AI-SDV 2022: Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity Susanne Tropf (Syngenta, Switzerland) Kornel Marko (Averbis, Germany)
Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity
Susanne Tropf (Syngenta, Switzerland)
Kornel Marko (Averbis, Germany)
Canalizing the Maelstrom of Metadata - Extensions on the Hourglass ModelBrecht Declercq
This presentation describes the Hourglass Model, a theoretical framework for audiovisual archives to develop or let evolve their (mainly descriptive) metadata creation strategy, taking into account recent evolutions in automatically extracted metadata, user generated metadata and linked metadata.
As presented at the CCAAA Joint Technical Symposium, Singapore, 7 March 2016
The StuffNPack Solution is software that ensures the correct dispatch of goods from manufacturers. It generates packing reports with barcode scanning to check for the right product, quantity, and sequence of goods being dispatched. All data is securely stored on the server. The software costs Rs. 40,000 initially and Rs. 10,000 annually for maintenance. It is intended to help the logistics units of manufacturing and warehouse companies efficiently manage their dispatch processes.
This document summarizes the new features and capabilities of the VITEK 2 Compact system for microbial identification. Key points include:
- The system has redesigned test cards with 64 wells, pre-inserted straws, and barcodes for improved accuracy, fewer manual steps, and full traceability.
- Four new test cards (GN, GP, BCL, YST) have been developed to identify different types of microorganisms, achieving identification rates of 85-100% within 10-18 hours.
- The system features an intuitive interface, automated functions to reduce manual steps and increase productivity, and software compliant with regulatory standards.
This document provides a tutorial on using the MultiQuant software to perform quantitative analysis of mass spectrometry data acquired using multiple reaction monitoring (MRM). It describes how to perform both relative and accurate quantitation using MRM data. For relative quantitation, it shows how to create results tables and reports to compare sample quantities. For accurate quantitation, it demonstrates how to generate calibration curves from standard samples and use them to determine concentrations in unknown samples.
Engineering plant facilities 16 artificial intelligence in manufacturing oper...Luis Cabrera
The document discusses using artificial intelligence and computer systems to manage manufacturing operations more efficiently. It describes how current operations management involves many repetitive meetings and inefficiencies. An AI system could integrate existing ERP and automation data to make instant operational decisions in real-time without as many meetings. The system would use algorithms and simulations to evaluate solutions and provide a dashboard for managers to monitor production status across all functions. This could help address current problems and improve strategic thinking compared to preparing reports.
This document provides instructions for sampling cassava plants for genotyping using the Cassavabase database and Coordinate mobile app. It describes how to:
1. Design a field trial and generate barcodes in Cassavabase
2. Use the Coordinate app to collect samples according to the trial template and export the sample data
3. Upload the template and sample data to Cassavabase
4. Submit the genotyping order to the service provider
The goal is to efficiently collect samples from the field trial and prepare the data for genotyping.
Fresh food producers are increasingly using barcode technology to meet traceability and compliance requirements. Barcode labeling in the field, production lines, and storage/shipping helps producers identify products through the supply chain. Software solutions track inventory, lot numbers, and shipments for growers, processors, and shippers. An effective system provides ease of use, detailed traceability, and flexibility for commingling and repackaging while maintaining speed for perishable foods.
Canalizing the Maelstrom of Metadata - Extensions on the Hourglass ModelBrecht Declercq
This presentation describes the Hourglass Model, a theoretical framework for audiovisual archives to develop or let evolve their (mainly descriptive) metadata creation strategy, taking into account recent evolutions in automatically extracted metadata, user generated metadata and linked metadata.
As presented at the CCAAA Joint Technical Symposium, Singapore, 7 March 2016
The StuffNPack Solution is software that ensures the correct dispatch of goods from manufacturers. It generates packing reports with barcode scanning to check for the right product, quantity, and sequence of goods being dispatched. All data is securely stored on the server. The software costs Rs. 40,000 initially and Rs. 10,000 annually for maintenance. It is intended to help the logistics units of manufacturing and warehouse companies efficiently manage their dispatch processes.
This document summarizes the new features and capabilities of the VITEK 2 Compact system for microbial identification. Key points include:
- The system has redesigned test cards with 64 wells, pre-inserted straws, and barcodes for improved accuracy, fewer manual steps, and full traceability.
- Four new test cards (GN, GP, BCL, YST) have been developed to identify different types of microorganisms, achieving identification rates of 85-100% within 10-18 hours.
- The system features an intuitive interface, automated functions to reduce manual steps and increase productivity, and software compliant with regulatory standards.
This document provides a tutorial on using the MultiQuant software to perform quantitative analysis of mass spectrometry data acquired using multiple reaction monitoring (MRM). It describes how to perform both relative and accurate quantitation using MRM data. For relative quantitation, it shows how to create results tables and reports to compare sample quantities. For accurate quantitation, it demonstrates how to generate calibration curves from standard samples and use them to determine concentrations in unknown samples.
Engineering plant facilities 16 artificial intelligence in manufacturing oper...Luis Cabrera
The document discusses using artificial intelligence and computer systems to manage manufacturing operations more efficiently. It describes how current operations management involves many repetitive meetings and inefficiencies. An AI system could integrate existing ERP and automation data to make instant operational decisions in real-time without as many meetings. The system would use algorithms and simulations to evaluate solutions and provide a dashboard for managers to monitor production status across all functions. This could help address current problems and improve strategic thinking compared to preparing reports.
