US 12,242,792 B2
Presenting intelligently suggested content enhancements
Erez Kikin Gil, Bellevue, WA (US); and Benjamin David Smith, Woodinville, WA (US)
Assigned to Microsoft Technology Licensing, LLC, Redmond, WA (US)
Appl. No. 17/791,260
Filed by Microsoft Technology Licensing, LLC, Redmond, WA (US)
PCT Filed Jan. 8, 2021, PCT No. PCT/US2021/012684
§ 371(c)(1), (2) Date Jul. 7, 2022,
PCT Pub. No. WO2021/142248, PCT Pub. Date Jul. 15, 2021.
Claims priority of application No. 2024634 (NL), filed on Jan. 9, 2020.
Prior Publication US 2023/0351091 A1, Nov. 2, 2023
Int. Cl. G06F 17/00 (2019.01); G06F 40/106 (2020.01); G06F 40/166 (2020.01)
CPC G06F 40/106 (2020.01) [G06F 40/166 (2020.01)] 18 Claims
OG exemplary drawing
 
1. A data processing system comprising:
a processor; and
a memory in communication with the processor, the memory comprising executable instructions that, when executed by, the processor, cause the data processing system to perform functions of:
examining content of a document to identify a plurality of portions of content in the document, wherein for each of the plurality of portions of content a suggested visual enhancement can be identified;
identifying the suggested visual enhancement for each of the plurality of portions of content;
enabling display of a first user interface element for previewing a plurality of suggested visual enhancements as applied to the content, each of the plurality of suggested visual enhancements being one of the identified suggested visual enhancements;
receiving a request to select one of the identified suggested visual enhancements; and
upon receiving the request, enabling display of a second user interface element for accepting the identified suggested visual enhancement,
wherein the first user interface element provides a preview of the document post enhancement, and
wherein the instructions further cause the processor to cause the data processing system to perform functions of:
collecting user feedback data regarding rejection or acceptance of the identified suggested visual enhancement,
ensuring that the user feedback data is privacy compliant;
storing the user feedback data; and
using the user feedback data in training a machine-learning model for identifying suggested visual enhancements.