Repair Data Analysis for Accurate Automated Maintenance Decisions
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Solution Overview
Problem
Existing search engines and techniques struggle with identifying and utilizing specific search terms effectively, leading to an overabundance of irrelevant information and difficulty in finding relevant repair and maintenance information for computing devices or other entities.
Innovation Solution
An Automated Repair Information Determination (ARID) system that combines image-based and textual-based analysis to extract, categorize, and link repair data from structured and unstructured sources, enabling efficient and accurate identification of relevant repair information for computing devices or other entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing search engines are used to identify information from vast datasets, then information retrieval can be performed, but the speed and accuracy of finding relevant repair information is insufficient and the process is overwhelmed by irrelevant data
Solution Approach 1:
The patent segments the vast dataset into structured repair data (from CMs, invoices, warranties) and unstructured repair data (from images, PDFs, handwritten notes). This segmentation allows the system to process and analyze each type of data using appropriate methods, improving the accuracy of retrieving relevant repair information while filtering out irrelevant data.
Solution Approach 2:
The patent introduces an intermediary automated system that acts as a mediator between the vast datasets and the user. This system includes components for automated data extraction, validation, and analysis that filter and process information before presenting it to users, thereby improving accuracy while eliminating the need for users to wade through overwhelming irrelevant data.
2Measurement precision
If manual analysis of repair data is performed, then detailed repair information can be extracted, but the speed and productivity of information retrieval is too slow
Solution Approach 1:
The patent implements self-service through automated data extraction and validation systems that process repair data without human intervention. The system automatically extracts information from structured sources (CMs, invoices) and unstructured sources (images, PDFs), validates the data against predefined criteria, and generates repair information determinations autonomously, thereby maintaining high accuracy while dramatically improving speed and productivity.
Solution Approach 2:
The patent replaces manual mechanical analysis with automated electronic processing systems. Optical character recognition (OCR) technology converts images and handwritten notes into machine-readable text, while automated validation rules and algorithms process the data rapidly, substituting slow manual analysis with fast electronic processing while maintaining or improving extraction accuracy.
3Measurement precision
If comprehensive repair data is stored and analyzed, then accurate repair information can be determined, but storage and computational requirements become excessive
Solution Approach 1:
The patent extracts only the essential repair information from comprehensive datasets using automated validation rules and analysis algorithms. Instead of storing and processing all raw data, the system extracts key repair determinations, validations, and conclusions, thereby maintaining accurate repair determination while significantly reducing storage requirements by keeping only the extracted essential information.
4Loss of information
If sensitive repair information is retained, then complete repair records are available, but privacy and security risks increase
Solution Approach 1:
The patent extracts and retains only the essential repair information needed for repair determinations while removing or anonymizing sensitive personal data. The system validates and processes complete repair records but stores only the extracted repair determinations, validations, and essential information, thereby maintaining record completeness for repair purposes while reducing privacy and security risks through selective data retention.
Data Source
AI summary
Techniques are described for performing automated operations related to identifying and using repair and maintenance information, such as extracting and linking data about repair and maintenance activities performed on various devices or other entities, determining specific repair and/or maintenance information of one or more specified types in response to queries (e.g., for one or more particular such devices that are identified based on those queries), and subsequently using the identified repair information in further automated manners in some situations (e.g., to automatically initiate repair or maintenance actions on a particular target computing device). The extracting of repair and maintenance data may include analyzing information from multiple source documents (from one or more repair activity providers) and/or across multiple repair encounters, and using a combination of both image-based and text-based analyses.