This document provides instructions for sampling cassava plants for genotyping using the Cassavabase database and Coordinate mobile app. It describes how to:
1. Design a field trial and generate barcodes in Cassavabase
2. Use the Coordinate app to collect samples according to the trial template and export the sample data
3. Upload the template and sample data to Cassavabase
4. Submit the genotyping order to the service provider
The goal is to efficiently collect samples from the field trial and prepare the data for genotyping.
Fresh food producers are increasingly using barcode technology to meet traceability and compliance requirements. Barcode labeling in the field, production lines, and storage/shipping helps producers identify products through the supply chain. Software solutions track inventory, lot numbers, and shipments for growers, processors, and shippers. An effective system provides ease of use, detailed traceability, and flexibility for commingling and repackaging while maintaining speed for perishable foods.
TechDays 2010 Portugal - Scaling your data tier with app fabric 16x9Nuno Godinho
This document discusses using Windows Server AppFabric caching to scale data layers. AppFabric caching provides a distributed, in-memory cache that can span machines and processes. It addresses issues like limited cache memory on individual servers. The document outlines how AppFabric caching works, how to install and configure it, and how to access the cache through the API. It also describes features like data distribution, eviction policies, and change notifications that allow the cache to efficiently scale to large workloads and data sets.
Buyers appreciate the catalog experience; they certainly don’t miss the avalanche of item listings. Sellers also like providing catalog experiences; their items get more exposure. With eBay’s built-in product definitions, sellers don’t need to take valuable time writing product descriptions or item specifics or even providing photos—eBay’s built-in product definitions take goods quickly to market. New this year: creating your own product listing within the eBay catalog using UPC codes and brand MPNs.
End To End Traceability – The Rising Challenge for Bakers.pdfSG Systems Global
The history of bread baking can be traced back to the Stone Age period at least 30,000 years ago. The Roman Empire is credited with creating the earliest known commercial baking processes in 300 BC. To be a baker was an important job and a highly respected and artistic profession.
1010 introducing the new sap global batch traceability gbt key to manage qual...Sri Kumar
This document discusses SAP Global Batch Traceability (GBT), a solution that allows companies to trace products through the supply chain and manage quality investigations and product recalls. GBT provides a single view of batch genealogy and distribution across systems, enabling fast identification of affected products to support precise product withdrawals and recalls. The document outlines key shortcomings of current batch management capabilities, and describes how GBT embedded in the product issue resolution process allows exploration of batch networks and single-run reporting to inform necessary actions.
Labeling in Genebanks - 2015 - Edwin RojasEdwin Rojas
This document discusses the use of barcode technologies in genebanks. It provides an overview of barcode concepts including symbologies, comparison of 1D and 2D barcodes, print technologies, and IP protection classes. It also summarizes the evolution and current status of the CIP barcode kit, use cases where barcodes have been implemented, and components of a barcode kit.
Slides of talk given at London Study of Enterprise Agile Meetup in June 2019.
We go over GitOps and how it affects delivery speed in software development and release.
Real time trend and failure analysis using TTA-Anand Bagmar & Aasawaree Deshmukhbhumika2108
This document describes Test Trend Analyzer (TTA), a tool that provides visualizations and reports on test automation results to gauge the health of a product portfolio. TTA collects test run data, performs trend and failure analysis, and generates customizable reports. It integrates with continuous integration systems to automatically collect results and provide stakeholders with a single click view of test status.
This document summarizes the CIP Genebank IT platform, which uses barcoding and mobile apps to manage one of the world's largest ex situ plant genetic resource collections. Key components of the platform include rugged mobile devices, barcode scanners, printers, labels, and a centralized GRIN-Global database. Various apps help with tasks like tracking accessions in vitro, in cryo storage, during field collection, and more. The platform aims to improve efficiency, data quality, and interoperability using international standards.
This document describes Pypet, a Python parameter exploration toolbox. Pypet allows for easy exploration of parameter spaces and storage of simulation results and parameters. It revolves around a trajectory container, which uses a tree data structure to manage parameters and results in a natural naming scheme. Pypet supports a variety of data formats and storage via HDF5. It provides tools for disentangling simulations from I/O, logging, version control integration, and parallelization. Pypet is open source, well tested, and documented.
The document is a product catalog for Creative Safety Supply that includes information about various products and services offered. It provides an overview of labeling and signage products including LabelTac printers and supplies in various sizes, labels, pipe marking labels, and accessories. It also describes floor marking tapes, signs, safety products, and measurement tools. The catalog highlights features like same-day shipping, custom products, and industry-leading warranties. It provides ordering information and states there is a 30-day return policy as well as payment and purchase order details.
AI-SDV 2022: Machine learning based patent categorization: A success story in...Dr. Haxel Consult
Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity
Susanne Tropf (Syngenta, Switzerland)
Kornel Marko (Averbis, Germany)
This document provides an introduction to the tools, coding standards, and codebase layout for developers working on InfoPay v5. It outlines best practices for using NetBeans as the IDE, ActiveCollab for project management, and Git for version control. Coding standards for PHP, JavaScript, and markup are defined, covering comments, errors, file paths, naming conventions, code style, and code libraries. The codebase is divided into core, frontend, and Yii sections, with guidelines on file locations, models/repositories, controllers/actions, and widgets. Credentials and access controls are also addressed.
