Automated Repair Information System for Natural Language Queries
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Solution Overview
Problem
Existing search engines face challenges in identifying and providing relevant information due to the need for specific search terms, overwhelming responses, and the obscuration of relevant information by non-relevant results, making it difficult to find specific answers to natural language queries.
Innovation Solution
The Automated Repair Information Determination (ARID) system uses natural language processing and machine learning techniques to summarize, encode, and identify specific repair or maintenance instructions by generating embedding vectors, employing a trained validation model to determine responsive answers, and providing them in an executable format for automated implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing search engines are used to find repair information, then a large amount of information is available, but the relevant information is obscured by non-relevant results and the response is too extensive to easily review
Solution Approach 1:
The system extracts only the specific repair instructions relevant to the diagnosed issue from the large corpus of device information, manuals, and repair guides. Instead of presenting all available information, it isolates and presents only the pertinent repair steps, thereby resolving the contradiction between having abundant information and making it easy to find relevant content.
Solution Approach 2:
The system introduces an intermediary processing layer that includes natural language processing, machine learning models, and validation components. This intermediary automatically filters, validates, and formats the repair information between the user query and the final output, eliminating the need for users to manually review extensive search results.
2Measurement precision
If specific search terms are required to find relevant information, then precision of search results improves, but the ease of use deteriorates due to the need for users to formulate specific technical queries
Solution Approach 1:
The system replaces the mechanical process of manual query formulation with automated natural language processing and machine learning. The system automatically interprets user-friendly descriptions, diagnoses issues, and retrieves precise repair information without requiring users to construct technically precise search terms, thus maintaining both precision and ease of use.
Solution Approach 2:
The system performs self-service by automatically understanding user intent, diagnosing device issues, and retrieving appropriate repair instructions without human intervention in the information retrieval process. This eliminates the burden on users to formulate specific search terms while still achieving precise results.
3Productivity
If automated repair determination is implemented, then speed of identifying repair actions improves, but device complexity increases due to the need for processing and validation systems
Solution Approach 1:
The automated repair determination system is segmented into distinct functional modules: device identification module, issue diagnosis module, repair instruction retrieval module, and validation module. Each module handles a specific aspect of the process, which manages the overall complexity by organizing functions into manageable, independent components that can be developed and maintained separately.
Solution Approach 2:
The system uses parameter changes in the form of machine learning model configurations, validation thresholds, and processing parameters to optimize the balance between automation speed and system complexity. By adjusting these parameters, the system can achieve high-speed automated repair determination while managing computational resources and system complexity effectively.
Data Source
AI summary
Techniques are described for performing automated operations related to identifying and using repair and maintenance information, such as summarizing and encoding such information for a number of problems, identifying specific repair or maintenance instructions in response to natural language queries, and using the identified instructions in further automated manners in some situations (e.g., to automatically initiate repair or maintenance actions on a particular target computing device). Identifying of specific instructions in response to a particular natural language query may include initially identifying a group of candidate groups of content that satisfy a defined similarity threshold to an encoded version of the natural language query, providing and using a trained validation model to evaluate each candidate content group and validate if it includes a responsive answer to the natural language query, and then further analyzing one or more validated candidate content groups to determine the actual responsive answer.