AI Input Translation Layer for Multi-Format Maintenance Data
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Solution Overview
Problem
Current approaches for translating different types of input data into formats usable by artificial intelligence models are inefficient, particularly when dealing with numerical matrix data, leading to deployment difficulties in machine learning and AI applications, such as vehicle maintenance systems.
Innovation Solution
A system and method that converts incoming data from various formats, including image, text, video, and audio files, into intermediate n-dimensional array representations, with additional processing for reshaping, resizing, and channel alignment, to create a common format for AI model input, enabling efficient use and development of AI models across multiple input types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current translational approaches are used to convert different input data types into AI model formats, then deployment can be achieved, but the process is inefficient and creates deployment difficulties
Solution Approach 1:
The patent implements a universal translation layer that handles multiple input data types (images, text, audio, video) through a single unified architecture. The system uses format-agnostic intermediate representations and standardized preprocessing pipelines that can process diverse inputs without requiring separate deployment configurations, thereby improving both translation efficiency and ease of deployment across different AI model types.
Solution Approach 2:
The patent introduces an intermediary translation layer between raw input data and AI model processing. This intermediary component converts various input formats into standardized intermediate representations (such as numpy arrays with consistent data types and structures), eliminating the need for model-specific preprocessing code and simplifying deployment while maintaining high translation efficiency.
2Adaptability or versatility
If multiple different types of input data are processed through current methods, then various AI input types can be used, but the translation process becomes inefficient
Solution Approach 1:
The patent segments the translation process into distinct modular stages: format detection, type-specific preprocessing, standardized conversion to intermediate representations, and final normalization. Each stage handles specific aspects of data transformation independently, allowing efficient processing of multiple input types simultaneously while maintaining code reusability and reducing overall translation complexity.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on input data type detection. The system automatically selects appropriate preprocessing configurations (such as image resolution, text tokenization methods, audio sampling rates) without manual intervention, enabling versatile handling of different input types while optimizing translation efficiency through parameter自适应 adjustment.
Data Source
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AI summary
A maintenance system(100) includes a maintenance task input unit (110) and one or more processors. The maintenance task input unit is configured to generate plural input data groups corresponding to a maintenance task. Each of the input data groups is in a corresponding one of plural formats. The one or more processors are coupled to the maintenance task input unit and configured to obtain the plural input data groups from the maintenance task input unit. The one or more processors are configured to identify a particular format of the formats for each input data group, and to prepare an intermediate representation of the input representation based on the particular format that is identified. Also the one or more processors are configured to modify the intermediate representation to provide a model input having a common format. The one or more processors are configured to modify intermediate representations corresponding to each of the input data groups to the common format, and to provide the model input to a maintenance system artificial intelligence modeler (190) that is configured to use the model input to at least one of develop or use a model (192) using the model input for vehicle maintenance.