Patents by Inventor Edmond D. Chow

Edmond D. Chow has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 10261993
    Abstract: A text analytics platform includes instructions embodied in one or more non-transitory machine accessible storage media configured to cause a computing device to retrieve text from at least one text source and implement one or more algorithms to determine a quantitative linguistics assessment for the retrieved text and provide as output a numeric value corresponding to the quantitative linguistics assessment. The quantitative linguistics assessment is based at least in part on a trained model.
    Type: Grant
    Filed: September 22, 2016
    Date of Patent: April 16, 2019
    Assignee: SRI International
    Inventors: John J. Niekrasz, Edmond D Chow
  • Publication number: 20170076219
    Abstract: Systems and methods for forecasting the prominence of various attributes in a future subject matter area are disclosed. An attribute is determined based on inputs received by a computing system. A set of indicators is determined based on the attribute and features extracted from an existing document set. The prominence of the attribute in the existing document set is determined. A prominence estimate of the attribute in a future document set is determined.
    Type: Application
    Filed: January 15, 2016
    Publication date: March 16, 2017
    Inventors: John J. Byrnes, Clint Frederickson, Kyle J. McIntyre, Tulay Muezzinoglu, Edmond D. Chow, William T. Deans
  • Patent number: 8015140
    Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.
    Type: Grant
    Filed: August 16, 2010
    Date of Patent: September 6, 2011
    Assignee: Fair Isaac Corporation
    Inventors: Shailesh Kumar, Edmond D. Chow, Michinari Momma
  • Publication number: 20100324985
    Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.
    Type: Application
    Filed: August 16, 2010
    Publication date: December 23, 2010
    Inventors: Shailesh Kumar, Edmond D. Chow, Michinari Momma
  • Patent number: 7801843
    Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.
    Type: Grant
    Filed: January 6, 2006
    Date of Patent: September 21, 2010
    Assignee: Fair Isaac Corporation
    Inventors: Shailesh Kumar, Edmond D. Chow, Michinari Momma
  • Patent number: 7685021
    Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.
    Type: Grant
    Filed: February 15, 2006
    Date of Patent: March 23, 2010
    Assignee: Fair Issac Corporation
    Inventors: Shailesh Kumar, Edmond D. Chow, Michinari Momma
  • Patent number: 7672865
    Abstract: The invention, referred to herein as PeaCoCk, uses a unique blend of technologies from statistics, information theory, and graph theory to quantify and discover patterns in relationships between entities, such as products and customers, as evidenced by purchase behavior. In contrast to traditional purchase-frequency based market basket analysis techniques, such as association rules which mostly generate obvious and spurious associations, PeaCoCk employs information-theoretic notions of consistency and similarity, which allows robust statistical analysis of the true, statistically significant, and logical associations between products. Therefore, PeaCoCk lends itself to reliable, robust predictive analytics based on purchase-behavior.
    Type: Grant
    Filed: October 21, 2005
    Date of Patent: March 2, 2010
    Assignee: Fair Isaac Corporation
    Inventors: Shailesh Kumar, Edmond D. Chow, Michinari Momma
  • Patent number: 6728695
    Abstract: A method and apparatus is disclosed for making predictions about entities represented in documents and for information analysis of text documents or the like, from a large number of such documents. Predictive models are executed responsive to variables derived from canonical documents to determine documents containing desired attributes or characteristics. The canonical documents are derived from standardized documents, which, in turn, are derived from original documents.
    Type: Grant
    Filed: May 26, 2000
    Date of Patent: April 27, 2004
    Assignee: Burning Glass Technologies, LLC
    Inventors: Anu K. Pathria, Krishna M. Gopinathan, Theodore J. Crooks, Edmond D. Chow, Mark A. Laffoon, Dayne B. Freitag