Ying Zhu Zheng Zhao, 5441711 Blossom Dr, Fremont, CA 94539

2003002, Jan 30, 2003

Jul 26, 2001

09/917409

Jeremy Cohen - Sunnyvale CA, US
Ashok Srivastava - Mountain View CA, US
Ying Zhao - Cupertino CA, US

G09G005/00

345/700000

The present invention provides management of entity profile data to effectively process, analyze, and review entity profile data. More specifically, the present invention provides a unified data analysis and processing scheme to break down and review entity profile data. The present invention also provides an interactive visualization tool for the strategists and web site-maintainers to effectively and efficiently review entity profile data. This tool provides strategists and site-maintainers an easy method of managing web-sites and optimizing web-site design for customers of interest.

2008008, Apr 10, 2008

Oct 19, 2007

11/875754

YING ZHAO - Cupertino CA, US
Charles Chuxin Zhou - Cupertino CA, US

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

G06F 15/18

706012000

A method searches for new, unique and interesting information using knowledge patterns discovered through data mining and text mining, machine learning (including supervised or unsupervised) and pattern recognition methods. The method is implemented as a computer program acting as an agent installed in a computer node or multiple nodes in a networked environment. The system is useful for improving search experience and used in knowledge discovery applications when new, unique and interesting information is critical. The system is also useful for introducing new concepts and products for business applications.

2008009, Apr 17, 2008

Dec 3, 2007

11/949049

YING ZHAO - Cupertino CA, US
Charles Zhou - Cupertino CA, US
Hsiu-Ying Sherry - San Jose CA, US

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

C40B 30/02

506008000, 435029000

This invention is to use knowledge pattern learning and search system for selecting microorganisms to produce useful materials and to generate clean energy from wastes, wastewaters, biomass or from other inexpensive sources. The method starts with an in silico screening platform which involves multiple steps. First, the organisms' profiles are compiled by linking the massive genetic and chemical fingerprints in the metabolic and energy-generating biological pathways (e.g. codon usages, gene distributions in function categories, etc.) to the organisms' biological behaviors. Second, a machine learning and pattern recognition system is used to group the organism population into characteristic groups based on the profiles. Lastly, one or a group of microorganisms are selected based on profile match scores calculated from a defined metabolic efficiency measure, which, in term, is a prediction of a desired capability in real life based on an organism's profile. In the example of recovering clean energy from treating wastewaters from food process industries, domestic or municipal wastes, animal or meat-packing wastes, microorganisms' metabolic capabilities to digest organic matter and generate clean energy are assessed using the invention, and the most effective organisms in terms of waste reduction and energy generation are selected based on the content of a biowaste input and a desired clean energy output. By selecting a microorganism or consortia of multiply microorganisms using this method, one can clean the water and also directly generate electricity from Microbial Fuel Cells (MFC), or hydrogen, methane or other biogases from microorganism fermentation. In addition, using similar screening method, clean hydrogen can be recovered first from an anaerobic fermentation process accompanying the wastewater treatment, and the end products from the fermentation process can be fed into a Microbial Fuel Cell (MFC) process to generate clean electricity and at the same time treat the wastewater. The invention can be used to first select the hydrogenic microorganisms to efficiently generate hydrogen and to select electrogenic organisms to convert the wastes into electricity. This method can be used for converting wastes to one or more forms of renewable energies.

2008021, Sep 4, 2008

Mar 5, 2008

12/042338

Ying Zhao - Cupertino CA, US
Charles Chuxin Zhou - Cupertino CA, US
Chetan K. Kotak - San Jose CA, US

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

G06F 17/30

707 5, 707E17061

The present invention is a method for detecting anomalies against normal profiles and for fusing and visualizing the results from multiple anomaly detection systems in a quantifying and unifying user interface. The knowledge patterns discovered from historical data serve as the normal profiles, or baselines or references (hereinafter, called “normal profiles”). The method assesses a piece of information against a collection of the normal profiles and decides how anomalous it is. The normal profiles are calculated from historical data sources, and stored in a collection of mining models. Multiple anomaly detection systems generate a collection of mining models using multiple data sources. When a piece of information is newly observed, the method measures the degree of correlation between the observed information and the normal profiles. The analysis is expressed and visualized through anomaly scores and critical event notifications that are triggered by fusion rules, thus allowing a user to see multiple levels of complexity and detail in a single view.

2011021, Sep 1, 2011

May 9, 2011

13/103121

YING ZHAO - Cupertino CA, US
Charles Chuxin Zhou - Cupertino CA, US
Chetan K. Kotak - San Jose CA, US

QUANTUM INTELLIGENCE, INC. - Cupertino CA

G06F 17/30

707751, 707E17044, 707E17122

The present invention is a method for detecting anomalies against normal profiles and for fusing and visualizing the results from multiple anomaly detection systems in a quantifying and unifying user interface. The knowledge patterns discovered from historical data serve as the normal profiles, or baselines or references (hereinafter, called “normal profiles”). The method assesses a piece of information against a collection of the normal profiles and decides how anomalous it is. The normal profiles are calculated from historical data sources, and stored in a collection of mining models. Multiple anomaly detection systems generate a collection of mining models using multiple data sources. When a piece of information is newly observed, the method measures the degree of correlation between the observed information and the normal profiles. The analysis is expressed and visualized through anomaly scores and critical event notifications that are triggered by fusion rules, thus allowing a user to see multiple levels of complexity and detail in a single view.

2011029, Dec 1, 2011

Aug 7, 2011

13/204713

Ying ZHAO - Cupertino CA, US
Charles C. ZHOU - Cupertino CA, US
Chetan KOTAK - San Jose CA, US

QUANTUM INTELLIGENCE, INC. - Santa Clara CA

G06F 15/18

706 12

The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules.

2012004, Feb 16, 2012

Oct 27, 2011

13/283442

Ying Zhao - Cupertino CA, US
Charles C. Zhou - Cupertino CA, US

Quantum Intelligence, Inc. - Santa Clara CA

G06F 15/18

706 10

One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research.

2012014, Jun 7, 2012

Feb 12, 2012

13/371469

Charles C. ZHOU - Cupertino CA, US
Ying ZHAO - Cupertino CA, US

G06F 15/18

706 12

Methods and systems for knowledge pattern search and analysis for selecting microorganisms based on desired metabolic properties or biological behaviors are disclosed in various embodiments of the invention. In one embodiment of the invention, a computer-implemented method for selecting a purpose-specific microorganism first compiles microorganisms' profiles by linking each microorganism's methanogenic, hydrogenic, electrogenic, another metabolic property, and/or another biological behavior to genetic and chemical fingerprints of metabolic and energy-generating biological pathways. Then, based on the compiled profiles of the microorganisms, the computer-implemented method groups the microorganisms into pathway characteristics using machine-learning and pattern recognition performed on a computer system, and subsequently generates a prediction called “discovered characteristics” for a desired metabolic property or a desired biological behavior of at least one microorganism. Furthermore, a profile match score may be calculated to indicate usefulness of one or more microorganisms for renewable energy generation from biological waste materials or wastewater.