Vehicle Service Recommendations from Historical and Sensor Data
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Solution Overview
Problem
Current vehicle service recommendation processes are prone to inaccuracies due to manual judgments and lack of personalized, data-driven recommendations, leading to users receiving undesirable services that obscure desirable ones.
Innovation Solution
A machine learning-based system that profiles vehicles and drivers, using historical data and sensor inputs to generate personalized service recommendations, which are optimized through user interaction and re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual inspection processes are used, then technician judgment is applied, but accuracy of service recommendations deteriorates due to potential unawareness of potential services or poor component health
Solution Approach 1:
The patent replaces manual inspection processes with an automated machine learning-based recommendation system. The system uses computer vision to capture vehicle images, processes them through trained models to identify component states, and generates service recommendations without human intervention, thereby eliminating the accuracy limitations of manual inspection while maintaining ease of operation through automated processing
Solution Approach 2:
The system enables self-service by automatically analyzing vehicle conditions and generating recommendations without requiring technician expertise. The machine learning models independently evaluate component health states and produce service suggestions, allowing the system to serve itself rather than relying on human technician judgment for accurate assessments
2Extent of automation
If automation mechanisms are used, then manual inspection is replaced, but accuracy deteriorates because inputs are driven by human inspections and heuristics
Solution Approach 1:
The patent replaces heuristic-based automation with machine learning models trained on extensive vehicle data. Instead of using human heuristics to drive automation, the system employs deep learning models that process visual and sensor data to generate accurate recommendations, achieving both high automation and high precision
Solution Approach 2:
The system changes the parameters of automation by transitioning from rule-based heuristics to data-driven machine learning models. The models are trained on diverse vehicle conditions and component states, enabling them to accurately determine service needs based on actual vehicle parameters rather than simplified heuristics
3Ease of operation
If manual transmission of service information is used, then users receive service recommendations, but relevance deteriorates because recommendations are not informed by prior decisions causing flooding with undesirable services
Solution Approach 1:
The patent implements feedback mechanisms where the system learns from user interactions with previous recommendations. The machine learning models analyze user responses and adjust future recommendations accordingly, creating a feedback loop that continuously improves relevance. This prevents flooding with undesirable services by adapting to user preferences based on actual user behavior
Solution Approach 2:
The system makes service recommendation transmission dynamic by adjusting recommendations based on real-time user interactions and historical data. Rather than static manual transmission, the system dynamically adapts its output based on user feedback, vehicle conditions, and learned preferences, ensuring high relevance in each interaction
Data Source
AI summary
A device receives current vehicle data that describes a current state of each of a plurality of components of the vehicle. The device accesses historical data describing prior services previously performed on the vehicle and applies the current vehicle data and the historical vehicle data to a trained machine learning model to obtain a set of recommended services, where the model was trained using labeled training data associated with additional vehicles having a threshold similarity to the vehicle. The machine learning model is trained to output a set of recommended services to be performed for a given vehicle based on inputs of given current vehicle data and given historical vehicle data for the given vehicle. The device outputs for display the set of recommended services to be performed on the vehicle to a user.