IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016

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1. IoT Innovation Insights from Patents

2. IoT Frontiers Insights from Patents

3. IoT Strategy Perspectives from Patents

4. IoT Innovation Exploiting Patents

5. IoT Patent Strategy

6. IoT Startup Patent Strategy

7. IoT for Business Growth Insight from Patents

8. Artificial Intelligence Innovation Insight from Patents

9. Big Data Innovation Insight from Patents

10. IoT + AI+ Big Data Integration Strategy Insight from Patents

Published in: Technology

IoT + AI + Big Data Integration Strategy Insights from Patents 3Q 2016

  1. 1. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com IoT +AI + Big Data IntegrationStrategy: Insightsfrom Patents 3Q 2016 Alex G. Lee (alexglee@techipm.com)
  2. 2. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 2 1. IoT Innovation Insights from Patents ……………………. ………….…...…... 3 2. IoT Frontiers Insights from Patents ……………………. ….………..………... 7 3. IoT Strategy Perspectives from Patents …................……………………... 16 4. IoT Innovation Exploiting Patents …………………..…….…………………... 18 5. IoT Patent Strategy …………..…………………..……………….………………... 32 6. IoT Startup Patent Strategy …….……………..……………….………………...57 7. IoT for Business Growth Insight from Patents..………….………………... 60 8. Artificial Intelligence Innovation Insight from Patents..…...…………...63 9. Big Data Innovation Insight from Patents..…...…………………………….. 75 10. IoT + AI+ Big Data Integration Strategy …………………………………... 88 Contents
  3. 3. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 3 1. IoT Innovation Insights from Patents Smart Home Patent Leadership Reference: Smart Home Patents Data 3Q 2016
  4. 4. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 4 1. IoT Innovation Insights from Patents Connected Car Patent Leadership Reference: Connected Car Patents Data 3Q 2016
  5. 5. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 5 1. IoT Innovation Insights from Patents Connected Health Patent Leadership Reference: Connected Health Patents Data 2Q 2016
  6. 6. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 6 1. IoT Innovation Insights from Patents IoT Connectivity Patent Leadership Reference: LTE Patents for Standards Data 2Q 2016
  7. 7. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 7 2. IoT Frontiers Insights from Patents Smart Home Audio and Video Analytics US20150364028 illustrates a smart home security system for detecting the home premise condition using audio analytics. Sounds (e.g., sound of doorbell, sound of breaking glass, or sound of baby crying) occurring on or near the premise can be detected and analyzed whether the sound belongs to a recognized class of sounds (e.g., security, health condition, property damage). In response to the analysis, the system performs tasks that are responsive to the detected sounds (e.g., sending messages to law enforcement or medical personnel). US20150194034 illustrates a system for monitoring an incapacitated person (e.g., unable to move or respond) in the monitored area using video analytics. The system processes captured images to detect an anomaly when a person becomes ill and incapacitated (e.g., sharp movements representing a fall). When such anomalies are detected, a notification can be transmitted to another location indicating that medical assistance is needed.
  8. 8. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 8 2. IoT Frontiers Insights from Patents Smart Home Robots Have you ever dreamed about future that your favorite food is ready automatically when you feel hungry? US20150290795 indicates a possibility by combing robotics and artificial intelligence. US20150290795 illustrates the computerized robotic food preparation system for food preparation by digitizing the food preparation process of professional and non-professional chef dishes and subsequently replicating a chef's cooking movements, processes and techniques with real-time electronic adjustments. US20140207282 illustrates a household robot that can take care of your home. The household robot can monitor the state of a home, clean the home and turn on/off various appliances for you automatically and transmit the surveillance data to you in an emergency condition detected by the robot. As illustrated in US20150224640 the household robot even can take care of your health.
  9. 9. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 9 2. IoT Frontiers Insights from Patents Self-aware Self-healing IoT Billions of interconnected devices that are connected to the internet should be self-aware and self-healing: Self-Aware Self-Healing IoT is concerned with systems that can auto-detect, analyze, and fix any issue with the IoT devices and networks without human intervention. Deployment of significant numbers of lighting devices with associated controllers and/or sensors and networking in smart cities presents increasing challenges for set-up and management of the system elements and network communication elements of the lighting system. US20150250042 illustrates the system of networked intelligent lighting devices that provides autonomous discovery and set-up at installation. The system also can provide autonomous detection of failure and fix of the failure.
  10. 10. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 2. IoT Frontiers Insights from Patents Emotion-Aware IoT An individual's mental state is important to general well-being and effective decision making. Mental states include a wide range of emotions and experiences from happiness to sadness, from contentedness to worry, or from excitation to calm. US20140200463 illustrates the emotion-aware IoT applications by determining well-being status from an analysis of facial information and physiological information of an individual. By monitoring the well-being status of an individual, the home lighting can adjust it while turning on the favorites music automatically when a person stressed. US20130054090 illustrates the emotion-aware IoT application for providing the safe driving service system by analyzing information necessary for cognizing emotion on the basis of bio signals sensed by in-vehicle sensors.
  11. 11. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 11 2. IoT Frontiers Insights from Patents Autonomous IoT The autonomous smart home system can recognize the contextual or semantic profiling of a person or place or devices (physical environment) based on sensed data by the IoT devices. The adaptable autonomous smart home system determines particular interpretation instructions (define particular IoT device control rules) that are associated with the particular physical environment and dynamically updates the control rules for changing physical environment. Qualcomm patent US9413827 is the first issued patent regarding the autonomous IoT system. The autonomous IoT system includes a variety of heterogeneous IoT devices to be able to communicate with each other. The IoT heterogeneous devices can recognize each other by virtue of a unique identifier. The autonomous IoT system detects a context of the IoT devices (e.g., space, time, location, status/events, etc.) and performs actions in response (e.g., issues a command or trigger a specific IoT device to perform a corresponding action or state change).
  12. 12. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 12 2. IoT Frontiers Insights from Patents Autonomous IoT -2 For example, if the sleep monitoring device senses that a child is sleeping in a room, the autonomous IoT system makes the lighting in the room remain off. If a parent walks in to check on the child (context of the parent and child being in the same room), the autonomous IoT system triggers an action of lighting up to a 10% dimmed state. The autonomous IoT system also can recognize the context with respect to relationship among different users. For example, if person A (e.g., husband) walks into the room when a music system in the room playing the genre of music for person B (e.g., wife), the music system can switch to a music genre that both person A and person B like.
  13. 13. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 13 2. IoT Frontiers Insights from Patents Augmented Reality US20150347850 illustrates an IoT AR (Augmented Reality) application in a smart home. A smart home IoT device communicates via a local network to a user AR device (e.g., smartphone) for providing the tracking data. The tracking data describes the smart home IoT device. The AR devices can recognize the smart home IoT device in the camera view based on the tracking data. Once the smart home IoT device is identified in the camera view, the AR application can augment the camera view with additional information and control interface about the smart home IoT device. The user can control the smart home IoT device using the AR device. US20140063064 illustrates an IoT AR application in a connected car. An AR head-up display in the connected car displays the overlaying a virtual image regarding the surrounding environmental information on an actual image of the external vehicle that is observed through the transparent display. The surrounding environmental information are obtained via the V2X communication system.
