A method for analyzing emotional change trend in an interactive situation and application thereof

By collecting multi-source heterogeneous data, using adaptive time window fusion, and employing nonlinear dynamic models, the problems of lagging emotion prediction and unclear attribution in existing technologies have been solved. This enables accurate prediction and proactive intervention of emotion change trends, improving the human-computer interaction experience and user emotional health.

CN122364762APending Publication Date: 2026-07-10ZHEJIANG QIANYING BIRDHOUSE FORESTRY DEV CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG QIANYING BIRDHOUSE FORESTRY DEV CO LTD
Filing Date
2026-04-16
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing technologies struggle to accurately predict the critical point of emotional evolution during long-term interactions and lack the ability to attribute situational causes to causality, making it impossible to proactively intervene before emotions spiral out of control.

Method used

By collecting multi-source heterogeneous data in real time, performing edge privacy desensitization processing and adaptive multi-scale time window fusion, analyzing the trajectory of emotion evolution using a nonlinear dynamic model, constructing a context-emotion dynamic causal graph, generating emotion attribution reports, and implementing targeted interactive intervention strategies.

Benefits of technology

It enables accurate prediction and proactive intervention of emotion change trends, improves human-computer interaction experience and user emotional health, and solves the problems of lagging emotion prediction and unclear attribution in traditional technologies.

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Abstract

This invention discloses a method and application for analyzing emotion change trends in interactive contexts, relating to the field of artificial intelligence technology. The method includes: Step S1: Real-time collection of multi-source heterogeneous data from the user in the current interactive context, wherein the multi-source heterogeneous data includes at least visual stream data, auditory stream data, text behavior stream data, brain current data, and environmental context data; Step S2: On-device privacy anonymization processing of the multi-source heterogeneous data, extraction of high-dimensional feature vectors, and construction of an adaptive multi-scale time window based on the semantic boundaries of interactive events, temporal alignment and fusion of feature vectors at different time granularities to generate multimodal fusion features for the current moment; Step S3: Loading the user's personalized emotional dynamic baseline. This invention improves the human-computer interaction experience, service success rate, and user emotional health level in application scenarios such as intelligent customer service, online education, intelligent cockpits, and mental health assistance.
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