Feature Extraction for ML Model Training
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Solution Overview
Problem
Existing machine-learning assisted code drafting technologies require the service provider to have direct access to the client's large and proprietary codebase, leading to burdensome data transfer, privacy risks, and incomplete model training due to incomplete or outdated data.
Innovation Solution
Allowing clients to extract and transmit only a limited set of data features from their codebase to the service provider, enabling the creation of custom machine learning models without the need for the service provider to access the entire codebase, thus reducing network resource usage and enhancing data security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the service provider has direct access to the client's complete codebase, then the machine learning model can be trained on comprehensive data, but the data transfer burden increases and privacy risks arise
Solution Approach 1:
The patent extracts only the essential features and metadata from the client's codebase rather than transferring the complete codebase. The feature extraction component identifies and extracts relevant characteristics (e.g., code structure, dependencies, configuration) while leaving the proprietary code on the client's system, thus reducing data transfer volume while maintaining model training effectiveness
Solution Approach 2:
The codebase is segmented into essential features and non-essential proprietary content. Only the essential features needed for model training are extracted and transmitted to the service provider, while the rest remains on the client's system. This segmentation allows selective data transfer that balances model training completeness with data privacy and transfer efficiency
2Measurement precision
If the service provider has direct access to the client's codebase, then complete models can be trained, but the client's proprietary data is at risk
Solution Approach 1:
The patent extracts only the essential features and metadata from the client's codebase rather than transferring the complete codebase. The feature extraction component identifies and extracts relevant characteristics (e.g., code structure, dependencies, configuration) while leaving the proprietary code on the client's system, thus reducing data transfer volume while maintaining model training effectiveness
Solution Approach 2:
The feature extraction component acts as an intermediary that processes the client's proprietary codebase locally and transforms it into anonymized features. This intermediary layer prevents direct exposure of proprietary code to the service provider while still enabling model training on derived features, thus mitigating data leakage risk
3Measurement precision
If the client transmits the entire codebase, then comprehensive model training is achieved, but network resources are inefficiently used
Solution Approach 1:
The patent extracts only the essential features and metadata from the client's codebase rather than transferring the complete codebase. The feature extraction component identifies and extracts relevant characteristics (e.g., code structure, dependencies, configuration) while leaving the proprietary code on the client's system, thus reducing data transfer volume while maintaining model training effectiveness
Solution Approach 2:
Instead of transmitting the entire codebase (excessive action), the system transmits only the necessary features and metadata (partial action) required for model training. This partial transmission approach reduces network resource consumption while still providing sufficient data for effective model training
4Ease of manufacture
If the client configures the service provider's service to build their codebase, then feature extraction can be performed, but the configuration process becomes burdensome
Solution Approach 1:
The feature extraction component runs on the client's own system using the client's existing build tools and environment. This self-service approach eliminates the need for the client to configure the service provider's system, as the extraction process leverages the client's own development environment to build and analyze their codebase locally
Data Source
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AI summary
Improving the results and process of machine learning service in computer program development. A clients codebase is accessed. A set of features are extracted from the clients codebase. One or more features from the set of features are then selected. Thereafter, at least one of the selected features is sent to a machine learning service that uses the received feature(s) to build custom model(s) for the client's computer system.