  14. 14. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 14 2. IoT Frontiers Insights from Patents Convergence US20120120930 illustrates a vehicle network system interconnected with a home network. When a predetermined request signal is transmitted from the vehicle network system to the home network, the home network performs an operation corresponding to the request signal. The home network determines whether the vehicle network system is detected within a predetermined range. The home network may include a RFID system to detect the presence of the vehicle network system. When the vehicle network system is detected within the predetermined range, the home network transmits parking information with respect to the vehicle equipped with the vehicle network. The transmitted parking information can then be displayed on the display unit in the vehicle.
  15. 15. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 15 2. IoT Frontiers Insights from Patents Fog Computing US20150261876 illustrates the network environment includes multiple fog computing devices each connected with a communication network. Fog computing pushes applications, data, and computing power (including computing services) away from centralized points in a network to logical extremes or edges of the network. Fog computing covers a wide range of technologies including wireless sensor and actuator networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to- peer ad hoc networking and processing. US9098344 illustrates the cloud-edge hybrid system that an application instance runs on the fog computing device and another application instance runs on the cloud-based computing resources.
  16. 16. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 16 3. Strategy Perspectives from Patents Cisco Cisco announced a strategic partnership with and Ericsson to create the networks of the future. Followings show the insights regarding Cisco strategy perspectives for developing the IoT networks of the future to make Cisco as the IoT networks innovation leader. Predictive Analytics for IoT networks Predictive analytics analyzes current and historical data to make predictions about future events and trends. US20150333992 illustrates the application of predictive analytics for managing the IoT Networks. Machine Learning for IoT networks Cisco is developing the intelligent autonomous IoT networks exploiting the machine learning (ML). US20150195192 illustrates the use of the ML in order to predict whether a network element failure is relatively likely to occur.
  17. 17. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 17 3. Strategy Perspectives from Patents IBM IBM announced a plan to invest more than $3 billion for four years to build the IoT business unit. Followings show the insights regarding IBM strategy perspectives for developing the IoT analytics to make IBM as the IoT business leader. Big Data Analytics IoT big data analytics are becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. US20140068180 illustrates a system capable of efficiently analyzing big data. Predictive Analytics US20140236650 illustrates the cost effective end-to-end analytics driven asset management by managing maintenance operations (e.g., scheduling, preventive maintenance, operating parameter control).
  18. 18. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 18 4. IoT Innovation Exploiting Patents White Space Analysis Patent information can provide many valuable insights that can be exploited for developing new IoT products/services. One can identify the potential R&D areas (“white space”) that can lead to new product/service development through the patent landscape analysis.
  19. 19. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 19 4. IoT Innovation Exploiting Patents Cross-competitor Analysis Patents can provide insights regarding the competitive advantage innovation strategy in alignment with the strategic move of a specific company for the IoT business leadership through the cross-competitor analysis.
  20. 20. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 20 4. IoT Innovation Exploiting Patents Scenarios Analysis Patents can be exploited to identify new IoT product/service development opportunity from scenarios analysis. The scenarios analysis can show potential interactions between the future user and the IoT product/service via the specific usage of the product/service and behavior of the user under environments provided by the product functionality. The scenarios analysis exploiting patent information can provide the new IoT product concept (e.g., specific benefits to the user, product design, product functionality and the technology for the product).
  21. 21. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 21 4. IoT Innovation Exploiting Patents Smart Home Scenarios Development • The first step in the scenario development is to do patent search and review for the state of the art of IoT innovation for smart home applications. The searched patents that are related to the smart home applications should be analyzed to find the measurable user benefits provided by the value propositions (automation, energy management, care, safety, entertainment) and method/device/system to offer the value propositions. • Then, develop a story of the future implementations of the state of the art of IoT innovation for smart home applications exploiting 5W1H – Who, When, Where, What, Why, How.
  22. 22. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 22 4. IoT Innovation Exploiting Patents Smart Home Scenarios Development -2 • The stakeholders (users) in the scenario for the IoT smart home are usually the member of home who can be specified by age, gender, profession. The time can be the morning, afternoon, evening. The place can be the bedroom, living room, bathroom, kitchen and outside. • Then, describe how the specific product (including system) that is implemented based on the patent disclosures performs (interacts with a user) to provide each value proposition under the circumstances for a specific purpose (expectations of the product’s future user or solutions for product’s future user’s problems). Base on the developed scenarios, potential improvements over the implemented product can be devised by listing new product features, functionality, UI/UX, integration method etc.
  23. 23. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 23 4. IoT Innovation Exploiting Patents Smart Home Scenarios Development -3 • Followings summarize the key elements of the IoT smart home scenarios for each value proposition. • Home Automation The smart home product should determine how to solve user’s problem or perform for a specific situation without constant guidance from the user. The smart home product should adapt to changing situation/environment. The smart home product should react properly to the changing situation/environment in accordance with the performance objectives. The smart home product should join the home networks without user’s manual configuration.
  24. 24. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 24 4. IoT Innovation Exploiting Patents Smart Home Scenarios Development -4 • Home Energy Management The smart home product should adapt the overall energy efficiency of the home to the comfort and quality of life. The smart home product should control the home heating for reducing energy consumption while ensuring comfort. The smart home product should control lighting based on user’s presence and environment. • Home Care The smart home product should proactively act for caring the user under the environment/situation on behalf of the user. The smart home product should provide UI/UX depend on the user context.
  25. 25. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 25 4. IoT Innovation Exploiting Patents Smart Home Scenarios Development -5 • Home Safety The smart home product should provide private space protection and control against hazard home environment. The smart home product should measure of gas, carbon monoxide, fire, smoke, water and weather conditions. The smart home product should provide remote access to home environment. • Home Entertainment The smart home product should provide personalize entertainment to the user. The smart home product should exploit semantic information regarding the home entertainment devices.
  26. 26. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 26 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios Following future scenarios are developed based on disclosures in the IoT patents. The scenarios describe possible situations in 2020 that a person will experience in his/her everyday life: home, highway, shopping mall, business meeting and medical emergency situation. Scenarios can provide several IoT business insights including the IoT products/services and application business models development. In the morning, at home Alex, married with Janet and has two daughters Alice and Jenny, wakes up at 6 am with the morning call music Feels So Good by Chuck Mangione, one of his favorites. Alex’s smart home system selects the Feels So Good for gloomy Friday morning due to heavy clouds in the sky of Alex’s Boston home. The wake up time and music were selected based on Alex’s personal profile for his favorite music, his yesterday’s busy schedule and current weather condition sensed from his front yard.
  27. 27. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 27 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios -2 As Alex stands up, his closet doors are opened and LED lighting from the bottom shins the sox that is match in color with his black coat for an important business meeting in the afternoon. The smart home system operates the closet doors and lightings depend on Alex’s position and movement. As Alex moves out from his bed room and moves into the living room, smart TV is turned on and automatically changes the TV channel for the traffic information. At that time, Kitchen lights flick to signal to Alex that his breakfast is ready in the rice cooker. As Alex finishes his breakfast, Roombo, a home serving robot, approaches and asks him whether he had his subscription pills. When Alex has his medicine, he takes the image of the pill and sends the image to his primary care doctor using his smartphone.
  28. 28. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 28 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios -3 On the way to shopping mall On her way to nearby shopping mall, Janet hears a voice massage that she needs to exit at the next to buy milk. The onboard screen in her car shows a map for nearby shops with milk price if a shop is on sale. When Janet dozes off at the wheel, the automated vehicle monitoring system changes her car to a self-driving mode. As Janet drives her car at 75 mph at 55 mph speed limit highway, her car smart control system automatically slows down. About 5 miles before the shopping mall, her car slows down to a complete stop because her car receives an accident notice from a car drives in the opposite direction. About 3 miles before the shopping mall, Janet hears a traffic congestion voice massage near the shopping mall exit.
