US 11,934,967 B2
Providing component recommendation using machine learning
Jayaprakash Vijayan, Dublin, CA (US); Ved Surtani, Gurgaon (IN); Nitika Gupta, Bengaluru (IN); Pratheek Manjunath Bharadwaj, Mysore (IN); and Indrajit Saha, Panagarh Bazar (IN)
Assigned to Tekion Corp, Pleasanton, CA (US)
Filed by Tekion Corp, Pleasanton, CA (US)
Filed on Mar. 22, 2023, as Appl. No. 18/125,028.
Application 18/125,028 is a continuation of application No. 17/403,477, filed on Aug. 16, 2021.
Prior Publication US 2023/0222365 A1, Jul. 13, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 30/0601 (2023.01); G06N 5/04 (2023.01); G06N 20/00 (2019.01); G06Q 10/20 (2023.01); G06Q 30/016 (2023.01); G06Q 30/0202 (2023.01)
CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01); G06Q 10/20 (2013.01); G06Q 30/016 (2013.01); G06Q 30/0202 (2013.01); G06Q 30/0631 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating recommended components for an object of a user comprising:
accessing, by a management system, a first plurality of historical component entries of a plurality of different entities, wherein each of the first plurality of historical component entries includes an identifier of a component of an object previously purchased by a respective user and an identifier of a service previously performed on the object at a respective entity from the plurality of different entities after the purchase of the object, the component of the object acquired in conjunction with the service being previously performed on the object;
training, by the management system, a machine learning model using training data that is based at least in part on the identifier of the component of the object and the identifier of the service included in each of the first plurality of historical component entries, the trained machine learning model configured to predict for each of a plurality of predetermined component classifications a likelihood of selection of a component corresponding to one of the plurality of predetermined component classifications;
receiving, by the management system, a request for a service to be performed on the object previously purchased by the user at an entity from the plurality of different entities, the request including attributes of the object that was previously purchased by the user and a specific service for the entity to perform on the object at the entity;
applying, by the management system, the attributes of the object to the trained machine learning model responsive to the request, the trained machine learning model outputting for each of the plurality of predetermined component classifications a prediction of the likelihood of user selection of the component corresponding to the predetermined component classification based on the attributes of the object applied to the trained machine learning model;
determining, by the management system, a recommended set of components for the object that are provided by the entity based on the prediction for each of the plurality of predetermined componenet classications; and
providing, by the management system, the recommended set of components for the object to acquire by the user in conjunction with the specific service that was requested by the user being performed on the object at the entity along with a response to the request for the service.