Vast.com, Inc.

Ojo Labs, Inc.

Joshua Howard Levy

David Wayne Franke

Lauri Janet Moore

Thomas Wyatt Wilbur

Ron Maire Kurti

Benjamin David Cohen

Gary Joseph Marcotte

David Ross Pratt

Jacy Myles Legault

Andrew Philip Dove

Andrew K Moore

David Robert Rubin

John Kenneth Berkowitz

John William Prior

Hans Ulrich Grasemann

Lauri Janet Feldman

William T Moose

Kenneth Czechowski

Alexa Breann Eun Taylor

Reed Coke

The disclosure herein provides systems, methods, and devices for determining and displaying market relative positions and/or attributes of unique items. A visualization system for generating electronic visualizations of market-relative attributes of unique items comprises an item selection receiver, a visualization compilation engine, and a rendering engine.

Systems, methods, and devices for determining and displaying market relative position of unique items

A machine learning of response selection to structured data input enables a machine to flexibly and responsively actively engage with a response recipient through a device, such as any electronic device connected to a data network. A system of one or more computers can be configured to respond to structured language messages, such as a textual messages, received from user devices with one or more templates that have been ranked using predetermined criteria including a machine learning classification of a momentum of a structured language message using a machine learning model. The momentum classification is correlated with templates. A ranking engine ranks the selection of candidate response templates in accordance with ranking criteria, including the momentum classifications for the candidate response templates. A highest ranked candidate response template is selected to provide a response to the textual messages received from the user devices.

Machine learning of response selection to structured data input including momentum classification

The disclosure herein provides methods, systems, and devices for measuring similarity of and generating recommendations for unique items. A recommendation system for generating recommendations of alternative unique items comprises an items information database, a penalty computation engine, a recommendation compilation engine, and one or more computers, wherein the penalty computation engine comprises a customizations filter, a condition filter, and a dissimilarity penalty calculator.

Systems, methods, and devices for measuring similarity of and generating recommendations for unique items

The disclosure herein provides methods, systems, and devices for measuring similarity of and generating recommendations for unique items. A recommendation system for generating recommendations of alternative unique items comprises an items information database, a penalty computation engine, a recommendation compilation engine, and one or more computers, wherein the penalty computation engine comprises a customizations filter, a condition filter, and a dissimilarity penalty calculator for determine the dissimilarity of attributes of alternative unique items from a plurality of selected items.

A supervised learning processing (SLP) system and method provide cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, provide prediction driven responses to input objects, and provide operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data. The SLP system can dynamically sequence SLP subsystem operations to improve resource utilization, training quality, and/or processing speed. A system monitor-controller can dynamically determine if process environmental data indicates initiation of dynamic subsystem processing sequencing. Concurrently training SLP's provides accurate predictions of input objects and responses thereto and enhances the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train with concurrent commercial operations.

Dynamic subsystem operational sequencing to concurrently control and distribute supervised learning processor training and provide predictive responses to input data

A supervised learning processing (SLP) system and non-transitory, computer program product provides cooperative operation of a network of supervised learning processors to concurrently distribute supervised learning processor training, generate predictions, and provide prediction driven responses to input objects, such as NL statements. The SLP system includes SLP stages that are distributed across multiple SLP subsystems. Concurrently training SLP's provides accurate predictions of input objects and responses thereto, the SLP system and non-transitory, computer program product enhance the network by providing high quality value predictions and responses and avoiding potential training and operational delays. The SLP system can enhance the network of SLP subsystems by providing flexibility to incorporate multiple SLP models into the network and train at least a proper subset of the SLP models while concurrently using the SLP system and non-transitory, computer program product in commercial operation.

Cooperatively operating a network of supervised learning processors to concurrently distribute supervised learning processor training and provide predictive responses to input data

A machine learning of response selection to structured data input enables a machine to flexibly and responsively actively engage with a response recipient through a device, such as any electronic device connected to a data network. In at least one embodiment, the response selection module improves response selection to the structure data input by initially filtering a library of templates to identify candidate templates that best respond to the input. In at least one embodiment, the response selection module ranks the identified candidate templates to provide the response to the device. The response selection module learns by receiving feedback, such as a linked recipient action result signal.

Machine learning of response selection to structured data input

The disclosure herein provides systems, methods, and devices for generating recommendations of dynamic or unique items. A system for generating recommendations of unique items comprises a data collection engine, a scoring engine, a user penalty calculator, and a recommendation compilation engine, wherein the recommendation engine is configured to transmit to a user access point system a list of recommended alternative unique items predicted to be preferred by a user of the user access point system.

Growing community of inventors

Austin, TX, United States of America

Joshua Howard Levy