AI Blockchain IoT Convergence for ESG Digital Transformation Webinar

Two key words for the post COVID-19 pandemic economic recovery will be the ESG (Environmental, Social, and Governance) management and digital transformation acceleration.

It is also expected that three core technologies in the data-centric digital economy - AI, Blockchain, IoT and their convergence - will support a long-term sustainable economic system development. 

Combining the two key words and three core technologies, Alex G. Lee, Ph.D., Esq. (https://www.linkedin.com/in/alexgeunholee/) will present how the AI, Blockchain, IoT and their convergence will enable the ESG digital transformation.

Alex will present the state of the art innovations of AI, Blockchain, IoT and their convergence, demos of specific implementations of the convergence systems, and use cases for the ESG digital transformation. 

Specifically, based on AI, Blockchain, IoT related patents and ESG related reports analysis, Alex will present specific technical details about the use cases in the most important 10 sub-fields of ESG digital transformation -

E: environmental sustainability, natural resources, climate change, energy & pollution, waste management; S: social responsibility, human rights, business relationship; G: corporate governance, code of conduct.

Alex will also present the industry-wise ESG performance maps, which can be used with the ESG digital transformation analysis in selecting best-in-class portfolio companies for ESG investment.

Place: Online Zoom meeting

Time: May 20, 2021 7 pm – 9.30 pm Eastern Time/EDT (US and Canada)

          May 20, 2021 4 pm – 6.30 pm Pacific Time/PST (US and Canada)

          May 21, 2021 8 am – 10.30 am GMT+9 Time Zone (S. Korea and Japan)

Registration: https://www.eventbrite.com/e/ai-blockchain-iot-convergence-for-esg-digital-transformation-webinar-tickets-153378221389?utm_source=eventbrite&utm_medium=email&utm_campaign=post_publish&utm_content=shortLinkNewEmail

AI, Blockchain, IoT Convergence Use Case System Implementation Insights from Patents

 

Contents

I. AI, Blockchain, IoT, and Their Convergence Technology Innovation Status

II. AI, Blockchain, IoT Convergence Use Case System Implementation Examples

1. Blockchain-based Privacy-Preserving Federated Learning System

2. Blockchain-based Decentralized IoT & AI Data Marketplace

3. Blockchain-based Trustworthy AI & IoT Systems

4. Blockchain-based Decentralized Parallel Edge Machine Learning

5. Predictive Maintenance Platform for Industrial Machine using Industrial IoT

6. AI+Blockchain System for Car Sharing Service

7. Connected Autonomous Vehicle Communication Management System

8. 5G-based AI+Blockchain+IoT Edge Computing System

Link: https://www2.slideshare.net/alexglee/ai-blockchain-iot-convergence-use-case-system-implementation-insights-from-patents

AI, Blockchain, IoT Convergence Insights from Patents

 AI, Blockchain, IoT Convergence Insights from Patents

Contents

I. AI, Blockchain, IoT Technology Innovation Status 

1. Technology Innovation Status in Innovation Entity

2. Technology Innovation Evolution

3. Technology Innovation Status in Innovation Country

4. Technology Innovation Status in CPC Classification

5. Technology Innovation Status in Specific Technology

A. Deep RL Technology Innovation Status

B. Deep Learning for Autonomous Vehicle Technology Innovation Status 

C. Deep Learning for 5G Technology Innovation Status 

D. Deep Learning for Cybersecurity Technology Innovation Status 

E. Blockchain Privacy Technology Innovation Status 

F. Blockchain Interoperability Technology Innovation Status 

G. Blockchain DID Technology Innovation Status 

H. Blockchain Toknization Technology Innovation Status 

II. AI Blockchain IoT Convergence Technology Innovation Status 

1. Technology Innovation Status in Innovation Entity

2. Convergence Technology Innovation Status in Convergence Field

3. Convergence Technology Innovation Evolution

4. Technology Innovation Status in Innovation Country

5. Technology Innovation Status in CPC Classification

6. Technology Innovation Status in Specific Application

Privacy-preserving Blockchain-based AI Data/Model Marketplace

Blockchain-based Decentralized Machine Learning Platform

Blockchain-based Secure Telehealth-diagnostic System

Peer-to-Peer Micro-Loan Transaction System

Predictive Maintenance Platform for Industrial Machine using Industrial IoT

Blockchain Augmented IoT System for Dynamic Supply Chain Tracking

Decentralized Energy Management Utilizing Blockchain Technology

Provisioning Edge Devices in Mobile Carrier Network as Compute Nodes in Blockchain Network