Mycotoxin Analysis in your hand – RIDA®SMART APP R-Biopharm AG
The RIDA®SMART APP is a complete new evaluation technology for the quantification of mycotoxins. This technology evaluates lateral flow tests (LFDs), which are used for immunochromatographic analysis of mycotoxins. The app provides reliable and accurate results, which can be forwarded via e-mail or easily sent to any supported printer. The app represents a more inexpensive, easier and faster alternative to conventional LFD reading devices.
Danone-a practitioner approach to packaging line productivity by Mathieu Lora...Monique Watkins
Mat shares Unique Strategies for Improving Packaging Line Productivity. You don't want to miss this. Recorded webinar can be found at http://www.plantseminars.com
The document discusses the use of barcode technology in seafood processing to improve traceability, productivity and compliance with labeling requirements. It describes the different challenges for rapidly processing variable products like salmon versus bulk processing of uniform products like shrimp. Mobile barcode printing and selection of attributes from a touchscreen can help address the need for quick, accurate labeling of diverse catch. Maintaining lot traceability during commingling or repackaging requires carton-based tracking in the software. Shipment verification when loading trucks prevents disputes over missing cartons. The key is choosing a flexible barcode system that handles the unique needs for identifying, tracking and documenting seafood from boat to market.
Code generation in Magento 2 automatically generates code to handle dependency injection, interception, and the service layer. Key types of generated code include factories for object instantiation, proxies for optional dependencies, interceptors for plugins, repositories for the service layer, extension attributes, and loggers. This code generation improves abstraction, avoids boilerplate code, and enables features like dependency injection, aspect-oriented programming and generic programming in Magento 2.
Use Cases for Big Data and the Connected EnterpriseESE, Inc.
This presentation showcases uses for big data in maintaining and improving production quality, identifying/quantifying opportunities for cost reduction, and a walk-through of a Connected Enterprise application.
WJAX 2019 - Taking Distributed Tracing to the next levelFrank Pfleger
The shift from monolithic applications to microservices led to many new challenges we haven’t had before. Especially analyzing problems and tracking down erroneous components of a distributed system has become much more difficult as slicing and decoupling applications advances. We now have to answer questions like: How do we find out which services were involved when processing a specific request and how long did it take to respond? How do we figure out which service is causing a request to fail and why? These issues are addressed by Distributed Tracing tools like Zipkin, Jaeger, OpenTracing and OpenCensus. But how can we leverage the data we are gathering using such tools to gain new insights into our business processes, instead of just focussing on the technical aspects?
Computational practices for reproducible scienceGael Varoquaux
Reconciling bleeding-edge scientific results and reproducible research may seem a conundrum in our fast-paced high-pressure academic world. I discuss the practices that I found useful in computational work. At a high level, it is important to navigate the space between rapid experimentation and industrial-grade software development. I advocate adopting more and more software-engineering best practices as a project matures. I will also discuss how to turn the computational work into libraries, and to ensure the quality of the resulting libraries. And I conclude on how those libraries need to fit in the larger picture of the exercise of research to give better science.
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementDr. Haxel Consult
This document describes a metadata list of patent holders in various countries and regions compiled by Muchiu (Henry) Chang. The list is sorted geographically and includes patent holder names from Canada, China, Hong Kong, Macao, Taiwan, the Middle East, and Europe between 2009-2022. Key features include Chinese-English compatibility and use of open source intelligence. The list has previously been utilized by the Region of Peel in Ontario, Canada.
AI-SDV 2022: Creation and updating of large Knowledge Graphs through NLP Anal...Dr. Haxel Consult
Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed.
Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.
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This document discusses using Windows Server AppFabric caching to scale data layers. AppFabric caching provides a distributed, in-memory cache that can span machines and processes. It addresses issues like limited cache memory on individual servers. The document outlines how AppFabric caching works, how to install and configure it, and how to access the cache through the API. It also describes features like data distribution, eviction policies, and change notifications that allow the cache to efficiently scale to large workloads and data sets.
Buyers appreciate the catalog experience; they certainly don’t miss the avalanche of item listings. Sellers also like providing catalog experiences; their items get more exposure. With eBay’s built-in product definitions, sellers don’t need to take valuable time writing product descriptions or item specifics or even providing photos—eBay’s built-in product definitions take goods quickly to market. New this year: creating your own product listing within the eBay catalog using UPC codes and brand MPNs.
End To End Traceability – The Rising Challenge for Bakers.pdfSG Systems Global
The history of bread baking can be traced back to the Stone Age period at least 30,000 years ago. The Roman Empire is credited with creating the earliest known commercial baking processes in 300 BC. To be a baker was an important job and a highly respected and artistic profession.
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This document discusses SAP Global Batch Traceability (GBT), a solution that allows companies to trace products through the supply chain and manage quality investigations and product recalls. GBT provides a single view of batch genealogy and distribution across systems, enabling fast identification of affected products to support precise product withdrawals and recalls. The document outlines key shortcomings of current batch management capabilities, and describes how GBT embedded in the product issue resolution process allows exploration of batch networks and single-run reporting to inform necessary actions.
