Patents by Inventor Patrick Hayes

Patrick Hayes 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).

  • Publication number: 20220121993
    Abstract: A disclosed example includes implementing a first worker instance and a second worker instance to operate in parallel running a first tuning operation via the first worker instance to tune first hyperparameters; running a second tuning operation via the second worker instance using a Bayesian-based optimization to determine a hyperparameter configuration to evaluate next; evaluating the hyperparameter configuration for an external model using a surrogate model; and selecting the hyperparameter configuration for the external model.
    Type: Application
    Filed: December 23, 2021
    Publication date: April 21, 2022
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Publication number: 20220114450
    Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.
    Type: Application
    Filed: October 22, 2021
    Publication date: April 14, 2022
    Inventors: Michael McCourt, Taylor Jackie Springs, Ben Hsu, Simon Howey, Halley Nicki Vance, James Blomo, Patrick Hayes, Scott Clark
  • Patent number: 11301781
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Grant
    Filed: February 20, 2020
    Date of Patent: April 12, 2022
    Assignee: Intel Corporation
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Publication number: 20220107850
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Application
    Filed: November 1, 2021
    Publication date: April 7, 2022
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 11274267
    Abstract: The present disclosure relates generally to cleaning compositions and, more specifically, to cleaning compositions containing an etheramine that is suitable for removal of stains from soiled materials.
    Type: Grant
    Filed: February 20, 2020
    Date of Patent: March 15, 2022
    Assignee: The Procter & Gamble Company
    Inventors: Stefano Scialla, Brian Joseph Loughnane, Karie Marie Henke, Gayle Marie Frankenbach, J. Frank Nash, Jr., Michael Patrick Hayes, Manuel G. Venegas, Bjoern Ludolph, Sophia Rosa Ebert, Christian Eidamshaus, Jan Richard Davis, Neil Thomas Fairweather
  • Patent number: 11270217
    Abstract: Systems and methods include receiving a tuning work request for tuning hyperparameters of a third-party model or system; performing, by a machine learning-based tuning service, a first tuning of the hyperparameters in a first tuning region; identifying tuned hyperparameter values for each of the hyperparameters based on results of the first tuning; setting a failure region based on the tuned hyperparameter values of the first tuning; performing, by the machine learning-based tuning service, a second tuning of the hyperparameters in a second tuning region that excludes the failure region; identifying additional tuned hyperparameter values for each of the hyperparameters based on results of the second tuning; and returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party model or system with one of the tuned hyperparameter values and the additional hyperparameter values.
    Type: Grant
    Filed: November 16, 2018
    Date of Patent: March 8, 2022
    Assignee: Intel Corporation
    Inventors: Kevin Tee, Michael McCourt, Patrick Hayes, Scott Clark
  • Patent number: 11268047
    Abstract: The present disclosure relates generally to cleaning compositions and, more specifically, to cleaning compositions containing an etheramine that is suitable for removal of stains from soiled materials.
    Type: Grant
    Filed: February 21, 2020
    Date of Patent: March 8, 2022
    Assignee: The Procter & Gamble Company
    Inventors: Stefano Scialla, Brian Joseph Loughnane, Karie Marie Henke, J. Frank Nash, Jr., Michael Patrick Hayes, Bjoern Ludolph, Sophia Rosa Ebert, Christian Eidamhaus
  • Patent number: 11163615
    Abstract: Systems and methods for implementing an application programming interface (API) that controls operations of a machine learning tuning service for tuning a machine learning model for improved accuracy and computational performance includes an API that is in control communication the tuning service that: executes a first API call function that includes an optimization work request that sets tuning parameters for tuning hyperparameters of a machine learning model; and initializes an operation of distinct tuning worker instances of the service that each execute distinct tuning tasks for tuning the hyperparameters; executes a second API call function that identifies raw values for the hyperparameters; and generates suggestions comprising proposed hyperparameter values selected from the plurality of raw values for each of the hyperparameters; and executes a third API call function that returns performance metrics relating to a real-world performance of the subscriber machine learning model executed with the propose
    Type: Grant
    Filed: January 14, 2020
    Date of Patent: November 2, 2021
    Assignee: Intel Corporation
    Inventors: Alexandra Johnson, Patrick Hayes, Scott Clark
  • Patent number: 11157812
    Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.
