Patents by Inventor Mark Pond
Mark Pond has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11676164Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale auctiType: GrantFiled: May 3, 2022Date of Patent: June 13, 2023Assignee: Capital One Services, LLCInventors: Yingbo Li, Senthil Natarajan, Mark Pond, Amitayu Pal
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Publication number: 20220261828Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale auctiType: ApplicationFiled: May 3, 2022Publication date: August 18, 2022Applicant: Capital One Services, LLCInventors: Yingbo LI, Senthil NATARAJAN, Mark POND, Amitayu PAL
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Patent number: 11354689Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale auctiType: GrantFiled: March 30, 2020Date of Patent: June 7, 2022Assignee: Capital One Services, LLCInventors: Yingbo Li, Senthil Natarajan, Mark Pond, Amitayu Pal
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Publication number: 20210304235Abstract: A computer-implemented method for determining auction prices of vehicles may include obtaining wholesale auction price data indicative of wholesale auction prices and vehicle attribute values of a plurality of vehicles; grouping the plurality of vehicles into a plurality of vehicle groups respectively associated with a plurality of characteristics; determining a plurality of regression models respectively based on the plurality of vehicle groups; obtaining information describing a target vehicle; based on the target-vehicle values and values for the one or more economic indicators, determining an estimated wholesale auction price of the target vehicle using an identified regression model of the plurality of regression models; determining a discount factor representing an expected discount of a post-repossession auction price of the target vehicle from the estimated wholesale auction price; and determining an estimated post-repossession auction price of the target vehicle based on the estimated wholesale auctiType: ApplicationFiled: March 30, 2020Publication date: September 30, 2021Applicant: Capital One Services, LLCInventors: Yingbo Li, Senthil NATARAJAN, Mark POND, Amitayu PAL
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Publication number: 20180165361Abstract: A system includes a processor and memory comprising executable instructions that cause the processor to effectuate operations. The operations include determining that a network resource has been implemented using a network element indicated by a data structure of a graph database comprising a network inventory. The operations also include establishing a first level model based on at least the data structure and a second level model indicative of the network resource. The operations include defining a topology of the second level model, wherein the topology comprises the first level model. The operations also include defining a persona associated with the first level model, the persona linking the first level model to the second level model.Type: ApplicationFiled: December 9, 2016Publication date: June 14, 2018Inventors: Richard R. Erickson, Lynn Williams, Mark Pond, Andrew Muller, Chesla Catherine Wechsler, Andrew Baxter, James Forsyth
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Patent number: 8135808Abstract: An automated provisioning system for provisioning a service or device in a communications network, which system comprises a listener (2) adapted to receive a request from a user and a driver adapted to control operation of the service or device. The driver supports a describe operation, which enumerates which operations are available for the user and co-operates with a control processor (22), which mediates between the driver and a data store holding information on which operations are available for the user. When a request to provision an available operation is received, the control processor (22) mediates between the data store and driver to determine what information is required to provision the service or device and to permit the driver to provision the service or device. The system dispenses with the table driven approach of known systems.Type: GrantFiled: June 2, 2006Date of Patent: March 13, 2012Assignee: Jacobs Rimell LimitedInventors: Keith Sterling, Mark Pond, David Jacobs
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Publication number: 20090172134Abstract: An automated provisioning system for provisioning a service or device in a communications network, which system comprises a listener (2) adapted to receive a request from a user and a driver adapted to control operation of the service or device. The driver supports a describe operation, which enumerates which operations are available for the user and co-operates with a control processor (22), which mediates between the driver and a data store holding information on which operations are available for the user. When a request to provision an available operation is received, the control processor (22) mediates between the data store and driver to determine what information is required to provision the service or device and to permit the driver to provision the service or device. The system dispenses with the table driven approach of known systems.Type: ApplicationFiled: June 2, 2006Publication date: July 2, 2009Applicant: JACOBS RIMELL LIMITEDInventors: Keith Sterling, Mark Pond, David Jacobs