Agile Framework Recommendation Engine for Objective Methodology Selection

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Solution Overview

Problem

The conventional methods for selecting an agile software framework methodology are often biased and lack knowledge-based decision-making, leading to suboptimal choices that can impact product quality, timeline, cost, and team wellness, and fail to consider human disabilities.

Innovation Solution

A recommendation engine that analyzes user inputs, including voice, text, and sign language, to recommend agile software frameworks based on training data, provides confidence scores, and assists in creating and executing development tasks.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional manual selection of agile methodology is used, then personal expertise and judgment can be applied, but selection is biased and lacks objectivity leading to suboptimal choices

Engineering Contradiction:

Improvemethodology selection accuracyVSAvoidselection objectivity

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

An AI-based recommendation engine is introduced as an intermediary between the user and agile methodology selection. The engine analyzes project requirements, team characteristics, and historical data to provide data-driven recommendations, eliminating personal bias while maintaining expertise through machine learning models trained on successful project outcomes.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system incorporates feedback mechanisms where selection outcomes and project performance data are continuously fed back into the recommendation engine. This allows the system to learn from actual results and improve future recommendations, ensuring both objectivity and increasing accuracy over time through iterative optimization.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If comprehensive analysis of multiple methodologies is performed, then better informed decisions can be made, but time and computational resources are consumed

Engineering Contradiction:

Improvemethodology evaluation thoroughnessVSAvoidselection process duration

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary analysis by pre-processing and storing characteristics of multiple agile methodologies in advance. When a user needs a recommendation, the system quickly retrieves and compares relevant pre-analyzed information rather than conducting full analysis from scratch, significantly reducing selection time while maintaining thoroughness.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The recommendation engine dynamically adjusts analysis depth and parameters based on project complexity and user needs. For simple projects, it provides quick recommendations with essential parameters; for complex projects, it performs more comprehensive analysis only where needed, optimizing the balance between thoroughness and time consumption.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If traditional selection processes are used, then existing knowledge bases can be leveraged, but human disabilities and exclusions are not addressed

Engineering Contradiction:

Improveprocess inclusivityVSAvoidaccessibility for all users

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system is designed to serve diverse user needs through multiple input modalities including voice, text, and sign language recognition. This universal design approach ensures that users with different abilities can equally access and benefit from the methodology selection process, making the tool inclusive while leveraging existing knowledge bases.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Manufacturing precision

If detailed analysis of each methodology is conducted, then selection quality improves, but complexity of the selection system increases

Engineering Contradiction:

Improvemethodology matching precisionVSAvoidselection system complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The selection system is segmented into modular components: requirement analysis module, methodology database, matching algorithm, and recommendation generation module. Each component handles specific aspects of the selection process independently, allowing detailed analysis where needed while keeping the overall system manageable and maintainable through clear separation of concerns.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS12536017B2Method and system for recommending agile software framework methodology

Publication Date: 2026.01.27 JPMORGAN CHASE BANK NA

AI summary

A method and a system for recommending agile software framework methodology are disclosed. The method includes receiving at least one input from a user. The method further includes analyzing the at least one input to authenticate and authorize the at least one input. Further, the method includes recommending at least one agile software framework methodology with an associated confidence score. The method further includes receiving a response input from the user on the recommended agile software framework methodology. Further, the method includes creating a set of tasks for the recommended agile software framework methodology based on a positive response from the user on the recommended agile software framework. Thereafter, the method includes executing the set of tasks associated with the recommended agile software frame methodology for development of a software.