Labeling in Genebanks - 2015 - Edwin RojasEdwin Rojas
This document discusses the use of barcode technologies in genebanks. It provides an overview of barcode concepts including symbologies, comparison of 1D and 2D barcodes, print technologies, and IP protection classes. It also summarizes the evolution and current status of the CIP barcode kit, use cases where barcodes have been implemented, and components of a barcode kit.
Slides of talk given at London Study of Enterprise Agile Meetup in June 2019.
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This document describes Test Trend Analyzer (TTA), a tool that provides visualizations and reports on test automation results to gauge the health of a product portfolio. TTA collects test run data, performs trend and failure analysis, and generates customizable reports. It integrates with continuous integration systems to automatically collect results and provide stakeholders with a single click view of test status.
This document summarizes the CIP Genebank IT platform, which uses barcoding and mobile apps to manage one of the world's largest ex situ plant genetic resource collections. Key components of the platform include rugged mobile devices, barcode scanners, printers, labels, and a centralized GRIN-Global database. Various apps help with tasks like tracking accessions in vitro, in cryo storage, during field collection, and more. The platform aims to improve efficiency, data quality, and interoperability using international standards.
This document describes Pypet, a Python parameter exploration toolbox. Pypet allows for easy exploration of parameter spaces and storage of simulation results and parameters. It revolves around a trajectory container, which uses a tree data structure to manage parameters and results in a natural naming scheme. Pypet supports a variety of data formats and storage via HDF5. It provides tools for disentangling simulations from I/O, logging, version control integration, and parallelization. Pypet is open source, well tested, and documented.
The document is a product catalog for Creative Safety Supply that includes information about various products and services offered. It provides an overview of labeling and signage products including LabelTac printers and supplies in various sizes, labels, pipe marking labels, and accessories. It also describes floor marking tapes, signs, safety products, and measurement tools. The catalog highlights features like same-day shipping, custom products, and industry-leading warranties. It provides ordering information and states there is a 30-day return policy as well as payment and purchase order details.
AI-SDV 2022: Machine learning based patent categorization: A success story in...Dr. Haxel Consult
Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity
Susanne Tropf (Syngenta, Switzerland)
Kornel Marko (Averbis, Germany)
This document provides an introduction to the tools, coding standards, and codebase layout for developers working on InfoPay v5. It outlines best practices for using NetBeans as the IDE, ActiveCollab for project management, and Git for version control. Coding standards for PHP, JavaScript, and markup are defined, covering comments, errors, file paths, naming conventions, code style, and code libraries. The codebase is divided into core, frontend, and Yii sections, with guidelines on file locations, models/repositories, controllers/actions, and widgets. Credentials and access controls are also addressed.
Mycotoxin Analysis in your hand – RIDA®SMART APP R-Biopharm AG
The RIDA®SMART APP is a complete new evaluation technology for the quantification of mycotoxins. This technology evaluates lateral flow tests (LFDs), which are used for immunochromatographic analysis of mycotoxins. The app provides reliable and accurate results, which can be forwarded via e-mail or easily sent to any supported printer. The app represents a more inexpensive, easier and faster alternative to conventional LFD reading devices.
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Mat shares Unique Strategies for Improving Packaging Line Productivity. You don't want to miss this. Recorded webinar can be found at http://www.plantseminars.com
The document discusses the use of barcode technology in seafood processing to improve traceability, productivity and compliance with labeling requirements. It describes the different challenges for rapidly processing variable products like salmon versus bulk processing of uniform products like shrimp. Mobile barcode printing and selection of attributes from a touchscreen can help address the need for quick, accurate labeling of diverse catch. Maintaining lot traceability during commingling or repackaging requires carton-based tracking in the software. Shipment verification when loading trucks prevents disputes over missing cartons. The key is choosing a flexible barcode system that handles the unique needs for identifying, tracking and documenting seafood from boat to market.
Code generation in Magento 2 automatically generates code to handle dependency injection, interception, and the service layer. Key types of generated code include factories for object instantiation, proxies for optional dependencies, interceptors for plugins, repositories for the service layer, extension attributes, and loggers. This code generation improves abstraction, avoids boilerplate code, and enables features like dependency injection, aspect-oriented programming and generic programming in Magento 2.
Use Cases for Big Data and the Connected EnterpriseESE, Inc.
This presentation showcases uses for big data in maintaining and improving production quality, identifying/quantifying opportunities for cost reduction, and a walk-through of a Connected Enterprise application.
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The shift from monolithic applications to microservices led to many new challenges we haven’t had before. Especially analyzing problems and tracking down erroneous components of a distributed system has become much more difficult as slicing and decoupling applications advances. We now have to answer questions like: How do we find out which services were involved when processing a specific request and how long did it take to respond? How do we figure out which service is causing a request to fail and why? These issues are addressed by Distributed Tracing tools like Zipkin, Jaeger, OpenTracing and OpenCensus. But how can we leverage the data we are gathering using such tools to gain new insights into our business processes, instead of just focussing on the technical aspects?
Computational practices for reproducible scienceGael Varoquaux
Reconciling bleeding-edge scientific results and reproducible research may seem a conundrum in our fast-paced high-pressure academic world. I discuss the practices that I found useful in computational work. At a high level, it is important to navigate the space between rapid experimentation and industrial-grade software development. I advocate adopting more and more software-engineering best practices as a project matures. I will also discuss how to turn the computational work into libraries, and to ensure the quality of the resulting libraries. And I conclude on how those libraries need to fit in the larger picture of the exercise of research to give better science.