Distributed Handoff-related Processing for Wireless Networks

III. Appendix

1. AI Blockchain IoT Convergence AT A Glance

2. AI, Blockchain, IoT for Finance AT A Glance

3. AI, Blockchain, IoT for Healthcare AT A Glance

4. 5G Based AI + Blockchain + IoT Convergence AT A Glance

Link: https://www2.slideshare.net/alexglee/ai-blockchain-iot-convergence-insights-from-patents

 

인공지능•블록체인•사물인터넷 융합시스템 기술 및 사업 개발 특강 개최

 

서울--(뉴스와이어) 2020년 11월 05일 -- 4차 산업혁명 3대 핵심기술인 인공지능·블록체인·사물인터넷 융합 분야에 대한 이근호 박사 초청 특강이 11월 17일 열린다. 이 특강은 KAIST 4차산업혁명정책센터가 주최하고 테크아이피엠 주관하며 이근호 박사는 해당 분야 전문가이다.

 

KAIST 4차산업혁명정책센터(Korea Policy Center for the Fourth Industrial Revolution, 이하 KPC4IR)는 세계경제포럼(World Economic Forum)의 한국 연구파트너로 특강 발표 내용의 일부는 KPC4I의 연구과제 지원으로 이뤄졌다.

 

이번 특강에서는 4차 산업혁명 3대 핵심기술인 인공지능, 블록체인, 사물인터넷의 기술 혁신 현황을 살펴보고 각 기술 간 결합 시너지를 분석해 인공지능, 블록체인, 사물인터넷 융합을 종합적으로 살펴본다. 또한 금융, 헬스케어, 스마트제조, 자율주행차, 5G 등 주요 인공지능, 블록체인, 사물인터넷 응용분야 융합시스템 개발에 대한 기술 소개, 요소기술 개발, 활용 사례, 특허 분석, 사업 전략 및 사업 모델 개발, GDPR/데이터 3법 규제 준수 및 대응, 디지털 뉴딜과의 사업 연계 및 개발 등에 대해 강의한다.

 

이번 특강을 진행하는 이근호 박사는 신기술 및 지식재산 사업화 및 수익화 전문가로, 이번 특강을 주관하며 미국 보스턴에 본사를 둔 인공지능, 블록체인, 사물인터넷 융합 시스템 기술·특허·사업화·수익화 개발 인큐베이팅 및 컨설팅 전문회사 테크아이피엠의 대표이다. 이근호 박사는 IT 분야 25년 이상의 경력자로 미국 존스홉킨스대학에서 박사를 받았고 MIT 경영대학원 혁신 및 전략 경영자 과정을 이수했다. 미국 서픽대학 로스쿨을 거쳐 현재 뉴욕주 변호사, 미국 변리사, 공인 라이센싱 전문가로 활동하고 있다.

 

이번 특강은 온·오프라인으로 진행되며 서초동 월튼블록체인연구교육원에서 청강 및 유튜브 실황 방송 및 녹화 방송 청강으로 참여할 수 있다. 이번 특강 무료로 등록에 대한 자세한 내용은 온오프믹스 모임 링크를 참고하면 된다.

 

온오프믹스 모임 링크: https://onoffmix.com/event/225904

 

AI+Blockchain+IoT Integration Innovation Insights from Patents

Link: https://www.slideshare.net/alexglee/aiblockchainiot-integration-innovation-insights-from-patents

IoT Innovation Superpower Infographic

Top 100 IoT technology innovation entities insights from patents!

