Context-Aware Conversational Maps for Ambiguous Queries
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Solution Overview
Problem
Existing mapping technologies suffer from limited natural language understanding (NLU), inability to handle ambiguity, complexity in query formulation, reduced flexibility, and heavy dependency on structured data, leading to inaccurate information retrieval and increased input/output (I/O) due to reliance on semi-structured information retrieval systems.
Innovation Solution
Utilizing a Large Language Model (LLM) to process natural language questions or commands, extract contextual data, and generate responses, including clarifying questions and enriched prompts, to improve NLU, handle ambiguity, reduce query complexity, and leverage unstructured data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If semi-structured information retrieval systems are used, then information retrieval can be performed, but natural language understanding is limited and query formulation becomes complex
Solution Approach 1:
A language model serves as an intermediary between the user's natural language query and the structured information retrieval system. The language model translates unstructured natural language queries into structured query formats that the retrieval system can process, thereby improving NLU while maintaining system functionality.
Solution Approach 2:
The patent replaces manual query formulation mechanisms with an automated language model-based query generation system. Instead of requiring users to construct complex structured queries, the system automatically generates appropriate queries from natural language inputs, reducing operational complexity.
2Adaptability or versatility
If structured data dependency is increased, then information retrieval accuracy can be maintained, but flexibility and ability to handle ambiguity decrease
Solution Approach 1:
The system changes the data processing parameters by introducing a language model layer that can handle both structured and unstructured data. This allows the system to maintain accuracy with structured data while gaining flexibility to process ambiguous and unstructured queries through the language model's interpretive capabilities.
Solution Approach 2:
The patent creates a composite information retrieval system that combines structured data sources with unstructured language model capabilities. This composite architecture allows the system to leverage the precision of structured data while incorporating the flexibility and ambiguity-handling abilities of language models.
3Measurement precision
If conversational context is incorporated, then NLU and ambiguity handling improve, but system complexity and processing requirements increase
Solution Approach 1:
The system performs preliminary actions by extracting and storing contextual information from previous conversation turns before processing the current query. This pre-processing of contextual data allows the language model to accurately interpret ambiguous queries without requiring complex real-time analysis during query processing.
Data Source
AI summary
Various embodiments discussed herein relate to using one or more language models and/or mapping platforms to generate a response to a natural language question or command regarding geographical information associated with a mapping platform. In response to receiving such natural language question or command, some embodiments first extract contextual data. Based at least in part on the extracting of the contextual data, various embodiments then provide the contextual data and the natural language command or question as input into one or more language models such that the one or more language models and/or mapping platforms generate a response. Some embodiments then cause presentation of an indication associated with the response.