Cerebri Ai Inc.

Ca, Inc.

Owl Computing Technologies, Inc.

Dell Software Inc.

Dell Products Inc.

Other

Owl Cyber Defense Solutions, LLC

Gabriel M Silberman

Jean Joseph Belanger

Steven Lloyd Greenspan

Serguei Mankovskii

Maria C Velez-Rojas

Carrie Elaine Gates

Michael L Roberts

Karen Bennet

Ronald Mraz

Alain Charles Briancon

Lee David Harper

Michael Thomas Wegan

Daniel Alexander Ford

Thejas Narayana Prasad

David Alexander Curry

Luke Philip Reding

Jay M Williams

Alain Briançon

Gregory Klose

James Stojanov

Andrew Kraemer

Arun Prakash

Rajesh Narayanan

Howard A Abrams

Stanislav Mitranic

James Cvetan Stojanov

Kenneth Lerman

Cheryl Morris

Tamar Silberman

Hessie Jones

In some implementations, a computing device may determine, from multiple data sources, multiple event timelines, with each event timeline associated with a customer. Each event in an event timeline represents an interaction between the customer and a vendor of goods and/or services. For N (N>1) marketing campaigns, N augmented timelines may be created for each timeline by augmenting each event timeline with the individual marketing campaigns. Thus, for M (M>1) customers, M×N augmented event timelines may be created. A trained machine learning model may perform an analysis of each augmented event timeline to predict results of executing each marketing campaign. The results may include total predicted revenue and total predicted cost resulting from executing each marketing campaign. A particular marketing campaign from the N marketing campaigns may be selected and execution of one or more marketing events may be initiated.

Predicting the effectiveness of a marketing campaign prior to deployment

Provided is process, including: obtaining interaction-event records; determining, based on at least some of the interaction-event records, sets of event-risk scores, wherein: at least some respective event-risk scores are indicative of an effective of a respective risk ascribed by a first entity to a respective aspect of a second entity; and at least some respective event-risk scores are based on both: respective contributions of respective corresponding events to a subsequent event, and a risk ascribed to a subsequent event; and storing the sets of event-risk scores in memory.

Dynamic business governance based on events

In some implementations, a computing device determines an event timeline that comprises one or more finance-related events associated with a person. A production classifier may be used to determine (i) an individual contribution of each event in the event timeline to a financial capacity of the person and (ii) a first decision regarding whether to extend credit to the person. A bias monitoring classifier may, based on the event timeline, determine a second decision whether to extend credit to the person. The bias monitoring classifier may be trained using pseudo-unbiased data. If a difference between the first decision and the second decision satisfies a threshold, the production classifier may be modified to reduce bias in decisions made by the production classifier.

Detecting and reducing bias (including discrimination) in an automated decision making process

In some implementations, an event timeline that includes one or more interactions between a customer and a supplier may be determined. A starting value may be assigned to individual events in the event timeline. A sub-sequence comprising a portion of the event timeline that includes at least one reference event may be selected. A classifier may be used to determine a previous relative value for a previous event that occurred before the reference event and to determine a next relative value for a next event that occurred after the reference event until all events in the event timeline have been processed. The events in the event timeline may be traversed and a monetized value index assigned to individual events in the event timeline.

Customer experience artificial intelligence management engine

Provided is a process that includes sharing information among two or more parties or systems for modeling and decision-making purposes, while limiting the exposure of details either too sensitive to share, or whose sharing is controlled by laws, regulations, or business needs.

Privacy and proprietary-information preserving collaborative multi-party machine learning

Disclosed herein are methods, systems, and processes for distributed logging for securing non-repudiable transactions. Credentials, request information, response information, and action items generated and received by a requesting computing system and a responding computing system, and transmitted between the requesting computing system and the responding computing system are separately recorded and stored in a requestor log maintained by the requesting computing system and in a responder log maintained by the responding computing system.

Distributed logging for securing non-repudiable multi-party transactions

Collaborative multi-parties/multi-sources machine learning for affinity assessment, performance scoring, and recommendation making

Provided is a process, including: obtaining a first training dataset of subject-entity records; training a first machine-learning model on the first training dataset; forming virtual subject-entity records by appending members of a set of candidate action sequences to time-series of at least some of the subject-entity records; forming a second training dataset by labeling the virtual subject-entity records with predictions of the first machine-learning model; and training a second machine-learning model on the second training dataset.

Machine-learning models to leverage behavior-dependent processes

Verifying message authenticity with decentralized tamper-evident logs

Business artificial intelligence management engine

Monitoring and controlling continuous stochastic processes based on events in time series data

Growing community of inventors

Hastings-on-Hudson, NY, United States of America

Gabriel M Silberman