Link: https://www.slideshare.net/alexglee/iot-innovation-superpower-infographic

블록체인 + 빅데이터 + AI + IoT 융합 특강 (Blockchain + Big Data + AI + IoT Integration)

내용: 신뢰성 있는 비즈니스 트렌젝션을 가능하게 하는 블록체인과 IoT 및 AI와의 결합에 의한 빅데이터 디지털 자산의 공유, 거래, 활용을 가능하게 하는 블록체인 + 빅데이터 + AI + IoT 융합 시스템에 대한 기술 및 응용사례를 강의한다.

1. 블록체인, AI, IoT 기술소개

2. 블록체인, AI, IoT 벤처기업 동향

3. 블록체인 + 빅데이터 + AI + IoT 융합사례

4. 제나두기반 블록체인 + 빅데이터 + AI + IoT 융합 시스템 소개 및 데모

강사: 이근호 박사(미국 제나두 빅데이터 대표)

일시: 7월 3일(화) 오후 1:30 - 4:30

장소: 홍익대학교 빅데이터 센터(홍문관 9층)

등록비: 무료

등록신청: 이담호(damho1104@mail.hongik.ac.kr)에게 특강 등록요청 제목으로 성명/이메일 주소를 보내세요.

Blockchain + IoT + AI Integration Insights from Patents

Link:

https://www.slideshare.net/alexglee/blockchain-iot-ai-integration-insights-from-patents

Fourth Industrial Revolution & Xanadu: Big Data + IoT + Deep Learning Integration Strategy

Part I

The Fourth Industrial Revolution?

Big Data Introduction

Big Data Analysis Flow

Big Data Use Cases

https://www.youtube.com/watch?v=sHPfY4E_4T0&feature=youtu.be

Part II

Big Data Use Cases

IoT Introduction

IoT Use Cases

https://www.youtube.com/watch?v=4J3iuxP4B4U&feature=youtu.be

Part III

IoT Use Cases

Artificial Intelligence Overview

Deep Learning Introduction

Deep Learning Use Cases

https://www.youtube.com/watch?v=cC5SK4G27Mo&t=58s

Part IV

Deep Learning Use Cases

Big Data in IoT & Deep Learning

Challenges of IoT Big Data Analytics Applications

Distributed Deep Learning

Xanadu Functionality

Xanadu Performance BMT

Xanadu Fault Tolerance Test Demo

Xanadu Use Cases

Xanadu Commodity Storage System Use Case

Xanadu Cloud Computing Use Case

https://www.youtube.com/watch?v=DOW1vtGgKL0&t=17s

Part V

Xanadu Cloud Computing Use Case

Xanadu + Deep Learning + Spark + Hadoop Integration

Xanadu based Big Data Deep Learning System

https://www.youtube.com/watch?v=V0s9FxG_zuU&feature=em-upload_owner

Xanadu for Big Data + IoT + Deep Learning + Cloud Integration Strategy (YouTube Presentation Video)

Silicon Valley Xanadu Promotional Event Presentation Part I

Big Data in IoT & Deep Learning

Challenges of IoT Big Data Analytics Applications

Challenges of Cloud-based IoT Platform

Cloud-based IoT Platform Use Case: GE Predix for Smart Building Energy Management

Link: https://www.youtube.com/watch?v=tJpsq6pg3Sg

Silicon Valley Xanadu Promotional Event Presentation Part II

Fog/Edge Computing & Micro Data Centers

Deep Learning for IoT Big Data Analytics Introduction

Deep Learning for IoT Big Data Analytics Use Case

Distributed Deep Learning

Link: https://www.youtube.com/watch?v=7GK8RfFbHiU&t=34s

Silicon Valley Xanadu Promotional Event Presentation Part III

Big Data + IoT + Cloud + Deep Learning Insights from Patents

Big Data + IoT + Cloud + Deep Learning Strategy Development

Link: https://www.youtube.com/watch?v=dFmx6vSXPf8

Silicon Valley Xanadu Promotional Event Presentation Part IV

Designing Data-Intensive Applications

Xanadu Functionality

Xanadu Use Case

Xanadu + Deep Learning + Hadoop Integration

Link: https://www.youtube.com/watch?v=Cl_y5sTj-Tk&t=5s

Part I+ II + III + IV Presentation Slide

Link:  https://www.slideshare.net/alexglee/xanadu-for-big-data-iot-deep-learning-cloud-integration-strategy