  29. 29. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 29 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios -4 The onboard screen in her car shows a map for alternative route to the shopping mall. As Janet approaches the shopping mall, smart LED lights detects available parking spaces, then directs her to them. When Janet enters into the shopping mall, her smartphone receives indoor navigation guide maps, product information and promotional advertisements. At a gift shop, Janet buys Alex’s birthday gift and pays using her smartphone. At the business meeting Alex has a business meeting with John at Financial Building located in the Boston downtown. As Alex arrives in front of Financial Building, he receives a welcoming message of an access information service of Financial Building (displayed on his smartphone). Alex then moves on to Conference Room 313 by being provided with a WiFi signal based location guidance through his smartphone.
  30. 30. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 30 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios -5 While Alex is moving to Conference Room 313, automatic authentication of all doors located along the path may be realized, thereby allowing Alex to pass through all doors in order to reach Conference Room 313. When Alex enters Conference Room B, lights and lamps installed in the conference room are automatically turned on, and operation of a ventilation system is initiated. When John enters Conference Room 313, a projector is turned on, blinders of the conference room windows are shut down, and the lights are turned off. Alex discusses with John regarding the adoption of the smart inventory system that utilizes the 3D printing system in John’s warehouse.
  31. 31. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 31 4. IoT Innovation Exploiting Patents 2020 IoT Scenarios -6 In the evening, at home As Alex approaches his home the smart home system determines whether his car network system is detected within a radius of 2 miles. When the car network system is detected within the predetermined range, the smart home system transmits parking information. The transmitted parking information is displayed on the display unit in the car. Then, smart home system turns on the light in the front door and changes the living room temperature at Alex’s most comfortable temperature. While Alex is having a good evening time his smartphone notifies him that his garage door is open and his electric vehicle is not charging.
  32. 32. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 32 5. IoT Patent strategy Integrated IoT Patent Strategy
  33. 33. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 33 5. IoT Patent Strategy Patent Development Strategy Prior Arts Search Even if the internet of things (IoT) gets a huge attention recently, the concept of interconnected devices and connecting billions of devices to the internet in the IoT is not new and has been researched for over 10 years. Thus, a large number of non-patent prior arts including research papers and books can be existed. Therefore, the intensive search of non-patent prior arts is needed for preparing the IoT patent. US20150019710 illustrates a good example case that intensive search of non-patent prior arts is required. US20150019710 claims a method of maintaining interoperability amongst IoT devices connected via an IoT integration platform. The key elements of the claim 1 at issue are the interoperable control rule based on the semantic label associated with a user's IoT device.
  34. 34. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 34 5. IoT Patent Strategy Patent Development Strategy -2 Prior Arts Search -2 The USPTO rejected the claim 1 under the AIA 102(b) as being anticipated by a non-patent prior art regarding the semantic interoperability on the web. Semantic interoperability enables exchange of data among different web system. The inventor argued that the semantic label in the non-patent prior associated with a user's IoT device and amended claim to clarify the distinguished aspects of the claim form the prior art. However, the USPTO rejected the claim again in the final rejection based on newly founded non-patent prior art that explicitly mentions the IoT device in the disclosure.
  35. 35. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 35 5. IoT Patent Strategy Patent Development Strategy -3 Claim Drafting for Maximum Quality & Value When the IoT patent claims are drafted, several factors that can make the patent high quality and value should be considered. The factors are (1) claim scope and broadness, (2) value propositions of a claim’s novelty point (non-obvious inventive departure from the prior arts), (3) claim mix (degree to which different kind of statutory claim types (e.g. apparatus, process, manufactures, composition) are used in a patent), (4) claim diversity (degree to which different kind of claim formats (e.g. means-plus-function claim), terminology, arrangement in a given statutory claim type (e.g. number or order of claim elements) are used In a patent) and (5) claim setting (degree to which different kind of commercially significant settings (e.g. component/system or transmitter/receiver) are used in a patent).
  36. 36. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 36 5. IoT Patent Strategy Patent Development Strategy -4 Claim Drafting for Maximum Quality & Value -2 The features to be considered in the claim drafting to get the high quality and value patents with the optimal quality to cost (efforts and time in addition to monetary cost) ratio can be decided exploiting the insights for products/services that is going to be covered by the patent (e.g. attributes of a product/service that valued most by customers), the patent landscape to figure out the competitors’ patents (e.g. whether design around is required to avoid infringing competitors’ patents) and internal patenting process (including available patent budget). For example, if a patent is going to use for excluding the competitors, the key features to be considered in the claim drafting will be the claim diversity (to avoid potential infringement loophole) and claim mix (to provide protection against invalidation).
  37. 37. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 37 5. IoT Patent Strategy Patent Development Strategy -5 Claim Drafting for Maximum Quality & Value -3 The best practice in the IoT patent claims drafting is the Qualcomm patents for the IoT connectivity. Total of 273 patents issued in the USPTO for the IoT connectivity are reviewed. Following table summarizes the assessment.
  38. 38. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 38 5. IoT Patent Strategy Patent Development Strategy -6 Claim Drafting & Amendment under Post-Alice 101 Eligibility Test 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.
  39. 39. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 39 5. IoT Patent Strategy Patent Development Strategy -7 Claim Drafting & Amendment under Post-Alice 101 Eligibility Test -2 For example, claim 1 in US20140012945 claimed: A method of automatically detecting issues and facilitating the transmission of alert messages in a home network consisting of a plurality of consumer electronic devices coupled to a service node, the method comprising: storing information regarding home entities in an inventory database, the entities comprising the household, one or more users within the household, devices in the household, applications running on the devices, and a community of which the household is included; defining usage classes for each entity, the usage classes denoting a primary function associated with an entity, and comprising networking, productivity, and entertainment; assigning an index value to each usage class for each entity, the index value comprising an integer value within a fixed range, and reflecting a relative level of satisfaction associated with the entity for each respective usage class; monitoring data regarding usage trends, performance characteristics, and external variables associated with the entities;
  40. 40. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 40 5. IoT Patent Strategy Patent Development Strategy -8 Claim Drafting & Amendment under Post-Alice 101 Eligibility Test -3 modifying one or more of the index values based on the monitored data; and facilitating the transmission of messages regarding services or products related to the entities of the home from one or more parties, wherein the messages are conformed to index values. This claim was rejected under 101 by the USPTO because the claim is directed towards the abstract idea: storing data regarding entities, classifying entities, assigning index values, monitoring usage data, modifying the index values and transmitting messages regarding services or products related to the entities based on index values. Thus, simply transferring the data through the networks or processed the data for providing services will not sufficient to transform the nature of the claimed invention into a patent-eligible claim. Additional elements that are sufficient to amount to significantly more than the abstract idea are needed.
  41. 41. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 41 5. IoT Patent Strategy Patent Development Strategy -9 Claim Drafting & Amendment under Post-Alice 101 Eligibility Test -4 Potential claim elements that can overcome the reject may be the claim elements that embed/link to a specific machine/article (not a generic purpose computer) (e.g. medical device, thermostat, application specific microprocessor/controller) or that either recite a specific series of steps that resulted in a departure from a human activity or transform a particular thing to a different thing or improve the technology.
