Abstract: A method for using distributed representations of data items within a first set of data documents clustered in a first two-dimensional metric space to generate a cluster of distributed representations in a second two-dimensional metric space includes clustering in a first two-dimensional metric space, by a reference map generator, a set of data documents, generating a semantic map. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator generates a distributed representation using occurrence information about each data item. A sparsifying module receives an identification of a maximum level of sparsity and reduces a total number of set bits within the distributed representation. The reference map generator clusters, in a second two-dimensional metric space, a set of SDRs retrieved from the SDR database and selected according to a second at least one criterion, generating a second semantic map.

Abstract: A reference map generator clusters, into a semantic map, a set of data documents selected according to at least one criterion and associated with a medical diagnosis. A parser generates an enumeration of measurements occurring in the set of data documents. A representation generator generates for each measurement in the enumeration, a sparse distributed representation (SDR). The method includes storing, by a processor on a second computing device, in each of a plurality of memory cells on the second computing device, one of the generated SDRs. A diagnosis support module receives a document comprising a plurality of measurements. The representation generator generates a compound SDR for the document. Each of the plurality of bitwise comparison circuits determine a level of overlap between the compound SDR and the stored generated SDR. The diagnosis support module provides an identification of the medical diagnosis associated with a stored SDR.

Abstract: A method for identifying a level of similarity between binary vectors includes storing, by a processor on a computing device, in each of a plurality of memory cells on the computing device, one of a plurality of binary vectors, each of the plurality of memory cells including a bitwise comparison circuit. The processor provides, to each of the plurality of memory cells, a received binary vector. Each of the bitwise comparison circuits determines a level of overlap between the received binary vector and the binary vector stored in the memory cell associated with the bitwise comparison circuit. Each of the comparison circuits that determines that the level of overlap satisfies a threshold provides, to the processor, an identification of the stored binary vector with the satisfactory level of overlap. The processor provides an identification of each stored binary vector satisfying the threshold.

Abstract: A method enables identification of a similarity level between a user-provided data item and a data item within a set of data documents. The method includes a representation generator determining, for each term in an enumeration of terms, occurrence information. The representation generator generates, for each term, a sparse distributed representation (SDR) using the occurrence information. The method includes receiving, by a filtering module, a filtering criterion. The method includes generating, by the representation generator, for the filtering criterion, at least one SDR. The method includes generating, by the representation generator, for a first of a plurality of streamed documents received from a data source, a compound SDR. The method includes determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion SDR and the generated compound SDR. The method includes acting on the first streamed document, based upon the determined distance.

Abstract: A method for identifying a level of similarity between binary vectors includes storing, by a processor on a computing device, in each of a plurality of memory cells on the computing device, one of a plurality of binary vectors, each of the plurality of memory cells including a bitwise comparison circuit. The processor provides, to each of the plurality of memory cells, a received binary vector. Each of the bitwise comparison circuits determines a level of overlap between the received binary vector and the binary vector stored in the memory cell associated with the bitwise comparison circuit. Each of the comparison circuits that determines that the level of overlap satisfies a threshold provides, to the processor, an identification of the stored binary vector with the satisfactory level of overlap. The processor provides an identification of each stored binary vector satisfying the threshold.

Abstract: A method of mapping data items to sparse distributed representations (SDRs) includes clustering in a two-dimensional metric space, by a reference map generator, a set of data documents selected according to at least one criterion, generating a semantic map. The semantic map associates a coordinate pair with each of the set of data documents. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator determines, for each data item in the enumeration, occurrence information. The representation generator generates a distributed representation using the occurrence information. A sparsifying module receives an identification of a maximum level of sparsity. The sparsifying module reduces a total number of set bits within the distributed representation based on the maximum level of sparsity to generate an SDR having a normative fillgrade.