Similar to AI-SDV 2022: Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity Susanne Tropf (Syngenta, Switzerland) Kornel Marko (Averbis, Germany) (20)
AI-SDV 2022: Henry Chang Patent Intelligence and Engineering ManagementDr. Haxel Consult
This document describes a metadata list of patent holders in various countries and regions compiled by Muchiu (Henry) Chang. The list is sorted geographically and includes patent holder names from Canada, China, Hong Kong, Macao, Taiwan, the Middle East, and Europe between 2009-2022. Key features include Chinese-English compatibility and use of open source intelligence. The list has previously been utilized by the Region of Peel in Ontario, Canada.
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Knowledge Graphs are an increasingly relevant approach to store detailed knowledge in many domains. Recent advances in NLP allow to enrich Knowledge Graphs through automated analysis of large volumes of literature, reducing a lot the efforts in traditional manual information capturing. In our presentation we report the approach taken in a project with partner Fraunhofer SCAI in the life sciences where a knowledge graph organising detailed facts about psychiatric diseases has been computed.
Information of cause-effect relations between proteins, genes, drugs and diseases has been encoded in the BEL (Biological Expression Language) and imported into a Graph database to approach an indication-wide Knowledge Graph for the selected therapeutic area. Ultimately, updating the graph will amount to just rerunning the analysis on the newly published literature.
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IP rights create an incentive for R&D which ultimately leads to innovation. Analysis and insights from IP data can therefore help provide a better understanding of how the IP system is being used and where and what innovation is taking place. Research and analysis of IP data is a key input to the ongoing work of the UKIPO’s Green Tech Working Group which seeks to:
further the UK’s status as a global leader by making the UK’s IP environment the best for innovating green technology;
develop and deliver IP policies to support government’s ambition on climate change and green technologies; and
to help innovators best protect and commercialise their green tech innovations both at home and internationally.
The UKIPO has been developing a broad portfolio of ‘green’ IP analytics research. A series of patent analytics reports have been published looking at green technologies, and analysis of how the UK’s Green Channel scheme for accelerated processing of green patent applications has been conducted. Patents have been used to identify technological comparative advantage within different green technologies at a country level, and new insights uncovered by mapping green technology patents to the UN Sustainable Development Goals (SDGs). Trade mark data provides a timeliness and closeness to market factor that patent data does not, and complementary trade mark analysis of UK ‘green’ trade marks, identified using a machine learning algorithm, provides a commercialisation angle to our research.
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In 2013 we witnessed an evolutionary change in the NLP field evolved thanks to the introduction of space embeddings that, with the use of deep learning architectures, achieved human-level performances in many NLP tasks. With the introduction of the Attention mechanism in 2017 the results were further improved and, as result, embeddings are quickly becoming the de facto standards in solving many NLP problems. In this presentation, you will learn how generate and use space embedding for search purposes and provide comparison metrics to more traditional relevance-based search engines. Moreover, I will provide some initial results on a paper currently under review that provides an insight on hyperparameter tuning during the generation of embeddings.
AI-SDV 2022: Rolling out web crawling at Boehringer Ingelheim - 10 years of e...Dr. Haxel Consult
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AI-SDV 2022: Extracting information from tables in documents Holger Keibel (K...Dr. Haxel Consult
In our customer projects involving automated document processing, we often encounter document types providing crucial data in the form of tables. While established text analytics algorithms are usually optimized to operate on running text, they tend to produce rather poor results on tables as they do not capture the non-sequential relations inside them (e.g. interpret the content of a table cell relative to its column title, interpret line breaks inside a cell differently from line breaks between cells or rows). While there are elaborate information extraction products in the market for a few highly specific types of tabular documents, there is no general approach out there. The main cause for this is the fact that table structures can be encoded by a heterogenous range of layout means (e.g. column boundaries can be signaled by lines vs. aligned text vs. white space). In this talk, we will illustrate several solutions that we have developed for a range of challenges occurring in this context, both for scanned and digitally generated documents.
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Most scientific journals request, that the complete set of research data is published simultaneously with the peer-reviewed paper. The publication of the research data usually is carried out as so-called "Supplementary Material", attached to the original paper, or on a "Research Data Repository". Both forms have in common, that the data is published usually unstructured and not in an uniform machine processable format. This makes its further use in electronic tools for AI or data mining unnecessarily difficult or even impossible. A concept is presented, in which the data is digitally recorded, following the principle of FAIR data, as part of the publication process. This digital capture makes the data available to the scientific community for easy use in data mining and AI tools. The data in the repository contains links to the publication to document its origin. The concept is applicable for preprints, peer-review papers, diploma and doctoral theses and is particularly suitable for open access publications. Moreover, the presentation highlights correspondent activities, which were released in scientific publications recently.
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This document discusses using AI tools to improve patent search and analysis. It provides metrics on how well an AI system called IPscreener can retrieve patent citations compared to examiners. The metrics show recall rates increase with longer input text and when users provide additional context. Machine translation negatively impacts performance, but the AI can help users navigate patents by selecting relevant text segments. The goal is for AI to boost innovation by improving how users search for and understand prior art.