  42. 42. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 42 5. IoT Patent Strategy Patent Development Strategy -10 Patent Disclosure Drafting & Prosecution in Alignment with Business Strategy The IoT patents should support future development of the IoT businesses and commercial implementations of the IoT innovations. The disclosures of the IoT patent can be considered as a system that is consist of the basic building blocks of the IoT as a subsystem: (1) devices that can sense/recognize their surrounding environments and communicate with other devices, (2) connecting /communicating network medium/infrastructure that can interconnect devices and connect devices to the internet, (3) back-end IT systems that can process information (data) obtained by the IoT devices (e.g. cloud computing/big data analytics) and (4) provide the value added services exploiting the information. Then, the system as a whole and each basic building block as a subsystem can be construed in the disclosures to support future development of IoT businesses and commercial implementations of the IoT innovations.
  43. 43. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 43 5. IoT Patent Strategy Patent Disputes Risk Assessment As we have seen in the smartphone market development, it is expected that the super-competition to occupy the leadership in the lucrative IoT market can lead to another round of patent wars. The post-smartphone patent wars, however, will be more extensive because of more extensive participation of players across several different industries. The post-smartphone patent wars will also be more complex because of the recent rapid change in legal environment and the learning curve from the smartphone patent wars. • A large number of patents owned by commercially unsuccessful companies can be the potential patent disputes risk (e.g., smart home). • A large number of patents owned by the patent monetizing entities is a potential patent disputes risk (e.g., connected car, IoT connectivity). • A large number of patents owned by entities from different industries is a potential patent disputes risk (e.g., connected car, connected health).
  44. 44. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 44 5. IoT Patent Strategy Patent Disputes Risk Assessment -2
  45. 45. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 45 5. IoT Patent Strategy Patent Disputes Risk Assessment -3
  46. 46. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 46 5. IoT Patent Strategy Patent Disputes Risk Assessment -4
  47. 47. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 47 5. IoT Patent Strategy IoT Patent Wars 2020 Scenarios
  48. 48. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 48 5. IoT Patent Strategy Patent Monetization Even though there were so many patents that were issued up to now, however, the number of patents that were commercially exploited and monetized successfully was very small. This is similar to the situation that, even if there were so many innovators and entrepreneurs, only small numbers achieved commercial success from their ideas. In “The Innovator's DNA”, the authors identified the code of successful innovators that distinguishes them from others based on the case study of well known successful innovators such as Steve Jobses and Jeff Bezoses. The key lesson from the research of the innovator’s DNA was that the successful innovators’ DNA is not simply genetic traits endowed, but that they can be developed. Then, by the analogy, one can ask similar questions for patent: What make the monetizing patents different?; Can we find the code of the monetizing patents?; Can we identify and develop the monetizing patents?
  49. 49. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 49 5. IoT Patent Strategy Patent Monetization -2 Monetizing patents protect the market for commercially successful products/ services from competitors. Monetizing patents can be exploited as financial/ investment assets or through licensing to other parties for additional cash flow. Followings are the monetizing patent’s DNA uncovered from the case study of carefully selected monetizing patents through the integrated technology, legal and business analysis. Monetizing patents solve the right technological problem The right technological problem is a problem in great needs of improved/ innovative solution; The right technological problem is recognized well in an industry; The right technological problem leads to a solution that has great business impacts; The solution of the right technological problem follows the correct evolutionally pass for emerging technology.
  50. 50. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 50 5. IoT Patent Strategy Patent Monetization -3 Monetizing patents practice value innovation Monetizing patents provide unique value proposition with maximum market protection; Monetizing patents provide values to customers without additional cost/pain or infrastructure/process change requirements. Monetizing patents actively participate in the business ecosystem Monetizing patents can be a platform or integrated into a platform; Monetizing patents form a dominant design or industry standard; Monetizing patents form diverse value chains; Monetizing patents derive the value for M&A.
  51. 51. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 51 5. IoT Patent Strategy Patent Monetization -4 Monetizing patents are legal savvy Monetizing patents are created following the case laws and statutes to obtain maximum legal rights; Monetizing patents are the litigation-proof patents; Monetizing patents adapt to the dynamic changes of legal environment.
  52. 52. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 52 5. IoT Patent Strategy Patent Monetization -5 TiVo patent US6233389 is an invention regarding the digital video recorder (DVR). Today, many digital set-top boxes include the key functionality of US6233389. TiVo monetized US6233389 through licensing and litigation including $500 million settlement with EchoStar and $490 million settlement with Google and Cisco. Monetizing DNA Details solve the righ t technologic al problem The viewer stores TV programs onto magnetic tape using the VCR. The VCR can pause the program playback whenever he desires, fast forward through unwanted program material or commercials, and to replay favorit e scenes. However, a VCR cannot both capture and play back informatio n at the same time. The invention provides an easily manipulated, low co st multimedia storage and display system that allows the user to view a t elevision broadcast program with the option of instantly reviewing previo us scenes within the program. In addition, the invention allows the user t o store selected television broadcast programs while the user is simultan eously watching or reviewing another program: solve a problem in grea t needs of improved/innovative and recognized well in an industry
  53. 53. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 53 5. IoT Patent Strategy Patent Monetization -6 Monetizing DNA Details solve the righ t technologic al problem An embodiment of the invention accepts television (TV) input streams in a multitude of forms including digital forms such as Digital Satellite Syste m (DSS), Digital Broadcast Services (DBS), or Advanced Television Stan dards Committee (ATSC): leads to a solution that has great business impacts (with the development of digital TV and broadband internet access) The invention can be used in the detection of gambling casino crime. Th e input section of the invention is connected to the casino's video surveill ance system. Recorded video is cached and simultaneously output to ext ernal VCRs: follows the correct evolutionally pass for emerging tech nology (e.g. applications in smart home/building security system)
  54. 54. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 54 5. IoT Patent Strategy Patent Monetization -7 Monetizing DNA Details practice valu e innovation An embodiment of the invention accepts television (TV) input streams in a multitude of forms including digital forms such as Digital Satellite Syste m (DSS), Digital Broadcast Services (DBS), or Advanced Television Stan dards Committee (ATSC): provide values to customers without additi onal cost/pain or infrastructure/process change requirements by be ing included in the current digital systems Although a VCR is specifically mentioned, any multimedia recording devi ce (e.g., a Digital Video Disk-Random Access Memory (DVD-RAM) recor der) is easily substituted in its place: provide unique value proposition wit h maximum market protection; The invention can be used in the detectio n of gambling casino crime. The input section of the invention is connect ed to the casino's video surveillance system. Recorded video is cached a nd simultaneously output to external VCRs: provide unique value prop osition with maximum market protection
  55. 55. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 55 5. IoT Patent Strategy Patent Monetization -8 Monetizing DNA Details actively parti cipate in the business eco system HBS Professor Iansiti, the author of “The Keystone Advantage,” suggest ed technological assimilation as a new engine for technological innovatio ns in his article “Creative Construction.” In the technological assimilation frameworks, a core innovation that once provided stand-alone products o r services for a specific market can be the building blocks for mass mark et generating innovations through assimilation to broader platforms that were not existed at the time of innovations. A good example may be the GPS technology providing LBS (location based services) applications for smartphones and automobiles. Another good example may be the DVR t echnology of TiVo that was integrated into digital set-top box and smart home platforms: can be a platform or integrated into a platform
  56. 56. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 56 5. IoT Patent Strategy Patent Monetization -9 Monetizing DNA Details legally savvy 4 US Families/ Total of 16 independent claims/High in different kind of st atutory claim types: protection against invalidation Exploited in 9 litigations and won the cases: litigation-proof
