Virtual Assisted Task Assignment for Device Maintenance
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Solution Overview
Problem
The existing processes for assigning device maintenance related functions are inaccurate, inflexible, and resource-intensive, with coordinating multiple parties for repairs being unreliable and time-consuming.
Innovation Solution
A virtual assisted task maximization method that establishes secure connections between hardware devices and external databases to identify relevant tasks, compute potential scores, select optimal tasks based on availability and skills, and map tasks to available timeframes, using specialized hardware components like SVA integrated circuits for efficient task assignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual processes are used for assigning device maintenance functions, then flexibility and accuracy are poor, but the process requires large amounts of resources and is time-consuming
Solution Approach 1:
The system enables self-service by allowing the task assignment process to automatically identify suitable tasks, compute scores, select optimal tasks, and map them to available timeframes without requiring manual coordination. The hardware device autonomously performs maintenance function assignment by processing availability data, skills information, and task parameters through integrated circuits.
Solution Approach 2:
The patent replaces manual mechanical coordination processes with electronic computation and data processing. Instead of human operators manually assigning tasks and coordinating schedules, the system uses processors, computation circuits, and caching circuits to automatically match tasks with individuals based on computed potential scores and availability mappings.
2Reliability
If multiple parties are coordinated for multiple repairs, then comprehensive coverage is achieved, but the process becomes unreliable and time-consuming
Solution Approach 1:
The system performs preliminary action by pre-computing potential scores for tasks based on individual skills and task requirements, and by pre-identifying available timeframes before actual task assignment occurs. This advance preparation enables rapid and reliable task coordination without time-consuming real-time negotiations.
Solution Approach 2:
The system implements feedback by continuously comparing computed potential scores against maximum potential scores, automatically selecting tasks that optimize the score ratio, and mapping selected tasks to available timeframes. This closed-loop process ensures reliable task assignment by constantly refining matches based on performance data and availability feedback.
3Measurement precision
If manual task assignment processes are used, then resource consumption is high, but the process lacks accuracy and flexibility
Solution Approach 1:
The system applies parameter changes by computing potential scores based on multiple parameters including individual skills, task requirements, and availability timeframes. The computation circuit dynamically adjusts and evaluates these parameters to determine optimal task assignments, achieving high matching accuracy through quantitative parameter analysis rather than manual assessment.
Solution Approach 2:
The patent segments the task assignment process into distinct functional components: identifying tasks, determining availability, computing potential scores, selecting optimal tasks, and mapping to timeframes. This segmentation allows each component to be processed independently and efficiently by specialized hardware circuits, reducing overall resource consumption while maintaining precision.
Data Source
AI summary
A method and system for automatically generating a virtual assisted task is provided. The method includes establishing secure connections between a hardware device and databases external to the hardware device. A group of tasks associated with an individual are identified and available timeframes associated with an availability of the individual are determined. A first potential score and maximum score for each task are computed. The first potential score is compared to each maximum potential score and in response, a subset of tasks are automatically selected. The subset of tasks are mapped to the available timeframes and results of the mapping a cached within a caching circuit of the hardware device. Results of the caching are presented via the hardware device.