Dialog Expansion System Using ML Models to Skip Welcome Prompts
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Solution Overview
Problem
Natural language processing systems face challenges in providing relevant and efficient user interactions, particularly in anticipating user needs and reducing the cognitive burden of invoking skill functionality, as they often require users to navigate through welcome prompts and multiple turns to access desired information.
Innovation Solution
A system that employs machine learning models to determine relevant skills based on concluded dialog sessions, ranking them and providing prompt recommendations to skip welcome prompts, allowing users to access related tasks or information directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system requires users to navigate through welcome prompts and multiple turns to access skill functionality, then the system maintains a standardized interaction protocol, but the cognitive burden on users increases and interaction efficiency decreases
Solution Approach 1:
The system performs preliminary actions by analyzing concluded dialog sessions and proactively identifying relevant skills before the user requests them. Machine learning models rank these skills and prepare prompt recommendations in advance, allowing the system to present optimized interaction paths rather than requiring users to navigate standardized welcome prompts sequentially.
2Productivity
If the system provides standardized welcome prompts and multiple turns for skill invocation, then the interaction protocol remains consistent, but the number of turns required for tasks increases
Solution Approach 1:
The system implements skipping by allowing users to bypass welcome prompts and intermediate turns through prompt recommendations. When the system identifies relevant skills from concluded dialog sessions, it presents condensed prompts that enable users to jump directly to the core interaction, skipping unnecessary procedural steps while maintaining protocol consistency.
3Adaptability or versatility
If the system analyzes concluded dialog sessions to recommend relevant skills, then user needs are anticipated more accurately, but the processing complexity and computational resources increase
Solution Approach 1:
The system implements self-service by having machine learning models automatically analyze concluded dialog sessions, identify relevant skills, rank them by relevance, and generate prompt recommendations without human intervention. This automated self-service approach enables accurate need anticipation while managing processing complexity through algorithmic rather than manual methods.
4Loss of time
If the system presents prompt recommendations to skip welcome prompts, then users can access information more directly, but the system must process and rank multiple skills using machine learning models
Solution Approach 1:
The system performs preliminary processing by analyzing dialog sessions and pre-ranking skills using machine learning models before the user needs to access them. This advance preparation creates a ready-to-present list of prompt recommendations, reducing the time users wait for skill access while managing computational complexity through batch processing rather than real-time analysis.
Data Source
AI summary
Techniques for recommending a skill experience to a user after a user-system dialog session has ended are described. Upon a dialog session ending, the system uses a first machine learning model to determine potential intents to recommend to a user. The system then uses a second machine learning model to determine a particular skill and intent to recommend. The system then prompts the user to accept the recommended skill and intent. If the user accepts, the system calls the recommended skill to execute. As part of calling the skill, the system sends to the skill at least one entity provided in a natural language user input of the ended dialog session. This enables the skill to skip welcome prompts, and initiate processing to output a response based on the intent and the at least one entity of the ended dialog session.