  57. 57. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 57 6. IoT Startup Patent Strategy Best Practice Nest Labs Company information A smart home company headquartered in Palo Alto, California, that manufactures smart thermostats and smoke detectors. Founded in 2010 and acquired by Google for $3.2 billion in 2014. IP value recognition in M&A Build up 132 US issued patents (+ pending applications + international families) in four years Cf. SmartThings’ acquisition by Samsung for $200 million with no patent *notice the value difference: $3.2 billion v. $200 million
  58. 58. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 58 6. IoT Startup Patent Strategy Best Practice -2 Strategic development of patent portfolios • Patent fortress for core business Create 33 US issued utility patents and 28 US issued design patents regarding smart thermostat • Strategic patent acquisition Acquired 69 US issued patents and one US patent applications regarding smart sensors and sensor networks. Strategic exploitation of patent portfolios • Strategic financing Utilize 29 US issued patents for patent secured lending from Wells Fargo Bank *after Series A and B funding, before Series C funding Strategic patent licensing Licensed patents for accessing to Intellectual Ventures’ IP for defense
  59. 59. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 59 6. IoT Startup Patent Strategy IoT Innovative Startups Insights from Patents Magic Leap (http://www.magicleap.com) is a leading augmented reality (AR) startup company. Magic Leap had raised more than $1.3 billion of venture funding from Alibaba, Google, Qualcomm, and other investors since its foundation in 2010. Financial Times valued Magic Leap at $4.5 billion. Magic Leap filed 167 US patent applications including two issued patents as of 1Q 2016. Magic Leap’s AR technology innovation strategy are as follows. • Biomedical engineering approach to the complex human visual perception system for producing and providing the AR contents • AR technology that facilitates a comfortable, natural-feeling, rich presentation of virtual image elements • AR system that work with the visual configuration of a typical human to address various challenges in AR applications
  60. 60. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 60 7. IoT for Business Growth Insights from Patents Growth by generating revenue from new product/service offerings US9218740 illustrate a connected car cloud service to assist drivers with respect to improving driver safety. The cloud-based driver assistive system can warn drivers upon impending collisions. Using mobile devices and relatively inexpensive sensors and wireless connections to the cloud service, the cloud-based driver assistive system can be implemented inexpensively for enriching the driving experience without needing new roadside infrastructure for vehicle-to-infrastructure (V2I) communications and embedding the Dedicated Short-Range Communications (DSRC) device to every vehicle for inter-vehicle (V2V) communications.
  61. 61. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 61 7. IoT for Business Growth Insights from Patents Growth through strengthening competitive position by adding new functionality/performance for the existing product/service while lowering production cost and manufacturing quality control US20150351698 illustrates a system for analyzing physiological and health data (e.g., activity data) retrieved from wearable monitors using a smartphone to identify emergencies or medically significant events in real-time. The system generates medical notifications corresponding to the identified medical event and generated health level in real-time. The generated health level is transmitted by smartphone to an emergency response system when the identified medical event is an emergency medical event. US20160202692 illustrates an IoT system for efficiently and accurately managing a multi-part product production line in a factory environment. The factory is equipped with sensors for detecting technician’s location and surrounding environment within a particular zone of the production line. When the technician enters an assigned zone of the production line, the technician receives instructions and images for the designated task.
  62. 62. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 62 7. IoT for Business Growth Insights from Patents Growth through business model innovation by creating new market or by providing fundamentally different value propositions for the current market US20120165617 illustrates a system for early health and preventive care using data from wearable sensors. Data collected from the sensors is transmitted to a mobile cloud computing platform-as-a-service (PaaS). The predictive analytics analyzes the received data to predict diseases and other conditions to which the patient may be predisposed.
  63. 63. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 63 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights Deep learning is a machine learning technique that attempts to learn layered models of inputs that mimics the human brain’s reasoning process. Deep learning techniques, such as deep belief networks and deep convolutional networks, are layered artificial neural networks architectures in which the output of a first layer of neurons becomes an input to a second layer of neurons, the output of a second layer of neurons becomes and input to a third layer of neurons, and so on. The layers correspond to distinct levels of concepts where higher-level concepts are derived from lower-level concepts (hierarchy of complex concepts that are constructed out of simpler concepts). Reference AI Deep Learning Patents Data 3Q 2016
  64. 64. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 64 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights -2 Since the United States is such a large market for products and services from nearly all technology innovations, a patent counting for growth in patenting over a period of times in the US can be a good measuring tool for monitoring the evolution of technology innovations. Following figure shows the growth trends for the deep learning technology innovations base on the patent research of nearly 250 published applications and issued patents by the USPTO as of 3Q 2016. The figure indicates that very active deep learning technology innovations started from 2013 and keep increasing (since there is usually a time lag between the priority date and publication date by two to three years, the number of patents in 2015 is not fully accounted as of 3Q 2016).
  65. 65. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 65 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights -3
  66. 66. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 66 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights -4 Number of patents for an assignee divided by the total number of patents gives what percentage the assignee contributes to the deep learning technology innovations. Ranking the assignees by the number of patents is thus an important part of visualizing the innovations landscape. Following figure shows the top assignees for the deep learning technology innovations. The top deep learning technology innovators include Microsoft, NEC, IBM, Samsung Electronics, Google, and Qualcomm.
  67. 67. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 67 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights -4
  68. 68. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 68 8. Artificial Intelligent Innovation Insights from Patents Deep Learning Innovation Insights -5 Following figures show the key applications of the deep learning and use cases by industry. The figures indicate that Object/Image Recognition is the most used application of the deep learning and IT solution/software industry is the top industry that exploits the deep learning.
  69. 69. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 69 8. Artificial Intelligent Innovation Insights from Patents Artificial Intelligence for FinTech/Investment Insights from Patents US20160196610 illustrates a system for predicting global financial crises. The system searches state, local, federal and international government websites and databases, including political and financial news sources, to obtain real time financial insights. The system correlates this information to individual stocks, bonds, commodities or currencies and processes it using machine learning engine that predicts the price move for each stock or exchange traded fund. The system also aggregates and analyzes predictions from a geographical area based on country, country capitols and continents or geographical areas and regions. Thus, system offers automated geopolitical risk and determines the investing climate, business for individual countries.
  70. 70. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 70 8. Artificial Intelligent Innovation Insights from Patents Artificial Intelligence for Human Activities Discovery Insights from Patents With the advent of mobile video cameras, digital video recording technology integrates of into consumer electronic devices more and more. In computer vision, machine learning techniques are used to detect the presence of and recognize various elements of the visual scenes that are depicted in digital images. Thus, the captured video by mobile video cameras can be used for understanding human activities. US20140316881 illustrates a system for analyzing images of human reactions (e.g., facial expressions, head poses, and/or gestures) to external sensory and psychological stimuli (e.g., exposure to products, smells, tastes, sounds, etc.). The system can recognize such reactions automatically using machine learning techniques. For example, if an advertisement is displayed to a user’s smartphone, the smartphone simultaneously record the user's reaction obtained using the camera. The smartphone analyzes the reactions of the user. Based on the analysis, new advertisement or incentive can be displayed or delivered to the user.
