Telematics Match Evaluation Using Predicted vs Actual Profitability
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Solution Overview
Problem
Conventional telematics data collection is limited by the need for party-specific devices, requiring customers to install multiple devices sequentially for different parties, lacking universality in data collection and sharing.
Innovation Solution
A computer-implemented method and system for managing user information through a telematics marketplace, collecting and sharing data via sensing modules, determining telematics inferences, and updating match evaluations based on differences in predicted and actual profitability, while modifying predictive models for improved data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If party-specific devices are used for telematics data collection, then each party can collect data for their own use, but customers must install multiple devices sequentially for different parties, reducing universality and increasing device complexity
Solution Approach 1:
The patent implements a universal telematics device that can serve multiple parties simultaneously. The device collects telematics data from the vehicle and makes it available to multiple parties through a marketplace platform, eliminating the need for separate party-specific devices. This multi-functional approach maintains data collection reliability while significantly reducing device complexity and installation burden on customers.
Solution Approach 2:
The patent introduces a marketplace platform as an intermediary between the telematics device and multiple parties. The device communicates with the marketplace, which then distributes data to authorized parties. This intermediary architecture allows one device to serve multiple parties without requiring direct integration with each party's systems, resolving the contradiction between reliable data collection and device complexity.
2Reliability
If multiple party-specific devices are installed sequentially, then each party's data needs are met, but the process is time-consuming and reduces productivity
Solution Approach 1:
By deploying a single universal telematics device that can serve multiple parties simultaneously, the system eliminates the sequential installation process. The device collects data once and makes it available to all authorized parties through the marketplace platform, dramatically improving data collection efficiency while maintaining data availability for all parties.
Solution Approach 2:
The universal device is pre-configured to collect and transmit telematics data to the marketplace platform, which then prepares and distributes data to multiple parties in advance. This preliminary data preparation and distribution infrastructure eliminates the need for sequential device installations, significantly improving productivity while ensuring data availability.
3Reliability
If telematics data is collected for single-party use only, then data privacy and security are simplified, but data sharing and utilization across parties are limited
Solution Approach 1:
The marketplace platform serves as a secure intermediary that manages data sharing between parties. The universal telematics device uploads data to the marketplace, which then controls access and distribution to authorized parties based on their subscriptions and agreements. This intermediary architecture enables versatile data sharing across multiple parties while maintaining security through centralized access control and authentication mechanisms.
Solution Approach 2:
The system implements a universal data access model where a single data collection infrastructure serves multiple parties with different needs. The marketplace platform provides customized data access levels for different parties while maintaining a unified secure backend, enabling both broad data sharing capability and targeted security controls simultaneously.
Data Source
AI summary
Method, system, device, and non-transitory computer-readable medium for match evaluation. In some examples, a computer-implemented method includes: collecting a first set of operator data during a first time period prior to user acquisition; collecting a second set of operator data during a second time period after user acquisition; determining a first set of telematics inferences including an predicted profitability; determining a second set of telematics inferences including an actual profitability; determining and continually updating one or more match evaluations based at least in part upon one or more differences between the first set of telematics inferences and the second set of telematics inferences; and modifying the one or more predictive models based at least in part upon the one or more match evaluations.