32health Inc.

Shaju Puthussery

Deepak Ramaswamy

Ravindra Kompella

Sagala Chaithanya Kumar

The disclosure relates to systems, methods, and computer-readable media for an artificial intelligence (AI) conversational agent configured for enhanced user interaction in multi-channel contact centers. An example method includes receiving a query regarding a particular property of an asset during a real-time conversation, classifying an intent of the query into a predefined category, triggering, based on the classifying of the intent, a finetuned large language model for the predefined category to answer the query, generating, by the finetuned large language model, a response to the query based on at least one of an information data model and a knowledge graph data store, and outputting the response to the query.

Multi-channel, intelligent conversational agent

Systems and techniques are provided for automatically analyzing and processing domain-specific image artifacts and document images. A process can include obtaining a plurality of document images comprising visual representations of structured text. An OCR-free machine learning model can be trained to automatically extract text data values from different types or classes of document image, based on using a corresponding region of interest (ROI) template corresponding to the structure of the document image type for at least initial rounds of annotations and training. The extracted information included in an inference prediction of the trained OCR-free machine learning model can be reviewed and validated or corrected correspondingly before being written to a database for use by one or more downstream analytical tasks.

Growing community of inventors

Brookline, MA, United States of America

Shaju Puthussery