Emotion-Aware Content Recommendation Using Chronological Vital Data

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Solution Overview

Problem

Existing content recommendation systems struggle to accurately reflect a user's true intentions due to a lack of scientific basis in estimating user emotions, leading to reduced user confidence and inappropriate content recommendations.

Innovation Solution

A content recommendation system that utilizes vital sensors to continuously acquire and analyze chronological vital data, generating estimated emotion values through an emotion estimation calculator, and selects content from a content library to align with the user's target emotion, incorporating context information for precise recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If general meta-information (shopping history, viewing history, music genre, rhythm, tempo) is used to generate recommendation lists, then the recommendation system can operate with simple data collection and processing, but the accuracy of understanding user true intentions deteriorates

Engineering Contradiction:

Improveaccuracy of understanding user intentionsVSAvoidcomplexity of data collection and processing

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent replaces conventional meta-information collection (mechanical/system-based data gathering) with physiological signal-based emotion detection. Vital sensors continuously monitor heartbeat, skin conductance, and other biological markers to objectively measure user emotional states, substituting subjective or indirect behavioral data with direct physiological measurements for more accurate intention understanding

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces an emotion estimation calculator as an intermediary component that processes raw vital sensor data and converts it into meaningful emotion values. This intermediary layer bridges the gap between complex physiological signals and actionable recommendation insights, enabling accurate emotion-based recommendations without requiring direct complex analysis of all raw sensor data

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If conventional meta-information is used to select recommended content, then the system operates with simpler data requirements, but the suitability of recommended content for each individual user deteriorates

Engineering Contradiction:

Improvepersonalization of content recommendationsVSAvoidamount of data required

Core Design Contradiction:

Adaptability or versatilityVSQuantity of substance

Solution Approach 1:

The patent applies local quality by transitioning from general, uniform recommendation criteria applied to all users to personalized recommendation criteria tailored to each user's current emotional state. The system dynamically adjusts recommendation parameters based on individual physiological responses, ensuring that each user receives content specifically suited to their unique emotional context rather than generic recommendations

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent utilizes parameter changes by continuously monitoring vital sign parameters (heartbeat, skin conductance, temperature) and using these changing physiological parameters to dynamically adjust content recommendations. The emotion estimation calculator processes these varying parameters over time to detect emotional transitions, enabling the system to adapt recommendations in real-time as user emotional states evolve

Inventive Principle:

Principle #35Parameter changes

3Reliability

If content recommendations are made without accurate emotion estimation, then the system can operate with simpler algorithms, but user confidence in the recommendation system and continued service usage deteriorates

Engineering Contradiction:

Improveuser confidence and service continuityVSAvoidcomplexity of recommendation algorithms

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements feedback mechanisms where the system continuously monitors user emotional states via vital sensors, compares actual emotional responses with expected responses to recommended content, and uses this feedback to refine future recommendations. The emotion estimation calculator provides ongoing feedback about user emotional states, enabling the recommendation engine to adjust its algorithm and improve reliability over time through learned patterns of user emotional responses

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12619670B2Content recommendation system, content recommendation method, content library, method for generating content library, and target-input user interface

Publication Date: 2026.05.05 SONY GROUP CORP

AI summary

Provided is A content recommendation system that includes a vital-feature-amount generator that acquires chronological vital data that is vital data of a user that is continuously and chronologically sensed by a vital sensor, and generates chronological vital-feature-amount data from the chronological vital data. The content recommendation system further includes an emotion estimation calculator that generates, from the chronological vital-feature-amount data, an estimated emotion value that is an estimated value of an emotion of the user, a recommendation engine that acquires a target emotion value that is input through a user interface terminal apparatus and indicates an emotion that is a target of the user, and selects, from a content library, content used to reach the target emotion value from the estimated emotion value, and a content recommendation section that recommends the selected content to the user interface terminal apparatus.