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    ENTERPRISE CYBERSECURITY AI PLATFORM
    1.
    发明公开
    ENTERPRISE CYBERSECURITY AI PLATFORM 审中-公开

    公开(公告)号：US20230291755A1

    公开(公告)日：2023-09-14

    申请号：US17654371

    申请日：2022-03-10

    申请人： C3.ai, Inc.

    发明人： Thomas M. Siebel ,  Aaron W. Brown ,  Varun Badrinath Krishna ,  Nikhil Krishnan ,  Ansh J. Hirani

    IPC分类号： H04L9/40 ,  G06N20/00

    CPC分类号： H04L63/1425 ,  H04L63/1441 ,  G06N20/00

    摘要： A method includes obtaining data associated with operation of a monitored system. The method also includes using one or more first machine learning models to identify anomalies in the monitored system based on the obtained data, where each anomaly identifies an anomalous behavior. The method further includes using one or more second machine learning models to classify each of at least some of the identified anomalies into one of multiple classifications. Different ones of the classifications are associated with different types of cyberthreats to the monitored system, and the identified anomalies are classified based on risk scores determined using the one or more second machine learning models. In addition, the method includes identifying, for each of at least some of the anomalies, one or more actions to be performed in order to counteract the cyberthreat associated with the anomaly.

    Systems and methods for inventory management and optimization
    2.
    发明授权
    Systems and methods for inventory management and optimization 有权

    公开(公告)号：US11620612B2

    公开(公告)日：2023-04-04

    申请号：US16506672

    申请日：2019-07-09

    申请人： C3.AI, Inc.

    发明人： Henrik Ohlsson ,  Gowtham Bellala ,  Sina Khoshfetrat Pakazad ,  Dibyajyoti Banerjee ,  Nikhil Krishnan

    IPC分类号： G06N20/00 ,  G06Q10/087

    摘要： The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

    Systems and methods for providing cybersecurity analysis based on operational technologies and information technologies
    4.
    发明授权
    Systems and methods for providing cybersecurity analysis based on operational technologies and information technologies 有权

    公开(公告)号：US11411977B2

    公开(公告)日：2022-08-09

    申请号：US15891630

    申请日：2018-02-08

    申请人： C3.ai, Inc.

    发明人： Kuenley Chiu ,  Jeremy Kolter ,  Nikhil Krishnan ,  Henrik Ohlsson

    IPC分类号： H04L9/40 ,  G06Q10/06 ,  G06F21/57 ,  G06Q50/06 ,  G06N20/00

    摘要： The disclosed technology can acquire a first set of data from a first group of data sources including a plurality of network components within an energy delivery network. A first metric indicating a likelihood that a particular network component, from the plurality of network components, is affected by cyber vulnerabilities can be generated based on the first set of data. A second set of data can be acquired from a second group of data sources including a collection of services associated with the energy delivery network. A second metric indicating a calculated impact on at least a portion of the energy delivery network when the cyber vulnerabilities affect the particular network component can be generated based on the second set of data. A third metric indicating an overall level of cybersecurity risk associated with the particular network component can be generated based on the first metric and the second metric.

    Systems and methods for determining disaggregated energy consumption based on limited energy billing data
    6.
    发明授权
    Systems and methods for determining disaggregated energy consumption based on limited energy billing data 有权

    公开(公告)号：US11301771B2

    公开(公告)日：2022-04-12

    申请号：US14549955

    申请日：2014-11-21

    申请人： C3.ai, Inc.

    发明人： Zico Kolter ,  Nikhil Krishnan ,  Mehdi Maasoumy ,  Henrik Ohlsson

    IPC分类号： G06N20/00 ,  G06N7/00

    摘要： Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to train a Bayesian network model based on a given set of data. Information associated with a user can be received. The information can include aggregated energy consumption data at one or more low frequency time intervals. At least a portion of the information can be inputted into the Bayesian network model. A plurality of energy consumption values for a plurality of energy consumption sources associated with the user can be inferred based on inputting the at least the portion of the information into the Bayesian network model.

    GENERATIVE ARTIFICIAL INTELLIGENCE ENTERPRISE SEARCH
    8.
    发明公开
    GENERATIVE ARTIFICIAL INTELLIGENCE ENTERPRISE SEARCH 审中-公开

    公开(公告)号：US20240202221A1

    公开(公告)日：2024-06-20

    申请号：US18542481

    申请日：2023-12-15

    申请人： C3.ai, Inc.

    发明人： Thomas M. Siebel ,  Nikhil Krishnan ,  Louis Poirier ,  Michael Haines ,  Romain Juban

    IPC分类号： G06F16/335 ,  G06F16/33 ,  G06F16/332 ,  G06F16/338

    CPC分类号： G06F16/335 ,  G06F16/3326 ,  G06F16/334 ,  G06F16/338

    摘要： 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.