AI-SDV 2022: Where’s the one about…? Looney Tunes® Revisited Jay Ven Eman (CE...Dr. Haxel Consult
How do you find video when you only have sparse data? While you can wander the stacks (if you can still find open stacks) for inspiration, video either physical or digital, is difficult to discover. Wandering the virtual stacks is, well, virtually impossible. Discovery platforms on the whole have not replicated the inspirational experience of wandering the stacks.
More companies are using archivable video for internal communication of the various research projects, product developments, test results, and more that are being considered, in progress, or completed. Showing how an experiment was conducted can convey considerably more information that is very difficult to communicate via text. How do you find a company video that might be helpful for your project?
A case study is presented of the problems and the solutions that were implemented by a large, multinational chemical company. A suite of content discovery technologies was used including a video to text to tagging system connected to their documents database and automatically indexed using several chemical as well as conceptual systems (rule-based, NLP, inference engine). To build the system and support the manuscript and video submission there is a metadata extraction program which pulls and inserts the metadata into the submission forms so the author can move quickly through that process.
Copyright Clearance Center
A pioneer in voluntary collective licensing, CCC (Copyright Clearance Center) helps organizations integrate, access, and share information through licensing, content, software, and professional services. With expertise in copyright and information management, CCC and its subsidiary RightsDirect collaborate with stakeholders to design and deliver innovative information solutions that power decision-making by helping people integrate and navigate data sources and content assets. CCC recently acquired the assets and technology of Deep SEARCH 9 (DS9), a knowledge management platform that leverages machine learning to help customers perform semantic search, tag content, and discover new insights.
Lighthouse IP is the world’s leading provider of intellectual property content. The core business of Lighthouse IP is sourcing and creating content from the world’s most challenging authorities. Specialized in IP data, Lighthouse IP provides over 160 countries coverage for patents, over 200 authorities for trademarks and over 90 authorities for designs. Lighthouse IP data is available via several partners. The company is headquartered in Schiphol-Rijk in the Netherlands and has offices in the United States, China, Thailand, Vietnam, Egypt, Indonesia and Belarus. Globally a team of 150 experts works on the creation of this unique data collection.
CENTREDOC was created in 1964 as the technical information center of the swiss watchmaking industry. Building on a strong team of engineers, CENTREDOC now offers a complete range of services and solutions for the monitoring of strategic, technological and competitive information. CENTREDOC is also a leader in the research of patent, technical and business intelligence, and offers consulting expertise in the implementation of monitoring solutions.
AI-SDV 2022: Possibilities and limitations of AI-boosted multi-categorization...Dr. Haxel Consult
The everyday use of AI-driven algorithms for data search, analysis and synthesis comes with important time savings, but also reveals the need to understand and accept the limitations of the technology. Practical deployments on concrete topics are inevitable to assess and manage the challenges of neuronal network based AI. A workshop report.
AI-SDV 2022: Big data analytics platform at Bayer – Turning bits into insight...Dr. Haxel Consult
What if there was a platform where literature, conference abstracts, patents, clinical trials, news, grants and other sources were fully integrated? What if the data would be harmonized, enriched with standardized concepts and ready for analysis? After building our patent analytics platform we didn’t stop dreaming and built our big data analytics platform by semantically integrating text-rich, scientific sources. In my presentation I will talk about what we built and why we built it. And, of course, I will also address the challenges and hurdles along the way. Was it worth it and what comes next? Let’s talk about it!
The Artificial Intelligence Conference on Search, Data and Text Mining, Analy...Dr. Haxel Consult
AI-SDV 2022 The Meeting
AI-SDV 2022 Conference and Exhibition is planned to be held physically and virtually. Speakers, attendees and exhibitors are invited to join physically and virtually in Vienna, Austria. We are developing a range of digital formats that respond precisely to the speakers, attendees and exhibitors needs, enabling individuals and exhibitors from AI industry and related sectors worldwide to participate in the AI-SDV 2022 in Vienna.
The Artificial Intelligence Conference on Search, Data and Text Mining, Analytics and Visualization.
The 2022 AI-SDV Conference in Vienna, Austria, 10th - 11th October 2022.
AI-SDV 2022 is the place to be for everyone involved in advanced search and data applications, text mining and visualization technologies. Individuals and companies that are shaping the future of this exciting space that surrounds us and impacts us all will present their latest research findings, tech developments and visions for the future.
Enjoy a full-on two days of learning, networking and exploring technologies and concepts that are state of the art and will continue to change way we as individuals and organisations work, rest and play. The focus is on Artificial Intelligence (AI), Digitization 2.0 (about making companies, processes and people ready for AI), Deep Learning and other topics identified by our community, who are specialists working in scientific and technical information.
The event features approximately 22 speakers over two days, plus an exhibition to complementing the conference programme.
As past attendees tell us, Vienna is a fabulous location and beyond the formal meetings taking place in the conference and exhibition space, we make the most of this gorgeous location for informal evening receptions and networking dinners.
AI-SDV 2021: Jay ven Eman - implementation-of-new-technology-within-a-big-pha...Dr. Haxel Consult
Synonym breaks search! How? Why is this important? What synonym is and how it breaks search will be explained with real-world examples. AI-based solutions are proposed, and relevant standards are identified. How synonym solutions should be used for search are explained. Learn what you can do yourself. Tools help, but it doesn’t have to be complicated, nor expensive. It is as straight forward as setting priorities!