  71. 71. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 71 8. Artificial Intelligent Innovation Insights from Patents Artificial Intelligence for Autonomous Medical Diagnostics Insights from Patents US20160203263 illustrates a system for automatically analyzing a patient's medical images and provides quantitative metrics related to the patient's current health status and their risks for future health outcomes exploiting machine learning technique. The system compares a patient’s image data from medical imaging apparatus to images of the same or similar tissue regions from other individuals for whom the corresponding health status and/or outcomes are known. An unsupervised machine-learning algorithm is used to calculate metrics related to the degree of similarity between the patient's medical images and the comparison images. The system identifies a corresponding cohort of individuals represented by the comparison images whose images are the most similar to the patient's images based on these similarity metrics. The system calculates the quantitative status and/or risk metrics for the patient based on the known health status and/or outcomes for the corresponding cohort.
  72. 72. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 72 8. Artificial Intelligent Innovation Insights from Patents Amazon Artificial Intelligence Strategy Insights from Patents Autonomous Drones US20160196755 illustrates a system for operating the autonomous drones. The system exploits machine learning techniques to avoid collisions with the ground or other objects. The system uses various types of sensors (e.g., imaging, sonar, radar, lidar, infrared or laser) to detect a distance to an object. Machine learning techniques are used to indicate at what levels operating parameters (effective thrust of a propeller, speed of the airflow of the propeller, etc.) are expected to be for a given distance of a drone from an obstacle. Then, potential proximities to an obstacle can be determined based on monitored parameter levels during flight.
  73. 73. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 73 8. Artificial Intelligent Innovation Insights from Patents Amazon Artificial Intelligence Strategy Insights from Patents -2 Artificial Intelligence as a Service US20150379424 illustrate a system for a customizable, easy-to-use artificial intelligence as a service (AIaaS) that designed to support large numbers of users and a wide variety of problem. The AIaaS implements programmatic interfaces (e.g., APIs) for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. The programmatic interfaces guide non-expert users to start using machine learning best practices relatively quickly, without the users having to expend a lot of time and effort on tuning models, or on learning artificial intelligence techniques. At the same time, expert users can customize the parameters or settings they wish to use for various types of machine learning tasks, such as input record handling, feature processing, model building, execution and evaluation.
  74. 74. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 74 8. Artificial Intelligent Innovation Insights from Patents Amazon Artificial Intelligence Strategy Insights from Patents -3 Cyber Security US20160180078 illustrates a system for providing enhanced information security. The system authenticate a user using the biometric sensing devices (e.g., fingerprint authentication). After successful authentication, the system uses a machine learning classification algorithm within the trusted execution environment to the user biometric sensor data to identify whether an elevated risk of malicious attack exists (e.g., whether it is likely that the user authenticated under duress). The training data for the classification machine learning algorithm can be based on attack patterns that have already been observed by many other cyber security systems. If elevated risk exists, the system triggers a security response (e.g., increasing authentication requirements).
  75. 75. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 75 9. Big Data Innovation Insights from Patents Big Data Innovation Insights Since the United States is such a large market for products and services from nearly all technology innovations, a patent counting for growth in patenting over a period of times in the US can be a good measuring tool for monitoring the evolution of technology innovations. Following figure shows the growth trends for the deep learning technology innovations base on the patent research of nearly 200 published applications and issued patents by the USPTO as of 3Q 2016. The figure indicates that active big data innovations started from 2012 and peaked in 2013. Big data innovations, however, look like decreasing started from 2014 (since there is usually a time lag between the priority date and publication date by two to three years, the number of patents in 2015 is not fully accounted as of 3Q 2016). Reference Big Patents Data 3Q 2016
  76. 76. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 76 9. Big Data Innovation Insights from Patents Big Data Innovation Insights -2
  77. 77. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 77 9. Big Data Innovation Insights from Patents Big Data Innovation Insights -3 Number of patents for an assignee divided by the total number of patents gives what percentage the assignee contributes to the deep learning technology innovations. Ranking the assignees by the number of patents is thus an important part of visualizing the innovations landscape. Following figure shows the top assignees for the big data innovations. The top big data innovators include IBM, Fisher-Rosemount Systems, ETRI, Aerospike, Hitachi, NEC, and Cloudera.
  78. 78. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 78 9. Big Data Innovation Insights from Patents Big Data Innovation Insights -4
  79. 79. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 79 9. Big Data Innovation Insights from Patents Deep Learning Innovation Insights -5 Following figure shows the key big data ecosystem components. The figure shows that Big Data Analytics/AI Application is the most innovated big data ecosystem component followed by Distributed Computing/File System and NoSQL Database.
  80. 80. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 80 9. Big Data Innovation Insights from Patents Big Data Aggregation US20140297826 illustrates a system for big data aggregation in a sensor network. The most important part of the big data analytics is collecting data before storing the data. The Hadoop big data platform supports collecting data in Hadoop Distributed File System (HDFS). HDFS is an open source for storing big data dispersedly, that is, a technology for storing collected data reliably. The big data aggregation system includes a sensor network which comprises many sensor nodes connected to each other over a wired/wireless network and is configured to transfer sensor data generated by each of sensor nodes to a big data management unit by setting a destination address in the sensor data as an address of a big data management unit. The big data management unit configured to distribute and dispersedly store the sensor data based on the destination address of the sensor data.
  81. 81. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 81 9. Big Data Innovation Insights from Patents Big Data Aggregation -2
  82. 82. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 82 9. Big Data Innovation Insights from Patents Big Data Platform The Hadoop big data platform is based on the MapReduce framework. US7650331 describes the MapReduce framework. US20110313973 illustrates the MapReduce framework including the shuffle function using the DFS.
  83. 83. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 83 9. Big Data Innovation Insights from Patents Big Data Cloud US20130227569 illustrates the system that can gather data from thousands of the IoT sensors/devices and analyze the data in the cloud without the massive amount of investment in the server and big data analytics infrastructure. The cloud based IoT big data system provides a virtual IoT sensors/devices cloud as an Infrastructure as a Service (IaaS) and a service cloud as a Software as a Service (SaaS), to provide a flexible and scalable system. The IaaS provides flexibility by handling heterogeneous IoT sensors/devices. The SaaS provides scalability by relieving end users of computational overheads, and enabling on-demand sharing of IoT sensors/devices data to requesting end users. The SaaS also relieves end users from specifying IoT sensors/devices characteristics, locating physical IoT sensors/devices, and provisioning for the physical IoT sensors/devices. The end user, via a device (e.g., smartphone), requests and receives services provided by the system.
  84. 84. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 84 9. Big Data Innovation Insights from Patents Big Data Analytics US20150179079 illustrates a system and for real time monitoring a patient's cognitive and motor response to a stimulus. The big data analysis of massive data obtained by the IoT healthcare/medical devices can provide many value- added healthcare services. US20150186972 illustrate a big data analytics system for the business IoT applications. The business IoT devices can collect a large amount of data regarding products, product attributes, prices, and price attributes. To be understood by a person, this large amount of data and analytic output must be summarized, personalized, and organized in relevant terms. The summarization and personalization of such a large and complex set of data presents challenges in the selection and refinement of information as well as with respect to identification of patterns and arrangement of information in a user interface. The big data analytics system provides a user interface to summarize and personalize a large amount of price and product information, to identify patterns therein, and to generate recommendations in relation to the information.
