Patents by Inventor Richard B. Jones
Richard B. Jones 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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Publication number: 20220277058Abstract: A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.Type: ApplicationFiled: May 16, 2022Publication date: September 1, 2022Applicant: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Publication number: 20220277232Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activityType: ApplicationFiled: January 10, 2022Publication date: September 1, 2022Applicant: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: RICHARD B. JONES
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Publication number: 20220195860Abstract: A method, apparatus and system is provided for assessing risk for well completion, comprising: obtaining, using an input interface, a Below Rotary Table hours and a plurality of well-field parameters for one or more planned runs, determining, using at least one processor, one or more non-productive time values that correspond to the one or more planned runs based upon the well-field parameters, developing, using at least one processor, a non-productive time distribution and a Below Rotary Table distribution via one or more Monte Carlo trials; and outputting, using a graphic display, a risk transfer model results based on a total BRT hours from the Below Rotary Table and the non-productive time distribution produced from the one or more Monte Carlo trials.Type: ApplicationFiled: August 5, 2021Publication date: June 23, 2022Applicant: THE HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Patent number: 11334645Abstract: A system and method is described herein for data filtering to reduce functional, and trend line outlier bias. Outliers are removed from the data set through an objective statistical method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line calculations. Outlier data records are removed when the error values are greater than or equal to the user-supplied criteria. For optimization methods or other iterative calculations, the removed data are re-applied each iteration to the model computing new results. Using model values for the complete dataset, new error values are computed and the outlier bias reduction procedure is re-applied. Overall error is minimized for model coefficients and outlier removed data in an iterative fashion until user defined error improvement limits are reached. The filtered data may be used for validation, outlier bias reduction and data quality operations.Type: GrantFiled: April 26, 2018Date of Patent: May 17, 2022Assignee: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Patent number: 11328177Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activityType: GrantFiled: March 18, 2021Date of Patent: May 10, 2022Assignee: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Patent number: 11288602Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activityType: GrantFiled: September 18, 2020Date of Patent: March 29, 2022Assignee: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Publication number: 20220092346Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activityType: ApplicationFiled: March 18, 2021Publication date: March 24, 2022Applicant: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: RICHARD B. JONES
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Patent number: 11275645Abstract: Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of, mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.Type: GrantFiled: April 2, 2020Date of Patent: March 15, 2022Inventors: Richard B. Jones, Dwayne L. Mason
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Patent number: 11261231Abstract: The application relates to methods of treating chronic viral infection by modulating Tim-3 activity. In addition, the present application relates to methods of diagnosing or monitoring immune system activity or function, chronic viral infection and inflammatory disease using Tim-3 expression.Type: GrantFiled: July 13, 2016Date of Patent: March 1, 2022Assignees: Altor BioScience, LLC., The Regents of the University of CaliforiaInventors: Richard B. Jones, Mario Ostrowski, Douglas F. Nixon, Lishomwa C. Ndhlovu, James Rini
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Publication number: 20210110313Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activityType: ApplicationFiled: September 18, 2020Publication date: April 15, 2021Applicant: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: RICHARD B. JONES
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Publication number: 20200233740Abstract: Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of, mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.Type: ApplicationFiled: April 2, 2020Publication date: July 23, 2020Inventors: Richard B. Jones, Dwayne L. Mason
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Publication number: 20200202451Abstract: In some embodiments, the present disclosure provides a network of multi-functional sensors; where, based on a quality insurance, each multi-functional sensor is positioned in, on, or in a vicinity of: a transported cargo and/or a cargo container, containing the transported cargo; where each multi-functional sensor is configured to measure particular transport-related condition, particular cargo-related condition, or both, to form cargo transport sensor data and wirelessly transmit it to a server that is configured to dynamically predict, based on the cargo transport sensor data, a predicted quality loss of the transported cargo, determine a current loss value of the transported cargo and cause one or more remedial actions that include instantaneously instructing to pay a payout amount to an owner of the transported cargo to compensate for the current loss value and/or transmitting a remedial instruction with an adjustment to the operation of one or more of a cargo transport, the cargo container, and a cargo sType: ApplicationFiled: March 5, 2020Publication date: June 25, 2020Inventors: Richard B. JONES, Stefan C. Froehlich, Ronald KARGL, Richard Walter BRIDGES, Christoph KALINSKI, Veenet MUTHRAJA
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Publication number: 20200182847Abstract: A system and method is described herein for performing at least one industrial process at each facility of a plurality of facilities based on an industrial process standard generated by reducing functional, and trend line outlier bias in data of one or more process parameters as measured by one or more sensors. Outliers are removed from the data set through an objective method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line estimates. Outlier data records are removed when the error values are greater than or equal to one or more criteria.Type: ApplicationFiled: February 10, 2020Publication date: June 11, 2020Applicant: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: RICHARD B. JONES
