LLM Map Feedback Reporting With Structured Error Templates
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Solution Overview
Problem
Map service providers face challenges in integrating large language models (LLMs) for accurate and timely map feedback reporting due to outdated training data and the need to convert unstructured user feedback into structured data formats, which is costly and distracting for users.
Innovation Solution
A system utilizing LLMs to process natural language feedback, integrating geolocation and sensor data to verify and structure user inputs, and generate actionable map updates, with a map feedback AI assistant to facilitate seamless interaction and enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional map feedback methods are used, then users can report map errors, but the process is costly and distracting for users due to manual data entry requirements
Solution Approach 1:
The patent replaces manual mechanical data entry with automated natural language processing. Users speak or type feedback in natural language, and the system automatically processes this unstructured text to extract structured map error data, eliminating the need for users to manually fill out complex forms while reducing processing complexity through AI automation
Solution Approach 2:
The system enables self-service by automatically classifying error types, extracting relevant data fields, and structuring feedback without requiring user intervention in the data processing steps. The automated classification and data extraction mechanisms allow the system to serve itself in transforming unstructured feedback into structured data
2Measurement precision
If LLMs are trained specifically for map feedback, then classification accuracy improves, but training costs and time increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining error type categories and data field structures before processing user feedback. The system has predetermined classification schemas and extraction templates ready, allowing the LLM to immediately apply these structures to new feedback without requiring retraining for each new error type or data structure change
Solution Approach 2:
The system achieves universality by designing a flexible classification framework that can handle multiple error types and data formats using the same underlying LLM infrastructure. The pre-defined error categories and data field structures allow the system to process diverse feedback types without requiring specialized training for each scenario
3Manufacturing precision
If detailed data collection is performed, then map update accuracy improves, but user interaction time and distraction increase
Solution Approach 1:
The patent replaces manual user effort in data collection with automated NLP extraction. The system automatically extracts relevant data fields from unstructured user feedback through natural language processing, eliminating the need for users to manually provide detailed information while maintaining high accuracy in map updates
Solution Approach 2:
The system introduces an intermediary layer of automated data extraction between user feedback and map updates. This intermediary process automatically interprets unstructured user input and extracts structured data, serving as a mediator that reduces user interaction requirements while ensuring accurate data collection for map updates
Data Source
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AI summary
An approach is provided for large language model (LLM) map feedback reporting. The approach involves, for example, processing an input specifying a map error/feedback using an LLM to classify a map error type. The approach also comprises determining a map error template based on the map error type that specifies structured data fields for the map error type. The approach further involves using the template to construct prompts for the LLM to generate questions to collect data items for populating the data fields, and providing the prompts to the LLM to generate the questions and collect the data items from the user. The approach further involves using template to generate additional prompts to generate a map error report of the data items in a structured data format, and providing the additional prompts to the LLM to generate the map error report for transmission to a map feedback system.