  85. 85. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 85 9. Big Data Innovation Insights from Patents Big Data Monetization Big data monetization is to generate revenue from available big data products for use by third parties by instituting the discovery, capture, storage, analysis, dissemination, and use of the big data. US 20150089626 illustrate a system for providing a marketplace for big data applications. The system includes a cloud infrastructure to deploy big data applications to the target cloud nodes connected to the cloud infrastructure. The system includes a central control interface to provide a cloud user with control over the cloud infrastructure. The central control interface deploys, changes, configures, manipulates, controls, secures, sells, and rents the applications, data and infrastructure resources to the requesting cloud user. The system provides big data applications on demand from the cloud user and installs the demanded application on to the dedicated big data platform for an online big data processing. The system further includes a web interface to provide an access to the users.
  86. 86. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 86 9. Big Data Innovation Insights from Patents Big Data Monetization -2 US 20160050279 illustrate a system for operating the IoT (internet of Things) big data analysis service. The system analyzes the stored data from the IoT devices in the cloud and produces a big data analysis report. A client can purchase or sell information on the analyzed big data analysis report through the virtual big data marketplace. The big data analysis report can include information on a preliminary analysis report whose results vary according to analysis time or period. When a third party analyzes big data using the information on preliminary analysis report, a writer who uploaded the information on preliminary analysis report to the analysis report marketplace charges fees for the information.
  87. 87. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 87 9. Big Data Innovation Insights from Patents Big Data Monetization -3 Blockchain technology enables a unified decentralized peer-to-peer trusted financial network. Trading of big data products through blockchain enables secure transaction of big data products. For example, US20140344015 illustrate a big data trading platform for enabling consumers to monetize their personal data while managing their online privacy. The platform uses cryptocurrency through blockchain as the means for monetizing the value of personal data. US20150379510 illustrate another big data trading platform to monetize big data using the blockchain infrastructure. The platform matches a producer's data product with a data buyer's specifications. The platform enables micropayments and the maintenance of privacy of personal information. The platform enables fair and transparent marketplace for data producers and data buyers using the redundant distributed ledgers of transactions on peer to peer networks.
  88. 88. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 88 10. IoT + AI + Big Data Integration Strategy Smart Home Integration US20150227118 illustrates the IoT Cloud Big Data AI system for facilitating automatic control of the smart home devices based on past device behavior, current device events, sensor data, and server-sourced data. Cloud-based big data analytics is accessible via a server system for analyzing data associated with persons or buildings in a geographic region about the building, such as local news and weather information and data pertaining to appliances within the geographic region, such as a neighborhood, zip code, and so on. The analyzed data is used to develop the control rules to control smart home devices automatically.
  89. 89. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 89 10. IoT + AI + Big Data Integration Strategy Smart Home Integration -2
  90. 90. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 90 10. IoT + AI + Big Data Integration Strategy Smart Home Integration -3 The automatic control of the smart home devices enable various benefits, such as triggering lights to automatically turn on when a user enters a particular room at a particular time; activating a sprinkler system when server-side data indicates that a fire is nearby; automatically turning on a heater in advance of a home owner's return at a particular time when the home temperature is below a predetermined level; turning off a sound system and lights in various rooms after data indicates that a user is preparing to sleep; turning off lower priority devices that may conflict with higher priority devices, and so on.
  91. 91. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 91 10. IoT + AI + Big Data Integration Strategy Smart Home Integration -4 Cloud-based big data analytics can be used to make a prediction about the future device usage and/or device behavior and/or user behavior exploiting AI. The device usage and/or device behavior and/or user behavior predictions can be used to generate control rules. The prediction can be derived by comparing collected data with a sample table of data to determine whether a correlation exists between the collected data and data in the sample table of data. The prediction can be generated based on a correlation between the collected data and data in the sample table of data. The prediction also can be based on a frequency of occurrence of an instance of data in the collected data (and timing information associated with occurrences of the instances of data) to generate a probability estimate. The probability estimate is employed to determine the prediction.
  92. 92. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 92 10. IoT + AI + Big Data Integration Strategy Google Self-driving Artificial Intelligence Connected Car US9275308 illustrates a system that can be installed in an autonomous vehicle to detect objects. The system obtains the images of surrounding environments using an integrated camera on board the vehicle. The system uses the deep learning (neural networks) to detect objects in images. US20150248131 illustrates a system that enables an autonomous vehicle to request assistance from a remote operator in certain predetermined situations. During the operation of the autonomous vehicle, the vehicle senses the environment of the vehicle. Based on the sensed environmental data, the autonomous vehicle identifies a situation (e.g., a vehicle or other obstacle is blocking a lane within a road) from a set of predetermined situations for which it will request assistance. The vehicle then receives a response back from the remote assistor through wireless vehicular communications indicating how to proceed.
  93. 93. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 93 10. IoT + AI + Big Data Integration Strategy Toyota Self-driving Artificial Intelligence Connected Car Toyota announced its plans to make self-driving cars by around 2020. Toyota also creates new department to unify its connected car technologies. US20140005906 illustrates the system predicting the future position and velocity trajectory of a preceding vehicle. The dynamic state of the preceding vehicle can be predicted based on data received from surrounding vehicles using the V2V Communications using AI. US9031779 illustrates the navigation system with the hazard avoidance feature. The system navigation allows for vehicles and other entities to collaborate and share information via vehicular networks regarding hazards, defects, obstacles, flaws, and other abnormalities that exist in any environment. The system navigation automatically detects and catalog environmental hazards and/or obstacles for route planning. Routes can be planned that avoid these hazards reducing lost time or frustration.
  94. 94. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 94 10. IoT + AI + Big Data Integration Strategy Allstate Connected Car Big Data Monetization Allstate recently announced its launch of an independent telematics business Arity. Allstate patent US9269202 provides details regarding the connected car telematics business that provides data analytics products to third parties. The Arity telematics system collects vehicle operation data at regular time intervals. Thus, the collected data is time marked. The data can also be location marked using the GPS. The vehicle operation data includes data from on board diagnostic systems and car area network systems (e.g., speed, rate of acceleration, activation of brakes, degree and duration of steering direction, etc.) and other monitoring systems (e.g., presence and distance of objects behind or ahead of the vehicle, driver physiological status, etc.). The collected data transferred to the remote data processing system. The remote data processing system creates databases for evaluating driver behavior or operation characteristics (e.g., aggressiveness, recklessness, compliance with laws, etc.).
  95. 95. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 95 10. IoT + AI + Big Data Integration Strategy Allstate Connected Car Big Data Monetization -2 The database can be correlated or evaluated with other databases for a wide variety of uses. For example, the data can be cross-correlated to another information database that is also time or location specific. The time or location specific data includes applicable local speed laws, weather events, and traffic signals (e.g., operation of the vehicle without headlights in a rainy day, driving 80 mph in a 55 mph speed limit zone, etc.). The processed data can be provided to third parties (e.g., insurance companies, l ending institutions, car rental companies, product and service marketing companies, potential employers, etc.) to evaluate an individual's driving behavioral characteristics in a real life.