Xanadu for Big Data + Deep Learning + Cloud + IoT Integration Strategy Presentation

Link: https://www.slideshare.net/alexglee/xanadu-for-big-data-iot-deep-learning-cloud-integration-strategy

Xanadu for Big Data + Deep Learning + Cloud + IoT Integration Strategy

Event Description:

Alex G. Lee, a managing partner of Xanadu Big Data, LLC, will talk about Xanadu technology and use cases for Big Data + Deep Learning + Cloud + IoT Integration Strategy.

Xanadu is the most advanced big data management platform technology that is developed to take care of the requirement of high speed processing of diverse type of high volume data. Xanadu can provide a massively scalable fault tolerance system that can connect multiple storages. Xanadu can provide high throughput and low latency data management system. Xanadu provides ACID compliance data management system. Xanadu is designed to be a composable architecture in order to be selected and integrated with other big data system elements such as IT infrastructures and data analytics to satisfy specific big data use requirements. Xanadu is designed for the heaviest workloads that can supports concurrent queries without conflict. For example, Xanadu can support thousands of sensors accessing and updating data at the same time. Thus, Xanadu enables real-time IoT analytics for industrial IoT applications. Xanadu also can support data-intensive distributed deep learning applications involving massive volume multimedia data.

Please join to meet Alex G. Lee for lunch and introduction of Xanadu.

Date: 6/29/2017

Time: 11.30 am – 3 pm

Location: DLA Piper in Palo Alto, 2000 University Ave, Palo Alto, CA 94303

Agenda:

11.30 am – 12.00 pm Check-in

12.00 pm – 1.00 pm Lunch & Networking

1.00 pm – 1.10 pm Introduction by DLA Piper

1.10 pm – 2.30 pm Presentation by Alex G. Lee

2.30 pm – 3.00 pm Networking

3.00 pm Meeting adjourn

This event is by invitation only. If you want to attend the event please send RSVP to Alex G. Lee (alexglee@xanadubigdata.com) with your name, company name, title and email address.

Blockchain for IoT & Big Data Insights from Patents

Link: https://www.linkedin.com/pulse/blockchain-iot-big-data-insights-from-patents-lee-ph-d-j-d-clp?published=t

IoT Big Data Analytics Insights from Patents

IoT (Internet of things) big data analytics is becoming important to process unimaginably large amounts of information and data that are obtained by the sensor embedded interconnected IoT devices. The typical IoT big data analytics system is Hadoop, an open-source software framework that supports data-intensive distributed applications, and the running of applications on large clusters of commodity hardware. Hadoop, that is based on the architectural framework MapReduce, collects both structured data and unstructured data, processes the collected data set in a distributed network cluster in parallel, and extracts valuable information from the processed data set within a short time.

IBM patent application US20160070816 illustrates a system for processing large scale unstructured data in real -time. The interconnected IoT sensing devices continuously generate massive information at a very high speed. Thus a technology for effectively processing a huge amount of information in the form of a data stream in real-time is very important. The real time big data analysis system includes a receiver for receiving streamed input data from live data sources, a pattern generator for deriving emergent patterns in data subsets, a pattern identifier for identifying a repeating pattern and corresponding data subset within the emergent patterns, a compressor for reducing the identified data subset and identified pattern to a compressed signature and a repository for storing the streamed input data with the compressed signature and without the identified data subset in which the data subset can be rebuilt if necessary using the compressed signature.

In IoT, millions of events often generated from IoT sensors and devices. Thus, it is very important to develop real-time data streaming and processing systems for IoT analytics. There are several open-source real-time data streaming and processing systems are available including Apache Kafka, Storm, and Spark. Most of open-source real-time data streaming and processing systems offer default schedulers that evenly distribute processing tasks between the available computation resources. However, such schedulers are not cost effective because substantial computation resources are lost during assignment and re-assignment of tasks to the correct sequence of computation resources in the stream processing system, which results in significant latency in the system. Salesforce.com, inc. patent application US20170075693 illustrates a cost effective improved real-time data streaming and processing systems for IoT analytics.

