Evolv Technology Solutions, Inc.

Cognizant Technology Solutions U.S. Corporation

Sentient Technologies (barbados) Limited

Diego Guy M Legrand

Robert Severn

Nigel Duffy

Philip Michael Long

James O'Neill

Scott Henning

Matthew J Strom

Olivier Francon

Jingbo Jiang

Petr Tsatsin

A method of implementing a graphical user interface to collect information from a user is provided. The method includes (i) dynamically displaying, by the graphical user interface, K>1 groupings of M>1 documents from a catalog of documents in an embedding space, wherein a distance between each pair of the documents in the embedding space corresponds to a predetermined measure of dissimilarity between the pair of documents, and the K groupings are formed using K-medoid clustering analysis, (ii) receiving a user selection of one grouping of the K groupings, (iii) dynamically displaying a predetermined number P>0 documents of the cluster which corresponds to the selected grouping, (iv) receiving user feedback with respect to one of the Pdocuments of the selected grouping, (v) and dynamically displaying an identified subsequent document from the selected grouping in dependence on the set of liked documents and the set of disliked documents.

Implementing a graphical user interface to collect information from a user to identify a desired document based on dissimilarity and/or collective closeness to other identified documents

A method for finding a best solution to a problem is provided. The method includes evolving candidate individuals in a candidate pool by testing each candidate individual of the candidate individuals to obtain test results, assigning a performance measure to each of the tested candidate individuals in dependence upon the test results, discarding candidate individuals from the candidate pool in dependence upon their assigned performance measure, and adding, to the candidate pool, a new candidate individual procreated from parent candidate individuals remaining in the candidate pool, and repeating the evolution steps to evolve the candidate individuals in the candidate pool. The method further includes selecting, as a winning candidate individual, a candidate individual from the candidate pool having a best probability to beat a predetermined score, the probability to beat the predetermined score being determined in dependence upon a Bayesian posterior probability distribution of a particular candidate individual.

Method and system for finding a solution to a provided problem using family tree based priors in Bayesian calculations in evolution based optimization

A method for identifying a desired document is provided to include forming K clusters of documents and, for each cluster: for each respective document of the cluster determining a sum of distances between (i) the respective document and (ii) each of the other documents of the cluster; and identifying a medoid document of the cluster as the document of the cluster having the smallest sum of determined distances of all of the documents of the cluster. The method also includes selecting M representative documents for each cluster, identifying for dynamic display toward the user K groupings of documents, wherein each of the K groupings of documents identifies the selected M representative documents of a corresponding cluster, and, in response to user selection of one of the K groupings of documents, identifying for dynamic display toward the user P documents of the cluster that corresponds to the selected grouping.

Intelligently driven visual interface on mobile devices and tablets based on implicit and explicit user actions

The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector x, a favored document represented by a vector x, and K&#x3e;1 unfavored documents represented by vectors x, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors y, yand y, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yof [D(y,y)&#x2212;D(y, y)].

Content embedding using deep metric learning algorithms

A method for identifying a desired document is provided to include calculating a Prior probability score for each document of a candidate list including a portion of documents of an embedding space, the Prior probability score indicating a preliminary probability, for each document of the candidate list, that the document is the desired document, and identifying an initial (i=0) collection of N0&#x3e;1 candidate documents from the candidate list in dependence on the calculated Prior probability scores, the initial collection of candidate documents having fewer documents than the candidate list. The method further includes, for each i'th iteration in a plurality of iterations, beginning with a first iteration (i=1) and in response to user selection of an i'th selected document from the (i&#x2212;1)'th collection of candidate documents, identifying an i'th collection of Ni&#x3e;1 candidate documents from the candidate list in dependence on Posterior probability scores.

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San Francisco, CA, United States of America

Diego Guy M Legrand