Conversation-Based Skill Component for User-State Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech processing systems lack the ability to effectively analyze conversational interactions to assess a user's state and provide personalized recommendations for improving mental health or well-being.
Innovation Solution
A speech processing system that utilizes natural language understanding and machine learning to analyze user speech for tone, topic, and state, generating personalized recommendations based on conversational interactions, including features like tone detection and acoustic embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech processing systems use basic speech recognition and natural language understanding, then they can identify spoken words and execute commands, but they cannot effectively analyze conversational interactions to assess user state or provide personalized recommendations
Solution Approach 1:
The patent implements nested processing layers where speech recognition outputs are fed into natural language understanding, which then feeds into conversational analysis, state assessment, and recommendation generation. Each processing layer is contained within and builds upon the previous layer, creating a hierarchical structure that enables comprehensive analysis while maintaining modular organization.
Solution Approach 2:
The speech processing system is enhanced to perform multiple functions beyond basic command execution. It simultaneously performs speech recognition, natural language understanding, conversational analysis, user state assessment, and personalized recommendation generation, making the system universally applicable to various user needs and contexts.
2Measurement precision
If the system performs comprehensive conversational analysis to accurately assess user state, then it can provide tailored actions and resources, but it requires advanced machine learning and multiple processing components
Solution Approach 1:
The system incorporates feedback loops where conversational analysis results inform state assessment, which in turn guides recommendation generation. The system continuously monitors user responses to recommendations and adjusts its assessments and recommendations accordingly, improving accuracy through iterative refinement based on user feedback.
Solution Approach 2:
The patent introduces intermediate processing components that bridge basic speech recognition and final state assessment. Natural language understanding serves as an intermediary that translates spoken words into meaningful context, while conversational analysis acts as another intermediary layer that synthesizes multiple utterances to detect user state, enabling accurate assessment without requiring direct complex processing at each stage.
Data Source
AI summary
The present application provides techniques for implementing a skill component, configured to perform an assessment of a user, as part of a speech processing system. The system may receive a natural language user input requesting assistance. The skill component may, using one or more machine learning models, determine at least one characteristic of the natural language input (e.g., lexical embedding, acoustic embedding, topic, tone, etc.). The skill component may determine state data for a present session, where the state data indicates a topic of the natural language user input and/or a user state associated with the natural language user input. The skill component may determine past state data of one or more past sessions, and generate a question to the user based on the state data for the natural language user input and the past state data.