Common Trip Format for Telematics Data Integration

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Solution Overview

Problem

Existing telematics processing systems face challenges in integrating third-party source data with telematics data in a standardized format, which complicates the determination of customized driver outputs while maintaining accuracy.

Innovation Solution

A computing platform that connects with telematics sensors and third-party data sources, combines and validates telematics and third-party data using machine learning, and generates a standardized Common Trip Format (CTF) output based on validated data, facilitating customized driver output determinations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If telematics data and third party source data are integrated in a standardized format, then data compatibility is improved, but data processing complexity increases

Engineering Contradiction:

Improvedata compatibilityVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent introduces a Common Trip Format (CTF) as an intermediary standardized data structure that mediates between diverse telematics data sources and third-party source data. The CTF acts as a common language that enables data integration without requiring complex custom integration logic for each data source, thereby improving compatibility while managing processing complexity through standardization.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms heterogeneous data from multiple sources into a unified parameter structure defined by the Common Trip Format. By changing the parameter representation of data from source-specific formats to standardized CTF parameters, the system achieves consistent data compatibility across different sources while simplifying downstream processing.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If machine learning validation is applied to enriched telematics output, then data accuracy is improved, but processing time increases

Engineering Contradiction:

Improvedata accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary enrichment of telematics output with third-party source data before validation, organizing and preparing the data in advance. This preliminary action structures the data in a way that facilitates more efficient machine learning validation, reducing the overall processing time while maintaining accuracy requirements.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent creates enriched telematics output as a validated copy or representation of the original telematics data, enhanced with additional third-party source data. This copying approach allows the validation process to work on the enriched representation without repeatedly processing the original raw data, thereby improving accuracy while managing processing time through efficient data replication.

Inventive Principle:

Principle #26Copying

3Productivity

If enriched telematics output is stored for batch processing, then system resource utilization is improved, but data availability time increases

Engineering Contradiction:

Improvesystem resource utilizationVSAvoiddata availability time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system implements periodic batch processing of enriched telematics output, where data is collected and processed at regular intervals. This periodic action optimizes system resource utilization by consolidating processing operations, while the batch approach ensures that data becomes available systematically at defined intervals, balancing resource efficiency with data availability requirements.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS12264927B2Processing system having a machine learning engine for providing a common trip format (CTF) output

Publication Date: 2025.04.01 ALLSTATE INSURANCE COMPANY

AI summary

Aspects of the disclosure relate to enhanced telematics processing systems with improved third party source data integration features and enhanced customized driving output determinations. A computing platform may receive telematics data and third party source data. The computing platform may enrich the telematics data using the third party source data. After generating the enriched telematics data, the computing platform may use machine learning algorithms and datasets to validate the enriched telematics data. The computing platform may ingest, via a batch ingestion process, the enriched telematics data. For example, the computing platform may store the enriched telematics data and generate additional enriched telematics data until expiration of a predetermined period of time. The computing platform may ingest the enriched telematics data associated with each trip. Once the enriched telematics data has been ingested, the computing platform may generate a standardized common trip format output for each trip.