ML-Based Per-Project Software Licensing Control

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Solution Overview

Problem

Existing electronic design automation (EDA) systems lack a robust method for providing fail-safe software access licensing control on a per-project basis without prior knowledge of project details.

Innovation Solution

The implementation of a machine learning-based classification system that extracts feature values from projects, trains classifiers using these values, and determines software access permissions based on project classification, allowing for online training and adaptive licensing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If tokenized licenses are used to control software access, then software usage can be limited, but the system cannot provide per-project licensing control without prior knowledge of project details

Engineering Contradiction:

Improveper-project licensing controlVSAvoidlicensing control system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system automatically extracts project features, trains classifiers, and makes licensing decisions without manual intervention. The classifier self-adapts to new projects by learning from extracted feature values, enabling autonomous per-project licensing control.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual licensing evaluation mechanisms with an automated machine learning-based classification system. The classifier uses extracted project features to automatically determine software access permissions, eliminating the need for manual assessment of project details.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If manual tuning of licensing parameters is used, then licensing rules can be customized, but the system requires prior knowledge of project details and is time-consuming

Engineering Contradiction:

Improvelicensing configurationVSAvoidsystem setup time

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs self-configuration by automatically extracting project features and training classifiers without manual tuning. The classifier learns licensing parameters from the extracted data, eliminating the need for manual setup and reducing configuration time.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system pre-extracts project features and pre-trains classifiers before licensing decisions are needed. This preliminary processing enables rapid licensing evaluations without requiring manual configuration or prior knowledge of specific project details.

Inventive Principle:

Principle #10Preliminary action

3Reliability

If software access is granted without fail-safe control, then productivity is maintained, but security and licensing compliance cannot be ensured

Engineering Contradiction:

Improvelicensing complianceVSAvoidsoftware access speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs preliminary classification of projects using extracted features before granting software access. This pre-evaluation ensures licensing compliance is verified in advance, providing fail-safe control without delaying actual software usage once permissions are confirmed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system continuously monitors and evaluates project features against trained classifiers to provide feedback on licensing compliance. This feedback mechanism ensures reliable compliance control while maintaining efficient software access through automated decision-making.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12277200B2Fail-safe software access licensing control on a per project basis without a priori knowledge of project details

Publication Date: 2025.04.15 SYNOPSYS INC

AI summary

A request may be received to use a software on a first project. A first set of values may be extracted for a set of features of the first project. A classifier may be used to classify the first project based on the first set of values. It may be determined whether to grant the request to use the software on the first project based on an output of the classifier.