Vehicle Diagnostics via Agnostic Command Translation
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Solution Overview
Problem
Traditional vehicle diagnostic and testing systems are limited to specific vendors, models, and require firmware updates for new tests, leading to high costs and complexity, and face challenges with data collection, management, and access, especially with increasing vehicle connectivity and data demands.
Innovation Solution
A system that interprets agnostic mobile system data, generates adapted data, and performs diagnostics or tests on vehicles, using a server and mobile interface device to manage data collection and access across various network zones, reducing the need for firmware updates and enhancing data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional vehicle diagnostic systems use vendor-specific protocols and firmware-based tests, then diagnostic functionality is reliable for specific vehicles, but system adaptability and flexibility deteriorate
Solution Approach 1:
The system separates diagnostic functionality into distinct modules: a communication module that handles vendor-specific protocols, a translation module that converts proprietary data to standardized formats, and a diagnostic engine that performs analysis. This segmentation allows each component to specialize while maintaining overall system flexibility and reliability.
Solution Approach 2:
The patent introduces a data translation layer that acts as an intermediary between vendor-specific communication protocols and the diagnostic analysis engine. This intermediary translates proprietary data formats into standardized representations, enabling the system to work with multiple vehicle vendors without requiring vendor-specific diagnostic logic for each.
2Reliability
If firmware updates are required for new diagnostic tests, then system reliability is maintained, but productivity and deployment speed deteriorate
Solution Approach 1:
The system implements dynamic test configuration where diagnostic test parameters, thresholds, and analysis rules can be modified and deployed without firmware updates. The diagnostic engine loads test configurations from external sources, allowing rapid deployment of new tests by simply updating configuration files rather than requiring firmware re-certification.
Solution Approach 2:
The patent uses standardized data representations that can be copied and reused across different vehicle types and diagnostic scenarios. Instead of developing custom firmware for each test, the system copies and adapts existing diagnostic templates and analysis rules, significantly reducing deployment time while maintaining reliability through proven methodologies.
3Measurement precision
If extensive data collection is performed for comprehensive diagnostics, then measurement precision improves, but data management complexity and security risks worsen
Solution Approach 1:
The system applies different processing and storage quality levels to different data types based on their diagnostic importance. Critical diagnostic parameters receive high-precision processing and secure storage, while less critical data undergoes lighter processing. This local quality approach ensures measurement precision for essential diagnostics while reducing overall data management complexity.
Solution Approach 2:
The patent dynamically adjusts data collection parameters based on diagnostic needs, vehicle operating conditions, and data priority levels. The system can change sampling rates, resolution, and retention periods for different data streams, collecting high-precision data only when necessary for critical diagnostics while reducing data volume for routine monitoring, thereby balancing precision with manageability.
Data Source
AI summary
A method including: interpreting a user input; generating, based on the user input, a first agnostic vehicle diagnostic command; translating the first agnostic vehicle diagnostic command to a first adapted vehicle diagnostic command; interpreting a first adapted vehicle diagnostic data generated in response to executing the first adapted vehicle diagnostic command; translating the first adapted vehicle diagnostic data to first agnostic vehicle diagnostic data; generating a second agnostic vehicle diagnostic command based at least in part on the first adapted vehicle diagnostic data; translating the second agnostic vehicle diagnostic command to a second adapted vehicle diagnostic command; interpreting second adapted vehicle diagnostic data generated in response to executing the second adapted vehicle diagnostic command; translating the second adapted vehicle diagnostic data to second agnostic vehicle diagnostic data; and generating a state value for the vehicle based at least in part on the second adapted vehicle diagnostic data.