IP Library Granted Patent US 12,555,028
Granted Patent B2
US 12,555,028 · App. 17/213,004 · Granted Feb 17, 2026

System and method for coordinated agent learning and explanation using hierarchical factors

Inventors: Mark J. Stefik (Portola Valley, CA); Gregory Michael Youngblood (Minden, NV); Robert T. Krivacic (San Jose, CA); Jacob Le (Palo Alto, CA); Lester D. Nelson (Santa Clara, CA); Robert R. Price (Palo Alto, CA)
Assignee: Genesee Valley Innovations, LLC
G06N20/00G06F18/217G06N5/022G06N5/025G06N5/045
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Quick Facts
Patent No.
US 12,555,028
App. No.
17/213,004
Granted
Feb 17, 2026
Kind
B2
Abstract

A system and method for selection and explanation of solutions is provided. A hierarchy of aggregation factors is maintained for evaluating at least a partial solution. Competing solutions are generated and each solution includes at least a partial solution. Scores are calculated for each of the aggregation factors for each competing solution. A total evaluation score is calculated for each competing solution based on the scores for at least one of the aggregation factors. The competing solution with the best evaluation score is selected and a gist is generated. The gist is a narrative comparing the selected solution with the non-selected solutions based on the at least one aggregation factor. The gist is provided to a user as a rationale for selection of the solution.

Claims (61)

1 . A system for selection and explanation of solutions, comprising:

memory to maintain a hierarchy of aggregation factors for evaluating at least a partial solution; and

a server comprising a central processing unit, memory, an input port to receive the hierarchy of aggregation factors from the memory, and an output port, wherein the central processing-unit is configured to:

receive a request for a task or activity;

generate competing solutions for the task for activity via an AI agent, each solution comprising at least a partial solution;

calculate scores for each of the aggregation factors for each competing solution;

calculate an evaluation score based on the scores for at least one of the aggregation factors for each competing solution;

select the competing solution with the best evaluation score;

generate via the AI agent a gist comprising a narrative comparing the selected solution with the non-selected solutions based on the at least one aggregation factor for the task or activity and based on where the at least one aggregation factor falls in the hierarchy of aggregation factors; and

provide the gist to a user as a rationale for selection of the solution to the task or activity.

2 . A system according to claim 1 , wherein the gist identifies one of the aggregation factors as a main factor for selecting the solution with the best evaluation score and one of the aggregation factors as a main penalty that is bypassed during selection of the plan with a worst aggregate score.

3 . A system according to claim 2 , wherein the central processing unit performs the following:

identify the main factor as the aggregate factor that accounts for 40% or more of a penalty difference between the scores for each of the competing solutions; and

identify the main penalty as the aggregation factor that accepts a small penalty disadvantage in exchange for the penalty identified by the main factor, which is larger.

4 . A system according to claim 1 , wherein the central processing unit performs the following:

calculate a further evaluation score for a different set of the aggregation factors for each competing solution;

select the competing solution with a best further evaluation score;

generate a gist comprising a narrative comparing the selected solution with the non-selected solutions based on the at least one aggregation factor and the different set of aggregation factors.

5 . A system according to claim 1 , wherein one or more of the aggregation factors includes sub-factors, which are also assigned scores that are included in the evaluation score.

6 . A system according to claim 1 , wherein the central processing unit performs the following:

create a visualization of the competing solutions; and

display the visualization for each of the solutions on a world model.

7 . A system according to claim 6 , wherein the central processing unit performs the following:

provide visualizations for one or more of the aggregation factors on the world model.

8 . A system according to claim 7 , wherein the visualizations of the competing solutions and the aggregation factors are provided as an overlay on the world model.

9 . A system according to claim 6 , wherein the central processing unit performs the following:

generate a table for the aggregation factors; and

display the table with the world model.

10 . A system according to claim 9 , wherein the central processing unit performs the following

select at least one of the aggregation factors in the table; and

display the selected aggregation factor in the world model.

11 . A method for selection and explanation of solutions, comprising:

maintaining a hierarchy of aggregation factors for evaluating at least a partial solution;

receiving a request for a task or activity;

generating competing solutions for the task or activity via an AI agent, each solution comprising at least a partial solution;

calculating scores for each of the aggregation factors for each competing solution;

calculating for each competing solution an evaluation score based on the scores for at least one of the aggregation factors;

selecting the competing solution with the best evaluation score;

generating via the AI agent a gist comprising a narrative comparing the selected solution with the non-selected solutions based on the at least one aggregation factor for the task or activity and based on where the at least one aggregation factor falls in the hierarchy of aggregation factors; and

providing the gist to a user as a rationale for selection of the solution.

12 . A method according to claim 11 , wherein the gist comprises one of the aggregation factors as a main factor for selecting the solution with the best evaluation score and one of the aggregation factors as a main penalty that is bypassed during selection of the plan with a worst aggregate score.

13 . A method according to claim 12 , further comprising at least one of:

identifying the main factor as the aggregate factor that accounts for 40% or more of a penalty difference between the scores for each of the competing solutions; and

identifying the main penalty as the aggregation factor that accepts a small penalty disadvantage in exchange for the penalty identified by the main factor, which is larger.

14 . A method according to claim 11 , further comprising:

calculating a further evaluation score for a different set of the aggregation factors for each competing solution;

selecting the competing solution with a best further evaluation score;

generating a gist comprising a narrative comparing the selected solution with the non-selected solutions based on the at least one aggregation factor and the different set of aggregation factors.