Meet up Milano 14 _ Axpo Italia_ Migration from Mule3 (On-prem) to.pdfFlorence Consulting
Quattordicesimo Meetup di Milano, tenutosi a Milano il 23 Maggio 2024 dalle ore 17:00 alle ore 18:30 in presenza e da remoto.
Abbiamo parlato di come Axpo Italia S.p.A. ha ridotto il technical debt migrando le proprie APIs da Mule 3.9 a Mule 4.4 passando anche da on-premises a CloudHub 1.0.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
Discover the benefits of outsourcing SEO to Indiadavidjhones387
"Discover the benefits of outsourcing SEO to India! From cost-effective services and expert professionals to round-the-clock work advantages, learn how your business can achieve digital success with Indian SEO solutions.
Understanding User Behavior with Google Analytics.pdfSEO Article Boost
Unlocking the full potential of Google Analytics is crucial for understanding and optimizing your website’s performance. This guide dives deep into the essential aspects of Google Analytics, from analyzing traffic sources to understanding user demographics and tracking user engagement.
Traffic Sources Analysis:
Discover where your website traffic originates. By examining the Acquisition section, you can identify whether visitors come from organic search, paid campaigns, direct visits, social media, or referral links. This knowledge helps in refining marketing strategies and optimizing resource allocation.
User Demographics Insights:
Gain a comprehensive view of your audience by exploring demographic data in the Audience section. Understand age, gender, and interests to tailor your marketing strategies effectively. Leverage this information to create personalized content and improve user engagement and conversion rates.
Tracking User Engagement:
Learn how to measure user interaction with your site through key metrics like bounce rate, average session duration, and pages per session. Enhance user experience by analyzing engagement metrics and implementing strategies to keep visitors engaged.
Conversion Rate Optimization:
Understand the importance of conversion rates and how to track them using Google Analytics. Set up Goals, analyze conversion funnels, segment your audience, and employ A/B testing to optimize your website for higher conversions. Utilize ecommerce tracking and multi-channel funnels for a detailed view of your sales performance and marketing channel contributions.
Custom Reports and Dashboards:
Create custom reports and dashboards to visualize and interpret data relevant to your business goals. Use advanced filters, segments, and visualization options to gain deeper insights. Incorporate custom dimensions and metrics for tailored data analysis. Integrate external data sources to enrich your analytics and make well-informed decisions.
This guide is designed to help you harness the power of Google Analytics for making data-driven decisions that enhance website performance and achieve your digital marketing objectives. Whether you are looking to improve SEO, refine your social media strategy, or boost conversion rates, understanding and utilizing Google Analytics is essential for your success.
Ready to Unlock the Power of Blockchain!Toptal Tech
Imagine a world where data flows freely, yet remains secure. A world where trust is built into the fabric of every transaction. This is the promise of blockchain, a revolutionary technology poised to reshape our digital landscape.
Toptal Tech is at the forefront of this innovation, connecting you with the brightest minds in blockchain development. Together, we can unlock the potential of this transformative technology, building a future of transparency, security, and endless possibilities.
Gen Z and the marketplaces - let's translate their needsLaura Szabó
The product workshop focused on exploring the requirements of Generation Z in relation to marketplace dynamics. We delved into their specific needs, examined the specifics in their shopping preferences, and analyzed their preferred methods for accessing information and making purchases within a marketplace. Through the study of real-life cases , we tried to gain valuable insights into enhancing the marketplace experience for Generation Z.
The workshop was held on the DMA Conference in Vienna June 2024.
AI-SDV 2022: Machine learning based patent categorization: A success story in monitoring a complex technology with high patenting activity Susanne Tropf (Syngenta, Switzerland) Kornel Marko (Averbis, Germany)
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Patent Monitor
is a machine-learning based patent
categorization application
analyzes a large number of patents
(and NPL) …
… and automatically classifies
documents into freely definable
categories
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Patent & Literature Categorization
Pat_1
Category 2
Pat_2
Category 1
Pat_1
Category … n
Pat_i
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Machine Learning for Patent Identification
Example Patents (Category 1)
Example Patents (Category 2)
Example Patents (Category 3)
…
Computer
Machine Learning
Patent Collection
Model
Patent result list, sorted in
✓ Category 1
✓ Category 2
✓ Category 3
✓ …
Model
Computer
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Main Benefits of Machine Learning for Patent
Identification
General problem with (complex) keyword searches
formulated too broadly → many irrelevant hits
formulated too specifically → miss potentially relevant
documents
ML-based models can be trained very specifically and
applied to large set of documents, thus, both increasing
precision and recall.
This saves a substantial amount of time and increases
the quality of work!
