive at the venue also. Email us on - sharktankindia1@setindia.com)
Mandatory
Applicant’s Photo Identity proof & Address Proofs (Aadhar Card & Pan-Card)
Document proofs to validate accreditation & Audited Financials - Balance Sheets, Net worth Certificate (if any) and Income Statement for last 3 years. Please note, the certificate should be taken from the CA/CA Firm (on their letterhead)
Certificates evidencing any change of name, Memorandum of Association, Articles of Association, registered partnership deed, etc. or equivalent documents
No Objection Certificate (NOC), signed & dated, from all shareholders/co-founders/partners/investors
Company PAN CARD/GST Certificates
Income Tax Returns for the last 3 years (according to the business tenure)
If Applicable
Certificate of Incorporation or registration for your firm/partnership/LLP/company
#Node CC: Philippe RochePhilippe Roche : 2nd Connect
Founding & Managing Partner
The distinction between private equity (PE) and venture capital (VC)
They differ in:
- Their amounts of investment
- Types of firms they choose to invest in
- Timing of their involvement in a company's lifespan
- Equity they receive as a result of their investments.
VCs often invest $10M or less.
PEs often spend $50 million or more.
Early-stage startups are the focus of VCs.
PEs focus on seasoned businesses.
A small portion of the corporation is owned by VCs.
PEs purchase a controlling stake from the stockholders.
VCs research exit strategies and fast growth.
PEs consider scaling businesses to increase productivity and profits.
Examples
VC firms: Bessemer Venture Partners, Andreessen Horowitz, and Sequoia Capital
VC-backed businesses: Juul, Stripe, SpaceX, Waymon, and Ripple Labs
Private equity firms: The Blackstone Group Inc., CVC Capital Partners, and TPG Capital are
Companies with PE backing: EQ Office, Panera Bread, and Refinitiv
PEs and VCs are, of course, much more complicated than this.
Please feel free to remark with your observations.
#startups #entrepreneurship #venturecapital #investing #finance #crossborder #inovexus #startup #VC #earlystage
#galeshapley
#smartcontracts
Ms. Prity Khastgir (b. 26 Dec. 1984). Obtained B.A.Sc (Hons) in Applied Sciences specialising in Food technology from University of Delhi and M.Sc in Biotechnology and LLB from University of Rajasthan. Did Entrepreneurship course from Indian School of Business (ISB) sponsored by Goldman Sachs 10,000 Women Entrepreneurs Initiative. She is Certified Mediator for Commercial Disputes and Negotiator in India. Currently, she is working towards launching innovation hub in
domestic and international level.
She is passionate about technology, law and business on a global scale. Recently, she was invited by China Government for China International Big Data Expo 2018, Guiyang. She is active in international technology arena and recently participated in the 4th Annual Asia Pacific Spectrum Management Conference and in World Summit on the Information Society (WSIS) Forum 2018, Geneva.
Senior executive profile with featured publications: BBC World, Nature Group (Nature Reviews Drug Discovery), BusinessWorld, BioSpectrum Asia etc. Previous work experience with US Law Firm headquartered in Greater New York City Area. Problem solver and Business Strategist with 12+ yrs exp. Seasoned Patent Strategist with expertise in IP portfolio research, cross-border tech transactions, licensing agreements, product clearance, FTO opinion, patent infringement and invalidity, IPR R&D Consultancy.
Aligning and facilitating youth to be driven in the industry 4.0 era is her motto to take India’s GDP to next level. She is a guest faculty at IMT, Ghaziabad taking lectures on legal aspects of doing business in India and understanding the innovation ethos globally.
#ITU Guide to drive Citizens' engagement for #Innovation #OpenGeneva
https://www.itu.int/net4/wsis/forum/2020/en/Agenda
Julia Dallest
Chief Operation Officer
Open Geneva
Julia Dallest is the Chief Operation Officer at Open Geneva. After 4 years as a change management consultant in Paris, Julia worked 2 years for a humanitarian mission in Madagascar as a partnership manager. Wishing to continue her involvement in the associative world, Julia joined Open Geneva as a volunteer in 2018 and as Chief Operation Officer since 2019.
Cristina Bueti
Counsellor of ITU-T Study Group 20 “Internet of things (IoT) and smart cities and communities (SC&C)”
International Telecommunication Union
Cristina Bueti is the Counsellor of ITU-T Study Group 20 “Internet of things (IoT) and smart cities and communities (SC&C)” at the International Telecommunication Union (ITU). She is responsible for ITU-T's activities on IoT and smart sustainable cities. She is the ITU focal point for environment and climate change including e-waste management. She also serves as TSB focal point for Latin America.
Cristina Bueti graduated from the Faculty of Political Science, Law and International Cooperation and Development of the University of Florence, where she completed postgraduate studies in International Cooperation and Telecommunications Law in Europe. She also holds a specialization in Environmental Law with a special focus on Telecommunications.
In 2003, Ms. Bueti built on her academic credentials by completing a specialized course in peace keeping and international cooperation with special focus on telecommunications at the Faculty of Laws, University of Malta, before joining the International Telecommunication Union in Geneva in January 2004.
As part of the International Women's Day 2016, she was named as one of the twenty Geneva-based inspirational women working to protect the environment.
