Sri International

Fair Isaac Corporation

Fair Issac Corporation

Burning Glass Technologies, LLC

Edmond D Chow

Shailesh Kumar

Michinari Momma

John Niekrasz

Anu Kumar Pathria

Dayne B Freitag

John James Byrnes

Theodore James Crooks

William T Deans

Krishna Gopinathan

Mark Alan Laffoon

Tulay Muezzinoglu

Kyle J McIntyre

Clint Frederickson

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.

Prediction of future prominence attributes in data set

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.

Adaptable text analytics platform

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.

Method and apparatus for recommendation engine using pair-wise co-occurrence consistency

Method and apparatus for initiating a transaction based on a bundle-lattice space of feasible product bundles

Method and apparatus for retail data mining using pair-wise co-occurrence consistency

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.

Method and apparatus for making predictions about entities represented in documents

Growing community of inventors

Encinitas, CA, United States of America

Edmond D Chow