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公开(公告)号：US11665247B2
公开(公告)日：2023-05-30
申请号：US17715934
申请日：2022-04-07
发明人： Richard Simon , Richard Berglund , Erik Donahue , Joseph W. Norton , Vladyslava Matviyenko , Jeremy Lee Rambo , John M. VanAntwerp , Dan Kalmes , Burton J. Floyd , Thad Garrett Craft , Marc Anderson , Nick U. Christopulos , Patrick Mead
IPC分类号： G06F15/16 , H04L67/51 , G06F16/9535
CPC分类号： H04L67/51 , G06F16/9535
摘要： A computer-implemented method for retrieving information from information services and providing it to a public application programming interface (API) includes receiving a first request data message using a core discovery agent, the request data message including at least one requested datum, for which a value is sought, and at least one known datum, for which a value is known; calling a resource locator to request a location of an information service that provides a value for the requested datum; calling a resource façade to contact the information service; transmitting a first information service message including the requested datum and known datum from the resource façade to the information service; receiving a second information service message from the information service including a value for the requested datum; and transmitting a resolved data message including the requested datum and its value from the core discovery agent to the public API.
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2.发明申请公开(公告)号：US20210256615A1
公开(公告)日：2021-08-19
申请号：US16136365
申请日：2018-09-20
摘要： Techniques for implementing machine learning for insurance loss mitigation or prevention, and claims handling are disclosed. In some scenarios, the insurance loss mitigation and claims handling may be associated with a disability, worker's compensation, life or health insurance policy, and the machine-learning analytics model may be trained in accordance with data that is relevant to identifying appropriate predictions in accordance with these particular types of insurance products. For instance, the machine-learning analytics model may utilize information within a dynamic data set as training data, which may include electronically accessible information. The machine-learning analytics model may additionally be implemented to identify various predictions that are indicative of a risk of insuring an individual as well as one or more actions that, when performed, may reduce the initial calculation of risk.
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公开(公告)号：US11783422B1
公开(公告)日：2023-10-10
申请号：US16136387
申请日：2018-09-20
摘要： Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network. A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user's mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.
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公开(公告)号：US10979515B1
公开(公告)日：2021-04-13
申请号：US16703662
申请日：2019-12-04
发明人： Richard Simon , Jeremy Lee Rambo , John M. VanAntwerp , Dan Kalmes , Burton J. Floyd , Thad Garrett Craft , Marc Anderson , Nick U. Christopulos , Patrick Mead , Richard Berglund , Erik Donahue , Joseph W. Norton , Vladyslava Matviyenko
IPC分类号： H04L29/08 , G06F16/9535
摘要： A computer-implemented method for retrieving information from information services and providing it to a public application programming interface (API) includes receiving a first request data message using a core discovery agent, the request data message including at least one requested datum, for which a value is sought, and at least one known datum, for which a value is known; calling a resource locator to request a location of an information service that provides a value for the requested datum; calling a resource façade to contact the information service; transmitting a first information service message including the requested datum and known datum from the resource façade to the information service; receiving a second information service message from the information service including a value for the requested datum; and transmitting a resolved data message including the requested datum and its value from the core discovery agent to the public API.
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公开(公告)号：US20210390624A1
公开(公告)日：2021-12-16
申请号：US16136519
申请日：2018-09-20
摘要： Machine learning techniques for determining a risk level of a target building or other type of real property include receiving data indicative of various historical characteristics of and/or associated with real property, and/or receiving data included in historical, electronic claims pertaining to buildings/real properties, and utilizing the received data to train a machine learning or other model that identifies or discovers risk factors associated with buildings/real properties. The machine learning or other model may be applied to characteristic data associated with the target building/real property to generate risk factors and/or risk indicators of the target building/real property. The techniques may include analyzing the generated risk factors and/or risk indicators to determine a risk level of the target building/real property. The risk factors, risk indicators, and/or risk level may be used for many purposes, such as pricing, quoting, underwriting, or re-underwriting of insurance policies.
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公开(公告)号：US20210312567A1
公开(公告)日：2021-10-07
申请号：US17353621
申请日：2021-06-21
摘要： A method of determining loss reserves and/or providing automatic financial reporting related thereto via one or more processors includes (1) receiving a plurality of historical electronic claim documents, each respectively labeled with a claim loss amount; (2) normalizing each respective claim loss amount and training an artificial intelligence or machine learning algorithm, module, or model, such as an artificial neural network, by applying the plurality of electronic claim documents to the artificial intelligence or machine learning algorithm, module, or model. The method may include receiving a user claim and predicting a loss reserve amount by applying the user claim to the trained artificial intelligence or machine learning algorithm, module, or model, and may include unreported claims.
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公开(公告)号：US20210287297A1
公开(公告)日：2021-09-16
申请号：US16136401
申请日：2018-09-20
摘要： A method of determining loss reserves and/or providing automatic financial reporting related thereto via one or more processors includes (1) receiving a plurality of historical electronic claim documents, each respectively labeled with a claim loss amount; (2) normalizing each respective claim loss amount and training an artificial intelligence or machine learning algorithm, module, or model, such as an artificial neural network, by applying the plurality of electronic claim documents to the artificial intelligence or machine learning algorithm, module, or model. The method may include receiving a user claim and predicting a loss reserve amount by applying the user claim to the trained artificial intelligence or machine learning algorithm, module, or model, and may include unreported claims.
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公开(公告)号：US20230260048A1
公开(公告)日：2023-08-17
申请号：US18139131
申请日：2023-04-25
摘要： Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network, A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user's mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.
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公开(公告)号：US11373249B1
公开(公告)日：2022-06-28
申请号：US16136357
申请日：2018-09-20
IPC分类号： G06Q40/08 , G06N3/08 , G06V20/00 , G06V30/194
摘要： A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle. The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.
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公开(公告)号：US20210256616A1
公开(公告)日：2021-08-19
申请号：US16136370
申请日：2018-09-20
摘要： A method of determining an automobile-based risk level via one or more processors includes training a machine learning program, such as a neural network, to identify risk factors within electronic claim features, receiving information corresponding to one or both of (i) an automobile, such as an autonomous or semi-autonomous vehicle, and (ii) an automobile operator, analyzing the information using the trained machine learning program to generate one or more risk indicators, determining, by analyzing the risk indicators, a risk level corresponding to the automobile, and/or displaying, to a user, a quotation based upon analyzing the risk indicators. The risk factors, risk indicators, and/or risk level may be used for many purposes, such as pricing, quoting, and/or underwriting of insurance policies.
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