US 12,242,936 B2
Automated comprehension and interest-based optimization of content
Jacob M. Hofman, New York, NY (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
Filed on Oct. 31, 2023, as Appl. No. 18/385,559.
Application 18/385,559 is a continuation of application No. 15/620,400, filed on Jun. 12, 2017, granted, now 11,842,251.
Prior Publication US 2024/0062110 A1, Feb. 22, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 20/00 (2019.01); G06F 16/44 (2019.01); G06N 5/022 (2023.01)
CPC G06N 20/00 (2019.01) [G06F 16/44 (2019.01); G06N 5/022 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system, comprising:
at least one processor; and
a memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:
obtaining user content;
generating, for each segment of one or more segments of the user content using a machine-learned relevancy model, a predicted level of interest;
generating a suggested formatting change for a segment of the one or more segments that corresponds to a modification of the predicted level of interest for the segment;
providing, for user selection via a user interface, the suggested formatting change with an indicator of a predicted change in level of interest, wherein the predicted change in level of interest comprises an increase or a decrease; and
responsive to receiving a selection of the suggested formatting change via the user interface:
applying the suggested formatting change to at least one associated segment of the user content;
generating an updated predicted level of interest; and
providing, via the user interface, an indication of the updated predicted level of interest.