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Thomas Diez
FabCity
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Alibaba Bags Patent in Singapore in Three Months for Computational AI Innovation #AIpatent
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In
Force
20/06/2014 22/06/2016 1) ALIBABA GROUP
HOLDING LIMITED
TWO FACTOR
AUTHENTICATION
1) D
NAPI
2) RO
2 10201802635Q Patent
In
Force
29/08/2014 29/03/2018 1) ALIBABA GROUP
HOLDING LIMITED
DATA PROCESSING
BASED ON TWO-
DIMENSIONAL
CODE
1) D
NAPI
2) H
LLP
3 10201905273V Patent
In
Force
10/06/2019 10/06/2019 1) Alibaba Group
Holding Limited
METHOD AND
SYSTEM FOR
EVALUATING AN
OBJECT
DETECTION MODEL
1) SP
& FE
(ASI
LTD
4 11201510054Y Patent
In
Force
20/06/2014 08/12/2015 1) ALIBABA GROUP
HOLDING LIMITED
TWO FACTOR
AUTHENTICATION
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NAPI
2) RO
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In
Force
10/07/2014 08/12/2015 1) ALIBABA GROUP
HOLDING LIMITED
PROVIDING
HISTORY-BASED
DATA PROCESSING
1) D
NAPI
2) RO
6 11201510058R Patent
In
Force
25/07/2014 08/12/2015 1) ALIBABA GROUP
HOLDING LIMITED
METHOD AND
SYSTEM FOR
PROVIDING
RECOMMENDED
TERMS
1) D
NAPI
2) RO
7 11201510059V Patent
In
Force
18/08/2014 08/12/2015 1) ALIBABA GROUP
HOLDING LIMITED
METHOD AND
SYSTEM FOR
RECOMMENDING
ONLINE PRODUCTS
1) D
NAPI
2) RO
8 11201601233Q Patent
In
Force
29/08/2014 19/02/2016 1) ALIBABA GROUP
HOLDING LIMITED
DATA PROCESSING
BASED ON TWO-
DIMENSIONAL
CODE
1) D
NAPI
2) H
LLP
9 11201601572U Patent
In
Force
11/09/2014 02/03/2016 1) ALIBABA GROUP
HOLDING LIMITED
METHOD AND
APPARATUS OF
DOWNLOADING
AND INSTALLING A
CLIENT
1) D
NAPI
2) H
LLP
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15/04/2015 18/10/2016 1) ALIBABA GROUP
HOLDING LIMITED
METHOD, PUBLIC
ACCOUNT SERVER,
AND MOBILE
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GENERATING
CARDS
1) D
NAPI
2) YU
AUD
11 11201609418T Patent
In
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Holding Limited
VOICE DISPLAYING 1) M
CLER
SING
LLP
12 11201610289R Patent
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08/06/2015 08/12/2016 1) ALIBABA GROUP
HOLDING LIMITED
METHOD AND
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AUTHENTICATION
1)
MCLA
IP PT
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HOLDING LIMITED
METHOD AND
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MANAGING
RESIDUAL VALUE
IN DISTRIBUTED
PROCESSING OF
TRANSACTIONS
1) D
NAPI
2)
MCLA
IP PT
14 11201610765W Patent
In
Force
30/06/2015 22/12/2016 1) Alibaba Group
Holding Limited
PROMPTING LOGIN
ACCOUNT
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NAPI
2) M
CLER
SING
LLP
15 11201700639P Patent
In
Force
17/07/2015 25/01/2017 1) Alibaba Group
Holding Limited
INFORMATION
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1) D
NAPI
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CLER
SING
LLP
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In
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14/10/2015 13/03/2017 1) ALIBABA GROUP
HOLDING LIMITED
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MCLA
IP PT
17 11201702002T Patent
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HOLDING LIMITED
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AND PRESENTING
DATA FIELDS WITH
ERRONEOUS
INPUTS
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IP PT
18 11201703418P Patent
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30/11/2015 26/04/2017 1) ALIBABA GROUP
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1)
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IP PT
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In
Force
15/01/2016 06/06/2017 1) ALIBABA GROUP
HOLDING LIMITED
SYSTEM FOR
EFFICIENT
PROCESSING OF
TRANSACTION
REQUESTS
RELATED TO AN
ACCOUNT IN A
DATABASE
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IP PT
20 11201706026S Patent
In
Force
11/03/2016 24/07/2017 1) ALIBABA GROUP
HOLDING LIMITED
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WITHIN A GROUP
CHAT
1)
MCLA
IP PT
21 11201707475W Patent
In
Force
08/03/2016 13/09/2017 1) ALIBABA GROUP
HOLDING LIMITED
THREE-
DIMENSIONAL
MODELING
METHOD AND
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1) PI
PTE.
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In
Force
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HOLDING LIMITED
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SYNCHRONIZATION
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PTE.
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HOLDING LIMITED
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SYNTHESIZED
PICTURE
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PTE.
24 11201707791X Patent
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HOLDING LIMITED
PICTURE
SYNTHESIS
METHOD AND
APPARATUS
1) PI
PTE.
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In
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15/03/2016 22/09/2017 1) ALIBABA GROUP
HOLDING LIMITED
DATA HANDLING
METHOD AND
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1) PI
PTE.
Page 1 / 2 of 38 record(s) 1 2 Rows pe
6. 21
METHOD AND SYSTEM FOR EVALUATING AN OBJECT DETECTION MODEL
ABSTRACT
A method for evaluating performance of an object detection model includes
generating a predicted bounding box representing an object based on the object
detection model. The object is positioned proximate to one or more adjacent
objects. The method also includes determining an area of intersection between the
predicted bounding box and a groundtruth bounding box of the object, and
determining a modified area of union between the predicted bounding box and the
groundtruth bounding box of the object. Determining the modified area of union
includes determining a weighted area of union between the predicted and
groundtruth bounding boxes based on one or more weights, and adding to the
weighted area of union an area of intersection between the predicted bounding box
and at least one groundtruth bounding box of the one or more adjacent objects.
