AI Task Prioritization System for Failure Analysis
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Solution Overview
Problem
Existing technologies for analyzing factors responsible for task failures in digital platforms are inefficient, relying on manual intervention and lacking a guiding structure for prioritization and classification, which hinders effective task management and key performance indicator (KPI) improvement.
Innovation Solution
A method and system that utilize an artificial intelligence-based module to analyze information related to task failures and target parameters, generating a composite score for task prioritization recommendations on user devices, and updating the system based on feedback and market conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual intervention is used to analyze task failure factors, then analysis can be performed with simple tools, but efficiency and scalability deteriorate when analyzing larger datasets
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated AI-based analysis system. The system uses machine learning models to automatically analyze task failure factors, eliminating the need for manual intervention while significantly improving analysis efficiency and scalability for large datasets.
Solution Approach 2:
The system enables self-service analysis where the AI model automatically processes task failure data without requiring manual intervention. The system autonomously identifies failure factors, generates insights, and provides recommendations, allowing the organization to independently analyze and resolve task failures at scale.
2Adaptability or versatility
If no guiding structure is provided for task prioritization, then the system remains simple and flexible, but users cannot effectively prioritize and classify task failures
Solution Approach 1:
The patent introduces a composite scoring system that transforms multiple task failure parameters (risk, urgency, complexity, cost) into a single prioritization metric. This parameter transformation provides a clear guiding structure for prioritization while maintaining the underlying flexibility of the task management system.
Solution Approach 2:
The system segments task failures into distinct categories based on failure factors such as risk, urgency, complexity, and cost. This segmentation creates a structured approach to prioritization while allowing flexibility in how individual tasks are managed within each category.
3Ease of manufacture
If existing solutions are used for task failure analysis, then implementation is straightforward, but key performance indicators cannot be effectively improved
Solution Approach 1:
The patent implements a feedback mechanism where the AI analysis system continuously monitors task failure outcomes and uses this feedback to refine its analysis models. This closed-loop feedback system improves the reliability of task prioritization and enables continuous improvement of key performance indicators while maintaining ease of implementation.
Solution Approach 2:
The AI-based analysis system acts as an intermediary between raw task failure data and actionable insights. This intermediary layer processes and interprets failure data, providing structured recommendations that improve KPIs while keeping the overall system easy to implement and operate.
Data Source
AI summary
A method and a system for providing a task prioritization recommendation for a set of tasks on a set of user devices are disclosed. The method includes: receiving, by a processor, first information related to a failure of the set of tasks; retrieving, by the processor, a set of target parameters related to the failure of the set of tasks; analyzing, by the processor using an artificial intelligence-based module, the first information related to the failure of the set of tasks and the set of target parameters; generating, by the processor, a composite score for the set of tasks based on the analysis; and providing, by the processor on the set of user devices, the task prioritization recommendation based on the composite score.