View source for Google llc (20250004797). Training Pipeline for Training Machine-Learned User Interface Customization Models

=Training Pipeline for Training Machine-Learned User Interface Customization Models=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Lisa Michelle Jakobovits of San Carlos CA (US)|Lisa Michelle Jakobovits of San Carlos CA (US)]][[Category:Lisa Michelle Jakobovits of San Carlos CA (US)]]

[[:Category:Jonathan James Mulligan of San Francisco CA (US)|Jonathan James Mulligan of San Francisco CA (US)]][[Category:Jonathan James Mulligan of San Francisco CA (US)]]

[[:Category:Mert Dikmen of Belmont CA (US)|Mert Dikmen of Belmont CA (US)]][[Category:Mert Dikmen of Belmont CA (US)]]

[[:Category:Xinlong Bao of Los Altos CA (US)|Xinlong Bao of Los Altos CA (US)]][[Category:Xinlong Bao of Los Altos CA (US)]]

[[:Category:John Chen of Mountain View CA (US)|John Chen of Mountain View CA (US)]][[Category:John Chen of Mountain View CA (US)]]

[[:Category:Jing Wang of Mountain View CA (US)|Jing Wang of Mountain View CA (US)]][[Category:Jing Wang of Mountain View CA (US)]]

==Training Pipeline for Training Machine-Learned User Interface Customization Models==


This abstract first appeared for US patent application 20250004797 titled 'Training Pipeline for Training Machine-Learned User Interface Customization Models




==Original Abstract Submitted==

example embodiments of the present disclosure provide for an example method. the example method includes obtaining session data descriptive of a plurality of user sessions, the plurality of user sessions respectively including an interaction with an input element rendered at a user device and a request for a resource associated with the input element. the example method includes obtaining, using a first machine-learned model, a plurality of weights associated with the plurality of user sessions by, for a respective user session of the plurality of user sessions: inputting, to the first machine-learned model, data descriptive of one or more characteristics of the respective user session; and obtaining, from the first machine-learned model, a respective weight of the plurality of weights, the respective weight indicative of an incremental probability of the request conditioned on rendering of the input element. the example method includes updating, based on the plurality of weights, a second machine-learned model to optimize candidate proposals for participation in a real-time content selection process for populating a user interface with one or more selected input elements.



[[Category:G06F9/451]]
[[Category:G06N20/00]]
[[Category:CPC_G06F9/451]]