Application Network Graph for Software Recommendation

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Solution Overview

Problem

Organizations face challenges in accessing necessary applications due to high licensing costs, compatibility issues, and unavailability, leading to decreased productivity and increased costs.

Innovation Solution

A recommendation engine utilizing an application network or graph to suggest alternative applications based on characteristics, user feedback, and machine learning, which constructs a graph of applications associated by characteristics and weights, allowing for automated or user-driven recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If organizations purchase and license necessary applications, then application availability and functionality are improved, but licensing costs and software expenses increase

Engineering Contradiction:

Improveapplication availabilityVSAvoidlicensing costs

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent implements a recommendation engine that provides multiple alternative applications for the same functional needs, allowing organizations to use different applications interchangeably. This multi-functionality approach enables organizations to access necessary applications through alternatives when primary applications are unavailable or too expensive, reducing licensing costs while maintaining application availability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If organizations use a variety of applications for different work purposes, then functionality and task completion are improved, but complexity of application management and compatibility issues increase

Engineering Contradiction:

ImprovefunctionalityVSAvoidapplication management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements a feedback mechanism where user interactions, usage patterns, and satisfaction levels are continuously monitored and fed back to the recommendation engine. This feedback loop enables the system to learn from user behavior and improve its recommendations over time, providing more accurate alternatives that reduce management complexity while maintaining versatility.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The recommendation engine acts as an intermediary between users and the vast ecosystem of applications. Instead of users directly managing complex application selections and compatibility issues, the engine mediates by analyzing requirements and recommending suitable alternatives, simplifying application management while preserving functionality.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If organizations invest in expensive applications, then application performance and capabilities are improved, but return on investment and cost-effectiveness worsen

Engineering Contradiction:

Improveapplication performanceVSAvoidcost-effectiveness

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent enables organizations to use cheaper alternative applications when expensive primary applications are not cost-effective. The recommendation engine identifies and promotes affordable alternatives that can serve the same functional purposes, allowing organizations to substitute expensive applications with more cost-effective options while maintaining necessary performance levels.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Data Source

PatentUS11227323B2Smart software recommendation using an application network

Publication Date: 2022.01.18 EMC IP HLDG CO LLC

AI summary

Recommending applications is disclosed. An application graph is disclosed that represents applications. Each node of the graph corresponds to an application and edges relate applications that can handle the same file type. When an input application is identified, the graph can be used to recommend other applications that may be a suitable replacement for the input application. The recommendation is based on the graph and its links and on characteristics of the organization.