Model-Based Data Processing Pipeline With Standardized Transformation
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Solution Overview
Problem
Existing data processing systems face challenges in achieving a streamlined process for processing data from various sources and providing flexibility and customization in using models and applications, as they typically require specifically trained models configured for specific outputs based on formatted data.
Innovation Solution
A data processing system that includes a data storage system, an input data processing module, and a model execution environment, which transforms input data from various sources into a predefined format for machine learning models, allowing for flexible and customizable data processing and output encoding for different applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specifically trained models are used for specific outputs based on formatted data, then model accuracy and reliability are improved, but system flexibility and adaptability deteriorate
Solution Approach 1:
The patent implements a universal data processing pipeline that can handle multiple data sources and formats through a single standardized interface. The system uses a common data transformation layer that adapts different input formats to a standardized internal representation, allowing the same machine learning models to process diverse data types without requiring separate specialized models for each data source.
Solution Approach 2:
The patent introduces an intermediary data transformation layer between raw data inputs and machine learning models. This intermediary component standardizes data formats, handles data preprocessing, and provides a unified interface to models, thereby maintaining model accuracy while enabling flexible adaptation to various data sources and requirements.
2Productivity
If data from various sources is processed through standardized transformations, then system streamlined efficiency is improved, but loss of information from original data formats occurs
Solution Approach 1:
The patent creates standardized copies of data in a unified format while preserving the ability to reference or retrieve original data formats when needed. The transformation process generates standardized data representations for efficient processing while maintaining data fidelity and allowing reconstruction or access to original formats for auditing or analysis purposes.
3Adaptability or versatility
If flexible output formatting is provided for different applications, then system adaptability is improved, but device complexity increases
Solution Approach 1:
The patent performs data transformations and model executions in advance, storing results in standardized formats that can be efficiently retrieved and formatted for different applications. This preliminary processing reduces the complexity of real-time output formatting by pre-computing and standardizing data before it needs to be delivered to different applications.
Data Source
AI summary
A process flow for model-based applications, including: receiving data from one or more data sources; applying at least one first transformation on at least a portion of the data to generate transformed input data encoded according to a predefined format; providing the transformed input data to an executed instance of a model facilitating a prediction associated with the data; and exposing access to application data based on an output associated with the model.