The method further includes determining a score equal to the area of intersection
between the predicted bounding box and the groundtruth bounding box of the
object divided by the modified area of union. The score represents the
performance of the object detection model.
FIG. 1
7. 1 / 5
generating a predicted bounding box representing an
object based on the object detection model
determining a modified area of union between the
predicted bounding box and the groundtruth bounding
box of the object
determining an area of intersection between the
predicted bounding box and a groundtruth bounding
box of the object
108
106
104
102
determining a score equal to the area of intersection
between the predicted bounding box and the
groundtruth bounding box of the object divided by the
modified area of union
Figure 1
100
9. 3 / 5
evaluating each of the object detection models with
validation data to identify the object detection model
having the highest score
receiving unlabeled actual data and a plurality of object
detection models
306
304
302
detecting an object in the unlabeled actual data based
on the identified object detection model
Figure 3
300
12. 1
METHOD AND SYSTEM FOR EVALUATING AN OBJECT
DETECTION MODEL
FIELD OF INVENTION
[0001] The present invention relates broadly, but not exclusively, to methods and
systems for evaluating an object detection model, and to object detection method
and devices.
BACKGROUND
[0002] Optical character recognition (OCR) is the mechanical or electronic
conversion of images of typed, handwritten or printed text into machine-encoded
text, whether from a scanned document, a photo of a document, a scene-photo or
from subtitle text superimposed on an image, etc. To recognise the text, the first
step is to detect bounding boxes of each text segment. Algorithms for detection of
text belong to a field named object detection in computer vision.
[0003] In object detection, intersection over union (IoU) is a common standard
metric used to evaluate the accuracy of a detector and model selection. The
traditional IoU formula is defined as “area of intersection between predicted
bounding box and groundtruth bounding box divided by area of union between
predicted bounding box and groundtruth bounding box”. This formula works well for
most cases. However, in the case of text detection, the traditional IoU may fail to
select the best models/parameters and thus using the traditional IoU may
significantly reduce the final accuracy of text recognition.
[0004] For example, the traditional IoU formula fails to consider at least two
conditions, namely, (1) both a smaller intersection and a greater union lead to a
similarly smaller IoU; however, for text detection a smaller intersection is worse
than a greater union because it may cause some regions of text to be lost and
affect the following OCR result, and (2) the traditional IoU does not consider the
intersection between a predicted textbox and other groundtruth textboxes. Without
13. 2
considering these two conditions, a higher IoU value may not necessarily indicate a
better model.
[0005] A need therefore exists to provide methods and devices that can improve
the evaluation of object detection models for text detection.
SUMMARY
[0006] A first aspect of the present disclosure provides a method for evaluating
performance of an object detection model. The method includes generating a
predicted bounding box representing an object based on the object detection
model, wherein the object is positioned proximate to one or more adjacent objects;
determining an area of intersection between the predicted bounding box and a
groundtruth bounding box of the object; and determining a modified area of union
between the predicted bounding box and the groundtruth bounding box of the
object. Determining the modified area of union includes determining a weighted
area of union between the predicted and groundtruth bounding boxes based on
one or more weights; and adding to the weighted area of union an area of
intersection between the predicted bounding box and at least one groundtruth
bounding box of the one or more adjacent objects. The method further includes
determining a score equal to the area of intersection between the predicted
bounding box and the groundtruth bounding box of the object divided by the
modified area of union. The score represents the performance of the object
detection model.
[0007] A second aspect of the present disclosure provides an object detection
method. The method includes receiving unlabeled actual data and a plurality of
object detection models, wherein the object detection models are generated by a
neural network based on labeled training data; evaluating each of the object
detection models with validation data using the method as defined in the first
aspect to identify the object detection model having the highest score; and
detecting an object in the unlabeled actual data based on the identified object
detection model.
[0008] A third aspect of the present disclosure provides a system for evaluating an
object detection model. The system includes a processor, and a computer-
14. 3
readable memory coupled to the processor and having instructions stored thereon.
The instructions are executable by the processor to generate a predicted bounding
box representing an object based on the object detection model, wherein the object
is positioned proximate to one or more adjacent objects; determine an area of
intersection between the predicted bounding box and a groundtruth bounding box
of the object; and determine a modified area of union between the predicted
bounding box and the groundtruth bounding box of the object. The modified area of
union is a sum of a weighted area of union between the predicted and groundtruth
bounding boxes based on one or more weights; and an area of intersection
between the predicted bounding box and at least one groundtruth bounding box of
the one or more adjacent objects. The instructions are also executable by the
processor to determine a score equal to the area of intersection between the
predicted bounding box and the groundtruth bounding box of the object divided by
the modified area of union. The score represents the performance of the object
detection model.
[0009] A fourth aspect of the present disclosure provides an apparatus comprising
an object detection module configured to generate a predicted bounding box
representing an object based on an object detection model, wherein the object is
positioned proximate to one or more adjacent objects, and an evaluation module.
The evaluation module is configured to determine an area of intersection between
the predicted bounding box and a groundtruth bounding box of the object; and
determine a modified area of union between the predicted bounding box and the
groundtruth bounding box of the object. The modified are of union is a sum of a
weighted area of union between the predicted and groundtruth bounding boxes
based on one or more weights; and an area of intersection between the predicted
bounding box and at least one groundtruth bounding box of the one or more
adjacent objects. The evaluation module is further configured to determine a score
equal to the area of intersection between the predicted bounding box and the
groundtruth bounding box of the object divided by the modified area of union; and
evaluate a performance of the object detection model based on the score.
