Saffron Technology, Inc.

Intel Corporation

James S Fleming

Manuel Aparicio, Iv

Yen-min Huang

Michael J Lemen

Brian J McGiverin

David R Cabana

Paul Hofmann

Dan Ariely

Iv Manuel Aparicio

Provided are methods, systems, and computer program products that generate a plurality of query queues that correspond to a plurality of row-based identifiers corresponding to a plurality of physical memory locations in at least one of a plurality of associative memory networks in response to a semantic-space based query that includes at least one query search element, determine count information responsive to the plurality of query queues and compute information distances between the at least one query search element and ones of associative memory query result elements to determine closeness values corresponding to the information distances.

Methods, systems and computer program products for using a distributed associative memory base to determine data correlations and convergence therein

Systems, methods and computer program products are provided for a distributed associative memory base. Such methods may include providing a distributed memory base that includes a network of networks of associative memory networks. The memory base may include a network of associative memory networks, a respective associative memory network comprising associations among a respective observer entity and a plurality of observed entities that are observed by the respective observer entity. Ones of the associative memory networks are physically and/or logically independent from other ones of the associative memory networks. Methods include imagining associations from the associative memory base using a plurality of streaming queues that correspond to ones of a plurality of rows of ones of the associative memory networks.

Methods, systems and computer program products for providing a distributed associative memory base

Associative matrix compression methods, systems, computer program products and data structures compress an association matrix that contains counts that indicate associations among pairs of attributes. Selective bit plane representations of those selected segments of the association matrix that have at least one count is performed, to allow compression. More specifically, a set of segments is generated, a respective one of which defines a subset, greater than one, of the pairs of attributes. Selective identifications of those segments that have at least one count are stored. The at least one count that is associated with a respective identified segment is also stored as at least one bit plane representation. The at least one bit plane representation identifies a value of the at least one associated count for a bit position of the count that corresponds to the associated bit plane.

Associative matrix observing methods, systems and computer program products using bit plane representations of selected segments

Associative memory systems, methods and/or computer program products include a network of networks of associative memory networks. A network of entity associative memory networks is provided, a respective entity associative memory of which includes associations among a respective observer entity and observed entities that are observed by the respective observer entity, based on input documents. A network of feedback associative memory networks includes associations among observed entities for a respective positive and/or negative evaluation for a respective task of a respective user. A network of document associative memory networks includes associations among observed entities in a respective observed input source, such as a respective input document. A network of community associative memory networks includes associations among a respective observer entity, observed entities that are observed by the respective observer entity, and observed tasks of users in which the observer entity was queried. Associations may be observed into and imagined from the network of networks of associative memory networks.

Network of networks of associative memory networks for knowledge management

Associative matrix methods, systems and computer program products using bit plane representations of selected segments

Network of networks of associative memory networks

An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. A respective dendrite includes a respective power series of weights. The weights in a given power of the power series represent a maximal projection. A respective power also may include at least one switch, to identify holes in the projections. By providing maximal projections, linear scaling may be provided for the maximal projections, and quasi-linear scaling may be provided for the artificial neuron, while allowing a lossless compression of the associations. Accordingly, hetero-associative and/or auto-associative recall may be accommodated for large numbers of inputs, without requiring geometric scaling as a function of input.

Artificial neurons including weights that define maximal projections

An artificial neuron includes inputs and dendrites, a respective one of which is associated with a respective one of the inputs. Each dendrite includes a power series of weights, and each weight in a power series includes an associated count for the associated power. The power series of weights preferably is a base-two power series of weights, each weight in the base-two power series including an associated count that represents a bit position. The counts for the associated power preferably are statistical counts. More particularly, the dendrites preferably are sequentially ordered, and the power series of weights preferably includes a pair of first and second power series of weights. Each weight in the first power series includes a first count that is a function of associations of prior dendrites, and each weight of the second power series includes a second count that is a function of associations of next dendrites. More preferably, a first and second power series of weights is provided for each of multiple observation phases. In order to propagate an input signal into the artificial neuron, a trace preferably also is provided that is responsive to an input signal at the associated input. The trace preferably includes a first trace count that is a function of associations of the input signal at prior dendrites, and a second trace count that is a function of associations of the input signal at next dendrites. The first and second power series are responsive to the respective first and second trace counts. The input signal preferably is converted into the first and second trace counts, and a trace wave propagator propagates the respective first and second trace counts into the respective first and second power series of weights.

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James S Fleming