Machine Learning Identifier File Selection for Software Licensing
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Solution Overview
Problem
Identifying a unique identifier for a software product is challenging due to similarities among files used by various software products, making it difficult to verify and validate software licenses effectively.
Innovation Solution
A computer-implemented method using a machine learning algorithm, specifically artificial neural networks, to determine a unique identifier file for a software product by scanning the file system and selecting an appropriate file based on attributes such as name and size, which is then used to update the software product's license.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional file scanning methods are used to identify software identifier files, then the process is simple and fast, but the accuracy of identification is low due to similarities among files from various software products
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between file scanning and identifier selection. The model processes file attributes and software metadata to accurately identify identifier files, resolving the contradiction by adding an intelligent mediation layer that improves accuracy without requiring direct complex analysis of file similarities
Solution Approach 2:
The system changes from traditional file-based identification to attribute-based identification using machine learning. By transforming the identification process into a parameter-based classification problem (using file attributes, metadata, and contextual information), the system achieves higher accuracy while managing complexity through structured data processing
2Productivity
If manual identification of identifier files is performed, then accuracy can be ensured, but the productivity and efficiency of software licensing verification are reduced
Solution Approach 1:
The system implements self-service through automated machine learning-based identification. The model automatically analyzes file attributes and software metadata to identify identifier files without human intervention, achieving both high productivity through automation and high accuracy through intelligent analysis, eliminating the trade-off between manual accuracy and automated efficiency
3Measurement precision
If comprehensive file analysis is performed to distinguish similar files, then identification accuracy improves, but the time and computational resources required increase
Solution Approach 1:
The system applies partial analysis by focusing on the most discriminative file attributes and metadata rather than analyzing every aspect of each file. The machine learning model is trained to identify key distinguishing features, achieving accurate file distinction while minimizing analysis time and computational resources by avoiding exhaustive examination of all file characteristics
Data Source
AI summary
Embodiments of the present invention are directed to a computer-implemented method for selecting an identifier file for a software product. An example method includes installing the software product on a machine. The method further includes determining a list of files of the software product by scanning a file system of the machine. The method further includes selecting an identifier file from the list of files using a machine learning algorithm. The method further includes updating a license of the software product on the machine with the identifier file.