IP Library › Granted Patent US 11,417,228
Granted Patent B2
US 11,417,228 · App. 16/574,620 · Granted Aug 16, 2022

Modification of extended reality environments based on learning characteristics

Inventors: Zachary A. Silverstein (Jacksonville, FL); Sarbajit K. Rakshit (Kolkata, IN); Robert Huntington Grant (Atlanta, GA); Haley Ashlin (Dallas, TX)
Assignee: International Business Machines Corporation
G09B5/02G06F3/011G06T19/006G09B9/00H04L67/306
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Quick Facts
Patent No.
US 11,417,228
App. No.
16/574,620
Granted
Aug 16, 2022
Kind
B2
Abstract

Provided is a method, computer program product, and system for modifying a simulation based on learning characteristics. A processor may receive a user profile associated with a user. The user profile includes a set of learning characteristics related to the user. The processor may display a simulation on an extended reality display. The displayed simulation is based in part on the set of learning characteristics. The processor may monitor focus data related to the user. The focus data is generated while the user is viewing the simulation. The processor may compare the focus data with one or more focus thresholds. The processor may modify the simulation in response to the one or more focus thresholds being met.

Claims (41)

1. A system, comprising:

a processor; and

a computer-readable storage medium communicatively coupled to the processor and storing program instructions which, when executed by the processor, cause the processor to perform a method comprising:

receiving a user profile associated with a user, wherein the user profile includes a set of learning characteristics related to the user;

displaying a simulation associated with a learning session on an extended reality (XR) display, wherein non-critical content is occluded from the simulation to create a clean background surrounding critical content related to the learning session based in part on the set of learning characteristics, and wherein the non-critical content comprises objects within a learning environment that are not related to the learning session and the critical content comprises a teacher;

monitoring focus data from one or more communicatively coupled IoT devices, wherein the focus data is generated while the user is viewing the simulation;

comparing the focus data with one or more focus thresholds, wherein the focus thresholds are based in part on correlating historical focus data of the user and the set of learning characteristics; and

inserting a virtual artifact within the simulation in response to the one or more focus thresholds being met.

2. The system of claim 1 , wherein the method performed by the processor further comprises:

analyzing a plurality of learning characteristics related to a plurality of users; and

grouping the plurality of users into one or more learning groups based in part on the plurality of users having one or more similar learning characteristics,

wherein the displaying of the simulation is modified based in part on at least one of the set of learning characteristics of the user matching a learning characteristic within the one or more learning groups.

3. The system of claim 1 , wherein the set of learning characteristics are generated in part by analyzing a set of educational reports related to the user using natural language computing.

4. The system of claim 1 , wherein monitoring focus data from one or more IoT devices utilizes a feedback loop to capture interaction of the user when viewing the simulation.

5. The system of claim 1 , wherein the method performed by the processor further comprises:

monitoring one or more performance factors of the user when the simulation is modified; and

adjusting the one or more focus thresholds based in part on the performance factors.

6. The system of claim 1 , wherein the virtual artifact is inserted into the clean background surrounding the critical content related to the learning session.

7. The system of claim 1 , wherein the virtual artifact is determined from visual content preferences in the user profile.

8. The system of claim 7 , wherein the visual content preferences are based on a focus level for virtual artifacts that the user finds appealing, and wherein the focus level is determined in part from the focus data.

9. The system of claim 8 , wherein the virtual artifacts that the user finds appealing include animated virtual artifacts.

10. The system of claim 1 , wherein the learning characteristics are selected from a group of learning characteristics consisting of: behavioral data, focus data, learning history, educational grades, and educational content.

11. The system of claim 1 , wherein occluding non-critical content from the simulation comprises:

analyzing, using natural language computing, written and spoken content of the learning session associated to the simulation; and

determining, based on the analyzing, critical content and non-critical content related to the simulation.

12. The system of claim 1 , wherein the set of learning characteristics are generated in part through gamification.

13. The system of claim 1 , wherein the focus data includes heart rate data associated with the user.

14. A computer program product comprising a computer-readable storage medium having program instructions embodied therewith, wherein the computer-readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:

receiving a user profile associated with a user, wherein the user profile includes a set of learning characteristics related to the user;

displaying a simulation associated with a learning session on an extended reality (XR) display, wherein non-critical content is occluded from the simulation to create a clean background surrounding critical content related to the learning session based in part on the set of learning characteristics, and wherein the non-critical content comprises objects within a learning environment that are not related to the learning session and the critical content comprises a teacher;

monitoring focus data from one or more communicatively coupled IoT devices, wherein the focus data is generated while the user is viewing the simulation;

comparing the focus data with one or more focus thresholds, wherein the focus thresholds are based in part on correlating historical focus data of the user and the set of learning characteristics; and

inserting a virtual artifact within the simulation in response to the one or more focus thresholds being met.

15. The computer program product of claim 14 , wherein the method performed by the processor further comprises:

analyzing learning characteristics and focus data related to a plurality of users; and

grouping the plurality of users into one or more learning groups based in part on the plurality of users having one or more similar learning characteristics,

wherein the displaying of the simulation is modified based in part on at least one of the set of learning characteristics of the user matching a learning characteristic within the one or more learning groups.

16. The computer program product of claim 14 , wherein the set of learning characteristics are generated in part by analyzing a set of educational reports related to the user using natural language computing.

17. The computer program product of claim 14 , wherein the method performed by the processor further comprises:

monitoring one or more performance factors of the user when the simulation is modified; and

adjusting the one or more focus threshold based in part on the performance factors.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 18, 2019
From: SILVERSTEIN, ZACHARY A.; RAKSHIT, SARBAJIT K.; GRANT, ROBERT HUNTINGTON; ASHLIN, HALEY
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 050417/0331 →
Continuity (1)
Related Publication 20210082299A1 · Mar 18, 2021