In Industrial IoT (IIoT) applications (e.g., manufacturing, oil and gas, mining, transportation, power and water, renewable energy, heath care, retail, smart buildings, smart cities, and connected vehicles), it is not practical to send all of that data from sensors embedded in industrial machines to cloud storage because connectivity not enough bandwidth in a cost effective way and difficulty in practical implementation of effective real-time decision making and prediction systems. FogHorn Systems, Inc. patent application US20170060574 illustrates a real-time edge IoT analytics system (e.g., Fog Computing) that can handle the large amounts of data generated by industrial machines and provides intelligent edge computing platform.

Data monetization is a business model to generate revenue from available data sources or real time streamed data by instituting the discovery, capture, storage, analysis, dissemination, and use of the data. Data monetization leverages data generated through business operations as well as data associated with individual actors and with electronic devices and sensors participating in a given network. IoT can facilitate generating location data and other data from sensors and mobile devices. Big data system enables identification, analysis, selection and capitalization of the IoT data monetization opportunities. Data monetization value chain includes the data producers, data aggregators, data distributors and data consumers. Data as a service (DaaS) models for transactions involving big data can be possible.

mFrontiers, LLC patent application US20160050279 illustrate a system for operating the IoT big data analysis service for data monetization. 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.

New Technologies & Associates, Inc. patent application US20150179079 illustrates a healthcare IoT big data analytics system for real time monitoring of a patient's cognitive response to a stimulus. The big data analysis of massive data obtained by the sensing devices can provide many value-added healthcare services. The real time monitoring system includes a mobile or tablet device, a user interface disposed on the mobile device, sensors monitoring user interaction with the mobile device and capturing kinesthetic and cognitive data. The real time monitoring system also includes a processor for comparing the kinesthetic and cognitive data and comparing the data to a baseline, and identifying relative improvement and impairment of cognition skills from the comparison exploiting big data analytics.

 ©2017TechIPm, LLC All Rights Reservedhttp://www.techipm.com/

Connected Things 2017 Keynote Highlight

Connected Things 2017 explores how to accelerate the adoption of the Internet of Things and
how IoT could have the biggest impact on people, places and things.
Harel Kodesh, Vice President, Predix & CTO, GE Digital
Mac Devine, VP & CTO, Emerging Technology & Advanced Innovation, IBM Cloud Division
Alan Southall, SVP of Engineering, Head of IoT Predictive Maintenance, SAP
David Friend, CEO, BlueArchive

Link: https://www.slideshare.net/alexglee/connected-things-2017-keynote-highlights

The Enormous IoT Innovation R&D Costs Can Be Reduced

The enormous costs of unsuccessful IoT innovation R&D projects are often scrutinized. TechIPm patent research offers a way to decrease the risk of failures and its high costs.

(Newswire.net -- February 2, 2017) Burlington, MA -- Very few topics are as loaded as when it comes to the enormous costs of unsuccessful research and development projects. According to the Economist, the greatest controversy of the pharmaceuticals industry comes exactly from the fact that the cost of a new drug also includes the numerous attempts which failed to win approval, and the associated funds consumed by the Research and Development department. The road to a drug’s approval is filled with casualties, one example being the $800m that Pfizer blew on torcetrapib, a potential treatment for high cholesterol that the company gave up in 2006. But according to many, the skeptical attitude towards the startlingly high estimates for drug-development is justified. Even Sir Andrew Witty, the director of GlaxoSmithKline, one of the largest drug makers, said that even a low figure such as $1 billion is a 'myth' which could be broken if the R&D staff simply stopped failing so much.

The Internet of Things (IoT) is the network of physical objects with unique identity that are connected through the Internet. These objects can sense internal states or the external environment and communicate. The IoT is considered to be the third wave of internet that is expected to generate over $11 Trillion market by 2025. The high new market creation potential of the IoT leads to huge investments in IoT innovation R&D to produce new technologies, products, and services.

