- Number
- 12731044
- Published
- 2026-09-08
- Filed
- 2023-03-09
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Fidaleo; Douglas A., Kennedy; James R., Barron; Michael
- CPC
- G06N5/022
- Verdict
- Low Notable software
- First reported
- 2026-W37 (2026-09-11)
- Source
- Google Patents · FreePatentsOnline
The keeper's note
An AI interactive character (AIIC) reads emotional cues from what a user says and adjusts its responses to match — companion-bot emotional intelligence.
Abstract
A system includes a computing platform having a hardware processor and a memory storing software code, a memory data structure storing memory features for an artificial intelligence interactive character (AIIC), and a trained machine learning (ML) model. The hardware processor executes the software code to receive interaction data describing a communication by a user with the AIIC, predict, using the trained ML model and the interaction data, at least one user memory feature(s) of the communication, and identify, using the memory data structure, one or more of the memory features for the AIIC as corresponding to the user memory feature(s). The software code also determines, using the user memory feature(s) and the corresponding one or more of the memory features for the AIIC, an interactive communication for execution by the AIIC in response to the communication by the user; and outputs the interactive communication to the AIIC.
Background
BACKGROUND (1) Establishing a deep emotional connection between a human and an artificial intelligence (AI) character is an unsolved problem of significant importance to many fields. Although there is evidence in the psychology literature that sharing memories creates a sense of relationship closeness between individuals, that apparent ability to share memories has not heretofore been extended to AI characters. The emotional closeness engendered by the sharing of memories is often enhanced when these memories are relatable between the individuals, i.e., the individuals have similar experiences or interpretations of those experiences. Such closeness generally improves communication between individuals and tends to make interactions richer and more pleasurable. However, until now, AI agents have had only crude ability to mimic human emotional behavior, which may be off-putting rather than enjoyable. Thus, there exists a need in the art for systems and methods to improve the ability of AI characters to express language and behaviors in a manner similar to individuals having shared memories.
Claims
1. A system comprising: a computing platform having a hardware processor and a system memory; the system memory storing a software code, a memory data structure storing a plurality of memory features for an artificial intelligence interactive character (AIIC), and a trained machine learning (ML) model; the hardware processor configured to execute the software code to: receive interaction data describing a communication by a user with the AIIC; predict, using the trained ML model and the interaction data, at least one user memory feature of the communication; identify, using the memory data structure, one or more of the plurality of memory features for the AIIC as corresponding to the at least one user memory feature, wherein identifying comprises comparing the at least one user memory feature and the one or more of the plurality of memory features across multiple dimensions, and computing an aggregated similarity score using dimension weights; determine, using the at least one user memory feature of the communication and the corresponding one or more of the plurality of memory features for the AIIC, an interactive communication for execution by the AIIC in response to the communication by the user; and output the interactive communication to the AIIC. ||
11. A method for use by a system including a computing platform having a hardware processor and a system memory, the system memory storing a software code, a memory data structure storing a plurality of memory features for an artificial intelligence interactive character (AIIC), and a trained machine learning (ML) model, the method comprising: receiving, by the software code executed by the hardware processor, interaction data describing a communication by a user with the AIIC; predicting, by the software code executed by the hardware processor and using the trained ML model and the interaction data, at least one user memory feature of the communication; identifying, by the software code executed by the hardware processor and using the memory data structure, one or more of the plurality of memory features for the AIIC as corresponding to the at least one user memory feature, wherein identifying comprises comparing the at least one user memory feature and the one or more of the plurality of memory features across multiple dimensions, and computing an aggregated similarity score using dimension weights; determining, by the software code executed by the hardware processor, using the at least one user memory feature of the communication and the corresponding one or more of the plurality of memory features for the AIIC, an interactive communication for execution by the AIIC in response to the communication by the user; and outputting, by the software code executed by the hardware processor, the interactive communication to the AIIC.