    Type: Grant
    Filed: April 15, 2020
    Date of Patent: October 26, 2021
    Assignee: Intel Corporation
    Inventors: Michael McCourt, Taylor Jackie Springs, Ben Hsu, Simon Howey, Halley Nicki Vance, James Blomo, Patrick Hayes, Scott Clark
  • Publication number: 20210292619
    Abstract: The invention is related to fast setting, bio-based hot melt adhesive compositions and use thereof. The bio-based hot melt adhesive compositions are based on renewable-based feedstock, making them environmentally friendly, and particularly suitable for sealing cardboard case, carton, and cardboards.
    Type: Application
    Filed: June 10, 2021
    Publication date: September 23, 2021
    Inventors: Patrick HAYES, Michael Harwell, Jennifer Plummer
  • Publication number: 20210142224
    Abstract: Systems and methods for an accelerated tuning of hyperparameters of a model supported with prior learnings data include assessing subject models associated with a plurality of distinct sources of transfer tuning data, wherein the assessing includes implementing of: [1] a model relatedness assessment for each of a plurality of distinct pairwise subject models, and [2] a model coherence assessment for each of the plurality of distinct pairwise subject models; constructing a plurality of distinct prior mixture models based on the relatedness metric value and the coherence metric value for each of the plurality of distinct pairwise subject models, identifying sources of transfer tuning data based on identifying a distinct prior mixture model having a satisfactory model evidence fraction; and accelerating a tuning of hyperparameters of the target model based on transfer tuning data associated with the distinct prior mixture model having the satisfactory model evidence fraction.
    Type: Application
    Filed: October 15, 2020
    Publication date: May 13, 2021
    Inventors: Michael McCourt, Ben Hsu, Patrick Hayes, Scott Clark
  • Publication number: 20210034924
    Abstract: Systems and methods for tuning hyperparameters of a model include receiving a tuning request for tuning hyperparameters, the tuning request includes a first and a second objective function for the machine learning model. The first and second objective functions may output metric values that do not improve uniformly. Systems and methods additionally include defining a joint tuning function that is based on a combination of the first and second objective functions; executing a tuning operation; identifying a Pareto efficient frontier curve defined by a plurality of distinct hyperparameter values; applying metric thresholds to the Pareto efficient frontier curve; demarcating the Pareto efficient frontier curve into at least a first infeasible section and a second feasible section; searching the second feasible section of the Pareto efficient frontier curve for one or more proposed hyperparameter values; and identifying at least a first set of proposed hyperparameter values based on the search.
    Type: Application
    Filed: July 30, 2020
    Publication date: February 4, 2021
    Inventors: Michael McCourt, Bolong Cheng, Taylor Jackle Spriggs, Halley Vance, Olivia Kim, Ben Hsu, Sarth Frey, Patrick Hayes, Scott Clark
  • Publication number: 20200327412
    Abstract: A system and method for tuning hyperparameters and training a model includes implementing a hyperparameter tuning service that tunes hyperparameters of a model that includes receiving, via an API, a tuning request that includes: (i) a first part comprising tuning parameters for generating tuned hyperparameter values for hyperparameters of the model; and (ii) a second part comprising model training control parameters for monitoring and controlling a training of the model, wherein the model training control parameters include criteria for generating instructions for curtailing a training run of the model; monitoring the training run for training the model based on the second part of the tuning request, wherein the monitoring of the training run includes periodically collecting training run data; and computing an advanced training curtailment instruction based on the training run data that automatically curtails the training run prior to a predefined maximum training schedule of the training run.
    Type: Application
    Filed: April 15, 2020
    Publication date: October 15, 2020
    Inventors: Michael McCourt, Taylor Jackie Springs, Ben Hsu, Simon Howey, Halley Nicki Vance, James Blomo, Patrick Hayes, Scott Clark
  • Patent number: 10793757
    Abstract: Predominately bio-sourced polymer compositions that may be used as hot-melt adhesives are disclosed herein. The polymer compositions may contain a polymer that has the following structure: G1 and G2 are independently (CH2)x. Variable x is an integer ranging between 1 and 10. Variable y is an integer ranging between about 50 and about 500. Variable z is an integer ranging between about 100 and about 600. Variable n is an integer ranging between about 5 and about 10,000. X is H, a functionalized alkylene polymer block containing alcohol functional groups, or a mixture thereof. Y is H, an acyl group, a functionalized alkylene polymer containing carboxylic acid functional groups, or a mixture thereof.
