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Patent Agency Ranking
公开(公告)号：US10943464B1
公开(公告)日：2021-03-09
申请号：US16668072
申请日：2019-10-30
Inventor： Gregory L. Hayward , Meghan Sims Goldfarb , Nicholas U. Christopulos , Erik Donahue
Abstract: Machine learning systems, methods, and techniques for detecting damage and/or other conditions associated with a building, land, structure, or other real property using a real property monitoring system are disclosed. The property monitoring system is used in conjunction with machine learning techniques to determine and/or predict various conditions associated with the real property, including particular damage thereto, e.g., based upon dynamic characteristic data obtained via on-site sensors, static characteristic data, third-party input descriptive of an event impacting the building, etc. Accordingly, damage and/or loss associated with the building/real property is more quickly and/or accurately ascertained so that suitable mitigation techniques may be applied. In some scenarios, previously undetectable or uncharacterized damage and/or other conditions may be discovered and mitigated.
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公开(公告)号：US20220303355A1
公开(公告)日：2022-09-22
申请号：US17715934
申请日：2022-04-07
Inventor： 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: H04L67/51 , G06F16/9535
Abstract: 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：US20220284517A1
公开(公告)日：2022-09-08
申请号：US17752702
申请日：2022-05-24
Inventor： Gregory L. Hayward , Meghan Sims Goldfarb , Nicholas U. Christopulos , Erik Donahue
IPC: G06Q40/08 , G06N3/08 , G06V30/194 , G06V20/00
Abstract: 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：US20210398227A1
公开(公告)日：2021-12-23
申请号：US17466722
申请日：2021-09-03
Inventor： Gregory L. Hayward , Meghan Sims Goldfarb , Nicholas U. Christopulos , Erik Donahue
Abstract: 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：US10536536B1
公开(公告)日：2020-01-14
申请号：US15808575
申请日：2017-11-09
Inventor： 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: H04L29/08 , G06F16/9535
Abstract: 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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