AI Task Approval Routing for Time-Zone-Aware Workflows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional approval workflow techniques face inefficiencies due to predefined lists of exclusive approvers, leading to resource wastage and time zone complexities.
Innovation Solution
Dynamically generating task approval requests using artificial intelligence techniques to identify and transmit requests to appropriate users based on availability, historical data, and domain similarity, enabling near real-time decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predefined lists of exclusive approvers are used for task approval requests, then the approval workflow structure is simple and manageable, but resource wastage occurs and inefficiencies arise due to time zone complexities and approver availability issues
Solution Approach 1:
The patent implements dynamic approver identification by training a machine learning classifier on historical approval data to predict which approvers are most likely to act on specific requests. This replaces static predefined lists with a dynamic system that adapts to individual working patterns, time zones, and availability, thereby reducing resource wastage while maintaining workflow efficiency.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to automatically identify and route approval requests to the most appropriate approvers without manual intervention. The classifier autonomously analyzes historical data patterns and makes intelligent routing decisions, eliminating the need for manual configuration of approval lists and reducing administrative overhead.
2Ease of operation
If predefined lists of exclusive approvers are used for task approval requests, then the approval process is straightforward to implement, but inefficiencies occur due to time zone complexities and approver availability issues
Solution Approach 1:
The system dynamically adjusts approver selection based on time zone awareness and availability patterns learned from historical data. The machine learning classifier identifies when approvers are most likely to be available and routes requests accordingly, automatically adapting to time zone differences without requiring manual configuration or complex scheduling rules.
Solution Approach 2:
The system incorporates feedback loops where approval outcomes and timing data are continuously fed back into the machine learning model. This allows the system to learn from actual approval patterns, refine its understanding of approver availability across different time zones, and progressively improve its routing decisions, maintaining simplicity while reducing time losses.
3Device complexity
If conventional approval workflow techniques are used, then the system is easy to maintain, but resource wastage and inefficiencies persist due to inability to dynamically identify appropriate approvers
Solution Approach 1:
The patent replaces the mechanical system of manual approver list configuration with an intelligent machine learning-based classification system. The classifier automatically processes historical approval data to identify patterns and predict optimal approver selections, substituting automated intelligent routing for manual system configuration and maintenance while significantly improving workflow efficiency.
Solution Approach 2:
The machine learning system performs self-service by automatically training on historical data, continuously refining its classification algorithms, and autonomously making approval routing decisions without requiring manual system reconfiguration. This self-learning capability maintains relatively low system complexity while dramatically improving productivity through intelligent, adaptive approver identification.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for dynamically generating task approval requests using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining at least one task approval request generated in connection with at least one requesting user; identifying one or more users to receive the at least one task approval request by processing data associated with the at least one task approval request using one or more artificial intelligence techniques; automatically transmitting at least a portion of the at least one task approval request to at least one of the one or more identified users using one or more software applications executing on one or more devices associated with the at least one identified user; and performing one or more automated actions based on at least one response to the at least one task approval request from the at least one identified user.