Collaborative interaction system of companion robot based on emotional state portrait and hierarchical memory

By using a collaborative interactive system that combines emotional state profiling with hierarchical memory, the problem of superficial understanding of emotional states in elderly companion robots has been solved. This system enables continuous empathy and personalized responses, improving the consistency and safety of emotional interaction in companion robots.

CN122116884BActive Publication Date: 2026-07-14HEFEI UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HEFEI UNIV OF TECH
Filing Date
2026-04-28
Publication Date
2026-07-14

AI Technical Summary

Technical Problem

Existing technologies for elderly companion robots have a superficial understanding of emotional states, making it difficult to achieve continuous empathy. Furthermore, they lack joint modeling of speech rate, pauses, rhythm, historical emotional baselines, and long-term user personality preferences, leading to response mismatch and memory drift.

Method used

A collaborative interaction system based on emotional state profiling and hierarchical memory is adopted, including an entry layer, a stable context base layer, a semantic and emotional state layer, a security control layer, a memory layer, a retrieval enhancement layer, and a strategy planning layer. Through real-time voice processing, historical information integration, intent recognition, emotion fusion estimation, and security gating, coherent and personalized response content is generated.

Benefits of technology

It achieves continuous empathic response in elderly companion robots, improves the continuity and personalization of emotional interaction, ensures safe and reliable execution capabilities, and avoids problems such as overly strong, weak, or mismatched responses.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application provides a companion robot collaborative interaction system based on emotional state portrait and hierarchical memory, and relates to the technical field of artificial intelligence and emotional computing. The application receives and processes historical round verified information through a stable context base layer, outputs a stable context base, a historical companion portrait and a long-term preference portrait, breaks the short board of the prior art that lacks a historical emotion baseline and long-term personality preference modeling, provides a complete user historical trajectory basis, and avoids isolated consideration of a single round dialogue. The semantic and emotional state layer receives current round speech observation results, the stable context base and the historical emotion baseline, completes intent recognition, entity extraction and emotion fusion estimation, outputs the current round semantic state, realizes multi-dimensional joint modeling, accurately distinguishes different emotional states, and cooperates with the remaining layers, dynamically generates adaptive responses, effectively tracks cross-round emotional trends, and provides coherent and accurate empathetic responses for scenes such as elderly companionship.
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