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

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 ( with your name, company name, title and email address.

Friday, May 19, 2017

Xanadu Cloud Computing Use Case: Protection of PCs from Ransomware

Xanadu Cloud Computing Use Case
Demo: Daeil Foreign Language High School, S. Korea

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.

Contact: Alex G. Lee (

Monday, March 20, 2017

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., 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.

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Friday, March 17, 2017

LTE Standard Related Patents Landscape 1Q 2017

By exploiting a big data patent search and analysis tool - the IPlytics Platform (Ref., TechIPm, LLC ( extends its more than 6 years of custom research of LTE patents to include a large number of new candidates for the LTE standard essential patents (SEPs). The IPlytics Platform data sources cover over 80 million patent documents for 98 worldwide countries, over 60 million scientific articles, information regarding over 3 million companies, about 2 million standards documents from 96 standard setting organizations including over 200,000 SEPs declared at the major standard setting organizations (SSOs).

LTE issued patents for the LTE UE (cellular phones, smart phones, PDAs, mobile PCs, etc.), base station (eNB) products, and other RAN (Radio Access Network) related products are searched in the USPTO as of 1Q 2017.  The searched patents are further reviewed with respect to 3GPP Release 10 technical specifications (LTE-Advanced) for LTE RAN technical specifications: PHY: TS 36.101, 211, 212, 213, 305; L2/L3 Protocols: TS 36.300, 304, 321, 322, 323, 331, 355 and 3GPP Release 11 technical specifications for CoMP (TR 36.819), 3GPP Release 12 technical specifications for EPDCCH (TS 36.211, 213) and D2D (Device to Device; TR 36.843) Communications.
The identified LTE standard related patents cover not only ETSI declared SEPs but also candidates for SEPs that were not declared. Nearly 4000 US issued patents are finally selected as the LTE standard related patents. The identified LTE standard related patents are also updated for the current assignees.

The identified LTE standard related patents are further categorize through the evaluation process by technologies for implementations of the LTE baseband modem ( OFDM/OFDMA (Frame & Slot Structure, Modulation), SC-FDMA (PUSCH, PUCCH), Channel Estimation (UL RS, DL RS, CQI), Cell Search & Connection (PRACH, DL SS), MIMO (Transmit Diversity, Spatial Multiplexing), Resource Management (Resource Allocation, Scheduling), Coding (Convolution, Turbo), Power Control, HARQ, Carrier Aggregation, Relay, and Positioning Technology) and radio protocols (Random Access, HARQ, Channel Prioritization, Scheduling (Dynamic, SPS), Protocol Format (PDUs, SDUs), Radio Link Control (ARQ), PDCP Process (SRB, DRB, ROHC), Security (Ciphering, Integrity), System Information, Connection Control, Mobility (Handover, Inter-RAT, Measurements), QoS, MBMS, and Carrier Aggregation). To evaluate the essentiality of a LTE patent, patent disclosures in claims and detail description for each LTE related patent also are compared to the LTE technical specifications.

Leaders in LTE standard related patents IPR ownership as of 1Q 2107 are Qualcomm followed by InterDigital, Samsung Electronics, LG Electronics, Google, Ericsson, Nokia (+ Alcatel-Lucent), Apple, Panasonic, Optis Wireless Technology LLC, Intel, Huawei, NTT Docomo, ZTE, BlackBerry, Amazon, NEC, Texas Instruments, and ETRI. Here, most of Amazon’s LTE patents were acquired from LG Electronics.

For more information, please contact Alex Lee at .

Monday, March 13, 2017

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

Wednesday, March 1, 2017

Big Data Analysis for Standard Essential Patents


IPlytics Platform: Intelligence IP Analytics SaaS

It is excited to announce that TechIPm entered into an agreement with IPlytics for the promotion and sale of the IPlytics Platform in China, Japan, S. Korea, Taiwan and the United States of America.
The IPlytics Platform is an intelligence IP analytics SaaS to analyze patent strategy, technology trends, market developments and a company’s competitive position using the  innovative big data & machine learning techniques.

If you are interested in the IPlytics Platform, please contact Alex G. Lee (

Monday, February 6, 2017

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.

( -- 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.
Alex G. Lee, Ph.D., J.D.
Principal Consultant & Chief Strategist
(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
BurlingtonMA 01803
United States
(781) 270-1585

Saturday, January 7, 2017

CE Trends Insight from CES 2017

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