Numenta, Inc.

Palm, Incorporated

Hewlett-packard Development Company, L.p.

Handspring, Inc.

Qualcomm Incorporated

Palmsource Inc.

3com Corporation

Access Systems Americas, Inc.

Palm Computing, Inc.

Other

Access Company, Ltd.

Hewlett-packard Company

Tandy Corporation

Jeffrey Charles Hawkins

Robert Yuji Haitani

Peter Nils Skillman

William B Rees

Dileep George

Subutai Ahmad

Thomas Benton Bridgwater

Cathal Loughnane

David Hoenig

Phillip Hobson

Dennis Joseph Boyle

Ronald Marianetti, Ii

Joseph K Sipher

Francis James Canova, Jr

Scott D Lincke

Robert Gilchrist Jaros

Anosh Raj

Robert Gregory Twiss

Amy A Han

Traci Angela Neist

Madeleine L Francavilla

Elisha Avraham Tal

Yuwei Cui

Marcus Anthony Lewis

Frank Emmanuel Astier

Debbie O Chyi

Michael Alexander Yurochko

Martin Bone

Edward Thomas Colligan

Srikiran Prasad

Karl Alun Townsend

Monty Boyer

Vitaly Kruglikov

Lih-Shyng Tzeng

Chetan Surpur

Greg Kucala

Joe Sipher

John Raff

Rahul Kumar Agarwal

Charles Richard Lewis, Jr

Mark Thomas Davis

Gavin Peacock

Jeffrey L Edwards

Nancy Gayed

Gregory T Shirai

Malcolm Sloan Smith

Art Lamb

Daniel Sung-Hwe Kim

Ronald Marianetti

Michael Albanese

Priscilla S Cinque

Gisela Schmoll

Ron Marianetti, Ii

Michael Wise

Robert Yuj Haitani

Yoon Kean Wong

Jerome C Tu

Kulbir S Sandhu

Livius Dumitru Chebeleu

David Clifford Blight

Justin Evan Manus

James H McNamara

Ron Marianetti

Bernard Paul Cousineau

William Cooper Saphir

Paul Cousineau

Dallas R Grove

Arthur C Lamb

Charles Curry

Kenneth L Dulaney

Lindsay Sanford

Douglas Anderson

Michael A Yurochko

Melissa Trott

Steven O Elliott

Hamid Giuseppe-Maria Arjomand

Edward J Vertatschitsch

Debbie Beliveau

Justin Manus

Doug Anderson

Christie L Cadwell

Described herein are apparatus and methods for performing inference into the identity of an object. For an object of a plurality of objects, the apparatus receives feature-location information identifying a feature at first location on a first object of the plurality and a feature at a second location on a second object of the plurality. The apparatus activates a first set of location cells that collectively represent the first location on the first object corresponding to a feature on an object of the plurality of objects and a second set of location cells that collectively represent the second location on the second object corresponding to a feature on an object of the plurality of objects. The apparatus activates a set of displacement cells representing displacement of the first set of location cells and the second set of location cells and identifies one or more objects by processing the displacement cells.

Displacement processor for inferencing and learning based on sensorimotor input data

An inference system performs inference, such as object recognition, based on sensory inputs generated by sensors and control information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The control information describes movement of the sensors or known locations of the sensors relative to a reference point. For a particular object, an inference system learns a set of object-location representations of the object. An object-location representation is a unique characterization of an object-centric location relative to the particular object. The inference system also learns a set of feature-location representations associated with the object-location representation that indicate presence of features at the corresponding object-location pair. The inference system can perform inference on an unknown object by identifying candidate object-location representations consistent with feature-location representations observed from the sensory input data and control information.

Location processor for inferencing and learning based on sensorimotor input data

Embodiments relate to performing inference, such as object recognition, based on sensory inputs received from sensors and location information associated with the sensory inputs. The sensory inputs describe one or more features of the objects. The location information describes known or potential locations of the sensors generating the sensory inputs. An inference system learns representations of objects by characterizing a plurality of feature-location representations of the objects, and then performs inference by identifying or updating candidate objects consistent with feature-location representations observed from the sensory input data and location information. In one instance, the inference system learns representations of objects for each sensor. The set of candidate objects for each sensor is updated to those consistent with candidate objects for other sensors, as well as the observed feature-location representations for the sensor.

Inferencing and learning based on sensorimotor input data

One or more multi-layer systems are used to perform inference. A multi-layer system may correspond to a node that receives a set of sensory input data for hierarchical processing, and may be grouped to perform processing for sensory input data. Inference systems at lower layers of a multi-layer system pass representation of objects to inference systems at higher layers. Each inference system can perform inference and form their own versions of representations of objects, regardless of the level and layer of the inference systems. The set of candidate objects for each inference system is updated to those consistent with feature-location representations for the sensors as well as object representations at lower layers. The set of candidate objects is also updated to those consistent with candidate objects from other inference systems, such as inference systems at other layers of the hierarchy or inference systems included in other multi-layer systems.

Embodiments relate to a processing node in a temporal memory system that performs temporal pooling or processing by activating cells where the activation of a cell is maintained longer if the activation of the cell were previously predicted or activation on more than a certain portion of associated cells in a lower node was correctly predicted. An active cell correctly predicted to be activated or an active cell having connections to lower node active cells that were correctly predicted to become active contribute to accurate prediction, and hence, is maintained active longer than cells activated but were not previously predicted to become active. Embodiments also relate to a temporal memory system for detecting, learning, and predicting spatial patterns and temporal sequences in input data by using action information.

Temporal processing scheme and sensorimotor information processing

Embodiments relate to a first processing node that processes an input data having a temporal sequence of spatial patterns by retaining a higher-level context of the temporal sequence. The first processing node performs temporal processing based at least on feedback inputs received from a second processing node. The first processing node determines whether learned temporal sequences are included in the input data based on sequence inputs transmitted within the same level of a hierarchy of processing nodes and the feedback inputs received from an upper level of the hierarchy of processing nodes.

Feedback mechanisms in sequence learning systems with temporal processing capability

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

Detecting patterns and sequences associated with an anomaly in predictions made a predictive system. The predictive system makes predictions by learning spatial patterns and temporal sequences in an input data that change over time. As the input data is received, the predictive system generates a series of predictions based on the input data. Each prediction is compared with corresponding actual value or state. If the prediction does not match or deviates significantly from the actual value or state, an anomaly is identified for further analysis. A corresponding state or a series of states of the predictive system before or at the time of prediction are associated with the anomaly and stored. The anomaly can be detected by monitoring whether the predictive system is placed in the state or states that is the same or similar to the stored state or states.

Anomaly detection in spatial and temporal memory system

Embodiments relate to a processing node of a hierarchical temporal memory (HTM) system with a union processor that enables a more stable representation of sequences by unionizing or pooling patterns of a temporal sequence. The union processor biases the HTM system so a learned temporal sequence may be more quickly recognized. The union processor includes union elements that are associated with incoming spatial patterns or with cells that represent temporal relationships between the spatial patterns. A union element of the union processor may be activated if a persistence score of the union element satisfies a predetermined criterion. The persistence score of the detector is updated based on the activation states of the spatial patterns or cells associated with the detector. After activation, the union element remains active for a period longer than a time step for performing the spatial pooling.

Union processing of sequences of patterns

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Atherton, CA, United States of America

Jeffrey Charles Hawkins