Innovation Frontline: Artificial Intelligence

Showing posts with label Artificial Intelligence. Show all posts

Saturday, November 28, 2020

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

Friday, July 17, 2020

AI+Blockchain+IoT Integration Innovation Insights from Patents

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

Thursday, July 9, 2020

AI Innovation Superpower Infographic

Top 100 AI technology innovation entities insights from patents!

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

Friday, January 24, 2020

AI (Artificial Intelligence) Patents Landscape for Innovation Leadership 4Q 2019

Patent information can provide insights regarding the state of the art of the AI technology innovation. Patents regarding the AI technology innovation that specifically describe the major AI technologies are a good indicator of the AI innovations in a specific innovation entity.

To find AI technology innovation leadership for the US market, patents in the USPTO database are searched using relevant AI keywords. Total of nearly 20,000 published patent applications regarding AI technology innovation are identified as of 4Q 2019.

IBM is the leader in AI patent application followed by Microsoft, Samsung Electronics, Intel, Google, AT&T, Accenture, Qualcomm, LG Electronics, Siemens , General Electric, Toyota, Baidu, Comcast, Cisco, General Motors, Honda, Sony, Adobe, and Facebook.

It is interesting to find several automobile manufactures' AI innovation leadership: Toyota, GM, and Honda. It is also interesting to find Apple's relatively low innovation activity in AI compare to its competitors.

Patent application activity chart shows a dramatic increase in technology innovation within five years.

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

http://www.techipm.com/

Friday, June 15, 2018

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.

Friday, April 13, 2018

Blockchain + IoT + AI Integration Insights from Patents

Link:

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

Wednesday, March 28, 2018

Xanadu Based Medical Big Data CBIR System for Automated Diseases Diagnosis

For some diseases, assessment of historical medical records in the database is sufficient for quick and accurate diagnosis. Many of such historical medical records are in form of images, such as images of affected body parts of the patients indicating a disease or abnormality. The doctors can study new images by comparing them to similar images available in the database. Moreover, since such historical records are generally stored along with their corresponding diagnoses in the database, it becomes easier for the doctors to diagnose a patient.

As the number of records in the database increases, the database may become comprehensive and exhaustive resulting into a consequent improvement in accuracy of a diagnosis based on the database. However, handling of such large amount of data poses a challenge. It is difficult to implement an architecture that enables archiving of such large number of records that allows quick retrieval of relevant records on demand at low cost.

Xanadu is a big data management technology. Xanadu provides resilient, durable, scalable, and consistent distributed big data database. Xanadu enables competitive big data management in the clouds or enterprises.

Xanadu’s high scalability makes it an ideal choice for the above mentioned type of problem. Xanadu distributed hash means thousands of images can be stored and retrieved efficiently using commodity hardware with a very low cost per GB. Xanadu’s query system can also be leveraged to retrieve images quickly even when the total images in the database grows into the millions, or even billions. For details regarding Xanadu, please see following references.

Xanadu Introduction Video

Xanadu Introduction Brochure

A content based image retrieval (CBIR) system enables search and retrieval of images similar to a target image in large databases base on contents of images (e.g. colors, shapes, textures). A common use-case of CBIR in medical diagnosis is where imaging methods are used to highlight small areas (lesions) in otherwise healthy tissue. Early breast cancer can be seen as small shadows on a Mamogram (X-Ray of the breast), PET scans highlight small areas of increased metabolic activity that can characterize cancerous growths and Retinal images show small bleeds (microaneurysms) that highlight eye disease as well as wider metabolic problems such as type II diabetes.

To demonstrate the performance and capability of Xanadu based medical big data CBIR system, a prototype CBIR system for retinal images is developed. In the case of the retina (the light sensing surface at the rear of the eye), a simple non-invasive photograph of the eye is sufficient to determine whether a patient suffers from a range of diseases. Indeed, the signs of other diseases such as diabetes, high blood pressure and other circulatory disorders can be diagnosed and assessed from a single retinal image.

The prototype CBIR system collects each retinal image together with its expert reviewed diagnosis. Then, the system breaks each image into small patches that are as small as possible without losing the ability to contain the typical lesions that can indicate disease. The system utilizes the PCA technique to cluster images together in a way that naturally groups similar images. For improving the accuracy, the system uses machine learning techniques (e.g. random forest or deep neural networks). The machine learning techniques also enable to infer an overall diagnosis of a patch given the disease “score” and “distance” (in image pixel terms) from known examples. The result is a detailed (pixel by pixel) report for doctors where all areas of concern have been highlighted and a detailed list of comparative images (showing the similarities) can be viewed in the graphic user interface for final clinical assessment.

