Saturday, August 18, 2018

Blockchain Patent Landscape for Innovation Leadership 3Q 2018





To find blockchain technology innovation leadership for the US market, patents in the USPTO database are searched using relevant blockchain keywords. Total of more than 1200 published patent applications regarding blockchain innovation are identified as of 3Q 2018.

IBM is the leader in blockchain patent application followed by, Bank of America, Mastercard, Wal-Mart, TD Bank, Intel, American Express, Accenture, Cognitive Scale, Inc., and Coinbase. It is interesting to find innovation leadership of Wal-Mart (a retail giant) and Intel (a semiconductor chip manufacturing leader) in blockchain technology innovation.

The identified blockchain patents are further classified by key technologies for blockchain applications: security, transaction, cryptocurrency, database, smart contract, blockchain network, consensus, decentralized application, and AI. Technology regarding security is identified as the top blockchain technology in blockchain technology innovation.

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



©2018 TechIPm, LLC All Rights Reserved
http://www.techipm.com/


Thursday, August 9, 2018

Global 5G Standard Essential Patents Landscape 2Q 2018


To find the key IPR holders for the 5G NR (New Radio) Standard Essential Patents (SEPs) for global market, patents in the lists of patents declared essential to 3GPP TS 38 Release 15 specification series (http://www.3gpp.org/DynaReport/38-series.htm) appear at the ETSI IPR Online website (https://ipr.etsi.org/) are searched. Total of 6920 5G NR declared SEPs (WIPO patent applications) are searched as of 2Q 2018.


Huawei is the leader in global 5G NR SEPs declaration followed by Intel, Ericsson , Qualcomm, and Sharp. It is interesting to find the IPR leadership in global 5G NR SEPs declaration of Intel (cf. US case: http://techipm-innovationfrontline.blogspot.com/2018/08/5g-standard-essential-patents-landscape.html). It is also interesting to find the global 5G NR SEPs declaration by a US company IDAC Holdings, which is one of Subsidiaries of InterDigital.

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


©2018 TechIPm, LLC All Rights Reserved
http://www.techipm.com/

Wednesday, August 8, 2018

5G Standard Essential Patents Landscape 2Q 2018




To find the key IPR holders for the 5G NR (New Radio) Standard Essential Patents (SEPs) for the US market, patents in the lists of patents declared essential to 3GPP TS 38 Release 15 specification series (http://www.3gpp.org/DynaReport/38-series.htm) appear at the ETSI IPR Online website (https://ipr.etsi.org/) are searched. Total of 4935 5G NR declared SEPs (including 2110 pending & 930 published USPTO patent applications) are searched as of 2Q 2018.

Ericsson is the leader in 5G NR SEPs declaration followed by Samsung Electronics, Sharp, Huawei (including Futurewei Technology), Qualcomm, and LG Electronics. It is interesting to find the IPR leadership in 5G NR SEPs declaration of Sharp and Huawei. It is also interesting to find InterDigital’s small number of 5G NR SEPs declaration and Nokia’s no declarations, which are one of IPR leaders in 3G & 4G SEPs.

The top 6 most declared specifications for the 5G NR SEPs are TS 38.213 (Physical layer procedures for control), TS 38.214 (Physical layer procedures for data), TS 38.211 (Physical channels and modulation), TS 38.331 (Radio Resource Control (RRC); Protocol specification), TS 38.212 (Multiplexing and channel coding), and TS 38.321 (Medium Access Control (MAC) protocol specification).

For more information, please contact Alex G. Lee (alexglee@techipm.com).
Reference: 5G New Radio Patents for Standards Data 2Q 2018
Link: https://www.slideshare.net/alexglee/5g-new-radio-patents-for-standards-data-2q-2018





©2018 TechIPm, LLC All Rights Reserved
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 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: 7 3() 오후 1:30 - 4:30

장소: 홍익대학교 빅데이터 센터(홍문관 9)

등록비: 무료

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

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.


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)

Sunday, November 19, 2017

Xanadu Based Big Data Deep Learning for Medical Data Analysis

Contents

Part I
Deep Learning for Medical Data Analysis Introduction
Automated Skin Cancer Classification
Automated Diabetic Retinopathy Classification
Brain Tumor Research
Alzheime Prediction
A Survey on Medical Image Deep Learning Research
Cardiac Arrhthymia Detection
ICU Patient Care

Part II
Deep Learning Introduction
Convolution Process Details
Issues with Big Data Deep Learning
Distributed Deep Learning for Medical Big Data Analysis
Challenges of Deep Learning for Medical Data Analysis
Content Based Image Retrieval (CBIR)

Part III
Xanadu Functionality
Xanadu Commodity Storage System Use Case
Xanadu Cloud Computing Use Case
Xanadu + Deep Learning + Hadoop + Spark Integration
Xanadu based Big Data Deep Learning System for Medical Data Analysis
Xanadu CBIR Demo

Link: https://www.slideshare.net/alexglee/xanadu-based-big-data-deep-learning-for-medical-data-analysis

Friday, October 27, 2017

(Seminar) Xanadu Big Data Deep Learning System for Medical Data Analysis

의료 빅데이터 딥러닝 시스템 및 빅데이터 센터 심포지움
일시: 2017년 11월 9일(목요일), 오전 10:00 ~ 12:00
장소: 중앙보훈병원 중앙관 지하2층 대강당 (http://seoul.bohun.or.kr/020info/info01.php?left=1)
내용:
10.00 ~ 10.50
제나두 기반 의료 빅데이터 딥러닝 시스템
이근호 박사 (미국 제나두 빅데이터 대표)
10.50 ~ 11.05
홍익대학교 빅데이터 센터 소개
표창우 교수 (홍익대학교 공대학장)
11.05 ~ 11.30
Pub/Sub 기반 이종 의료 정보 실시간 패턴 분석 (CEP) 및 전파
윤영 교수 (홍익대학교 컴퓨터공학과)
11.30 ~ 12.00
패널토론: 제나두 기반 의료 빅데이터 딥러닝 시스템 구축 및 상호협력 연구 프로젝트 추진 방안
사회: 김억 교수 (홍익대학교 빅데이터 센터)
패널: 이근호 박사, 표창우 교수, 윤영 교수, 보훈병원 김봉석 기조실장 및 관계자들

Monday, September 25, 2017

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

Part II
Big Data Use Cases
IoT Introduction
IoT Use Cases

Part III
IoT Use Cases
Artificial Intelligence Overview
Deep Learning Introduction
Deep Learning Use Cases

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

Part V
Xanadu Cloud Computing Use Case
Xanadu + Deep Learning + Spark + Hadoop Integration
Xanadu based Big Data Deep Learning System

Monday, July 10, 2017

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

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

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

Silicon Valley Xanadu Promotional Event Presentation Part IV
Designing Data-Intensive Applications
Xanadu Functionality
Xanadu Use Case
Xanadu + Deep Learning + Hadoop Integration

Part I+ II + III + IV Presentation Slide