Granted Patent B2

US 12,572,384 · App. 18/138,605 · Granted Mar 10, 2026

Inventor: Ravi S. Jonnalagadda (Waltham, MA)

Assignee: Hexagon Technology Center GmbH

G06F9/4881

Abstract

Claims (50)

1 . A method, comprising:

executing, using a computing system including a processor and a memory operatively coupled with the processor, a plurality of tasks of a collaborative process for a plurality of iterations, the plurality of tasks including a user-system conditioning task;

determining a plurality of scenario characteristics and a plurality of outcomes in response to executing the plurality of tasks for the plurality of iterations;

generating a first machine learning model using the plurality of scenario characteristics and the plurality of outcomes;

inputting a first scenario characteristic of the plurality of scenario characteristics into the first machine learning model;

outputting a first proficiency score from the first machine learning model, wherein the first proficiency score corresponds to a likelihood a user will complete a first task of the plurality of tasks;

adjusting the user-system conditioning task in response to the first proficiency score;

executing, using the computing system, the collaborative process including the adjusted user-system conditioning task;

generating a second machine learning model using the plurality of scenario characteristics and the plurality of outcomes;

inputting a second scenario characteristic of the plurality of scenario characteristics into the second machine learning model; and

outputting a second proficiency score from the second machine learning model,

wherein the first proficiency score corresponds to a user and the first user-system conditioning task,

wherein the plurality of tasks includes the second user-system conditioning task of the collaborative process, and

wherein the second proficiency score corresponds to the user and the second user-system conditioning task;

adjusting the second user-system conditioning task in response to the second proficiency score; and

executing, using the computing system, the collaborative process including the second adjusted user-system conditioning task.

2 . The method of claim 1 , wherein executing the collaborative process including the adjusted user-system conditioning task includes removing all tasks after the adjusted user-system conditioning task in response to adjusting the user-system conditioning task.

3 . The method of claim 1 , wherein the user-system conditioning task includes a user subtask assigned to a user and a machine subtask.

4 . The method of claim 3 , wherein the machine subtask includes transmitting instructions to the user, and wherein adjusting the user-system conditioning task includes eliminating the machine subtask.

5 . The method of claim 3 , wherein the plurality of outcomes includes a user performance metric for the user-system conditioning task for each iteration, and wherein the machine subtask includes waiting for the user to complete the user subtask and determining the user performance metric.

6 . The method of claim 1 , wherein the plurality of iterations corresponds to one user, and wherein the computing system executes the collaborative process including the adjusted user-system conditioning task with the user.

7 . The method of claim 1 , comprising:

tagging the plurality of tasks; and

determining a plurality of user-system conditioning tasks using the tagged plurality of tasks.

8 . The method of claim 1 , wherein an outcome includes a user response score and the scenario characteristic includes at least one of a familiarity score or an environmental condition.

9 . A computer program product for use on a computer system including a processor and a memory operatively coupled with the processor, for executing a collaborative process with a user, the computer program product comprising a tangible, non-transient computer usable medium having computer readable program code executable by the processor thereon, the computer readable program code comprising:

program code for executing a plurality of tasks of the collaborative process for a plurality of iterations, the plurality of tasks including a user-system conditioning task of the collaborative process;

program code for determining a plurality of scenario characteristics and a plurality of outcomes in response to executing the collaborative process for the plurality of iterations;

program code for generating a first machine learning model using the plurality of scenario characteristics and the plurality of outcomes;

program code for inputting a first scenario characteristic of the plurality of scenario characteristics into the first machine learning model;

program code for outputting a first proficiency score from the first machine learning model, wherein the first proficiency score corresponds to a likelihood a user will complete a first task of the plurality of tasks;

program code for adjusting the user-system conditioning task in response to the first proficiency score;

program code for executing, using the computing system, the collaborative process including the adjusted user-system conditioning task;

program code for generating a second machine learning model using the plurality of scenario characteristics and the plurality of outcomes;

program code for inputting a second scenario characteristic of the plurality of scenario characteristics into the second machine learning model; and

program code for outputting a second proficiency score from the second machine learning model,

wherein the first proficiency score corresponds to a user and the first user-system conditioning task,

wherein the plurality of tasks includes the second user-system conditioning task of the collaborative process, and

wherein the second proficiency score corresponds to the user and the second user-system conditioning task;

program code for adjusting the second user-system conditioning task in response to the second proficiency score; and

program code for executing, using the computing system, the collaborative process including the second adjusted user-system conditioning task.

10 . The computer program product of claim 9 , wherein executing the collaborative process including the adjusted user-system conditioning task includes removing all tasks after the adjusted user-system conditioning task in response to adjusting the user-system conditioning task.

11 . The computer program product of claim 9 , wherein the user-system conditioning task includes a user subtask assigned to a user and a machine subtask.

12 . The computer program product of claim 11 , wherein the machine subtask includes transmitting instructions to the user, and wherein adjusting the user-system conditioning task includes eliminating the machine subtask.

13 . The computer program product of claim 11 , wherein the plurality of outcomes includes a user performance metric for the user-system conditioning task for each iteration, and wherein the machine subtask includes waiting for the user to complete the user subtask and determining the user performance metric.

14 . The computer program product of claim 9 , wherein the plurality of iterations corresponds to one user, and wherein the computing system executes the collaborative process including the adjusted user-system conditioning task with the user.

15 . The computer program product of claim 9 , comprising:

program code for tagging the plurality of tasks; and

program code for determining a plurality of user-system conditioning tasks using the tagged plurality of tasks.

16 . The computer program product of claim 9 , wherein an outcome includes a user response score and the scenario characteristic includes at least one of a familiarity score or an environmental condition.

Assignments (1)

Continuity (1)

References Cited (9)