[0010] A fifth aspect of the present disclosure provides an object detector. The
object detector includes a receiver module configured to receive unlabeled actual
data and a plurality of object detection models. The object detection models are
generated by a neural network based on labeled training data. The object detector
15. 4
also includes the apparatus as defined in the fourth aspect coupled to the receiver
module and configured to evaluate each of the object detection models with
validation data to identify the object detection model having the highest score. The
object detection module is further configured to detect an object in the unlabeled
actual data based on the identified object detection model.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] Embodiments of the invention will be better understood and readily
apparent to one of ordinary skill in the art from the following written description, by
way of example only, and in conjunction with the drawings, in which:
[0012] Figure 1 shows a flow chart illustrating a method for evaluating an object
detection model according to an embodiment.
[0013] Figures 2a and 2b show schematic diagrams illustrating an implementation
to determine the modified area of union in the method of Figure 1.
[0014] Figure 3 shows a flow chart illustrating an object detection method
according to an embodiment.
[0015] Figure 4 shows a schematic diagram illustrating an object detector
according to an embodiment.
[0016] Figure 5 shows a schematic diagram illustrating a computer system suitable
in implementing the methods of Figures 1 and 3 and the object detector of Figure
4.
[0017] Skilled artisans will appreciate that elements in the figures are illustrated for
simplicity and clarity and have not necessarily been depicted to scale. For example,
the dimensions of some of the elements in the illustrations, block diagrams or
flowcharts may be exaggerated in respect to other elements to help to improve
understanding of the present embodiments.
16. 5
DETAILED DESCRIPTION
[0018] The present disclosure provides methods and devices in which an object
detection model is evaluated based on an improved IoU formula that takes into
account the considerations that (1) for text detection, a smaller intersection is
worse than a greater union because it may cause some regions of text to be lost
and affect the following OCR result, and (2) for text detection, the intersection
between a predicted textbox and other groundtruth textboxes may also adversely
affect the following OCR result. As described in more details below, the evaluation
is based on a score that not only penalizes a smaller intersection and greater union
combination (which the traditional IoU formula does), but also penalizes more on a
smaller intersection than on a greater union. In addition, it penalizes an intersection
between a predicted textbox and other groundtruth textboxes. Accordingly, given a
number of different object detection models, it is possible to identify or select the
model that is most suitable for text detection.
[0019] Embodiments will be described, by way of example only, with reference to the
drawings. Like reference numerals and characters in the drawings refer to like
elements or equivalents.
[0020] Some portions of the description herein are explicitly or implicitly presented in
terms of algorithms and functional or symbolic representations of operations on data
within a computer memory. These algorithmic descriptions and functional or symbolic
representations are the means used by those skilled in the data processing arts to
convey most effectively the substance of their work to others skilled in the art. An
algorithm is here, and generally, conceived to be a self-consistent sequence of steps
leading to a desired result. The steps are those requiring physical manipulations of
physical quantities, such as electrical, magnetic or optical signals capable of being
stored, transferred, combined, compared, and otherwise manipulated.
[0021] Unless specifically stated otherwise, and as apparent from the following, it will
be appreciated that throughout the present specification, discussions utilizing terms
such as “scanning”, “receiving”, “calculating”, “detecting”, “determining”, “replacing”,
“generating”, “initializing”, “outputting”, “evaluating”, or the like, refer to the action and
processes of a computer system, or similar electronic device, that manipulates and
transforms data represented as physical quantities within the computer system into
17. 6
other data similarly represented as physical quantities within the computer system or
other information storage, transmission or display devices.
[0022] The present specification also discloses apparatus for performing the
operations of the methods. Such apparatus may be specially constructed for the
required purposes, or may comprise a computer or other device selectively activated or
reconfigured by a computer program stored in the computer. The algorithms and
displays presented herein are not inherently related to any particular computer or other
apparatus. Various machines may be used with programs in accordance with the
teachings herein. Alternatively, the construction of more specialized apparatus to
perform the required method steps may be appropriate. The structure of a computer
suitable for executing the various methods / processes described herein will appear
from the description herein.
[0023] In addition, the present specification also implicitly discloses a computer
program, in that it would be apparent to the person skilled in the art that the individual
steps of the method described herein may be put into effect by computer code. The
computer program is not intended to be limited to any particular programming language
and implementation thereof. It will be appreciated that a variety of programming
languages and coding thereof may be used to implement the teachings of the
disclosure contained herein. Moreover, the computer program is not intended to be
limited to any particular control flow. There are many other variants of the computer
program, which can use different control flows without departing from the spirit or
scope of the invention.
[0024] Furthermore, one or more of the steps of the computer program may be
performed in parallel rather than sequentially. Such a computer program may be stored
on any computer readable medium. The computer readable medium may include
storage devices such as magnetic or optical disks, memory chips, or other storage
devices suitable for interfacing with a computer. The computer readable medium may
also include a hard-wired medium such as exemplified in the Internet system, or
wireless medium such as exemplified in the GSM, GPRS, 3G or 4G mobile telephone
systems, as well as other wireless systems such as Bluetooth, ZigBee, Wi-Fi. The
computer program when loaded and executed on such a computer effectively results in
an apparatus that implements the steps of the preferred method.
