Sansa Ai Inc.

Sansa Al Inc.

James B Robinson

Robert J Munro

Schuyler D Erle

Gary C King

Brendan D Callahan

Jason Brenier

Tyler J Schnoebelen

Jessica D Long

Paul A Tepper

Christopher Walker

Edgar Nunez

Tripti Saxena

Veena Basavaraj

Ross Mechanic

Haley Most

Sarah K Luger

Andrew Gilchrist-Scott

Michelle Casbon

Ujjwal Sarin

Aneesh Nair

Martha G Hinrichs

Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Methods for generating natural language processing systems

Systems, methods, and apparatuses are presented for a trained language model to be stored in an efficient manner such that the trained language model may be utilized in virtually any computing device to conduct natural language processing. Unlike other natural language processing engines that may be computationally intensive to the point of being capable of running only on high performance machines, the organization of the natural language models according to the present disclosures allows for natural language processing to be performed even on smaller devices, such as mobile devices.

Methods and systems for providing universal portability in machine learning

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San Francisco, CA, United States of America

James B Robinson