Stateful Advice System Using Generative AI for Rule Generation
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Solution Overview
Problem
Stateful advice systems based on rules are difficult to scale due to the effort required to comprehend and translate problem and solution domains into computer languages, necessitating a cross-functional team that is time-consuming and cost-prohibitive.
Innovation Solution
A method utilizing a generative artificial intelligence model to generate a majority of the rules/content for stateful advice systems, with user-provided configuration data guiding the model to determine applicable strategies, allowing for faster scaling and more accurate results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based stateful advice systems are implemented, then advice functionality is provided, but scaling difficulty increases due to manual comprehension and translation effort
Solution Approach 1:
The patent replaces the manual mechanical process of comprehending and translating problem-solution domains into computer languages with an automated AI-based system. The AI model automatically generates the domain model, model schema, and advice rules from natural language inputs, eliminating the need for manual rule-based programming and enabling rapid scaling of advice functionality.
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously generate and update the domain model and advice rules without requiring manual intervention from cross-functional teams. The automated workflow includes the AI model receiving inputs, generating the domain model, determining applicable strategies, and producing advice rules independently, thereby accelerating system scaling.
2Reliability
If cross-functional teams are assembled to build rule-based systems, then system functionality is achieved, but time consumption and costs increase
Solution Approach 1:
The patent substitutes the manual work of cross-functional teams with an automated AI system that performs domain comprehension, model generation, and rule creation. This automation eliminates the time-consuming collaborative process involving product managers, user experience designers, domain experts, and software engineers, reducing development time while maintaining system functionality.
Solution Approach 2:
The AI model performs preliminary actions by pre-generating the domain model and model schema from natural language inputs before the advice generation process begins. This preliminary automation of complex analytical work that would otherwise require extensive human effort significantly reduces the time needed to build functional advice systems.
3Measurement precision
If manual rule creation is used, then system accuracy can be controlled, but the complexity of the system increases
Solution Approach 1:
The patent replaces complex manual rule creation processes with AI-based automated generation. The AI model systematically analyzes the domain model and generates accurate advice rules based on learned patterns, maintaining measurement precision while reducing the operational complexity of system creation and maintenance.
Solution Approach 2:
The system changes the parameter of rule generation from manual specification to AI-driven automated generation. This parameter change allows the system to maintain accuracy through the AI model's learning capabilities while reducing complexity by eliminating the need for manual comprehension and translation of domain knowledge into rules.
Data Source
AI summary
A method for operating a stateful advice system includes determining, based on configuration data, whether a selected mode for the stateful advice system corresponds to a first mode in which a domain model is directly modified to implement one or more of a plurality of different strategies or a second mode in which the domain model is indirectly modified via a model schema of the domain model. The method includes providing a prompt to a generative artificial intelligence (AI) model configured to determine applicability of each of the plurality of different strategies to the domain model. The prompt includes the domain model when the selected mode corresponds to the first mode or the model schema when the selected mode corresponds to the second mode. The method includes receiving one or more recommendations from the generative AI model.