US 11,899,733 B2
Method and system for activity prediction, prefetching and preloading of computer assets by a client-device
Michael Shalai, New York, NY (US); Joseph Catalano, Melville, NY (US); Bo Lin, New York, NY (US); Dustin Zelle, New York, NY (US); and Rami Al-Rfou, Mountain View, CA (US)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Appl. No. 17/792,965
Filed by Google LLC, Mountain View, CA (US)
PCT Filed Jan. 14, 2020, PCT No. PCT/US2020/013488
§ 371(c)(1), (2) Date Jul. 14, 2022,
PCT Pub. No. WO2021/145862, PCT Pub. Date Jul. 22, 2021.
Prior Publication US 2023/0050882 A1, Feb. 16, 2023
Int. Cl. G06F 7/00 (2006.01); G06F 16/957 (2019.01)
CPC G06F 16/9574 (2019.01) 20 Claims
OG exemplary drawing
 
1. A computer implemented method for activity prediction, the method comprising:
building, by a MLM builder associated with the computing device, a machine learning model using activity log data received by the MLM builder, wherein the MLM builder extracts activity information and groups the extracted activity information by session;
training, by the computing device, the machine learning model by applying training activity datasets to the machine learning model, wherein the machine learning model is continuously trained to serve an up-to-date machine learning model;
converting, by the computing device, the machine learning model into a web browser compatible format; and
uploading, by the computing device, the machine learning model to a server that is arranged to deploy the machine learning model to a plurality of communicating devices, wherein the machine learning model is arranged to:
receive, by a machine learning model, as input a sequence of one or more prior activities on one communicating device of a plurality of communicating devices, the sequence comprising the one or more prior activities during a session in chronological order, the one or more prior activities comprising at least one of page transitions, button clicks, or remote procedure calls (RPCs);
analyze, by the machine learning model, the sequence of one or more prior activities on the one communicating device, wherein the analyzing includes calculating relevance weights for search results, wherein the relevance weights are indicative of a relevance to prediction data, and identifying the results having the greatest relevance to the prediction data;
predict, by the machine learning model, a next activity on the one communicating device based on the analysis of the sequence of one or more prior activities;
search a computer network based on the predicted next activity to find a computer asset, wherein the computer asset comprises a next application; and
preload the computer asset to a storage in the one communicating device.