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公开(公告)号：US20230385468A1
公开(公告)日：2023-11-30
申请号：US17828014
申请日：2022-05-30
Applicant: Accenture Global Solutions Limited
Inventor： Zaid Tashman , Matthew Kujawinski , Sanjoy Paul , Neda Abolhassani
IPC: G06F30/12
CPC classification number: G06F30/12 , G06F2111/08
Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to generate digital twins having extended capabilities. To generate the digital twin, an ontology may be obtained and modified to define additional types of data, such as events and metrics, for incorporation into the digital twin. The ontology, once modified, may be instantiated as a knowledge graph having the additional types of data embedded therein. The embedded data may be used to convert the knowledge graph to a probabilistic graph model that may be queried to extract information from the digital twin in a probabilistic manner. Additionally, multiple ontologies may be utilized to create a digital twin-of-digital twins, which enables more complex digital twins to be generated (e.g., digital twins of entire ecosystems), and enables new insights and understanding of the various components and interactions between the components of the ecosystem.
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公开(公告)号：US11574216B2
公开(公告)日：2023-02-07
申请号：US16906759
申请日：2020-06-19
Applicant: Accenture Global Solutions Limited
Inventor： Teresa Sheausan Tung , Mohamad Mehdi Nasr-Azadani , Yao A. Yang , Zaid Tashman , Maziyar Baran Pouyan
Abstract: A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.
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公开(公告)号：US11531328B2
公开(公告)日：2022-12-20
申请号：US17301252
申请日：2021-03-30
Applicant: Accenture Global Solutions Limited
Inventor： Jurgen Albert Weichenberger , Mohamad Mehdi Nasr-Azadani , Zaid Tashman , Matin Momeni , Teresa Sheausan Tung
Abstract: In some implementations, a control system may obtain historical data associated with usage of a distillation column during a historical time period. The control system may configure a prediction model to monitor the distillation column for a hazardous condition. The prediction model may be trained based on training data that is associated with occurrences of the hazardous condition. The control system may monitor, using the prediction model, the distillation column to determine a probability that the distillation column experiences the hazardous condition within a threshold time period. The prediction model may be configured to determine the probability based on measurements from a set of sensors of the distillation column. The control system may perform, based on the probability satisfying a probability threshold, an action associated with the distillation column to reduce a likelihood that the distillation column experiences the hazardous condition within the threshold time period.
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Patent Agency Ranking
公开(公告)号：US11232368B2
公开(公告)日：2022-01-25
申请号：US16792696
申请日：2020-02-17
Applicant: Accenture Global Solutions Limited
Inventor： Zaid Tashman , Paul Hofmann , Teresa Sheausan Tung
Abstract: A system receives sensor data from sensing parameters of a piece of factory equipment. The system includes a first model to generate predicted degradation states of the piece of factory equipment by being trained to generate a stochastic degradation model for classification of the predicted degradation states of a particular asset. The system includes a second model to which the predicted degradation states are provided. The second model trained to generate a covariate indicative of a failure condition of the piece of factory equipment. The system may supply the covariate to the first model to generate predicted degradation states compensated with the covariate. From the predicted degradation states compensated with the covariate a policy of a maintenance action may be generated with the system to optimize life expectancy of the piece of factory equipment. The system may adjust operation of the piece of factory equipment based on the maintenance action.
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公开(公告)号：US20230274170A1
公开(公告)日：2023-08-31
申请号：US17681699
申请日：2022-02-25
Applicant: Accenture Global Solutions Limited
Inventor： Zaid Tashman , Matthew Kujawinski , Neda Abolhassani , Sanjoy Paul , Thien Quang Nguyen , Eric Annong Tang , Jessica Huey-Jen Yeh
Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support ontology driven processes to create digital twins that extend the capabilities of knowledge graphs. A dataset including an ontology and domain data corresponding to a domain associated with the ontology is obtained. A knowledge graph is constructed based on the ontology and the domain data is incorporated into the knowledge graph. The knowledge graph is exploited to derive random variables of a probabilistic graph model. The random variables may be associated with probability distributions, which may include unknown parameters. A learning process is executed to learn the unknown parameters and obtain a joint distribution of the probabilistic graph model, which may enable querying of the probabilistic graph model in a probabilistic and deterministic manner.
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6.发明申请公开(公告)号：US20200265331A1
公开(公告)日：2020-08-20
申请号：US16792696
申请日：2020-02-17
Applicant: Accenture Global Solutions Limited
Inventor： Zaid Tashman , Paul Hofmann , Teresa Sheausan Tung
Abstract: A system receives sensor data from sensing parameters of a piece of factory equipment. The system includes a first model to generate predicted degradation states of the piece of factory equipment by being trained to generate a stochastic degradation model for classification of the predicted degradation states of a particular asset. The system includes a second model to which the predicted degradation states are provided. The second model trained to generate a covariate indicative of a failure condition of the piece of factory equipment. The system may supply the covariate to the first model to generate predicted degradation states compensated with the covariate. From the predicted degradation states compensated with the covariate a policy of a maintenance action may be generated with the system to optimize life expectancy of the piece of factory equipment. The system may adjust operation of the piece of factory equipment based on the maintenance action.
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公开(公告)号：US20250053918A1
公开(公告)日：2025-02-13
申请号：US18230901
申请日：2023-08-07
Applicant: Accenture Global Solutions Limited
Inventor： Sanjoy Paul , Mohamad Mehdi Nasr-Azadani , Zaid Tashman , Jacob Charles Metzger , Maxwell Ryan Murphy , Aaron Crow
IPC: G06Q10/087 , G06Q10/0635 , G06Q30/0202
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for obtaining inventory data associated with a product or service; generating, using the inventory data, a monotonically increasing function indicative of cumulative demand; predicting future inventory events using the monotonically increasing function and a machine learning model that includes survival curve analysis; in response to predicting future inventory events using the monotonically increasing function of data and survival curve analysis, generating an instruction configured to procure one or more items of inventory; and transmitting the instruction to an inventory system.
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公开(公告)号：US11556850B2
公开(公告)日：2023-01-17
申请号：US16749717
申请日：2020-01-22
Applicant: Accenture Global Solutions Limited
Inventor： Andrew Nam , Yao Yang , Teresa Sheausan Tung , Mohamad Mehdi Nasr-Azadani , Zaid Tashman , Ruiwen Li
Abstract: The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.
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