AI Task Scoring With Confidence Models for Effort Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional scoring techniques for development tasks in project management are inconsistent across software solutions, leading to inefficient project planning and resource allocation due to arbitrary criteria and lack of accurate effort estimation.
Innovation Solution
A method utilizing artificial intelligence to facilitate automated scoring of development tasks through predictive analytics, involving data aggregation, structured data set generation, and model-based scoring to provide consistent and objective task assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional scoring techniques are used for development tasks, then each development team can individually score tasks based on their criteria, but the scoring becomes inconsistent across large collections of software solutions and lacks objective accuracy
Solution Approach 1:
The patent transforms subjective scoring parameters into objective quantitative parameters by using machine learning models that predict task scores based on feature engineering from project data. The system changes the scoring from arbitrary team-based parameters to data-driven parameters including complexity, effort, and risk metrics that can be consistently measured across all software solutions.
Solution Approach 2:
The patent replaces the mechanical manual scoring process with an automated machine learning-based scoring system. Instead of human evaluators assigning points based on their judgment, the system uses trained models that automatically process task descriptions and project data to generate consistent, objective scores without human bias or variability.
2Ease of operation
If manual scoring of development tasks is performed, then teams can assign points based on their judgment, but accurate estimation of effort required is not achieved leading to inefficient project planning
Solution Approach 1:
The system enables self-service scoring where the machine learning models automatically perform the scoring function without requiring manual intervention. The models process task data independently and generate scores based on learned patterns from historical project data, eliminating the need for human evaluators while maintaining high accuracy in effort estimation.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from actual project outcomes and adjusts its scoring models accordingly. Historical project data is used to train and refine the models, allowing the system to improve its estimation accuracy over time by feedback from real-world project performance.
3Adaptability or versatility
If inconsistent scoring is used across projects, then each project can be scored according to local needs, but project management tools become inefficiently utilized and resource allocation is compromised
Solution Approach 1:
The patent creates a universal scoring system that can be applied across all software solutions while accommodating local needs through configurable parameters. The machine learning models are designed to handle diverse project types and requirements, providing consistent scoring that works universally across the organization while still allowing customization where needed.
Data Source
AI summary
A method for facilitating automated scoring of software development tasks by using predictive analytics is disclosed. The method includes receiving, via a graphical user interface, search requests for various tasks; aggregating raw data that corresponds to the tasks, the raw data including information in a natural language format; generating structured data sets from the raw data based on predetermined parameters; determining, by using a first model, a task score for each of the tasks based on the structured data sets; determining, by using a second model, a confidence score for the task score; and computing predictive outputs for the tasks based on the corresponding task score and the corresponding confidence score.