AI Chatbot Multi-Pass Processing for Accurate Complex Queries
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Solution Overview
Problem
Existing AI/ML models often provide ambiguous or inaccurate responses to complex, compound questions due to probabilistic processing, leading to increased costs and reduced user confidence.
Innovation Solution
A computer system that processes user prompts through multiple stages, using AI/ML models to generate data processing code, analyzing the code for multiple data processing stages, and retrieving data from a database system to provide precise responses, while also providing users with interpretation information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI/ML models process complex questions through probabilistic processing, then the system can provide responses to user prompts, but the responses become ambiguous or inaccurate and user confidence decreases
Solution Approach 1:
The patent segments the complex question processing into multiple distinct passes or stages. Each pass focuses on specific aspects of the query, allowing the system to break down ambiguous complex questions into smaller, more manageable components that can be processed more accurately and reliably.
Solution Approach 2:
The patent implements preliminary analysis passes before the final response generation. These initial passes evaluate the question, identify key components, and prepare structured representations that guide the subsequent response generation, thereby improving accuracy before the probabilistic modeling stage occurs.
2Stability of the object's composition
If AI/ML models are used for all processing stages, then the system can maintain consistency, but the cost increases and latency increases
Solution Approach 1:
The patent creates a multi-functional processing architecture where different types of operations are performed by different components: deterministic operations (data retrieval, code execution, structured analysis) are handled by traditional systems, while probabilistic operations (natural language understanding, response generation) are handled by AI/ML models. This universal approach maintains consistency through standardized interfaces while reducing costs by avoiding unnecessary AI/ML usage.
Solution Approach 2:
The patent introduces intermediary components that bridge AI/ML models and deterministic systems. These intermediaries translate between probabilistic outputs and deterministic inputs, allowing the system to leverage both approaches without requiring AI/ML processing at every stage, thereby reducing computational costs while maintaining overall system consistency.
3Reliability
If the system provides detailed interpretation information for each processing stage, then user confidence increases, but the device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where the system generates and provides interpretation information about its processing stages back to the user. This feedback loop allows users to understand how their questions are processed, what data is retrieved, and how responses are formed, thereby increasing confidence while the feedback is structured to avoid overwhelming complexity.
Solution Approach 2:
The patent provides interpretation information selectively rather than comprehensively at all stages. It offers partial explanations focused on the most relevant and impactful processing steps, giving users sufficient information to build confidence without presenting the full complexity of the entire system architecture.
4Measurement precision
If the system retrieves and processes data through multiple stages, then the precision of responses improves, but the latency increases
Solution Approach 1:
The patent performs preliminary data retrieval and processing in advance, before the final response generation. By pre-fetching and pre-processing data in earlier passes, the system reduces the computational burden during the final response stage, thereby maintaining high precision while reducing overall latency.
Solution Approach 2:
The patent segments data processing into parallel or overlapping stages where independent operations can be executed concurrently. This segmentation allows the system to maintain precise multi-stage processing while reducing sequential dependencies that would increase latency.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for multi-pass processing for artificial intelligence chatbots. In some implementations, a system obtains code or instructions generated by one or more artificial intelligence or machine learning (AI/ML) models, where the code or instructions specify criteria to retrieve data from a data source to respond to a prompt from a user. The system determines that the code or instructions specify multiple stages of data processing. The system generates a set of results from the data source based on the generated code or instructions, and obtains a response to the prompt that the one or more AI/ML models generate using at least a portion of the set of results. The system generates an interpretation statement that describes each of the multiple stages of data processing and provides output that includes (i) the response to the prompt and (ii) the generated interpretation statement.