18. 7
[0025] The present invention may also be implemented as hardware modules. More
particularly, in the hardware sense, a module is a functional hardware unit designed for
use with other components or modules. For example, a module may be implemented
using discrete electronic components, or it can form a portion of an entire electronic
circuit such as an Application Specific Integrated Circuit (ASIC) or Field Programmable
Gate Array (FPGA). Numerous other possibilities exist. Those skilled in the art will
appreciate that the system can also be implemented as a combination of hardware and
software modules.
[0026] According to various embodiments, a "circuit" may be understood as any
kind of a logic implementing entity, which may be special purpose circuitry or a
processor executing software stored in a memory, firmware, or any combination
thereof. Thus, in an embodiment, a "circuit" may be a hard-wired logic circuit or a
programmable logic circuit such as a programmable processor, e.g. a
microprocessor (e.g. a Complex Instruction Set Computer (CISC) processor or a
Reduced Instruction Set Computer (RISC) processor). A "circuit" may also be a
processor executing software, e.g. any kind of computer program, e.g. a computer
program using a virtual machine code such as e.g. Java. Any other kind of
implementation of the respective functions which may be described in more detail
herein may also be understood as a "circuit" in accordance with an alternative
embodiment.
[0027] Figure 1 shows a flow chart 100 illustrating a method for evaluating
performance of an object detection model according to an embodiment.
[0028] At step 102, a predicted bounding box representing an object is generated
based on the object detection model. The object is typically positioned proximate to
one or more adjacent objects. For example, the object may be a text string (e.g. a
word or phrase) that is proximate to other text strings (e.g. other words and
phrases within a sentence or paragraph). Parameters such as size and position of
the predicted bounding box may vary from one object detection model to another.
The performance of the object detection model is therefore dependent on the
predicted bounding box.
[0029] At step 104, an area of intersection between the predicted bounding box
and a groundtruth bounding box of the object is determined. Typically, a greater
area of intersection between the predicted bounding box and the groundtruth
19. 8
bounding box of the object indicates a greater degree of overlap between the
bounding boxes and may be more desirable than a smaller area.
[0030] At step 106, a modified area of union between the predicted bounding box
and the groundtruth bounding box of the object is determined by (1) determining a
weighted area of union between the predicted and groundtruth bounding boxes
based on one or more weights and (2) adding, to the weighted area of union, an
area of intersection between the predicted bounding box and at least one
groundtruth bounding box of one or more adjacent objects.
[0031] As further illustrated below with reference to Figure 2a, since the area of
union between two objects is a sum of the area of their intersection and the areas
of non-intersection, in the present examples, the weighted area of union between
the predicted and groundtruth bounding boxes may be determined by assigning
appropriate weights to respective constituent areas, including the areas of non-
intersection between the predicted bounding box and the groundtruth bounding box
of the object.
[0032] The area of intersection between the predicted bounding box and at least
one groundtruth bounding box of the one or more adjacent objects is further
illustrated with reference to Figure 2b.
[0033] The net result of step 106 is a modified area of union that is greater if the
predicted bounding box does not overlap well with the groundtruth bounding box of
the object and/or overlaps with the groundtruth bounding box(es) of adjacent
object(s). Typically, a greater modified area of union is less desirable than a
smaller area.
[0034] At step 108, a score equal to the area of intersection between the predicted
bounding box and the groundtruth bounding box of the object divided by the
modified area of union is determined. The score represents the performance of the
object detection model. For example, if the object detection model gives rise to a
greater modified area of union, the score is lower. On the other hand, if the object
detection model gives rise to a smaller modified area of union, the score is higher.
A score closer to 1 indicates a greater object detection accuracy.
[0035] Figures 2a and 2b show schematic diagrams illustrating an implementation
to determine the modified area of union in the method of Figure 1. In this
20. 9
implementation, the object and the adjacent objects are text strings containing
characters and numerals, and the bounding boxes are interchangeably referred to
textboxes. However, it will be appreciated by a person skilled in the art that text
detection is just an example, and that the method can be applied to other types of
object detection.
[0036] With reference to Figures 2a-2b, the predicted bounding box can be labeled
as set A, the groundtruth bounding box of the object can be labeled as set B, and
the groundtruth bounding boxes of adjacent objects can be labeled as sets C and
D respectively. Given labeled validation data, the sizes and positions of B, C and D
are known, while the size and position of A can vary based on the detection model
used. While the bounding boxes of two adjacent objects are shown in this example,
it will be appreciated that the calculation can be applied to a greater number of
adjacent objects.
[0037] The modified area of union !"#$%$&#'( ) *+ in step 106 of Figure 1 can be
represented as:
!"#$%$&#'( ) *+ , -&$./0&#'( ) *+ 1 ( 2 3 1 ( 2 4 (I)
where -&$./0&#'( ) *+ represents the weighted area of union
between sets A and B, ( 2 3 represents the intersection between
sets A and C, and ( 2 4 represents the intersection between sets A
and D. The areas corresponding to ( 2 3 and ( 2 4 are shown in
Figure 2b.
[0038] Referring to Figure 2a, while the standard area of union is the sum of the
area of intersection (( 2 *) and the areas of non-intersection (A1 and B1), the
value of -&$./0&#'( ) *+ in equation (I) is determined by taking into the
consideration that, a greater B1 means a smaller intersection with the groundtruth
bounding box, whereas a greater A1 leads to greater union. Therefore, in the
present embodiments, B1 is penalized more than A1.
[0039] For example, -&$./0&#'( ) *+ can be expressed as:
-&$./0&#'( ) *+ , -5(5 1 -6*5 1 '( 2 *+ (II)
In equation (II), weights -5 and -6 are used to adjust penalties for A1 and B1.