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Patent number: 10621674Abstract: In some embodiments, the present disclosure provides a network of multi-functional sensors; where, based on a quality insurance, each multi-functional sensor is positioned in, on, or in a vicinity of: a transported cargo and/or a cargo container, containing the transported cargo; where each multi-functional sensor is configured to measure particular transport-related condition, particular cargo-related condition, or both, to form cargo transport sensor data and wirelessly transmit it to a server that is configured to dynamically predict, based on the cargo transport sensor data, a predicted quality loss of the transported cargo, determine a current loss value of the transported cargo and cause one or more remedial actions that include instantaneously instructing to pay a payout amount to an owner of the transported cargo to compensate for the current loss value and/or transmitting a remedial instruction with an adjustment to the operation of one or more of a cargo transport, the cargo container, and a cargo sType: GrantFiled: October 12, 2018Date of Patent: April 14, 2020Assignee: Munich Reinsurance CompanyInventors: Richard B. Jones, Stefan C. Froehlich, Ronald Kargl, Richard Walter Bridges, Christoph Kalinski, Veenet Muthraja
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Publication number: 20200104651Abstract: In at least one embodiment, the present description is directed to a computer system, having at least components of a server, including a processor and a non-transient storage subsystem, storing a computer program including instructions that, when executed by the processor, cause the processor to at least: electronically receive a model for one or more operating conditions, one or more threshold criteria, and facility operating data for each respective facility of a plurality of facilities; validate the one or more threshold criteria to be one or more acceptable bias criteria; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the non-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.Type: ApplicationFiled: September 28, 2018Publication date: April 2, 2020Inventor: Richard B. Jones
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Patent number: 10557840Abstract: A system and method is described herein for performing at least one industrial process at each facility of a plurality of facilities based on an industrial process standard generated by reducing functional, and trend line outlier bias in data of one or more process parameters as measured by one or more sensors. Outliers are removed from the data set through an objective method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line estimates. Outlier data records are removed when the error values are greater than or equal to one or more criteria.Type: GrantFiled: October 4, 2018Date of Patent: February 11, 2020Assignee: HARTFORD STEAM BOILER INSPECTION and INSURANCE COMPANYInventor: Richard B. Jones
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Publication number: 20200004802Abstract: Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measurable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.Type: ApplicationFiled: September 10, 2019Publication date: January 2, 2020Inventor: Richard B. Jones
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Patent number: 10514977Abstract: Disclosed is a system and method for the analysis of event data that enables analysts to create user specified datasets in a dynamic fashion. Performance, equipment and system safety, reliability, and significant event analysis utilizes failure or performance data that are composed in part of time-based records. These data identify the temporal occurrence of performance changes that may necessitate scheduled or unscheduled intervention like maintenance events, trades, purchases, or other actions to take advantage of mitigate or compensate for the observed changes. The criteria used to prompt a failure or performance record can range from complete loss of function to subtle changes in performance parameters that are known to be precursors of more severe events. These specific criteria applied to any explicit specific application and this invention is relevant to this type of data taxonomy and can be applied across all areas in which event data may be collected.Type: GrantFiled: March 14, 2014Date of Patent: December 24, 2019Inventors: Richard B. Jones, Dwayne L. Mason
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Patent number: 10409891Abstract: Systems, methods, and apparatuses for improving future reliability prediction of a measurable system by receiving operational and performance data, such as maintenance expense data, first principle data, and asset reliability data via an input interface associated with the measurable system. A plurality of category values may be generated that categorizes the maintenance expense data by a designated interval using a maintenance standard that is generated from one or more comparative analysis models associated with the measureable system. The estimated future reliability of the measurable system is determined based on the asset reliability data and the plurality of category values and the results of the future reliability are displayed on an output interface.Type: GrantFiled: April 11, 2015Date of Patent: September 10, 2019Assignee: HARTFORD STEAM BOILER INSPECTION AND INSURANCE COMPANYInventor: Richard B. Jones
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Publication number: 20190271673Abstract: A system and method is described herein for performing at least one industrial process at each facility of a plurality of facilities based on an industrial process standard generated by reducing functional, and trend line outlier bias in data of one or more process parameters as measured by one or more sensors. Outliers are removed from the data set through an objective method. Bias is determined based on absolute, relative error, or both. Error values are computed from the data, model coefficients, or trend line estimates. Outlier data records are removed when the error values are greater than or equal to one or more criteria.Type: ApplicationFiled: October 4, 2018Publication date: September 5, 2019Applicant: HARTFORD STEAM BOILER INSPECTION and INSURANCE COMPANYInventor: RICHARD B. JONES