IP Library › Granted Patent US 12711043
Granted Patent B1
US 12711043 · App. 18/212,098 · Granted Aug 18, 2026

Issue impact prediction

Inventors: Neilesh Chorakhalikar (San Jose, CA); Manas Joshi (Santa Clara, CA); Arunachalam Arunachalam (Austin, TX); Bipin Prabhakar Todur (Santa Clara, CA)
Assignee: NVIDIA Corporation
G06F11/3672
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Quick Facts
Patent No.
US 12711043
App. No.
18/212,098
Granted
Aug 18, 2026
Kind
B1
Abstract

Apparatuses, systems, and techniques to identify users or sessions impacted by one or more issues of one or more software programs, based, at least in part, on characteristics shared by the users and other users of the programs who have indicated the one or more issues.

Claims (31)

1 . A processor comprising:

one or more circuits to perform software to:

identify, as users impacted by one or more problems, one or more first users who have experienced the one or more problems with using one or more software programs and who have not been identified as having provided feedback indicating an occurrence of the one or more problems, based, at least in part, on:

a determination that a number of second users is at least a calculated threshold number, wherein the second users share one or more characteristics and have been identified as having provided feedback indicating the one or more problems with using the one or more software programs; and

a determination that the one or more first users who have not been identified as having provided feedback share the one or more characteristics with the second users who have been identified as having provided feedback.

2 . The processor of claim 1 , wherein the one or more characteristics comprise session information of one or more sessions of the one or more software programs.

3 . The processor of claim 1 , wherein the one or more circuits are to identify the one or more characteristics based on clustering feedback information of the second users of the one or more programs who have indicated the one or more problems.

4 . The processor of claim 1 , wherein the calculated threshold number of the second users is determined using one or more values of the one or more characteristics observed over an interval of time.

5 . The processor of claim 1 , wherein the one or more characteristics include at least one of a region, title, or device associated with the one or more software programs.

6 . The processor of claim 1 , wherein the one or more first users are identified, based, at least in part, predicting a number of impacted users during a period of time associated with the one or more problems.

7 . The processor of claim 1 , wherein the one or more circuits use one or more machine learning models to identify the one or more first users who have experienced the one or more problems.

8 . A method comprising:

identifying, as users impacted by one or more problems, one or more first users who have experienced the one or more problems with using one or more software programs and who have not been identified as having provided feedback indicating an occurrence of the one or more problems, based, at least in part, on:

determining that a number of second users is at least a calculated threshold number, wherein the second users share one or more characteristics and have been identified as having provided feedback indicating the one or more problems with using the one or more software programs; and

determining that the one or more first users who have not been identified as having provided feedback share the one or more characteristics with the second users who have been identified as having provided feedback.

9 . The method of claim 8 , wherein the one or more characteristics comprise session information of one or more sessions of the one or more software programs.

10 . The method of claim 8 , further comprising identifying the one or more characteristics based on clustering feedback information of the second users of the one or more programs who have indicated the one or more problems.

11 . The method of claim 8 , wherein the calculated threshold number of the second users is determined using one or more values of the one or more characteristics observed over an interval of time.

12 . The method of claim 8 , wherein the one or more characteristics include at least one of a region, title, or device associated with the one or more software programs.

13 . The method of claim 8 , wherein the one or more first users are identified, based, at least in part, predicting a number of impacted users during a period of time associated with the one or more problems.

14 . The method of claim 8 , using one or more machine learning models to identify the one or more first users who have experienced the one or more problems.

15 . A system comprising:

one or more processors to:

identify, as users impacted by one or more problems, one or more first users who have experienced the one or more problems with using one or more software programs and who have not been identified as having provided feedback indicating an occurrence of the one or more problems, based, at least in part, on:

a determination that a number of second users is at least a calculated threshold number, wherein the second users share one or more characteristics and have been identified as having provided feedback indicating the one or more problems with using the one or more software programs; and

a determination that the one or more first users who have not been identified as having provided feedback share the one or more characteristics with the second users who have been identified as having provided feedback.

16 . The system of claim 15 , wherein the one or more characteristics comprise session information of one or more sessions of the one or more software programs.

17 . The system of claim 15 , wherein the one or more processors are to identify the one or more characteristics based on clustering feedback information of the second users of the one or more programs who have indicated the one or more problems.

18 . The system of claim 15 , wherein the calculated threshold number of the second users is determined using one or more values of the one or more characteristics observed over an interval of time.

19 . The system of claim 15 , wherein the one or more characteristics include at least one of a region, title, or device associated with the one or more software programs.

20 . The system of claim 15 , wherein the one or more first users are identified, based, at least in part, predicting a number of impacted users during a period of time associated with the one or more problems.