Evolv Technology Solutions, Inc.

Robert Severn

Diego Guy M Legrand

Matthew J Strom

Scott Henning

James O'Neill

Jingbo Jiang

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 is generally directed to massively multivariate testing, conversion rate optimization, and product recommendation and, in particular, directed to automatically and autonomously placing conversion code (e.g., scripts) in webpages of a host website without requiring any affirmative action on the part of the host. The conversion code modifies display and functionality of a particular portion of a host webpage without modifying other portions of the host webpage. The conversion code is placed by a website modification service which is limitedly authorized by the host to modify only the particular portion of the host webpage under a product recommendation and/or conversion rate optimization scheme.

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Berkeley, CA, United States of America

Robert Severn