Numenta, Inc.

Palm, Incorporated

Medtronic Minimed, Inc.

Klue, Inc.

Hewlett-packard Development Company, L.p.

3com Corporation

Palmsource Inc.

Handspring, Inc.

Ronald Marianetti, Ii

Subutai Ahmad

Jeffrey Charles Hawkins

Katelijn Vleugels

Anosh Raj

Scott D Lincke

Robert Yuji Haitani

Frank Emmanuel Astier

Joseph K Sipher

Dileep George

Jeffrey L Edwards

Steven C Lemke

Neal A Osborn

Eric Michael Lunsford

Scott R Johnson

Nicholas Twyman

William Cooper Saphir

Wiliam C Saphir

Robert Gosselin

A sensing device monitors and tracks food intake events and details. A processor, appropriately programmed, controls aspects of the sensing device to capture data, store data, analyze data and provide suitable feedback related to food intake. More generally, the methods might include detecting, identifying, analyzing, quantifying, tracking, processing and/or influencing, related to the intake of food, eating habits, eating patterns, and/or triggers for food intake events, eating habits, or eating patterns. Feedback might be targeted for influencing the intake of food, eating habits, or eating patterns, and/or triggers for those. The sensing device can also be used to track and provide feedback beyond food-related behaviors and more generally track behavior events, detect behavior event triggers and behavior event patterns and provide suitable feedback.

Method and apparatus for tracking food intake behaviors and providing relevant feedback

Method and apparatus for tracking of food intake and other behaviors and providing relevant feedback

A processing node in a temporal memory system includes a spatial pooler and a sequence processor. The spatial pooler generates a spatial pooler signal representing similarity between received spatial patterns in an input signal and stored co-occurrence patterns. The spatial pooler signal is represented by a combination of elements that are active or inactive. Each co-occurrence pattern is mapped to different subsets of elements of an input signal. The spatial pooler signal is fed to a sequence processor receiving and processed to learn, recognize and predict temporal sequences in the input signal. The sequence processor includes one or more columns, each column including one or more cells. A subset of columns may be selected by the spatial pooler signal, causing one or more cells in these columns to activate.

Sparse distributed representation for networked processing in predictive system

Temporal memory using sparse distributed representation

A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Hierarchical temporal memory (HTM) system deployed as web service

A spatial and temporal memory system (STMS) processes input data to detect whether spatial patterns and/or temporal sequences of spatial patterns exist within the data, and to make predictions about future data. The data processed by the STMS may be retrieved from, for example, one or more database fields and is encoded into a distributed representation format using a coding scheme. The performance of the STMS in predicting future data is evaluated for the coding scheme used to process the data as performance data. The selection and prioritization of STMS experiments to perform may be based on the performance data for an experiment. The best fields, encodings, and time aggregations for generating predictions can be determined by an automated search and evaluation of multiple STMS systems.

Pattern detection feedback loop for spatial and temporal memory systems

Growing community of inventors

Campbell, CA, United States of America

Ronald Marianetti, Ii