Greater B1 leads to smaller intersection, whereas greater A1 leads to greater
21. 10
union. Therefore, B1 should be penalized more, which means -5 < -6. The exact
value of -5 and -6 may be selected based on e.g. practical data and may vary
depending on the situation. In one non-limiting example, -5 + -6= 2. The areas
corresponding to A1, B1 and ( 2 *7are shown in Figure 2a.
[0040] By combining the above two equations (I) and (II):
!"#$%$&#'( ) *+ , -5(5 1 -6*5 1 '( 2 *+ 1 ( 2 3 1 ( 2 4 (III)
[0041] Referring to step 108 of Figure 1, the score can be calculated as:
89":& ,
;2<
=>? ! "?';)<+
,
;2<
#$;$%#&<$%';2<+%;2'%;2(
(IV)
In equations (III) and (IV), -5 < -6 and in one non-limiting example, -5 + -6= 2.
[0042] As can be seen from the above equation (IV), while the numerator is the
same as that used to determine the tradition IoU, the denominator is adapted to
penalize (1) more on a small intersection between sets A and B and less on a
greater union between sets A and B (as -5 < -6), and (2) any intersection between
sets A and C or between sets A and D (as the components ( 2 3 and7( 2 4 are
non-zero). In other words, the score is lower if either or both of conditions (1) and
(2), which are indicative of a low performance by the object detection model,
happen. Conversely, if there is a large intersection between sets A and B, and no
intersection between sets A and C or between sets A and D, the score is high. A
higher score (i.e. closer to 1) represents a better accuracy of the object detection
model.
[0043] The evaluation method as described above can be used for object detection
by first identifying the appropriate model for the detection task before using that
model for the actual data to be analysed. Figure 3 shows a flow chart 300
illustrating an object detection method according to an embodiment.
[0044] At step 302, unlabeled actual data and a plurality of object detection models
are received. The object detection models may be generated by a neural network,
e.g. a convolutional neural network, based on labeled training data. The unlabeled
actual data may be in the form of image data of an optical image of the object. For
example, the optical image may be a scanned document or a photograph of a
22. 11
document uploaded by a customer which contains textual information that the
customer wishes to provide.
[0045] At step 304, given labeled validation data, each of the object detection
models is evaluated using the method as described above with reference to
Figures 1 and 2a-2b, to identify the object detection model having the highest
score. At step 306, the identified object detection model is used to detect an object
in the unlabeled actual data.
[0046] In an embodiment of the object detection method of Figure 3, the object
detection models in step 302 may have associated hyperparameters, and the
object detection model with the associated hyperparameters having the highest
score is identified in step 306. The identified model and associated
hyperparameters are then used to detect the object in the unlabeled actual data.
[0047] Figure 4 shows a schematic diagram illustrating an object detector 400
according to an embodiment. The object detector includes a receiver module 402
coupled to an apparatus 404. The apparatus 404 includes an object detection
module 406 and an evaluation module 408. Typically, the evaluation module 408 is
provided with a set of labeled validation data 410. The receiver module 402 is
configured to receive unlabeled actual data and a plurality of object detection
models. The object detection models are generated by a neural network based on
labeled training data. The apparatus 404 can evaluate each of the object detection
models based on the validation data to identify the object detection model having
the highest score.
[0048] In an example, the object detection module 406 is configured to generate a
predicted bounding box representing an object based on an object detection
model. The object is positioned proximate to one or more adjacent objects. The
evaluation module 408 is configured to determine an area of intersection between
the predicted bounding box and a groundtruth bounding box of the object, and
determine a modified area of union between the predicted bounding box and the
groundtruth bounding box of the object. The modified area of union is a sum of a
weighted area of union between the predicted and groundtruth bounding boxes
based on one or more weights and an area of intersection between the predicted
bounding box and at least one groundtruth bounding box of the one or more
adjacent objects. The evaluation module 408 is further configured to determine a
score equal to the area of intersection between the predicted bounding box and the
23. 12
groundtruth bounding box of the object divided by the modified area of union, and
evaluate a performance of the object detection model based on the score. The
steps are performed for each of the object detection models to identify the object
detection model having the highest score.
[0049] The object detection module 406 is can then detect an object in the
unlabeled actual data based on the identified object detection model.
[0050] The methods, systems and devices as described when applied to text
detection can improve the performance of model selection in textbox detection and
increase the final accuracy of OCR. A detection model that provides a small
intersection between the predicted textbox and the groundtruth textbox of the
target text string outputs data that may be incomplete or truncated. Likewise, a
detection model that provides some degree of intersection between the predicted
textbox and groundtruth textboxes of adjacent text strings outputs data that may be
noisy or inaccurate. According to the present embodiments, such detection models
have low evaluation scores and will not be selected. Instead, a model that provides
a large intersection between the predicted textbox and the groundtruth textbox of
the target text string, and no intersection between the predicted textbox and
groundtruth textboxes of adjacent text strings, has a high evaluation score and will
be selected. When used to detect textboxes in a real/actual optical image, the
selected model can output accurate data which can help to improve the accuracy
of subsequent OCR steps.
[0051] Figure 5 depicts an exemplary computing device 500, hereinafter
interchangeably referred to as a computer system 500, where one or more such
computing devices 500 may be used for the object detector 400 of Figure 4, or for
implementing some or all steps of the methods of Figures 1 and 3. The following
description of the computing device 500 is provided by way of example only and is not
intended to be limiting.
