Blockchain + AI + Big Data Technology Integration Demo

Part I. Cryptocurrency Payment System for E-Commerce Applications
Secure Multi Coin Decentralized Wallet Demo
Emotion AI Chatbot Demo
E-Commerce Integration Demo
AI Chatbot Sentiment Analysis + Big Data Analytics
Hyperledger Fabric Token Wallet Implementation
High Throughput Token Wallet Implementation Performance
Hyperledger Fabric MainNet Prototype Implementation
Mobile Wallet Token Transaction over MainNet Prototype Demo

Part II. Xanadu Based Healthcare Information Retrieval & Sharing System
Xanadu Based Big Data Archive
Xanadu Introduction Video
Xanadu Use Case Demo
Xanadu based Medical CBIR System Demo for Diabetic Retinopathy Diagnosis
Blockchain-Xanadu Database Integration Demo
Xanadu Base Big Data Deep Learning Demo
Intellectual Property Development

Presentation: https://www.slideshare.net/alexglee/blockchain-ai-big-data-technology-integration-demo

Part I video: https://www.youtube.com/watch?v=RzmjxzsiVE4&feature=youtu.be

Part II video: https://www.youtube.com/watch?v=iH6L3NOQiSU&feature=youtu.be

Blockchain + AI + Big Data Technology Integration Roadshow

Registration: https://www.eventbrite.com/e/blockchain-ai-big-data-technology-integration-roadshowsan-francisco-bay-area-tickets-69895400023

Description

I am pleased to invite you to the first Elamachain & Xanadu Joint San Francisco Bay Area Roadshow on September 23, 2019. In the roadshow, implementations of blockchain + AI + big data technology integration will be presented for FinTech, E-Commerce, and Helathcare applications.

I. Cryptocurrency payment system by Elamachain in collaboration with Cloudmate group and QuillHash
Demo of hyperledger fabric token wallet implementation with high throughput chain code development
Demo of multicoin wallet implementation with advanced UI/UX development
Demo of emotion AI chatbot implementation for E-commerce applications
Demo of sentiment analysis implementation with smart chatbot voice recognition interface

II. Healthcare information retrieval & sharing system by Xanadu in collaboration with HashBlock
Demo of NoSQL DB implementation with ACID compliance
Demo of content based medical image retrieval system
Demo of blockchain based medical image sharing system
Introduction of blockchain based decentralized distributed storage technology development

III. Taekwon Block Project Introduction by Taekwon Block Inc. in collaboration with Elamachain and Xanadu
AI martial arts school recommendation and sharing system development
Blockchain based training history management system development

Date/Time: September 23 (Monday), 2019 17:30 – 21:30

Venue: Seaport Conference Center, 459 Seaport Ct, Redwood City, CA 94063
Map: http://www.seaportconferencecenter.com/contact/

Agenda:
17:30 - 18:10 pm: check-in/registration & dinner
18:10 - 19:30 pm: Elamachain demo
19:30 - 19:40 pm: Coffee Break
19:40 - 20:30 pm: Xanadu demo
20:30 - 21:00 pm: Taekwon Block Introduction
21:00 - 21:30 pm: Q&A and networking

Participating Companies:
Boston based Elamachain USA, LLC is the R&D center for Elamachain Foundation (https://www.elamachain.io/). Elamachain is the world first Hyperledger based blockchain payment solution in integration with emotion AI.

Xanadu Big Data, LLC (http://www.xanadubigdata.com/)  is based in Boston, and is operated for Xanadu big data management platform technology in integration with AI and blockchain development, commercialization, and monetization.

Cloudmate group (https://cloudmategroup.com/) provides end-to-end AI chatbot solutions.
Cloudmate group has also expertise in web & mobile applications development.

QuillHash Technologies Pvt. Ltd. (https://www.quillhash.com/)  is a Blockchain dev-house based in India with expertise in providing NFT development and Blockchain Security Audits along with enterprise grade Blockchain solutions on Private and public blockchains, Supply chain and DEX development.

Boston based HashBlock USA LLC is the R&D center for HashBlock Inc. (http://www.hashblk.com/en/index-en.php), which is a strategic partner of Xanadu Big Data, LLC in S. Korea.

Taekwon Block Inc. (http://www.taekwonblock.io/)  is a startup company in S. Korea established to create a global Taekwondo community platform. Taekwon Block project is for making taekwondo training easier and to record the value of that precious process.

블록체인 + 빅데이터 + AI + IoT + 5G 융합 특강

내용: 신뢰성 있는 비즈니스 트렌젝션을 가능하게 하는 블록체인과 IoT, AI, 5G 등 신기술간의 결합에 의한 빅데이터 디지털 자산의 공유, 거래, 활용을 가능하게 하는 블록체인 + 빅데이터 + AI + IoT + 5G 융합 시스템에 대한 기술 및 응용사례를 강의한다.

