C3.ai, Inc. patent applications on February 27th, 2025
Patent Applications by C3.ai, Inc. on February 27th, 2025
C3.ai, Inc.: 2 patent applications
C3.ai, Inc. has applied for patents in the areas of G05B23/02 (1), G05B13/04 (1), G06N3/088 (1), G06N3/045 (1) G05B23/0294 (1), G06N3/088 (1)
With keywords such as: values, components, tags, subsystem, comprise, digital, assemblies, representation, determining, and estimated in patent application abstracts.
Patent Applications by C3.ai, Inc.
Inventor(s): Lila Fridley of Redwood City CA (US) for c3.ai, inc., Henrik Ohlsson of Redwood City CA (US) for c3.ai, inc., Sina Koshfetrat Pakazad of Redwood City CA (US) for c3.ai, inc.
IPC Code(s): G05B23/02, G05B13/04
CPC Code(s): G05B23/0294
Abstract: the present disclosure provides system, methods, and computer program products for predicting and detecting anomalies in a subsystem of a system. an example method may comprise (a) determining a first plurality of tags that are indicative of an operational performance of the subsystem. the tags can be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system that are not in the subsystem. the method may further comprise (b) processing measured values of the first plurality of tags using an autoencoder trained on historical values of the first plurality of tags to generate estimated values of the first plurality of tags; (c) determining whether a difference between the measured values and estimated values meets a threshold; and (d) transmitting an alert that indicates that the subsystem is predicted to experience an anomaly if the difference meets the threshold.
Inventor(s): Louis Poirier of Paris (FR) for c3.ai, inc., Willy Douhard of Paris (FR) for c3.ai, inc., Shouvik Mani of San Jose CA (US) for c3.ai, inc., Dan Constantini of Paris (FR) for c3.ai, inc.
IPC Code(s): G06N3/088, G06N3/045
CPC Code(s): G06N3/088
Abstract: the present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. an example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. the digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.