[0052] As shown in Figure 5, the example computing device 500 includes a processor
504 for executing software routines. Although a single processor is shown for the sake
of clarity, the computing device 500 may also include a multi-processor system. The
processor 504 is connected to a communication infrastructure 506 for communication
with other components of the computing device 500. The communication infrastructure
506 may include, for example, a communications bus, cross-bar, or network.
24. 13
[0053] The computing device 500 further includes a main memory 508, such as a
random access memory (RAM), and a secondary memory 510. The secondary
memory 510 may include, for example, a hard disk drive 512 and/or a removable
storage drive 514, which may include a floppy disk drive, a magnetic tape drive, an
optical disk drive, or the like. The removable storage drive 514 reads from and/or writes
to a removable storage unit 518 in a well-known manner. The removable storage unit
518 may include a floppy disk, magnetic tape, optical disk, or the like, which is read by
and written to by removable storage drive 514. As will be appreciated by persons
skilled in the relevant art(s), the removable storage unit 518 includes a computer
readable storage medium having stored therein computer executable program code
instructions and/or data.
[0054] In an alternative implementation, the secondary memory 510 may additionally
or alternatively include other similar means for allowing computer programs or other
instructions to be loaded into the computing device 500. Such means can include, for
example, a removable storage unit 522 and an interface 520. Examples of a removable
storage unit 522 and interface 520 include a program cartridge and cartridge interface
(such as that found in video game console devices), a removable memory chip (such
as an EPROM or PROM) and associated socket, and other removable storage units
522 and interfaces 520 which allow software and data to be transferred from the
removable storage unit 522 to the computer system 500.
[0055] The computing device 500 also includes at least one communication interface
524. The communication interface 524 allows software and data to be transferred
between computing device 500 and external devices via a communication path 526. In
various embodiments of the inventions, the communication interface 524 permits data
to be transferred between the computing device 500 and a data communication
network, such as a public data or private data communication network. The
communication interface 524 may be used to exchange data between different
computing devices 500 which such computing devices 500 form part an interconnected
computer network. Examples of a communication interface 524 can include a modem,
a network interface (such as an Ethernet card), a communication port, an antenna with
associated circuitry and the like. The communication interface 524 may be wired or
may be wireless. Software and data transferred via the communication interface 524
are in the form of signals which can be electronic, electromagnetic, optical or other
signals capable of being received by communication interface 524. These signals are
provided to the communication interface via the communication path 526.
25. 14
[0056] As shown in Figure 5, the computing device 500 further includes a display
interface 502 which performs operations for rendering images to an associated display
530 and an audio interface 532 for performing operations for playing audio content via
associated speaker(s) 534.
[0057] As used herein, the term "computer program product" may refer, in part, to
removable storage unit 518, removable storage unit 522, a hard disk installed in hard
disk drive 512, or a carrier wave carrying software over communication path 526
(wireless link or cable) to communication interface 524. Computer readable storage
media refers to any non-transitory tangible storage medium that provides recorded
instructions and/or data to the computing device 500 for execution and/or processing.
Examples of such storage media include floppy disks, magnetic tape, CD-ROM, DVD,
Blu-rayTM
Disc, a hard disk drive, a ROM or integrated circuit, USB memory, a
magneto-optical disk, or a computer readable card such as a PCMCIA card and the
like, whether or not such devices are internal or external of the computing device 500.
Examples of transitory or non-tangible computer readable transmission media that may
also participate in the provision of software, application programs, instructions and/or
data to the computing device 500 include radio or infra-red transmission channels as
well as a network connection to another computer or networked device, and the
Internet or Intranets including e-mail transmissions and information recorded on
Websites and the like.
[0058] The computer programs (also called computer program code) are stored in
main memory 508 and/or secondary memory 510. Computer programs can also be
received via the communication interface 524. Such computer programs, when
executed, enable the computing device 500 to perform one or more features of
embodiments discussed herein. In various embodiments, the computer programs,
when executed, enable the processor 504 to perform features of the above-described
embodiments. Accordingly, such computer programs represent controllers of the
computer system 500.
[0059] Software may be stored in a computer program product and loaded into the
computing device 500 using the removable storage drive 514, the hard disk drive 512,
or the interface 520. Alternatively, the computer program product may be downloaded
to the computer system 500 over the communications path 526. The software, when
26. 15
executed by the processor 504, causes the computing device 500 to perform functions
of embodiments described herein.
[0060] It is to be understood that the embodiment of Figure 5 is presented merely by
way of example. Therefore, in some embodiments one or more features of the
computing device 500 may be omitted. Also, in some embodiments, one or more
features of the computing device 500 may be combined together. Additionally, in some
embodiments, one or more features of the computing device 500 may be split into one
or more component parts.
[0061] It will be appreciated that the elements illustrated in Figure 5 function to provide
means for performing the various functions and operations of the servers as described
in the above embodiments.
[0062] In an implementation, a server may be generally described as a physical device
comprising at least one processor and at least one memory including computer
program code. The at least one memory and the computer program code are
configured to, with the at least one processor, cause the physical device to perform the
requisite operations.
[0063] It will be appreciated by a person skilled in the art that numerous variations
and/or modifications may be made to the present invention as shown in the specific
embodiments without departing from the spirit or scope of the invention as broadly
described. For example, methods, systems and devices as described can be suitably
adapted for different types of object detection, e.g. facial detection or vehicle detection.
The present embodiments are, therefore, to be considered in all respects to be
illustrative and not restrictive.
