ML-Based SaaS License Lifecycle Optimization

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Solution Overview

Problem

SaaS application rationalization is challenging for organizations, leading to unnecessary economic burdens and compliance issues due to inefficient license management, as existing methods rely on arbitrary criteria and are costly and time-prohibitive to implement.

Innovation Solution

The use of machine learning models to analyze user interaction patterns, such as recency and frequency of usage, to convert traditional software licenses into a consumption model, enabling continuous license optimization, automated access provisioning, and smart alerting for optimal license allocation and revocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of manufacture

If traditional arbitrary criteria are used for license management, then implementation is simple, but cost and time consumption increase significantly

Engineering Contradiction:

Improveease of license managementVSAvoidtime consumption

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The patent replaces manual, arbitrary license management criteria with an automated machine learning-based system. The ML models analyze user behavior patterns, usage frequency, and engagement metrics to automatically determine license allocation and revocation decisions, eliminating the need for time-consuming manual reviews and arbitrary decision-making processes.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service license management by automatically provisioning, allocating, and revoking licenses based on real-time user behavior analysis. The machine learning models continuously monitor usage patterns and autonomously make optimization decisions without requiring manual intervention from administrators.

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If traditional arbitrary criteria are used for license management, then implementation is simple, but cost increases significantly

Engineering Contradiction:

Improveease of license managementVSAvoidcost

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes the parameters for license management from arbitrary criteria to data-driven metrics including usage frequency, recency of use, engagement levels, and predicted future usage patterns. This transformation enables optimized license allocation that reduces costs while improving accuracy.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If machine learning models are used to analyze user interaction patterns, then license optimization accuracy improves, but system complexity increases

Engineering Contradiction:

Improvelicense optimization accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces machine learning models as intermediary components that bridge the gap between raw user behavior data and license management decisions. These models process complex patterns from authentication logs and activity data, transforming them into actionable insights for automated license optimization without requiring direct complex analysis in the decision-making system.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Reliability

If continuous license optimization is implemented, then compliance improves, but implementation complexity increases

Engineering Contradiction:

ImprovecomplianceVSAvoidimplementation complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements continuous license optimization by continuously monitoring user behavior patterns, training machine learning models on new data, and dynamically adjusting license allocations. This continuous operation ensures ongoing compliance with evolving organizational needs and regulatory requirements without requiring periodic manual interventions.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The patent incorporates feedback mechanisms where usage data from authentication logs and activity patterns continuously feeds back into the machine learning models. This feedback loop enables the system to learn from actual usage behavior and continuously refine its license optimization decisions, ensuring improved compliance over time.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20240386338A1End-to-end enterprise SAAS license lifecycle optimization

Publication Date: 2024.11.21 SNOWFLAKE INC

AI summary

The subject technology analyzes a set of authentication logs of users of an application. The subject technology generates a baseline of activity for the application based at least in part on the analyzing. The subject technology trains, using the baseline of activity, a machine learning model for each user of the application. The subject technology generates, using the trained machine learning model, a probability of usage for the application over a particular period of time. The subject technology triggers a license revocation process based at least in part on the probability of usage, the license revocation process revoking a set of licenses for the application. The subject technology allocates the set of licenses to a new set of users for using the application.