This is why there is a rising demand for companies which offer a specific tailored type of consultancy for providing successful cost-effective IoT innovation R&D strategy.

TechIPm, LLC  is a professional research and consulting company specializing in strategy for emerging technology and related intellectual property development and monetization.

As far as health is concerned, many companies in healthcare industry may benefit from TechIPM’s custom research, which is based on the analysis of the published patent applications and issued patents in the USPTO regarding the IoT (Internet of Things).  Connected health and is called IoT Connected Health Patents Data 4Q 2016. The used methodology consists of searching the USPTO database for the IoT connected health related published patent applications and issued patent as of 4Q 2016. Then, the searched patents for the key IoT connected health patents are reviewed by categorizing the identified patents by application systems such as Clinical Health, Fitness, Workout, Medication and many other Management systems. It also categorizes the patents by key connected health technology innovations such as R&D, which includes the university as well as individual inventors. Also, it classifies the findings by key connected health technology innovations. Healthcare Network System, Intelligent Medical Diagnosis/Treatment, Personal Health Management, Personalized Medicine are among many which are included.

Another industry which is heavily impacted by new patents is of course, may benefit from TechIPM’s custom research is the car industry. Currently the world eagerly awaits Apple’s very first car, Forbes reports based on speculation and noncommittal comments from Apple’s CEO, Tim Cook, about the company’s plans to enter the automotive business. Let’s not forget how many lifestyles were affected by the introduction of Land Rover, Audi, Bugatti Veyron, considered as the Concorde of cars and Toyota’s Prius which started the Hybrid chapter.

TechIPm also offers custom research services to assess car related patents and applies the same above described methodology via its IoT Connected Car Patents Data 4Q 2016. Using patent data information provides new insights regarding the state current car innovations, helps to identify new opportunities by identifying new R&D areas that can lead to new product or service development. Also, by using the patent analysis, the company can re-evaluate its competitive strategy’s strengths and weaknesses and its alignment to the company’s overall leadership, therefore helping the management in directing the company’s next strategic move. This type of analysis also gives a better overview of the company’s value chain and its individual chain members. And finally, it is a great prevention measure against dispute risks as it offers an angle from which potential disputes can be seen before they arise.

Last, but not least, this type of custom research method is also applied to smart home innovations through IoT Smart Home Patents Data 4Q 2016. Risk management is an essential feature of any company’s successful performance as well as a key ingredient in ensuring the company continues to operate in the foreseeable future. As the world is constantly moving towards a more green way of living, there is an increased demand in intelligent lighting and energy management systems. Although challenging and highly costly to innovate in these areas, TechIPm is a good associate in reducing the company’s risk of failed innovations and therefore related costs which weigh heavily on financial performance. Not surprisingly, Sony, Samsung and Microsoft are among many of their satisfied clients.

Contact:

Alex G. Lee, Ph.D., J.D.
Principal Consultant & Chief Strategist
alexglee@techipm.com
(781) 270-1585 

About TechIPm, LLC

TechIPm is a professional research and consulting company specializing in strategy for technology and intellectual property manangement and monetization. Our mission is to create value from technology innovations for contributing to the development of intellectual society. We serve technology and IP professionals providing custom research and consulting services for technology and intellectual property monetization & management.

TechIPm, LLC

15 District Ave
Burlington, MA 01803
United States
(781) 270-1585
alexglee@techipm.com
http://www.techipm.com/index.html

Source: http://newswire.net/newsroom/blog-post/00095016-the-enormous-io-t-innovation-r-d-costs-can-be-reduced.html

IoT Connected Health Patents Data 4Q 2016

IoT Connected Health Patents Data 4Q 2016 is a custom research of TechIPm, LLC (www.techipm.com) based on the analysis of the published patent applications and issued patents in the USPTO regarding the IoT (Internet of Things) connected health.

