Cerebri Ai Inc.

Other

Michael L Roberts

Karen Bennet

Gabriel M Silberman

Jean Joseph Belanger

Jay M Williams

Jennifer Fritsch

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

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

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

Verifying message authenticity with decentralized tamper-evident logs

Business artificial intelligence management engine

A modular, temporary safety railing system that provides fall restraint protection during masonry wall construction projects includes a plurality of anchoring sleeves which are embedded in wet grout within a hollow cavity of a masonry block onto which posts and rail sections are assembled. The embedded pins receive and secure respective vertical posts via interconnecting locking pins comprising spring pin fasteners. A plurality of top and intermediate rail sections are attached to the vertical posts using connecting hardware designed to adjustable position said rail sections at an offset distance allowing additional courses of block to be added to the wall without interference from said railing system. As construction continues, the vertical posts are detached from the embedded sleeves and reused with newly installed sleeves.

Safety rail system

A utensil in the form of a spoon having a spill resistant bowl. The bowl may have an angled tip at the front portion of the bowl and a secondary reservoir at or near the rear portion of the bowl. In some embodiments, portions of the inner surface of the bowl may be textured near the perimeter edge along the sides of the bowl. The utensil may also have an ergonomic handle having a depression on the top and concavity on the bottom with a flattened portion at the rear end. The flattened portion allows the utensil to stand upright.

Easy use utensil

A particular continuous downward spiraling cereal bowl is disclosed which allows for exceptional control over a mixing process by keeping dry substances separated from liquids until mixed in user defined portions and timeframes. The disclosure comprises a slightly asymmetrically round bowl consisting of two contiguous areas with an inner surface that continually slopes downward between the two areas and an S-shaped guide rail between these two areas. The shape of the guide rail is curved in order to hold cereal in the upper part of the spiral at a level above the fill line of liquid held in the lower portion of the spiral. In the preferred embodiment, the bowl further comprises a thumb groove underneath the inner surface of the dry area which may serve as an embedded handle.

Spiraling bowl

A method for garbage collection in which resources used by data objects in a computer program that are not accessed in the future are reclaimed, the method consists of the following steps of 1. Providing a tracing garbage collector which is both parallelized and incremental; 2. Integrating the collector with the computer program to create the mutator; 3. Instructing the mutator to scan and collect resources used by data objects in a computer program that are not accessed in the future simultaneously on all threads in small amounts; and 4. Interleaving the mutator scanning and collection with unrelated processing by the computer program such that all of the threads in the application are never stopped simultaneously for any portion of the collection cycle.

Growing community of inventors

Austin, TX, United States of America

Michael L Roberts