Patents by Inventor Diego Alvaro Goyret

Diego Alvaro Goyret 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).

  • Patent number: 11430040
    Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform tracking respective duration data for each respective item of items in a catalog based on purchase histories; measuring a reorder rate for the each respective item within one or more first periods of time; generating a Weibull distribution for the each respective item; training a machine learning model based on previous orders by the users; generating, using the machine learning model, as trained, a ranked list of one or more first items for a user of the users, a respective predicted quantity for each of the one or more first items, and an average basket size for the user; receiving a request for recommended items from the user using a user interface; and sending the ranked list of the one or more first items to be displayed on the user interface. Other embodiments are disclosed.
    Type: Grant
    Filed: January 29, 2020
    Date of Patent: August 30, 2022
    Assignee: WALMART APOLLO, LLC
    Inventors: Kannan Govindan, Deepak Arora, Varun Srivastava, Diego Alvaro Goyret, Prashant Gupta
  • Publication number: 20210233148
    Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform tracking respective duration data for each respective item of items in a catalog based on purchase histories; measuring a reorder rate for the each respective item within one or more first periods of time; generating a Weibull distribution for the each respective item; training a machine learning model based on previous orders by the users; generating, using the machine learning model, as trained, a ranked list of one or more first items for a user of the users, a respective predicted quantity for each of the one or more first items, and an average basket size for the user; receiving a request for recommended items from the user using a user interface; and sending the ranked list of the one or more first items to be displayed on the user interface. Other embodiments are disclosed.
    Type: Application
    Filed: January 29, 2020
    Publication date: July 29, 2021
    Applicant: Walmart Apollo, LLC
    Inventors: Kannan Govindan, Deepak Arora, Varun Srivastava, Diego Alvaro Goyret, Prashant Gupta
  • Patent number: 11030652
    Abstract: This application relates generally to systems and methods for identification of facets for associated items in a datastore. In an embodiment, a method performed by a computing device includes: receiving, from a supplier device, a first item dataset characterizing a first item purchasable on a sales platform. The method also includes associating the first item with a facet in accordance with an instruction from an internal merchant device, where the facet is separate from the first item dataset, and where the facet may be selected to differentiate the first item from a second item purchasable on the sales platform. The method also includes retrieving the facet from a datastore in response to an item request from a customer device. The method also includes retrieving the first item dataset from a datastore in response to a selection of the facet from the customer device.
    Type: Grant
    Filed: January 22, 2019
    Date of Patent: June 8, 2021
    Assignee: Walmart Apollo, LLC
    Inventors: Raghavendra Sarangapurkar, Phanindra Vuppalapati, Michael Jayson C. De Leon, Diego Alvaro Goyret, Siddarth Vasant Gaonkar, Zuzar F. Nafar, Rohit Deep
  • Publication number: 20200234334
    Abstract: This application relates generally to systems and methods for identification of facets for associated items in a datastore. In an embodiment, a method performed by a computing device includes: receiving, from a supplier device, a first item dataset characterizing a first item purchasable on a sales platform. The method also includes associating the first item with a facet in accordance with an instruction from an internal merchant device, where the facet is separate from the first item dataset, and where the facet may be selected to differentiate the first item from a second item purchasable on the sales platform. The method also includes retrieving the facet from a datastore in response to an item request from a customer device. The method also includes retrieving the first item dataset from a datastore in response to a selection of the facet from the customer device.
    Type: Application
    Filed: January 22, 2019
    Publication date: July 23, 2020
    Inventors: Raghavendra Sarangapurkar, Phanindra Vuppalapati, Michael Jayson C. De Leon, Diego Alvaro Goyret, Siddarth Vasant Gaonkar, Zuzar F. Nafar, Rohit Deep