Voice Dialogue Content Recommendation System
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Solution Overview
Problem
Conventional computer systems that employ voice recognition technology do not effectively generate content recommendations based on user-device dialogue, often requiring multiple requests from users to initiate actions related to the conversation context, leading to increased speech input, processing, and data exchange.
Innovation Solution
A system that processes speech-to-text data and determines topic and value associated with user requests, using a language model and machine learning model to generate prompt data with recommendations and questions, allowing users to confirm actions directly, thereby reducing unnecessary input and data exchange.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional voice recognition systems process user requests, then the system can respond to user queries, but the user must provide multiple separate requests to complete related actions, increasing speech input and processing demands
Solution Approach 1:
The system performs preliminary analysis of the user's speech input to identify not only the explicit request but also related potential actions. By anticipating follow-up actions before the user explicitly requests them, the system prepares recommendation data in advance, reducing the need for multiple user requests and decreasing speech input requirements.
Solution Approach 2:
The system provides feedback to the user in the form of recommended follow-up actions based on the current conversation context. This feedback loop allows the user to confirm or modify actions with minimal input (e.g., simple yes/no responses), thereby reducing the complexity of operation while maintaining high processing efficiency through automated context analysis.
2Ease of operation
If the system generates content recommendations based on dialogue context, then user input is reduced, but the system complexity increases due to additional processing requirements
Solution Approach 1:
The system segments the complex task of generating content recommendations into distinct processing stages: speech-to-text conversion, topic identification, context analysis, recommendation generation, and output formatting. By dividing the processing into modular segments, the system reduces overall complexity while enabling sophisticated recommendation capabilities through specialized sub-processes.
3Device complexity
If multiple requests are required to initiate related actions, then the system maintains simple processing logic, but network usage increases due to repeated data exchange
Solution Approach 1:
The system maintains continuous context awareness across the dialogue, keeping the conversation state and user preferences in memory throughout the interaction. This continuity allows the system to generate relevant recommendations without requiring users to repeat information, thereby reducing the number of data exchange cycles and minimizing network energy consumption while preserving relatively simple processing logic.
Data Source
AI summary
Techniques for generating a recommendation for content based on user-device dialogue are described. In an example, a computer system receives a request for audio output at a user device. The computer system determines a response for the request. The computer system determines a topic and value associated with the request and the response. The computer system also determines a recommendation for content based on the request and the response. The computer system generates prompt data including a question of whether to initiate an action associated with the recommendation. The computer system sends the prompt data and the response data to the user device or a different user device.