Asset Tracking Data Capture Triggers for Rich Analytics

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Solution Overview

Problem

Telematics systems face challenges in efficiently processing large amounts of data from asset tracking devices, leading to overwhelming technical infrastructure and a lack of rich detail for advanced analytics, as existing methods either discard valuable data or burden the system with excessive information.

Innovation Solution

Implementing a dual data capture approach where asset tracking devices apply a dataset simplification algorithm in parallel with a rich data capture algorithm, selectively transmitting simplified data for telematics services while logging and forwarding rich data for advanced analysis when specific triggers are met, using a combination of simplified and unsimplified data blocks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If all raw data is transmitted to the server for processing, then rich detail for advanced analytics is preserved, but the technical infrastructure becomes overwhelmed and processing efficiency decreases

Engineering Contradiction:

Improverich detail for advanced analyticsVSAvoiddata processing efficiency

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The data transmission process is segmented into two parallel paths: simplified data processing for telematics services and rich data capture for advanced analytics. This segmentation allows the system to handle different data types through different processing channels, preventing infrastructure overload while preserving necessary data detail.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Different quality levels of data are applied locally based on the specific processing need. Simplified data with aggregated and filtered information is used for routine telematics services, while high-fidelity raw data is preserved and transmitted only for advanced analytics when needed, optimizing both efficiency and information retention.

Inventive Principle:

Principle #3Local quality

2Productivity

If data is simplified before transmission, then processing efficiency improves and infrastructure load decreases, but rich detail for advanced analytics is lost

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidrich detail for advanced analytics

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system dynamically adjusts data processing based on trigger conditions. Normal operation uses simplified processing for efficiency, but when rich data capture triggers are satisfied (indicating events of interest), the system dynamically switches to capturing and transmitting unsimplified raw data blocks, allowing adaptive response to varying operational conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system performs preliminary filtering and simplification of data before transmission for routine processing, but maintains the capability to capture complete raw data blocks in advance when triggers indicate potential events of interest, ensuring both efficiency and analytical capability are preserved.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If rich data is captured and transmitted continuously, then advanced analytics capability is maintained, but system complexity and data management burden increase

Engineering Contradiction:

Improvedetail for advanced analyticsVSAvoiddata capture and management complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system extracts only the essential simplified data for continuous transmission to telematics services, while selectively extracting and transmitting complete raw data blocks only when rich data capture triggers are satisfied. This extraction approach minimizes continuous data management complexity while preserving analytical capability for important events.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

Instead of continuous rich data transmission, the system employs periodic rich data capture triggered by specific conditions. This periodic action reduces data management burden and system complexity while maintaining advanced analytics capability for significant events, balancing detail preservation with operational simplicity.

Inventive Principle:

Principle #19Periodic action

4Productivity

If simplified data processing is used for all data, then infrastructure load is reduced, but machine learning and advanced analytics suffer from lack of rich detail

Engineering Contradiction:

Improveinfrastructure processing capacityVSAvoidadvanced analytics accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system creates a composite data transmission strategy that combines simplified data processing for routine operations with selective rich data capture for analytical purposes. This composite approach uses both processed and raw data in appropriate contexts, maintaining infrastructure efficiency while ensuring sufficient data quality for machine learning and advanced analytics when needed.

Inventive Principle:

Principle #40Composite materials

Data Source

PatentUS11550337B2Data capture trigger configuration for asset tracking

Publication Date: 2023.01.10 GEOTAB INC

AI summary

Methods, systems, and devices for data capture trigger configuration for asset tracking are provided. Another example method capturing raw data involves obtaining a rich data capture trigger that defines when a controller of an asset tracking device onboard an asset is to identify and log an unsimplified block of raw data in raw data on the asset tracking device for rich data analysis, transmitting data capture instructions to the asset tracking device that contains the rich data capture trigger, and receiving the simplified set of raw data and the unsimplified block of raw data from the asset tracking device.