15 . A method according to claim 11 , wherein one or more of the aggregation factors includes sub-factors, which are also assigned scores that are included in the evaluation score.

16 . A method according to claim 11 , further comprising:

creating a visualization of the competing solutions; and

displaying the visualization for each of the solutions on a world model.

17 . A method according to claim 16 , further comprising:

providing visualizations for one or more of the aggregation factors on the world model.

18 . A method according to claim 17 , wherein the visualizations of the competing solutions and the aggregation factors are provided as an overlay on the world model.

19 . A method according to claim 16 , further comprising:

generating a table for the aggregation factors; and

displaying the table with the world model.

20 . A method according to claim 19 , further comprising:

selecting at least one of the aggregation factors in the table; and

displaying the selected aggregation factor in the world model.

Assignments (8)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 31, 2025
From: XEROX CORPORATION
To: GENESEE VALLEY INNOVATIONS, LLC
Reel/Frame 073225/0116 →
SECOND LIEN NOTES PATENT SECURITY AGREEMENT Recorded Jul 2, 2025
From: XEROX CORPORATION
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 071785/0550 →
FIRST LIEN NOTES PATENT SECURITY AGREEMENT Recorded Apr 11, 2025
From: XEROX CORPORATION
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 070824/0001 →
SECURITY INTEREST Recorded Feb 13, 2024
From: XEROX CORPORATION
To: CITIBANK, N.A., AS COLLATERAL AGENT
Reel/Frame 066741/0001 →
SECURITY INTEREST Recorded Nov 20, 2023
From: XEROX CORPORATION
To: JEFFERIES FINANCE LLC, AS COLLATERAL AGENT
Reel/Frame 065628/0019 →
CORRECTIVE ASSIGNMENT TO CORRECT THE REMOVAL OF US PATENTS 9356603, 10026651, 10626048 AND INCLUSION OF US PATENT 7167871 PREVIOUSLY RECORDED ON REEL 064038 FRAME 0001. ASSIGNOR(S) HEREBY CONFIRMS THE ASSIGNMENT. Recorded Jun 28, 2023
From: PALO ALTO RESEARCH CENTER INCORPORATED
To: XEROX CORPORATION
Reel/Frame 064161/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 20, 2023
From: PALO ALTO RESEARCH CENTER INCORPORATED
To: XEROX CORPORATION
Reel/Frame 064038/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 25, 2021
From: STEFIK, MARK J.; YOUNGBLOOD, GREGORY MICHAEL; KRIVACIC, ROBERT T.; LE, JACOB; NELSON, LESTER D.; PRICE, ROBERT R.
To: PALO ALTO RESEARCH CENTER INCORPORATED
Reel/Frame 055725/0405 →
Continuity (2)
Provisional Application 63002252 · Mar 30, 2020
Related Publication 20210303932A1 · Sep 30, 2021
References Cited (15)
US 20120136567A1 · Wang · 2012 [cited by examiner]
US 20170140468A1 · Peak · 2017 [cited by examiner]
US 20200409375A1 · Bowe · 2020 [cited by examiner]
Raj Korpan et al., “WHY: Natural Explanations from a Robot Navigator”, Sep. 27, 2017, arXiv:1709.09741v1, referenced as WHY. (Year: 2017). [cited by examiner]
Raj Korpan et al., “WHY: Natural Explanations from a Robot Navigator,” arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, Sep. 27, 2017, XP080824215. [cited by applicant]
Dieterich, Thomas. Hierarchical reinforcement learning with the MAXQ value function decomposition, Journal of Artificial Intelligence Research, 13, pp. 227-303, 2000. [cited by applicant]
Erwig, Martin, Fern, Alan, Murali, Magesh, Koul, Anurag. Explaining deep adaptive programs via reward decomposition. IJCAI, 2018. [cited by applicant]
Juozapaitis, Zoe, Koul, Anurag, Fern, Alan, Erwig, Martin, Doshi-Velez, Finale. Explainable Reinforcement Learning via Reward Decomposition, IJCAI 2019 Workshop on Explainable Artificial Intelligence (XAI), Macau, China… [cited by applicant]
Khan, Omar Zia, Poupart, Pascal, Black, James P. Minimal sufficient explanations for factored Markov decision processes. ICAPS, 2009. [cited by applicant]
Miller, George A., The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information. Psychological Review, 63, pp. 81-97, 1956. [cited by applicant]
Newell, Allen, Simon, Herbert, A. Human Problem Solving: The state of the theory in 1970. American Psychologist 26(2), pp. 145-159, 1970. [cited by applicant]
Ohlsson, Stellon. The problems with problem solving: Reflections on the rise, current status, and possible future of a cognitive research paradigm. The Journal of Problem Solving, 5(2), 2012. [cited by applicant]
Sacerdoti, Earl D., Planning in a Hierarchy of Abstraction Spaces, Artificial Intelligence, pp. 15-135, 1974. [cited by applicant]
Stefik, Mark. Planning with Constraints. Artificial Intelligence, pp. 111-139, Sep. 1980. [cited by applicant]
Stefik, Mark. Planning and Meta-Planning. Artificial Intelligence, pp. 141-169, Sep. 1980. [cited by applicant]