Conversational System Emotional Context Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing interactive communication programs, such as chatbots, struggle to generate human-like conversational responses that are sensitive to the emotional context of user interactions, particularly when users mention socially close individuals or specific events.

Innovation Solution

A conversational system that uses machine learning models to infer a user's mental state by detecting mentions of socially close persons and determining the context of their mention, thereby adjusting the language of responses to match the emotional tone.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If the conversational system uses standard response templates, then the system operation is simple and fast, but the responses lack emotional sensitivity and human-like qualities

Engineering Contradiction:

Improveemotional sensitivityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system segments the conversational response generation into multiple independent modules: (1) a social closeness detection module that identifies relationships between user and mentioned entities, (2) an emotional context determination module that analyzes the emotional tone, and (3) a response generation module that selects and customizes appropriate responses. This segmentation allows each module to specialize in one aspect of emotional intelligence while maintaining overall system manageability.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis of the user input to detect socially close persons and determine emotional context before generating the final response. By pre-processing the input to extract emotional and relational features, the system can then select and customize appropriate response templates, achieving emotional sensitivity without completely redesigning the response generation architecture.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If the system analyzes user mental state in detail, then the response quality improves, but the processing time increases

Engineering Contradiction:

Improvemental state detection accuracyVSAvoidresponse time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system applies different levels of analysis depth to different aspects of user input based on their importance. Socially close persons and emotional context keywords receive intensive analysis with multiple detection methods, while other parts of the conversation maintain standard processing. This localized deep analysis ensures high accuracy for critical emotional indicators without uniformly increasing processing time for all inputs.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system replaces complex manual psychological analysis with automated machine learning models and natural language processing algorithms. These computational models can rapidly analyze user input for emotional context and social closeness indicators, achieving high measurement precision in mental state detection without the time cost of human-level detailed analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If the system uses generic response templates, then the implementation is straightforward, but the responses lack contextual appropriateness

Engineering Contradiction:

Improvecontextual appropriatenessVSAvoidimplementation complexity

Core Design Contradiction:

Adaptability or versatilityVSEase of manufacture

Solution Approach 1:

The system maintains a library of response templates but dynamically changes key parameters of these templates based on detected emotional context and social closeness. For example, the same informational response can be delivered with different emotional tones, levels of formality, or accompanying empathetic statements depending on the user's mental state. This allows contextual appropriateness while building upon existing template structures.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system introduces an intermediate layer between the user input and the final response that processes emotional context and social closeness information. This intermediary module analyzes the input, determines the appropriate emotional tone and response style, and then selects or customizes templates accordingly. This mediator enables contextual appropriateness without requiring complete redesign of the response generation system.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12217008B2Modifying dynamic conversational responses based on detecting references to socially close people

Publication Date: 2025.02.04 CAPITAL ONE SERVICES LLC

AI summary

Methods and systems are described for generating dynamic conversational responses sensitive to different emotional contexts using machine learning models. The dynamic conversational responses may be generated in real time and reflect the likely emotional context by detecting socially close entities and events in user input.