    Type: Grant
    Filed: November 16, 2016
    Date of Patent: October 6, 2020
    Assignees: Henkel IP & Holding GmbH, Danimer Bioplastics, Inc.
    Inventors: Patrick Hayes, Daniel Carraway, Steven Wann, Rachelle Arnold, Joe B. Grubbs, III
  • Publication number: 20200302342
    Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.
    Type: Application
    Filed: June 8, 2020
    Publication date: September 24, 2020
    Inventors: Bolong Cheng, Olivia Kim, Michael McCourt, Patrick Hayes, Scott Clark
  • Patent number: 10740695
    Abstract: Systems and methods for tuning hyperparameters of a model includes: receiving a multi-criteria tuning work request for tuning hyperparameters of the model of the subscriber to the remote tuning service, wherein the multi-criteria tuning work request includes: a first objective function of the model to be optimized by the remote tuning service; a second objective function to be optimized by the remote tuning service, the second objective function being distinct from the first objective function; computing a joint tuning function based on a combination of the first objective function and the second objective function; executing a tuning operation of the hyperparameters for the model based on a tuning of the joint function; and identifying one or more proposed hyperparameter values based on one or more hyperparameter-based points along a convex Pareto optimal curve.
    Type: Grant
    Filed: November 26, 2019
    Date of Patent: August 11, 2020
    Assignee: SigOpt, Inc.
    Inventors: Bolong Cheng, Olivia Kim, Michael McCourt, Patrick Hayes, Scott Clark
  • Publication number: 20200202254
    Abstract: A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.
    Type: Application
    Filed: February 20, 2020
    Publication date: June 25, 2020
    Inventors: Patrick Hayes, Michael McCourt, Alexandra Johnson, George Ke, Scott Clark
  • Patent number: 10691922
    Abstract: A system detecting counterfeit items based on machine learning and analysis of visual and textual data is disclosed. The system may comprise a data access interface to receive product data associated with a protected product from a user device. The product data may comprise multimodal data that describes the protected product. The system may also comprise a search term generator to generate search terms based on the received product data. The system may comprise a processor to identify one or more potential counterfeit items from the at least one web source using a crawling technique to obtain data associated with to similar products from the at least one web source, identifying at least one match for similar products, and using image processing and analysis to determine if the at least one match for similar products comprises at least one or more potential counterfeit items.
    Type: Grant
    Filed: May 17, 2018
    Date of Patent: June 23, 2020
    Assignee: ACCENTURE GLOBAL SOLUTIONS LIMITED
    Inventors: Julianne Madeleine Chaloux, Jeremiah Patrick Hayes, Mohit Aggarwal, Penelope Daphne Tsatsoulis
  • Publication number: 20200190434
    Abstract: The present disclosure relates generally to cleaning compositions and, more specifically, to cleaning compositions containing an etheramine that is suitable for removal of stains from soiled materials.
    Type: Application
    Filed: February 21, 2020
    Publication date: June 18, 2020
    Inventors: Stefano Scialla, Brian Joseph Loughnane, Karie Marie Henke, J. Frank Nash, JR., Michael Patrick Hayes, Bjoern Ludolph, Sophia Rosa Ebert, Christian Eidamhaus
  • Publication number: 20200193079
    Abstract: The present disclosure relates generally to cleaning compositions and, more specifically, to cleaning compositions containing an etheramine that is suitable for removal of stains from soiled materials.
    Type: Application
    Filed: February 20, 2020
    Publication date: June 18, 2020
    Inventors: Stefano SCIALLA, Brian Joseph LOUGHNANE, Karie Marie HENKE, Gayle Marie FRANKENBACH, J. Frank NASH, JR., Michael Patrick HAYES, Manuel G. VENEGAS, Bjoern LUDOLPH, Sophia Rosa EBERT, Christian EIDAMSHAUS, Jan Richard DAVIS, Neil Thomas FAIRWEATHER