Cerebri Ai Inc.

Fujitsu Corporation

Pranav Mahesh Makhijani

Jean Joseph Belanger

Alain Charles Briancon

Michael Henry Engeling

Gregory Klose

James Cvetan Stojanov

Hongshi Li

Max Changchun Huang

Mounib Mohamad Ismail

Christopher Michael Coovrey

Wei-Peng Chen

Lei Liu

Thejas Narayana Prasad

Eyal Ben Zion

Sara Amini

Jian Deng

Ngoc Thu Nguyen

Provided is a process, including: obtaining a first training dataset, training a first machine-learning model on the first training dataset, obtaining a set of candidate question sequences, forming virtual subject-entity records, forming a second training dataset, training a second machine-learning model, and storing the adjusted parameters of the second machine-learning model in memory.

Customer journey management engine

Provided is a process of modeling methods organized in racks of a machine learning pipeline to facilitate optimization of performance using modelling methods for implementation of machine learning design in an object-oriented modeling (OOM) framework, the process including: writing classes using object-oriented modelling of optimization methods, modelling methods, and modelling racks; writing parameters and hyper-parameters of the modeling methods as attributes as the modeling methods; scanning modelling racks classes to determine first class definition information; selecting a collection of rack and selecting modeling method objects; scanning modelling method classes to determine second class definition information; assigning racks and locations within the racks to modeling method objects; and invoking the class definition information to produce object manipulation functions that allow access the methods and attributes of at least some of the modeling method objects, the manipulation functions being configured to effectuate writing locations within racks and attributes of racks.

Machine learning pipeline optimization

A method includes extracting, from a labelled repository, a plurality of true endpoint descriptions associated with a plurality of web APIs and retrieving a documentation corpus associated with the plurality of web APIs. The method further includes determining a plurality of false endpoint descriptions associated with the plurality of web APIs based on the retrieved documentation corpus and the extracted plurality of true endpoint descriptions. The method further includes extracting feature information associated with the plurality of web APIs, generating a training dataset based on the extracted feature information, and obtaining a ML model based on the generated training dataset. The method further includes providing a set of inputs to the ML model and classifying the provided set of inputs as one of a true description or a false description associated with an endpoint based on a prediction result of the ML model for the provided set of inputs.

Predicting API endpoint descriptions from API documentation

Growing community of inventors

Austin, TX, United States of America

Pranav Mahesh Makhijani