Methodology

1. Search for the IoT connected health related patents.

Search the USPTO database for the IoT connected health related published patent applications and issued patent as of 4Q 2016

2. Review the searched patents for the key IoT connected health patents.

Categorize the identified patents by the key connected health application system:

Clinical Health Management System, Fitness/Workout Management System, Health ICT System, Home Healthcare System, Medication Management System, Personal Health Management System, Physical Activity Monitoring System, Physiological Monitoring System, Sleep Monitoring System, Smart Cloth/Footwear, Telemedicine System

Categorize the identified patents by the key connected health value chain player:

Consumer Electronics Manufacture, Consulting Service, Financial Service, Healthcare Service,  Healthcare Solution, ICT Solution, Medical/Health Device Manufacture, Patent Monitization Entity, R&D (including university, individual inventor), Semiconductor Device Manufacture, Sports Goods/Equipment Manufacturer

Categorize the identified patents by the key connected health technology innovation:

Activity Monitoring Automation, Healthcare Information System, Healthcare Network System, Intelligent Medical Diagnosis/Treatment, Personal Health Management, Personalized Medicine,    Physiological Information Sensing, Remote Health Monitoring

Deliverables

MS excel file for current assignee, patent number (hyperlinked to Google Patent), title, application system category, value chain player category, technology innovation category

Examples for Data Use Cases

Innovation Status

Patent information can provide insights regarding the state of the art of connected health innovations.

Innovation Opportunity

Using patent information, one can identify the potential innovation R&D areas (“white space”) that can lead to new connected health products/services development through the patent analysis.

Cross-competitor Analysis

Patent information can provide insights regarding the competitive advantage innovation strategy in alignment with the strategic move of a specific company for connected health business leadership through the cross-competitor analysis.

Value Chain

Patent information can provide insights regarding the state of the art of connected health innovations of the value chain players.

Patent Dispute Risk

Patent information can provide insights regarding the potential patent dispute risks of connected health applications.

For more information, please contact Alex G. Lee at alexglee@techipm.com .

IoT Connected Car Patents Data 4Q 2016

IoT Connected Car Patents Data 4Q 2016 is a custom research of TechIPm, LLC (www.techipm.com) based on the analysis of the published patent applications and issued patents in the USPTO regarding the IoT (Internet of Things) connected car.

Methodology

1. Search for the IoT connected car related patents.

Search the USPTO database for the IoT connected car related published patent applications and issued patent as of 4Q 2016

2. Review the searched patents for the key IoT connected car patents.

Categorize the identified patents by the key connected car technology/application:

Accident Avoidance, Control Automation, Driving Automation, Information Sharing, Location Finding, Maintenance/Diagnostic/Alert, Map/Route Information, Network Security, Traffic Control, V2X (V2V, V2I using DSRC, LTE)

Categorize the identified patents by the key connected car product/service:

ADAS, Autonomous Driving System, Cloud Service, Infotainment System, ITS, Navigation System, Parking Assist System, Telematics, Vehicular Communication System, Driver Status/Health Monitoring

Categorize the identified patents by the key connected car value chain player: 

Auto Manufacturer (OEM), Auto Parts Manufacturer, CE Manufacturer, ICT Solution, ICT Service, Insurance, Internet/Computer, PME (Patent Monetization Entity), R&D (including university), Semiconductor, Telecom Operator, Telecom OEM, Agricultural Equipment Manufacture

Deliverables

MS excel file for current assignee, patent number (hyperlinked to Google Patent), title, technology/application category, product/service category, value chain player category

Examples for Data Use Cases

Innovation Status

Patent information can provide insights regarding the state of the art of connected car innovations.

Innovation Opportunity

Using patent information, one can identify the potential innovation R&D areas (“white space”) that can lead to new connected car products/services development through the patent analysis.

Cross-competitor Analysis

Patent information can provide insights regarding the competitive advantage innovation strategy in alignment with the strategic move of a specific company for connected car business leadership through the cross-competitor analysis.

Value Chain

Patent information can provide insights regarding the state of the art of connected car innovations of the value chain players.

Patent Dispute Risk

Patent information can provide insights regarding the potential patent dispute risks of connected car applications.

For more information, please contact Alex G. Lee at alexglee@techipm.com .