Advice Engine Human Validation Conversation Tree
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Solution Overview
Problem
Advice engines lack the ability to provide high-quality, human-like advice efficiently, especially when dealing with complex or unbounded advice scenarios, as they often rely solely on machine-generated recommendations without human oversight.
Innovation Solution
A semi-supervised advice engine system that interacts with a human professional to dynamically edit and improve advice conversations, ensuring that advice statements are validated and refined before being presented to users, utilizing a conversation tree structure to manage and refine advice based on user input and professional feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If advice engines rely solely on machine-generated recommendations, then automation is improved, but advice quality and reliability deteriorate
Solution Approach 1:
The patent introduces a human professional as an intermediary between the machine-generated advice and the final recommendation to the user. The system presents machine-generated advice conversations to a human professional who reviews, validates, and approves them before they are presented to users. This intermediary role ensures that automated advice maintains high quality and reliability while preserving the benefits of automation for generating and managing advice conversations.
2Reliability
If human professionals review all advice conversations, then advice quality is improved, but system complexity and processing time worsen
Solution Approach 1:
The system performs preliminary actions by having machine algorithms generate and structure advice conversations before presenting them to human professionals. The advice conversations are pre-organized in a structured format with multiple potential responses and paths, allowing human professionals to efficiently review and approve content without having to create advice from scratch. This preliminary structuring reduces the complexity of human review while maintaining advice quality.
3Reliability
If human professionals validate each advice statement, then advice relevance is improved, but productivity decreases
Solution Approach 1:
The system applies partial validation by having human professionals review and approve advice conversations selectively rather than every single statement. The structured conversation tree format allows professionals to approve entire conversation paths or branches at once, rather than validating each individual advice statement separately. This partial validation approach maintains advice relevance while significantly improving productivity compared to statement-by-statement review.
Data Source
AI summary
A method of providing advice to a user may be provided. A method may include receiving a topic from the user. The method may also include presenting one or more potential advice conversations to a human professional, wherein each potential advice conversation of the one or more potential advice conversations includes one or more advice statements. Further, the method may include selecting an advice statement from the one or more advice statements, and presenting the selected advice statement to the user upon receiving approval of the selected advice statement from the human professional.