Generative AI Dialogue System for Internal Data Analysis
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Solution Overview
Problem
Current systems for analyzing and generating data are cumbersome, fail to provide clear guidance for users, and do not account for nuances in language and user interpretation, leading to inefficiencies and miscommunication.
Innovation Solution
A computer-implemented method using generative AI models to identify impactful elements in internal database information, generating dialogue outputs that improve user understanding by analyzing customer feedback, market feedback, and project information, and providing context and alternative phrases to enhance survey questions and product pitches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current systems generate data products using internal data, then data generation is achieved, but user understanding of the reasoning process is lost
Solution Approach 1:
The patent introduces an intermediary component that translates the reasoning process into natural language explanations. This mediator layer sits between the data generation engine and the user, converting internal system decisions into comprehensible dialogue that preserves transparency without compromising generation efficiency.
Solution Approach 2:
The system implements feedback loops where user interactions and comprehension levels inform subsequent explanations. The system continuously adapts its reasoning presentations based on user responses, improving clarity over time while maintaining efficient data generation processes.
2Reliability
If current systems direct users to human elements for questions, then complex queries are answered, but timing and communication efficiency deteriorate
Solution Approach 1:
The patent implements a self-service dialogue system where the AI model autonomously handles complex queries by accessing internal data and generating informed responses. This eliminates the need for human intervention in routine complex queries, providing accurate answers immediately without timing delays or communication breakdowns.
Solution Approach 2:
An AI-mediated system serves as an intermediary between users and human experts, filtering and resolving queries that can be answered autonomously while escalating only truly complex cases to human elements, thereby reducing overall response time while maintaining reliability.
3Productivity
If current systems rely on numerical feedback or keywords for survey generation, then survey creation is achieved, but language nuance and user interpretation are lost
Solution Approach 1:
The patent replaces mechanical keyword-matching systems with a generative AI model capable of natural language understanding. This substitution enables the system to process and preserve language nuances, contextual meanings, and interpretive subtleties while maintaining efficient survey generation through automated natural language processing.
Data Source
AI summary
Systems and methods are described for identifying impactful elements in database information to generate a dialogue output. The method may include: (1) receiving, by one or more processors, internal database information at a generative artificial intelligence (AI) model, wherein the internal database information includes data associated with interaction dialogue; (2) analyzing, by the one or more processors, the internal database information via the generative AI model to generate an internal database analysis; (3) identifying, by the one or more processors and based upon at least the internal database analysis, one or more impact elements regarding human understanding of the internal database information via the generative AI model; and (4) generating, by the one or more processors and based upon at least the one or more impact elements, a dialogue output (or visual or virtual output) regarding the data via the generative AI model.