(ATI=( plant% OR crop% OR cereal% OR flower% OR grass or grasses OR
Arabidopsis OR (A thaliana) OR (A halleri) OR (A lyrata) OR nicotiana OR (N
tabacum) OR tobacco OR tobaco OR Physcomitrella OR (P patens) OR corn% OR
maize% OR Zea or (Z mays) or (Z vulgaris) OR rice% OR oryza OR oryzeae OR (O
sativa) OR (O australiensis) OR (O glaberrima) OR paddy OR wildrice OR riz OR
soybean% OR soya OR (Glycine max) OR (glycine hispida) OR (glycine soja) OR (G
max) OR (G hispida) OR (G soja) OR (phaseolus max) OR (P max) OR (S dolichos)
OR (S hispida) OR soja OR sojabean% OR sojbean% OR soyabean% OR soia OR
soiabean% OR soy OR triticum OR wheat OR (T compactum) OR (T sativum) OR (T
vulgare) OR (T aestivum) OR (T durum) OR (T trugidum) OR brassica OR (B napus)
OR (B oleifera) OR (B campestri%) OR (B rapa) OR (B napobrassica) OR rape% OR
rapeseed% OR colza% OR rapa OR canola% OR potato* OR (solanum near tuberosum)
OR (solanum near esculentum) OR (lycopersicon near tuberosum) OR (lycopersicon
near esculentum) OR poppy OR papaver OR medicago OR (M near truncatula) OR (M
near sativa) OR (M near vulgaris) OR tomato* OR (lycopersic* near esculentum)
OR (lycopersic* near lycopersicum) OR (solanum near lycopersicum) OR cotton%
OR gossypium OR (g near arboreum) OR (g near barbadense) OR (g near herbaceum)
OR (g near hirsutum) OR cucurbit* OR barley% OR (hordeum near vulgare) OR
(hordeum near sativum) OR oat OR oats OR (avena near sativa) OR esculentum OR
rye OR secale OR bean OR beans OR (phaseolus vulgaris) OR (faba vicia) OR
(faba bona) OR (faba vulgaris) OR beet% OR sugarbeet% OR (beta vulgaris) OR
(beta esculenta) OR cabbage% OR carrot% OR daucus OR carota OR lettuce% OR
salad% or (lactuca sativa) OR (lactuca capitata) OR spinach* OR (spinacia
oleracea) OR (spinacia glabra) OR (spinacia domestica) OR paprika% OR (pepper
red) or (pepper sweet) or (pepper bell) OR (pepper bullnose) OR paprica% OR
(sweet chillies) OR (capsicum annuum) OR (C annuum) OR (pea% near garden) OR
(pea common) OR (pea green) OR (pea shelling) OR (pea field) OR (pea grey) OR
(pisum speciosum) OR (pisum arvense) OR (pea sativum) OR grape% OR grapevine%
OR vine% OR (vitis vinifera) OR mustard% OR sinapis OR (s alba) OR (s hirta)
OR cacao OR caotree% OR (theobroma cacao) OR tea OR teas OR (thea sinensis) OR
(camellia sinensis) OR coffee OR coffeetree% OR (coffea arabica) OR (coffea
vulgaris) OR (coffea canephora) OR (coffea robusta) OR (coffea laurentzi) OR
(coffea liberica) OR sugarcane% OR …
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How do I integrate Patent Monitor into my daily work?
Stand-alone application Integration in your IT environment
Full Patbase/PatKM connectivity Full integration in CENTREDOC
Rapid5
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➢ Add training examples in PatBase folders
➢ (Directly) Import folders in Patent Monitor
➢ Train the classifier
➢ Add patents to be categorized to PatBase folder
➢ (Directly) Import folder in Patent Monitor
➢ Classify documents and inspect/export results
➢ Create Alert in PatKM
➢ Configure Workflow in Patent Monitor to create weekly inspections for
categories
➢ View weekly inspections directly in PatKM (!)
Step-by-step workflow with PatBase @Syngenta
Setup
Ad-hoc categorization
Weekly alerting
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Setup: Create folders in PatBase
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Setup: Import folders from PatBase
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➢ Add training examples in PatBase folders
➢ (Directly) Import folders in Patent Monitor
➢ Train the classifier
➢ Add patents to be categorized to PatBase folder
➢ (Directly) Import folder in Patent Monitor
➢ Classify documents and inspect/export results
➢ Create Alert in PatKM
➢ Configure Workflow in Patent Monitor to create weekly inspections for
categories
➢ View weekly inspections directly in PatKM (!)
Step-by-step workflow with PatBase @Syngenta
Setup
Ad-hoc categorization
Weekly alerting
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Ad-hoc categorization
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➢ Add training examples in PatBase folders
➢ (Directly) Import folders in Patent Monitor
➢ Train the classifier
➢ Add patents to be categorized to PatBase folder
➢ (Directly) Import folder in Patent Monitor
➢ Classify documents and inspect/export results
➢ Create Alert in PatKM
➢ Configure Workflow in Patent Monitor to create weekly
inspections, sorted in categories
➢ View weekly inspections directly in PatKM (!)
Step-by-step workflow with PatBase @Syngenta
Setup
Ad-hoc categorization
Weekly alerting
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Weekly alerting
PatKM Alert
(weekly/monthly)
PatKM Archive
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Weekly alerting
PatKM Alert
(weekly/monthly)
PatKM Archive
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Weekly
alerting
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PatKM Archive
… publishing classification results
in PatBase Express will come soon!
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Patent Monitor reduces manual
effort by 90% to identify relevant
patents, with an accuracy of > 97%
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Summary & Take Away
Machine learning for categorization is a great improvement for patent identification
less irrelevant hits, more relevant hits
pre-sorting of results
80% of time saving in average
Same accuracy compared to manual categorization by IP experts
Seamless integration into PatBase and PatKM workflows (but other data sources supported as well)
Also integrated in CENTREDOCs RAPID5
Outlook: PatBase Express integration
Save time and focus on relevant documents only ! Increase the quality of your work !
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Find. Understand. Predict.
Interested?
Get in Touch Kornél Markó
kornel.marko@averbis.com