Charles S Zhou

US Patent:

7757215, Jul 13, 2010

Filed:

Apr 11, 2006

Appl. No.:

11/402348

Inventors:

Charles Jianping Zhou - Santa Clara CA, US
Yuguang Wu - Mountain View CA, US

Assignee:

Oracle America, Inc. - Redwood City CA

International Classification:

G06F 9/44

US Classification:

717124, 717126, 717127, 717128, 717129, 717131, 717153, 717162, 717163, 714 2, 714 25, 714 38

Abstract:

An approach is provided for performing software fault injection code testing in a framework that allows testers to place flexible tracing and monitoring actions into algorithmic scripts which provide instructions for dynamically switching called software program functions to corresponding fault injected functions during program execution and that allows testers to perform fault injection testing without requiring modification or access to the underlying source code of the software program being tested. The framework suspends execution of the software program when certain conditions are met, removes any existing linking to called functions, changes the software program's runtime dynamic linking, performs any other instructions provided by the algorithmic script, and then resumes the software program's execution until execution of the program is complete or until the framework is again instructed to change the software program's runtime dynamic linking.

US Patent:

20040243882, Dec 2, 2004

Filed:

May 27, 2003

Appl. No.:

10/445700

Inventors:

Charles Zhou - Mountain View CA, US

Assignee:

Sun Microsystems, Inc.

International Classification:

H04B001/74

US Classification:

714/038000

Abstract:

A system and method for validating error-handling code by fault injection. In one embodiment, the system may include a software module operable to communicate with a function provider configured to provide designated functions in response to calls initiated by the software module. The system may further include an error handling block configured to respond to a plurality of error conditions, and a fault injection layer operable to intercept a function call generated by the software module. The fault injection layer may thereby prevent a corresponding function from being performed by the function provider, and instead return an error condition in response to the function call.

US Patent:

20080086436, Apr 10, 2008

Filed:

Oct 19, 2007

Appl. No.:

11/875754

Inventors:

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

Assignee:

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

International Classification:

G06F 15/18

US Classification:

706012000

Abstract:

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.

US Patent:

20080090736, Apr 17, 2008

Filed:

Dec 3, 2007

Appl. No.:

11/949049

Inventors:

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

Assignee:

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

International Classification:

C40B 30/02

US Classification:

506008000, 435029000

Abstract:

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.

US Patent:

20080195719, Aug 14, 2008

Filed:

Feb 12, 2007

Appl. No.:

11/673915

Inventors:

Yuguang Wu - Mountain View CA, US
Charles J. Zhou - Mountain View CA, US

International Classification:

G06F 15/167

US Classification:

709213

Abstract:

A system and method for allocating physical memory in a distributed, shared memory system and for maintaining interaction with the memory using a reservation protocol is disclosed. In various embodiments, a processor node may broadcast a memory request message to a first subset of nodes connected to it via a communication network. If none of these nodes is able to satisfy the request, the processor node may broadcast the request message to additional subsets of nodes until a positive response is received. The reservation protocol may include a four-way handshake between the requesting processor node and a memory node that can fulfill the request. The method may include creation of a reservation structure on the requesting processor and on one or more responding memory nodes. The reservation protocol may facilitate the use of a proximity-based search methodology for memory allocation in a system having an unreliable underlying transport layer.

US Patent:

20080215576, Sep 4, 2008

Filed:

Mar 5, 2008

Appl. No.:

12/042338

Inventors:

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

Assignee:

QUANTUM INTELLIGENCE, INC. - SANTA CLARA CA

International Classification:

G06F 17/30

US Classification:

707 5, 707E17061

Abstract:

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.

US Patent:

20110213788, Sep 1, 2011

Filed:

May 9, 2011

Appl. No.:

13/103121

Inventors:

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

Assignee:

QUANTUM INTELLIGENCE, INC. - Cupertino CA

International Classification:

G06F 17/30

US Classification:

707751, 707E17044, 707E17122

Abstract:

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.

US Patent:

20110295783, Dec 1, 2011

Filed:

Aug 7, 2011

Appl. No.:

13/204713

Inventors:

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

Assignee:

QUANTUM INTELLIGENCE, INC. - Santa Clara CA

International Classification:

G06F 15/18

US Classification:

706 12

Abstract:

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.