Emotion-Aware Food Recommendation Using Coordinate-Based Mood Input

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Solution Overview

Problem

Existing food information presentation systems fail to accurately consider emotions, leading to inadequate selection of food items that may not provide user satisfaction.

Innovation Solution

A system that recognizes user information, current emotions, and uses a prediction model to recommend food items based on machine-learned correlations between emotions, user preferences, and candidate food items, presenting information through a coordinate-based emotion model.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If the system uses fixed parameters (preferences, circumstances) to recognize recommended food, then the system complexity is reduced and ease of operation is improved, but the accuracy of emotion recognition deteriorates and the reliability of food recommendation deteriorates

Engineering Contradiction:

Improveease of emotion inputVSAvoidemotion recognition accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system uses biological information recognition technology to automatically acquire user emotion information without requiring manual input. The emotion recognition unit recognizes emotions by analyzing biological signals (facial expressions, voice tone, heartbeat, etc.), allowing the system to serve itself in obtaining accurate emotion data while maintaining ease of operation for the user.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual emotion input mechanisms with biological information recognition. Instead of requiring users to self-report emotions through forms or interfaces, the system uses sensors and AI to detect and interpret biological signals, substituting mechanical/manual processes with automated biological detection.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If the system requires users to accurately express and input their emotions, then the emotion recognition accuracy is improved, but the ease of operation deteriorates and the time required increases

Engineering Contradiction:

Improveemotion recognition accuracyVSAvoidtime for emotion input

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary action by continuously or periodically acquiring biological information in advance of when food recommendations are needed. The emotion recognition unit is prepared with up-to-date emotion data, so when a food recommendation is requested, the system can immediately use the pre-acquired emotion information without requiring additional time for emotion input or processing.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system automatically performs emotion recognition without requiring user intervention. The emotion recognition unit independently analyzes biological signals and updates emotion information, freeing the user from the time-consuming task of manually expressing and inputting emotions while maintaining high accuracy.

Inventive Principle:

Principle #25Self-service

3Reliability

If the system uses a prediction model with machine learning to recognize recommended food based on emotions and user information, then the reliability of food recommendation is improved, but the device complexity increases

Engineering Contradiction:

Improvefood recommendation reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a prediction model as an intermediary between the emotion recognition unit and the food recommendation output. This prediction model serves as a mediator that processes the complex relationship between multiple input parameters (emotions, user information, food database) and generates reliable recommendations. The intermediary handles the computational complexity internally while presenting a simple interface to the user.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The prediction model performs multiple functions: it processes emotion data, analyzes user information, searches the food database, and generates recommendations. By consolidating these functions into a single multi-functional prediction model, the system achieves high reliability without proportionally increasing overall system complexity, as the model integrates multiple roles that would otherwise require separate components.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12511336B2Food information presentation system, food information presentation method, food information presentation device, food information presentation program, and storage medium on which program is recorded

Publication Date: 2025.12.30 KIKKOMAN CORP

AI summary

A presentation system S includes: a user information recognition unit configured to recognize user information; a current emotion recognition unit configured to recognize a current emotion of a user; a recommended food recognition unit configured to recognize a recommended food item by using a prediction model which receives an input of the user information and the current emotion and outputs the recommended food item; and a food information presentation unit configured to obtain and present the user with food information about the recommended food item. The current emotion recognition unit is configured to estimate, as the current emotion, coordinates selected by the user from an emotion model in which coordinates are defined based on a plurality of basic emotions.