Creation, use and training of computer-based discovery avatars

Abstract

In embodiments of the present invention improved capabilities are described for developing, training, validating and deploying discovery avatars embodying mathematical models that may be used for document and data discovery and deployed within large data repositories.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of the following provisional application, which is hereby incorporated by reference in its entirety:[0002]App. No. 61 / 491,140 filed on May 27, 2011, and entitled “CURIOSITY ENGINE FOR CONTENT DISCOVERY.”BACKGROUND[0003]1. Field[0004]The invention is related to data management, discovery, and organization within voluminous data repositories.[0005]2. Description of the Related Art[0006]With the rapid increase in data creation and the capability to cheaply and reliably store vast volumes of data has come an increasing complexity in organizing, searching and discovering data elements within large data repositories. One result is that traditional techniques for searching data for needed elements, such as keyword searching, Boolean operators, and enhanced search are insufficient to cull wanted data from large data repositories because even a small mismatch between, for example, a keyword and data included in ...

AI Technical Summary

Benefits of technology

"The patent describes a method for creating and using computer-based avatars to assist human analysts in conducting analysis and exploration of large data sets. The avatars are created by using machine learning processes to analyze data and identify relevant information. The avatars can be used in a wide range of applications, such as national security, enterprise management, and forensic analysis. The method involves extracting data features from the source data, scoring them based on their relevance to a particular topic, and optimizing the scoring based on a comparison of the data features. The avatars can also be trained to improve their performance over time. Overall, the method provides a way to automate the analysis of data and improve the efficiency of human analysts."

Problems solved by technology

With the rapid increase in data creation and the capability to cheaply and reliably store vast volumes of data has come an increasing complexity in organizing, searching and discovering data elements within large data repositories.

One result is that traditional techniques for searching data for needed elements, such as keyword searching, Boolean operators, and enhanced search are insufficient to cull wanted data from large data repositories because even a small mismatch between, for example, a keyword and data included in a document, may result in the document being omitted from the search results.

Similarly, the presence of a keyword in too many documents within a data stream may result in over-inclusive searching, producing search results that are too voluminous for a human to review in an acceptable amount of time.

Further, a keyword match may lack intelligence and produce data query results that combine documents simply on the basis of sharing a word (e.g. “state”), even though that keyword has substantively different meanings in the documents (e.g., “solid state” and “state of mind,”).

Also, individuals may have a strong intuitive sense of what information is valuable within a set of results, but may not be able to develop keywords that properly reflect that intuition.

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