If you are interested in collaboration regarding medical big data CBIR applications utilizing medical big data archive, please let me know: Alex G. Lee (alexglee@xanadubigdata.com)

Monday, June 12, 2017

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.

Thursday, November 24, 2016

Silicon Valley IoT Meetup, Startups Greater Asia Meetup, and IEEE Consultants' Network Joint Workshop

Subject: IoT + AI + Big Data Integration Strategy & IoT Innovation Insights from Patents and IP-Based Innovation Platform

Speaker: Alex G. Lee, TechIPm & Liquidax Capital

Venue: Carr & Ferrell LLP, 120 Constitution Drive, Menlo Park, CA 94025

Date and Time: Friday, December 9, 2016 from 12:00 PM to 2:00 PM (PST)

12:00 -12:30 pm: Lunch and Networking, 12:30 -2:00 pm: Talk and Q&A

Register Online: https://www.eventbrite.com/e/workshop-iot-ai-big-data-integration-strategy-iot-innovation-insights-from-patents-and-ip-based-tickets-29584945368

*Registration is complimentary

Have questions about Workshop: Contact Christina Hsiang (christina.hsiang@gmail.com) or Joseph Wei (joseph.wei@ieee.org)

Abstract:

Goldman Sachs, Mckinsey, and other top firms have long been predicting a boom and new era of technology with the ripening of the Internet of Things (IoT) marketing but WHERE are all the players PLACING THEIR BETS?

Exploring innovation insights and analyzing patent trends in verticals such as wearables, smart homes and cities, connected car, and etc. we'll dive into the state of the art of IoT innovations. We will also look at potential innovation R&D areas that can lead to new products/services development and how to take advantage of the Liquidax Capital private equity fund and its IoT Innovation Platform. We will further touch on the impact of fast-growing cross-border technology transfer transactions particularly investments flowing from Asia to the West as well as transfer and distribution of technologies from the West to Asia.

This talk will cover the following topics:

IoT Innovation Frontiers Insights from Patents,

IoT Innovation Development Exploiting Patents

IoT + Big Data + AI Integration Strategy

IoT Innovation Platform for Business Growth

Cross-border IoT Tech Transfer Deal Trends

Leveraging Strengths in the Asian and US IoT Markets

Speaker Bio:

Dr. Alex G. Lee brings over 25 years of unique experiences and expertise consulting with Fortune 500 companies patent monetization firms, law firms, investment firms, and research institutions on technology R&D, technology commercialization, business development, intellectual property (IP) management, and business strategy consulting into business, technology, and IP integrated strategy development and execution.

Some leading companies and research institutions that Dr. Lee has worked with include Samsung, Korea Telecom, KMW, MIC Radio Research Laboratory, Boston University, and Georgia Tech. Dr. Lee also has founded and managed several companies and industry organizations such as TechIPm, LLC for IP strategy consulting, Xanadu Big Data, LLC for the big data technology licensing and commercialization, and u-City Forum for the IoT smart city development through public-private partnership.

Dr. Lee earned his Ph.D. in physics from the Johns Hopkins University and J.D. from the Suffolk University Law School. He is registered to practice before the US Patent and Trademark Office, is a Certified Licensing Professional (CLP) and passed New York State Bar Exam. Dr. Lee is now participating in the MIT Sloan School of Management Executive Program for strategy and innovation.

Monday, September 26, 2016

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

Contents

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

=> Link

Friday, September 23, 2016

AI Deep Learning Patents Data 3Q 2016

AI Deep Learning Patents Data 3Q 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 AI (Artificial Intelligence) deep learning techniques.

Methodology

1. Search for the AI deep learning related patents.

l Search the USPTO database for the AI deep learning related published patent applications and issued patent as of 3Q 2016

2. Review the searched patents for the key AI deep learning patents.

l Categorize the identified patents by Industry

Aerospace, Agriculture, Automotive, Consulting, Consumer Electronics. Defense, Education, Entertainment, Government, Industrial, Internet, IT solution, Medical, Device, Pharmaceuticals, PME (Patent Monetization Entity), R&D (including university), Security, Semiconductor, Software, and Telecom

l Categorize the identified patents by the key application

Automation Rule Creation, Character Recognition, Cloud Computing,

Computer Vision, Control Automation, Cyber Security, Data Processing,

Detecting Abnormalities, Document Classification, Feature Extraction,

Financial/Investment Analysis, Human Action Recognition,

Human Emotion Recognition, Human Mental State Recognition ,

Image Classification, Natural Language Processing, Object/Image Recognition,

Online Search, Pattern Recognition, People Counting,

Predictive Analytics/Decision Supporting System, Recommendation System,

Speech Recognition, and Telecom/Computer Network Management

Deliverables

MS excel file for assignee, patent number (hyperlinked to Google Patent), title, priority year, application category, industry category