Vehicle Telematics Model for Personalized Acquisition Cost
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Solution Overview
Problem
Existing vehicle maintenance plans are 'one size fits all' and do not account for individual vehicle usage patterns, leading to inefficient cost allocation and lack of personalized savings opportunities for vehicle owners.
Innovation Solution
A system and method that utilize historical vehicle telematics data and vehicle data to build a model predicting the acquiring cost of a vehicle for a predetermined time period, including predefined operational parameters that, if met, allow for reduced costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a one size fits all maintenance plan is used, then all vehicle owners are covered for standard maintenance, but individual savings opportunities are lost and cost allocation is inefficient
Solution Approach 1:
The patent applies parameter changes by using telematics data to dynamically adjust maintenance plan parameters based on actual vehicle usage. The system changes cost parameters, maintenance intervals, and coverage levels according to measured driving patterns, mileage, and operational conditions, enabling personalized plans without manual configuration of each parameter.
Solution Approach 2:
The system implements self-service by automatically collecting telematics data, analyzing usage patterns, and generating personalized maintenance plans without requiring manual input from vehicle owners. The system serves itself by using its own collected data to determine appropriate maintenance parameters and cost allocations for each user.
2Productivity
If telematics data collection and modeling is implemented, then personalized cost optimization is achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The patent applies universality by designing a multi-functional system that simultaneously performs data collection, analysis, modeling, and cost calculation. The same telematics infrastructure serves multiple purposes: monitoring vehicle health, analyzing usage patterns, predicting maintenance needs, and determining personalized costs, thereby reducing overall system complexity despite the range of functions.
Solution Approach 2:
The system uses an intermediary modeling layer that processes raw telematics data and transforms it into actionable insights for cost calculation. This intermediary model acts as a mediator between data collection and cost optimization, simplifying the complexity by providing a structured intermediate representation that bridges raw data and financial decisions.
Data Source
AI summary
Provided herein is a computing system including a processor in communication with at least one memory. The processor is configured to (i) build a model using historical vehicle telematics data and historical vehicle data, the model configured to: (a) predict an operating cost of a vehicle based upon a vehicle type and user data and (b) output operational parameters for operating the vehicle, (ii) receive a vehicle acquisition request for a selected vehicle including vehicle data for the selected vehicle and selected user data for the user of the selected vehicle, (iii) input the vehicle acquisition request data into the model, and (iv) output from the model an acquisition cost and operational parameters for the selected vehicle and the selected user including a procurement cost for the selected vehicle and an operating cost for operating the selected vehicle within the operational parameters for a predetermined time period.