  96. 96. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 96 10. IoT + AI + Big Data Integration Strategy Hyundai 5G Connected Car 5G (the fifth generation mobile communications) is scheduled to launch in around early 2020s. Even if it is not determined yet regarding the standard technology details, many researchers expect that 5G will transfer 1000 times more data, and thus, can connect billions of IoT (Internet of Things) devices at the same time. Key candidate technologies that enable 5G to support IoT devices connection are millimeter wave communication, massive MIMO (multiple input and multiple output) technology, cloud RAN/network function virtualization (NFV)/ software defined network (SDN), ultra dense network (UDN) and low latency network (a maximum response rate of about 1 ms or less). 5G connected car enables real-time processing of sensor information from surrounding vehicles for proving autonomous driving. Mass transmission supported by the 5G can provide the big data services to the vehicle. 5G enables direct vehicle to vehicle communication using the Device-to-Device (D2D) connectivity. 5G can support the multihop mesh networks among many vehicles.
  97. 97. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 97 10. IoT + AI + Big Data Integration Strategy Hyundai 5G Connected Car -2 US20160278153 illustrates the connected car exploiting 5G multi-antenna beamforming technique for transmitting (receiving) sensor data into (from) a particular region or to (from) a particular vehicle. The sensor data includes surrounding driving condition information regarding the surrounding area of the vehicle. The sensor data also can include position and speed information regarding the surrounding vehicles. Using the surrounding driving condition, position, and speed information, the dynamic route guidance system can guide drivers along the traveling route based on the dynamic traffic information. Using the share accident-associated information, the advanced driver assistant system can prepare potential collision by controlling the speed of the vehicle using the advanced smart cruise control.
  98. 98. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 98 10. IoT + AI + Big Data Integration Strategy Vehicle Autonomous Platooning Vehicular convoying enables vehicles to follow closely behind each other in a convenient and safe manner. Vehicular convoying reduces the drag caused by the substantially turbulent airflow at the rear. This drag is responsible for the majority of fuel used in transportation, especially for heavy trucks (fuel is about 40% of operating expenses). Vehicular convoying provides significant fuel savings both for the lead and the trailing vehicles. US20160054735 illustrates a system for convoying vehicles by coordinating and controlling vehicles to follow closely behind each other. Platooning - how it works video: Link https://www.youtube.com/watch?v=X7vziDnNXEY
  99. 99. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 99 10. IoT + AI + Big Data Integration Strategy Vehicle Autonomous Platooning -2 Vehicle Linking A long range communication system and a central server provide a communication link to each of vehicles. The server or an onboard computer in each vehicle makes decisions and suggestions based on knowledge of vehicle location, destination, load, weather, traffic conditions, vehicle type etc. When a linking opportunity exists, the driver is notified via driver display. The driver can manually adjust the vehicle speed to reduce the distance between the vehicles, or the system can automatically adjust the speed (e.g., using Adaptive Cruise Control). Once the vehicles are sufficiently close together, a short range communications link for communicating among the candidate vehicles causes the vehicles to move into a linked position. The control processor in the lead vehicle communicates its status to the control processor in the trailing vehicle, and vice versa, to cause the vehicles to move into close proximity to one another.
  100. 100. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 10. IoT + AI + Big Data Integration Strategy Vehicle Autonomous Platooning -3 Vehicle Platooning Vehicle control unit responsive to the short range communications link manages vehicular functions to safely maintain a linked position among the vehicles. During the linked, close-following mode, the system controls the engine torque and braking, with no driver intervention required. Relative distance between the vehicles is determined by one of RADAR, LIDAR, cameras, and precision GPS etc. The vehicle control units, in either the lead vehicle or a following vehicle, receive characteristics of vehicle performance in determining whether to maintain or discontinue the linking.
  101. 101. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 10. IoT + AI + Big Data Integration Strategy Industrial IoT US20150040051 illustrates the industrial monitoring system that provides monitoring capabilities for various types of industrial machines and systems. For example, the industrial monitoring system can monitor operational parameters of the gas turbine system using a number of sensors. US20140163763 illustrates the energy management system exploiting the predictive analytics. Utility companies are experiencing a shortage of electrical generating capacity due to increasing customer demand for electricity. The energy management system predicts on-peak hours and/or off-peak hours based on data obtained from various monitoring devices using the predictive analytics. *GE Predix: Cloud platform for the Industrial Internet. It is generally available for developers, data scientists, and other IT professionals who want to build powerful industrial-strength apps and achieve unprecedented processing mobility between the edge and the cloud.
  102. 102. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 10. IoT + AI + Big Data Integration Strategy Industrial IoT -2 A cyber-physical system (CPS) is a system that supports efficient control and determination by interlocking the real world and the virtual world. A CPS is a hybrid system in which embedded systems are combined based on a network, and has the characteristics of both continuous elements such as physical elements and discrete elements such as software elements. CPS applications include the smart electric grid, smart transportation, smart buildings, smart medical technologies, air traffic management, and smart manufacturing. CPSs are the key enabling technologies of the Industrial IoT (IIoT).
  103. 103. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 10. IoT + AI + Big Data Integration Strategy Industrial IoT -3 US20160253495 illustrates s system for detecting anomalous CPS behavior due to cyber and/or physical attacks. Traditional analysis tools are unable to predict adequately CPS behavior because of the complexity of CPS. The system collects information regarding the physical system dynamics from sensors associated with the CPS (e.g., image sensors, depth cameras, infrared and/or thermal sensors). The system, then, classifies system behaviors and identifies the anomalous CPS behavior by detecting a deviation from the identified system behaviors Using a machine learning technique.
  104. 104. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 10. IoT + AI + Big Data Integration Strategy Industrial IoT -4 US20160196527 illustrates the cyber-physical supply chain logistics transportation system for predictive estimation of QoS across supply chains using condition monitoring and predictive analytics. The system includes sensors that monitor conditions associated with cyber-physical system. Sensors monitor individual cargo units and the transportation environment within cyber-physical system. Sensors include door latch sensors, tire pressure sensors, oxygen-level sensors, stress sensors, chemical sensors, reefer sensors for refrigerated trailers, temperatures sensors. Sensors are distributed across several locations within cyber-physical system or embedded within a product itself. Sensors generate sensor data based on monitored conditions and transmit the sensor data to quality management system via wireless network. The quality management system processes the collected data to predict QoS parameter values and recommend pertinent actions.
  105. 105. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 References Smart Home Patents Data 3Q 2016: http://www.techipm.com/alexdb/IoT%20Smart%20Home%20Patents%20Data%203 Q%202016_Intro.pdf Connected Car Patents Data 3Q 2016: http://www.techipm.com/alexdb/IoT%20Connected%20Car%20Patents%20Data%2 03Q%202016_Intro.pdf Connected Health Patents Data 2Q 2016: http://www.techipm.com/alexdb/IoT%20Connected%20Health%20Patents%20Data %202Q%202016_Intro.pdf AI Deep Learning Patents Data 3Q 2016: http://www.techipm.com/alexdb/AI%20Deep%20Learning%20Patents%20Data%20 3Q%202016_Intro.pdf Big Data Patents Data 3Q 2016: http://www.techipm.com/alexdb/Big%20Data%20Patents%20Data%203Q%202016_I ntro.pdf LTE Patents for Standards Data 2Q 2016: http://www.techipm.com/alexdb/LTE%20Patents%20for%20Standards%20Data%20 2Q%202016_Intro.pdf
  106. 106. ©2016 TechIPm, LLC All Rights Reserved www.techipm.com 10 Thank you! If you have any questions please contact Alex G. Lee at alexglee@techipm.com

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