1. 블록체인, AI, IoT, 5G 기술 및 활용사례 소개

2. 블록체인 + 빅데이터 + AI + IoT + 5G 융합 기술개발 및 활용사례

3. 제나두기반 블록체인 융합 시스템 소개 및 데모

강사: 이근호 박사(미국 제나두 빅데이터 대표)

일시: 9월 19 일(수요일) 오후 1:30 - 4:30

장소: 홍익대학교 빅데이터센터

등록비: 무료

등록신청: https://docs.google.com/forms/d/e/1FAIpQLScpMbUmLQVDWYxL-L7NHtM9m-yb3sZonQp9xld6r3Za6GNnYA/viewform

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.

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)

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

(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

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.

Xanadu Cloud Computing Use Case: Protection of PCs from Ransomware

Xanadu Cloud Computing Use Case

Demo: Daeil Foreign Language High School, S. Korea

https://www.youtube.com/watch?v=FQcMrd6Jczw&feature=youtu.be

Xanadu is a key-value NoSQL big data management platform technology that provides fault tolerant ACID property and high throughput/low latency with massive scalability. Xanadu is designed for the heaviest workloads, and supports support concurrent queries without conflict.

Xanadu can be exploited for the back-end storage technology that allows remote client computers can access data and computing/application resources via the standard iSCSI network protocol. With iSCSI supported natively by any operating system, Xanadu makes it easy to securely store and access data from any machine on the network. Xanadu also can be used for providing services that allow remote computer systems to “boot” from a stored system drive image. With diskless units on users’ desks and all data (including the operating system disk) remains in the secure cloud servers, administrators are free to deploy diskless PCs to the desktop with their inherent advantages of higher data security, quicker disaster recovery, smaller office footprint and better energy consumption.

In-built deduplication functionality of Xanadu enable saving of cloud data storage resources a lot. For example, Xanadu can store thousands of 25GB basic Windows 7 disk images in only a few hundred gigabytes of actual storage. Xanadu, therefore, enables a simple and highly efficient means to centrally manage cloud data storage, particularly for standardised PC installations that need to be booted almost identically in many places. Time stamping functionality of Xanadu also offers an efficient snapshot capability that enables users to “reset” their stores to a previous saved “good” state. Especially, this resetting capability will be a good solution for proving protection of client PCs from malwares including notorious Ransomwares.

Xanadu for Protection of PCs from Ransomware.

https://www.youtube.com/watch?v=lB3-mfupjNo&feature=youtu.be

Contact: Alex G. Lee (alexglee@xanadubigdata.com)

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

Big Data Analysis for Standard Essential Patents

Link: https://www.linkedin.com/pulse/big-data-analysis-standard-essential-patents-lee-ph-d-j-d-clp?published=t

CE Trends Insight from CES 2017

Contents
IoT + Big Data + AI + 3D Printing became the key enabling CE technologies
AR/VR Products are widely adopted in CE market
Robots became an essential part of smart home
Drone became a mainstream CE business
Industry crossover and convergence will be accelerated

Link: http://www.slideshare.net/alexglee/ce-trends-insight-from-ces-2017

Xanadu as Global Big Data Collaboration Platform

4차 산업혁명과 빅데이터 세미나 Alex G. Lee (미) Xanadu Big Data, LLC 대표 발표 내용

4th Industrial Revolution & Big Data Seminar

4차 산업혁명에서는 정보통신(ICT) 신기술이 제조업 등 다양한 산업들과 결합되고 빅데이터로 분석되어 지금까지는 볼 수 없던 새로운 형태의 제품과 서비스, 비즈니스를 만들어 광활한 글로벌시장을 형성할 것으로 예측되고 있습니다. 따라서 한국이 현재의 경제적 어려움을 극복하고 도약하기 위해서는 빅데이터가 ICT산업의 중심적 역할을 하여 산학연이 함께 미래산업 전반을 비약적으로 발전시키는 4차 산업혁명을 통해 ICT 강국으로 거듭나는 것이 필수 불가결한 과제라 할 것 입니다. 이에 산학연 빅데이터 전문가들을 모시고 우리나라 빅데이터의 현주소를 이해하고 4차 산업혁명시대의 성공 전략을 찾기 위한 세미나를 개최하니 관심 있는 분들의 많은 참여 바랍니다.

○ 주    제 : “ 4차 산업혁명과 빅데이터 플랫폼 실증방안 ”

○ 행사일시 : 2016년 12월 15일 (목) 14 : 00 ∼ 17 : 00

○ 장    소 : 중소기업중앙회 대회의실 (여의도)

○ 주    최 : 변재일 국회위원, 배덕광 국회의원

○ 주    관 : (재)파이터치연구원, (사)한국ICT이용자보호원

○ 후    원 : (미) Xanadu Big Data LLC, 미래창조과학부, 국회뉴스, 조선비즈

○ 일    정

교통안내

지하철이용

5호선 여의도역 3번 출구 하차시, 도보로 약 15분 소요  9호선 국회의사당역 3번출구, 공원방면에서 도보로 약 5분 소요  5호선 여의나루역 1번 출구 하차시, 도보로 약 20분 소요

사전등록(무료) 안내

담당: 이창근 국장 (chang@kw.ac.kr)

이메일 등록신청 필요사항: 성명, 소속, 이메일, 연락처

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.