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公开(公告)号：US20240078618A1
公开(公告)日：2024-03-07
申请号：US18492765
申请日：2023-10-23
申请人： C3.ai, Inc.
摘要： A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.
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公开(公告)号：US20240022483A1
公开(公告)日：2024-01-18
申请号：US18473181
申请日：2023-09-22
申请人： C3.ai, Inc.
发明人： Jeremy Kolter , Giuseppe Barbaro` , Mehdi Maasoumy Haghighi , Henrik Ohlsson , Umashankar Sandilya
CPC分类号： H04L41/16 , H04L67/12 , G06N20/00 , G01R19/2513 , G06N7/01
摘要： The present disclosure provides systems and methods that may advantageously apply machine learning to detect and ascribe network interruptions to specific components or nodes within the network. In an aspect, the present disclosure provides a computer-implemented method comprising: mapping a network comprising a plurality of islands that are capable of dynamically changing by splitting and/or merging of one or more islands, wherein the plurality of islands comprises a plurality of individual components; and detecting and localizing one or more local events at an individual component level as well as at an island level using a disaggregation model.
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公开(公告)号：US11810204B2
公开(公告)日：2023-11-07
申请号：US17592781
申请日：2022-02-04
申请人： C3.ai, Inc.
摘要： The present disclosure provides systems and methods that may advantageously apply machine learning to accurately identify and investigate potential money laundering. In an aspect, the present disclosure provides a computer-implemented method for anti-money laundering (AML) analysis, comprising: (a) obtaining, by the computer, a dataset comprising a plurality of accounts, each of the plurality of accounts corresponding to an account holder among a plurality of account holders, wherein each account of the plurality of accounts comprises a plurality of account variables, wherein the plurality of account variables comprises financial transactions; (b) applying, by the computer, a trained algorithm to the dataset to generate a money laundering risk score for each of the plurality of account holders; and (c) identifying, by the computer, a subset of the plurality of account holders for investigation based at least on the money laundering risk scores of the plurality of account holders.
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公开(公告)号：US20230333521A1
公开(公告)日：2023-10-19
申请号：US18300215
申请日：2023-04-13
申请人： C3.ai, Inc.
发明人： Timothy P. Holtan , Qiwei Li , Shouvik Mani , Suman Tripathy
IPC分类号： G05B13/02
CPC分类号： G05B13/0265 , G05B13/021 , G05B13/026
摘要： A method includes analyzing information to be processed, where analyzing the information includes classifying invalid data contained in the information and substituting replacement data in place of at least some of the invalid data in the information. The method also includes training at least one machine learning model based on some of the analyzed information. The method further includes providing other of the analyzed information to the at least one trained machine learning model, where the at least one trained machine learning model is used to generate one or more recommendations based on the analyzed information. In addition, the method includes translating each of the one or more recommendations into one or more actions and generating one or more control instructions based on the one or more actions for at least one of the one or more recommendations.
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公开(公告)号：US20230297901A1
公开(公告)日：2023-09-21
申请号：US17655740
申请日：2022-03-21
申请人： C3.ai, Inc.
IPC分类号： G06Q10/06
CPC分类号： G06Q10/063
摘要： A method includes obtaining information associated with multiple collocation events. Each collocation event is associated with an occurrence where multiple entities were collocated with one another. The information associated with each collocation event identifies the entities associated with the collocation event, a duration of the collocation event, one or more locations associated with the collocation event, and a time associated with the collocation event. The method also includes identifying strengths of different relationships involving different ones of the entities based on the information. The strengths are determined using one or more features of the collocation events.
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6.发明公开公开(公告)号：US20230297742A1
公开(公告)日：2023-09-21
申请号：US17699395
申请日：2022-03-21
申请人： C3.ai, Inc.
IPC分类号： G06F30/27
CPC分类号： G06F30/27
摘要： A simulation method includes providing a physics-based simulation model including model parameters for simulating a physical process using input data from different sources of operational data including time series data, the physics-based simulation model generating output data including simulated predictions that are calculated using the model parameters, an artificial intelligence (AI)-based-system including an AI-based proxy model. The AI-based proxy model responsive to receiving an update of the input data processes the updated input data to generate a proxy prediction for at least one selected prediction from the simulated predictions or a variable derived from the simulated prediction as a replacement for or as a supplement to the selected prediction or the variable derived from the selected prediction.
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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.
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.
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公开(公告)号：US20230101023A1
公开(公告)日：2023-03-30
申请号：US17932862
申请日：2022-09-16
申请人： C3.ai, Inc.
摘要： A method includes identifying, using at least one processor, uncertainty distributions for multiple variables. The method also includes identifying, using the at least one processor, one or more hyperparameters. The method further includes performing, using the at least one processor, multiple simulations to simulate effects of future requests using the one or more hyperparameters and at least one of the uncertainty distributions. The simulations involve sampling of the at least one uncertainty distribution to simulate at least one uncertainty associated with at least one of the variables on the future requests. In addition, the method includes selecting, using the at least one processor, one or more of the simulated future requests.
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公开(公告)号：US20220253769A1
公开(公告)日：2022-08-11
申请号：US17665417
申请日：2022-02-04
申请人： C3.ai, Inc.
摘要： A method includes obtaining information identifying (i) multiple processing units in a facility, (ii) multiple interconnections between the processing units, and (iii) constraints associated with the processing units and the interconnections. The method also includes identifying an optimization problem associated with production of multiple products by the processing units in the facility, where the optimization problem is associated with a cost function. The method further includes removing one or more terms from the optimization problem to generate a relaxed optimization problem. In addition, the method includes generating one or more solutions to the relaxed optimization problem, where each solution represents a proposed production schedule.
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10.发明申请公开(公告)号：US20220245573A1
公开(公告)日：2022-08-04
申请号：US17665428
申请日：2022-02-04
申请人： C3.ai, Inc.
摘要： A method includes obtaining information defining multiple customer orders for one or more products over time. The method also includes using machine learning to identify one or more of the customer orders that are likely to change based on classification of the customer orders. The method further includes using machine learning to estimate one or more lengths of time that the one or more customer orders are likely to change based on regression of the customer orders. In addition, the method includes generating a consumption plan for a facility based on the one or more estimated lengths of time that the one or more customer orders are likely to change.
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