Intelligent Results

First Data Corporation

Robert Jasper

Michael M Meyer

Kelly A Pennock

Shawn Rutledge

Matthew S Jacobsen

Embodiments of the present invention provide methods, systems, and machine-readable media for identifying and sharing common trends represented in a corpus of private information without revealing the private information. Identifying and sharing common trends can comprise reading a first set of private information and a second set of private information. The first set of private information can be compared to the second set of private information and common information between the first set of private information and the second set of private information can be identified. Once identified, a determination can be made as to whether the identified common information represents an emerging trend, i.e., the common information re-occurs or develops over time. In response to determining the identified common information represents an emerging trend, the emerging trend can be reported without revealing the first set of private information or the second set of private information.

Identifying and sharing common trends

A predictive modeling system and methodology makes predictions using unstructured content as an input, either alone or in conjunction with structured content. Content transformation rules are selected for application to the unstructured content, such as emails, call center notes, and other forms of human communication, by identifying the rules that are likely to improve the performance of a predictive modeling system.

Predictive selection of content transformation in predictive modeling systems

The present invention provides for a system and method of assigning attribute scores, including those for abstract and diverse concepts such as sentiment (e.g., happiness, anger, sadness) to documents containing unstructured content (natural language content). Such content may include, but is not limited to, Web pages, e-mails, word processing documents, computer logs, chat logs, audio files, graphical images, text files, books, magazines, articles, etc. The attributes that are scored are specialized views of the unstructured content. Accordingly, attribute scoring denotes the processes of assigning one or more new measures to a document containing unstructured content. The attribute score or measure indicates the degree of the attributes found within the document.

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Sammamish, WA, United States of America

Robert Jasper