27. 16
CLAIMS
1. A method for evaluating performance of an object detection model, the
method comprising the steps of:
generating a predicted bounding box representing an object based on the
object detection model, wherein the object is positioned proximate to one or more
adjacent objects;
determining an area of intersection between the predicted bounding box
and a groundtruth bounding box of the object;
determining a modified area of union between the predicted bounding box
and the groundtruth bounding box of the object, wherein determining the modified
area of union comprises:
determining a weighted area of union between the predicted and
groundtruth bounding boxes based on one or more weights; and
adding to the weighted area of union an area of intersection between
the predicted bounding box and at least one groundtruth bounding box of
the one or more adjacent objects; and
determining a score equal to the area of intersection between the predicted
bounding box and the groundtruth bounding box of the object divided by the
modified area of union, wherein the score represents the performance of the object
detection model.
2. The method as claimed in claim 1, wherein the object comprises a text
string, and wherein the adjacent objects comprise adjacent text strings.
3. The method as claimed in claim 1 or 2, wherein the one or more weights
comprise:
a predetermined first weight associated with a portion of the predicted
bounding box not intersecting with the groundtruth bounding box of the object,
a predetermined second weight associated with a portion of the groundtruth
bounding box of the object not intersecting with the predicted bounding box; and
wherein the first weight is less than the second weight.
4. The method as claimed in claim 3, wherein determining the weighted area
of union comprises assigning the first and second weights to the associated
28. 17
portions and summing weighted areas of said portions with the area of intersection
between the predicted bounding box and groundtruth bounding box of the object.
5. The method as claimed in any one of the preceding claims, wherein a score
closer to 1 represents a higher accuracy of the object detection model.
6. An object detection method comprising:
receiving unlabeled actual data and a plurality of object detection models,
wherein the object detection models are generated by a neural network based on
labeled training data;
evaluating each of the object detection models with validation data using the
method as claimed in any one of the preceding claims to identify the object
detection model having the highest score; and
detecting an object in the unlabeled actual data based on the identified
object detection model.
7. The method as claimed in claim 6, wherein receiving the object detection
models comprises receiving hyperparameters associated with the respective
models, and wherein the object detection model together with the associated one
or more hyperparameters having the highest score is identified.
8. The method as claimed in claim 6 or 7, wherein detecting the object in the
unlabeled actual data comprises detecting a text string in image data of an optical
image of the object.
9. A system for evaluating an object detection model, comprising:
a processor; and
a computer-readable memory coupled to the processor and having
instructions stored thereon that are executable by the processor to:
generate a predicted bounding box representing an object based on
the object detection model, wherein the object is positioned proximate to
one or more adjacent objects;
determine an area of intersection between the predicted bounding
box and a groundtruth bounding box of the object;
29. 18
determine a modified area of union between the predicted bounding
box and the groundtruth bounding box of the object, wherein the modified
area of union comprises a sum of:
a weighted area of union between the predicted and
groundtruth bounding boxes based on one or more weights; and
an area of intersection between the predicted bounding box
and at least one groundtruth bounding box of the one or more
adjacent objects; and
determine a score equal to the area of intersection between the
predicted bounding box and the groundtruth bounding box of the object
divided by the modified area of union, wherein the score represents the
performance of the object detection model.
10. The system as claimed in claim 9, wherein the object comprises a text
string, and wherein the adjacent objects comprise adjacent text strings.
11. The system as claimed in claim 9 or 10, wherein the one or more weights
comprise:
a predetermined first weight associated with a portion of the predicted
bounding box not intersecting with the groundtruth bounding box of the object,
a predetermined second weight associated with a portion of the groundtruth
bounding box of the object not intersecting with the predicted bounding box; and
wherein the first weight is less than the second weight.
12. The system as claimed in claim 11, wherein the instructions are executable
by the processor to assign the first and second weights to the associated portions
and sum weighted areas of said portions with the area of intersection between the
predicted bounding box and the groundtruth bounding box of the object to
determine the weighted area of union.
13. The system as claimed in any one claims 9 to 12, wherein a score closer to
1 represents a higher accuracy of the object detection model.
14. An apparatus comprising:
30. 19
an object detection module configured to generate a predicted bounding
box representing an object based on an object detection model, wherein the object
is positioned proximate to one or more adjacent objects; and
an evaluation module configured to:
determine an area of intersection between the predicted bounding
box and a groundtruth bounding box of the object;
determine a modified area of union between the predicted bounding
box and the groundtruth bounding box of the object, wherein the modified
are of union comprises a sum of:
a weighted area of union between the predicted and
groundtruth bounding boxes based on one or more weights; and
an area of intersection between the predicted bounding box
and at least one groundtruth bounding box of the one or more
adjacent objects;
determine a score equal to the area of intersection between the
predicted bounding box and the groundtruth bounding box of the object
divided by the modified area of union; and
evaluate a performance of the object detection model based on the
score.
15. An object detector comprising:
a receiver module configured to receive unlabeled actual data and a
plurality of object detection models, wherein the object detection models are
generated by a neural network based on labeled training data; and
the apparatus as claimed in claim 14 coupled to the receiver module and
configured to evaluate each of the object detection models with validation data to
identify the object detection model having the highest score;
wherein the object detection module is further configured to detect an object
in the unlabeled actual data based on the identified object detection model.
16. The object detector as claimed in claim 15, wherein the input module is
further configured to receive hyperparameters associated with the respective
object detection models, and wherein the object detection model together with the
associated one or more hyperparameters having the highest score is identified.
31. 20
17. The object detector as claimed in claim 15 or 16, wherein the object
comprises a text string, and the unlabeled actual data comprises image data of an
optical image of the object.