32health Inc.

Endovigilant Inc

Other

Ravindra Kompella

Vineet Sachdev

Deepak Ramaswamy

Shaju Puthussery

H David Helsley, Jr

Om P Chaurasia

Hamed Pirsiavash

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.

Domain-specific processing and information management using machine learning and artificial intelligence models

A set of enhancements to further improve the performance of deep learning artificial intelligence algorithms trained to detect and localize colon polyps. The enhancements spanning training data mining efficiencies and automation, training data augmentation, early detection of polyps enable a more performant colon polyp detection solution for use on colonoscopy procedure recordings or live procedures in endoscopy centers.

Deep learning for real-time colon polyp detection

An endoscopy video feature enhancement platform (EVFEP) is connected to the output of any type endoscope system, and inputs and captures the output video. The video is visually augmented live with indicators of possible polyp detection and localization, polyp attributes, and procedure metrics, based on the collective learning of the output results of many different types of endoscopy systems on a large scale. An artificial intelligence model is trained on confirmed polyp detection previously determined by this and other EVFEP devices used with many different types of endoscope systems on a large scale. Augmented video, images and automatically generated short video clips of key procedure segments are passed to a reporting system, and supplemented with meta data.

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Hyderabad Telangana, India

Ravindra Kompella