Experience-Based Repair Platform for Vehicle Diagnostics
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Solution Overview
Problem
DIY consumers face challenges in identifying the necessary parts and tools for vehicle repairs, often requiring multiple trips to the store and lacking guidance on labor costs, leading to inefficiencies and increased complexity.
Innovation Solution
The Experience-Based Repair Solution (EBRS) platform receives vehicle diagnostic information, evaluates the issues, and generates lists of required and recommended parts, tools, along with complexity information and purchase interfaces, facilitating one-stop shopping and providing DIY cost estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If DIY consumers attempt vehicle repairs without comprehensive guidance, then they can save on labor costs, but they face increased complexity and require multiple trips to the store
Solution Approach 1:
The system performs preliminary actions by generating comprehensive part lists and complexity information before the consumer begins shopping or attempting repairs. The host system evaluates vehicle diagnostic information and pre-identifies all required parts and tools, allowing consumers to prepare in advance and avoid multiple trips to the store.
Solution Approach 2:
The EBRS host system acts as an intermediary between vehicle diagnostic information and the consumer's repair process. It processes diagnostic data, generates part lists, determines complexity levels, and provides guidance materials, thereby mediating the complex interaction between diagnostic information and repair execution.
2Adaptability or versatility
If DIY consumers shop for parts without comprehensive guidance, then they have flexibility in selection, but they require multiple trips to the store and spend excessive time
Solution Approach 1:
The system generates complete part lists with compatibility information before the consumer visits the store, allowing them to purchase all necessary parts in a single trip. The host system pre-evaluates diagnostic information and identifies all required parts, eliminating the need for multiple trips and excessive shopping time.
Solution Approach 2:
The system uses feedback loops where consumer purchases and repair outcomes are tracked. This feedback refines the part recommendation algorithms, improving the accuracy of future part lists and ensuring consumers receive increasingly precise guidance that maintains flexibility while reducing time spent shopping.
3Measurement precision
If the system provides detailed part lists and complexity information, then repair accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex repair guidance task into distinct functional modules: diagnostic information evaluation, part list generation, complexity determination, and guidance material provision. This segmentation allows each module to handle specific aspects independently, improving accuracy while managing system complexity through modular design.
Solution Approach 2:
The EBRS host system performs multiple functions using a unified architecture: it evaluates diagnostic information, generates part lists, determines complexity levels, and provides guidance materials. This multi-functionality reduces overall system complexity by consolidating operations into a single versatile platform rather than requiring separate specialized systems.
4Productivity
If consumers receive comprehensive repair information upfront, then the number of store trips decreases, but information processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant information from vehicle diagnostic data to generate part lists and complexity assessments. It filters out redundant or unnecessary information, providing consumers with concise, actionable guidance that improves repair efficiency without overwhelming them with excessive information processing requirements.
Solution Approach 2:
The system transforms raw diagnostic information into structured, standardized parameters such as part numbers, compatibility codes, and complexity ratings. This parameter transformation organizes information in a consistent format that is easier to process and act upon, improving productivity while reducing the cognitive load on consumers.
Data Source
AI summary
Embodiments are disclosed for facilitating Do-It-Yourself (DIY) repairs. In the context of a method, an example embodiment includes receiving, by an experience-based repair service (EBRS) host system, vehicle diagnostic information identifying one or more vehicle problems. This example embodiment of the method further includes evaluating the vehicle diagnostic information, generating, based on the evaluation of the vehicle diagnostic information, required part types and required tools for addressing the one or more vehicle problems, and generating, based on the evaluation of the vehicle diagnostic information, recommended part types for addressing the one or more vehicle problems. Finally, this example embodiment of the method further includes causing presentation of an interface facilitating purchase of required parts from each of the generated required part types, the required tools, and recommended parts from each of the generated recommended part types. Corresponding apparatuses and computer program products are also provided.