Other

Microsoft Technology Licensing, LLC

Symbol Technologies, LLC

Cosortium of Rheumatology Researchers of North America, Inc.

Timothy Lewis Harrington

Marc Alexander Najork

Rina Panigrahy

Patrick J Wallace

Rajesh Shenoy

Joel Kremer

George Reed

Allan Gibofsky

Jeffrey Greenberg

Architecture that provides a data structure to facilitate personalized ranking over recommended content (e.g., documents). The data structure approximates the social distance of the searching user to the content at query time. A graph is created of content recommended by members of the social network, where the nodes of the graph include content nodes (for the content) and recommending member nodes (for members of the social network who recommended the content). If a member recommends content, an edge is created between the member node and the content node. If a member is a 'friend' (tagged as related in some way) of another member, an edge is created between the two member nodes. Each node is converted to a lower dimensional feature set. Feature sets of the content are indexed and the feature set of the searching user is utilized to match and rank the search results at query time.

Social network recommended content and recommending members for personalized search results

An extensible architecture that enables developers to focus solely on the domain-specific nature of the stream processing algorithm to be implemented. It is positioned as an intermediary component between streaming data feeds and stream algorithms, thereby aggregating demand on data sources and hiding the complexity involved in managing active connections to different data sources. Per-algorithm stream throttling is provided so that individual algorithms do not get overloaded; thus, ensuring that algorithms receive fresh items from the data feeds to which the algorithms subscribe. Feed items can be discarded when an algorithm is not able to process the items in realtime to ensure that feed items are sampled at the fastest processing rate of the algorithm. Thus, a single instance of an algorithm can handle an entire data stream. Moreover, redundancy can be achieved by running the same configuration on multiple machines.

Scalable and extendable stream processing

A system and method that employs standardized data collection forms for use by health care providers and patients to enable efficient collection, storage and management of patient data for treatment of diseases, such as rheumatoid arthritis.

Growing community of inventors

League City, TX, United States of America

Timothy Lewis Harrington