Predictive Resource Allocation for Skill-Based Task Scheduling

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Solution Overview

Problem

Existing technologies fail to identify an appropriate combination of resources with different skills necessary to complete a specific task efficiently.

Innovation Solution

A processing device and system that generate a predictive model to identify the optimal combination of resources based on task requirements and resource skills, and control robots to execute the task efficiently.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If a predictive model is generated to identify optimal resource combinations, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improvetask completion efficiencyVSAvoidprocessing system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by generating a predictive model in advance that evaluates multiple resource combinations. This model pre-calculates the most suitable resource allocation for given tasks, allowing the system to quickly identify optimal combinations without complex real-time computations when tasks are assigned, thus improving productivity while managing complexity through advance preparation

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The predictive model acts as an intermediary between task requirements and resource allocation decisions. Instead of directly complexly analyzing all possible resource combinations in real-time, the system uses this intermediate predictive model to translate task characteristics into recommended resource allocations, simplifying the overall system architecture while maintaining high productivity

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If resource combination identification is implemented, then task completion time is reduced, but measurement precision requirements increase

Engineering Contradiction:

Improvework time for task completionVSAvoidskill and task matching accuracy

Core Design Contradiction:

Loss of timeVSMeasurement precision

Solution Approach 1:

The system replaces manual or heuristic methods of matching resources to tasks with an automated predictive model based on machine learning or statistical algorithms. This substitution enables precise measurement and comparison of skill-task compatibility across multiple dimensions, accurately identifying optimal combinations while reducing the time required for resource allocation decisions

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The predictive model employs multiple parameters to characterize both tasks (type, complexity, required skills) and resources (skill level, availability, capacity). By changing and analyzing these parameters systematically, the system achieves high measurement precision in matching resources to tasks, enabling fast identification of optimal combinations that minimize completion time

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250363393A1Processing device, processing system, processing method, and recording medium

Publication Date: 2025.11.27 NEC CORP

AI summary

A calculator includes a memory configured to store instructions; and a processor configured to execute the instructions to: generate a predictive model for predicting a required work time of a task according to a combination of resources based on a combination of one or more types of tasks included in predetermined work and one or more skills required to execute the task, one or more types of resources for executing the predetermined work, a quantity of each of the resources, and a skill possessed by each of the resources; and identify the combination of the resources for completing the predetermined work based on the quantity of each of the tasks and the predictive model.