C3.ai, Inc. patent applications on June 20th, 2024
Patent Applications by C3.ai, Inc. on June 20th, 2024
C3.ai, Inc.: 5 patent applications
C3.ai, Inc. has applied for patents in the areas of G06F16/33 (2), G06F16/335 (1), G06F16/332 (1), G06F16/338 (1), G06F16/34 (1) G06F16/335 (1), G06F16/345 (1), G06F40/40 (1), G06N3/092 (1), G06N20/00 (1)
With keywords such as: data, respective, models, enterprise, systems, record, information, methods, records, and multimodal in patent application abstracts.
Patent Applications by C3.ai, Inc.
20240202221.GENERATIVE ARTIFICIAL INTELLIGENCE ENTERPRISE SEARCH_simplified_abstract_(c3.ai, inc.)
Inventor(s): Thomas M. Siebel of Woodside CA (US) for c3.ai, inc., Nikhil Krishnan of Los Altos CA (US) for c3.ai, inc., Louis Poirier of Paris (FR) for c3.ai, inc., Michael Haines of San Mateo CA (US) for c3.ai, inc., Romain Juban of San Francisco CA (US) for c3.ai, inc.
IPC Code(s): G06F16/335, G06F16/33, G06F16/332, G06F16/338
CPC Code(s): G06F16/335
Abstract: systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. a deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. these enterprise generative ai systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative ai providing traceable references and links to source information underlying the generative ai insights. these systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.
Inventor(s): Thomas M. Siebel of Woodside CA (US) for c3.ai, inc., Nikhil Krishnan of Los Altos CA (US) for c3.ai, inc., Louis Poirier of Paris (FR) for c3.ai, inc., Romain Juban of San Francisco CA (US) for c3.ai, inc., Michael Haines of San Mateo CA (US) for c3.ai, inc., Yushi Homma of Santa Clara CA (US) for c3.ai, inc., Riyad Muradov of Foster City CA (US) for c3.ai, inc.
IPC Code(s): G06F16/34, G06F16/33, G06F40/20
CPC Code(s): G06F16/345
Abstract: systems and methods managing, by an orchestrator, a plurality of agents to generate a response to an input. the orchestrator employs one or more multimodal models such as a large language models to process or deconstruct the prompt into a series of instructions for different agents. each agent employs one or more machine-learning models to process disparate inputs or different portions of an input associated with the prompt. the system generates, by the orchestrator, a natural language summary of the structured and unstructured data records. the system formulates output and transmits the natural language summary of the data records.
Inventor(s): Louis Poirier of Paris (FR) for c3.ai, inc., Romain Juban of San Francisco CA (US) for c3.ai, inc., Yushi Homma of Santa Clara CA (US) for c3.ai, inc., Riyad Muradov of Foster City CA (US) for c3.ai, inc., Michael Haines of San Mateo CA (US) for c3.ai, inc.
IPC Code(s): G06F40/40
CPC Code(s): G06F40/40
Abstract: systems and methods managing a plurality of agents to generate a response to a query using a multimodal model. an example method uses the plurality of agents to iteratively determine subsequent outputs of the multimodal model satisfies the query. it can generate a respective context associated with a respective output of the multimodal model. and determine, by the multimodal model based on the respective context, whether the respective subsequent output satisfies the query.
Inventor(s): Louis Poirier of Paris (FR) for c3.ai, inc., Romain Juban of San Francisco CA (US) for c3.ai, inc., Sina Pakazad of Redwood City CA (US) for c3.ai, inc., Yushi Homma of Santa Clara CA (US) for c3.ai, inc., Riyad Muradov of Foster City CA (US) for c3.ai, inc., Michael Haines of San Mateo CA (US) for c3.ai, inc.
IPC Code(s): G06N3/092
CPC Code(s): G06N3/092
Abstract: a plurality of different data domains of an enterprise information environment are scanned. a plurality of data records of multiple enterprise data sources of the different data domains are chunked. the chunking generates one or respective data record segments for each of the plurality of data records. respective contextual metadata is generated for each of the one or more respective data record segments. each respective contextual metadata indicates semantic or contextual descriptions of the respective data records segment, and at least one of the respective contextual metadata is capable of facilitating a determination of a relationship between one of the respective data record segments of a particular respective data record and another one the respective data segments of another respective data record. a respective segment embedding is generated for each data record segment based on the respective contextual metadata, and the segment embeddings are stored in an embeddings datastore.
20240202600.MACHINE LEARNING MODEL ADMINISTRATION AND OPTIMIZATION_simplified_abstract_(c3.ai, inc.)
Inventor(s): Louis Poirier of Paris (FR) for c3.ai, inc., Sina Pakazad of Redwood City CA (US) for c3.ai, inc., John Abelt of San Francisco CA (US) for c3.ai, inc., Aliakbar Panahi of Redwood City CA (US) for c3.ai, inc., Michael Haines of San Mateo CA (US) for c3.ai, inc., Romain Juban of San Francisco CA (US) for c3.ai, inc., Yushi Homma of Santa Clara CA (US) for c3.ai, inc., Riyad Muradov of Foster City CA (US) for c3.ai, inc.
IPC Code(s): G06N20/00, G06N5/04
CPC Code(s): G06N20/00
Abstract: systems and methods for a model inference service system that provides a technical solution for deploying and updating trained machine-learning models with support for specific use case deployments and implementations at scale with efficient processing. the model inference service system includes a hierarchical model registry for versioning models and model dependencies for each versioned model, a model inference service for rapidly deploying model instances in run-time environments, and a model processing system for managing multiple instances of deployed models. changes to deployed models are captured as new versions in the hierarchical model registry.
