AI Workspace Actor Selection for Real-Time Replacement Matching
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Solution Overview
Problem
The selection of appropriate actors (human, robots, or machines) in work environments is challenging due to their varying characteristics and capabilities, leading to inefficiencies and increased costs in achieving objectives, as conventional methods are often costly and resource-intensive.
Innovation Solution
The implementation of deep learning artificial intelligence for activity analytics, which analyzes activity information in real-time to determine suitable replacement actors by comparing deviations within acceptable thresholds, using geometric modeling and automated AI analysis, and rank-ordering candidates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional methods are used for actor selection, then the selection process is straightforward, but it becomes costly and resource-intensive
Solution Approach 1:
The patent replaces conventional manual or mechanical actor selection methods with an automated AI-based system that uses deep learning neural networks to analyze activity information and determine optimal actor assignments, thereby reducing resource consumption while maintaining ease of selection
Solution Approach 2:
The system enables self-service actor selection by automatically analyzing activity data and generating actor assignments without requiring manual intervention, allowing the system to select appropriate actors independently based on analyzed criteria
2Productivity
If deep learning AI analysis is implemented for actor selection, then selection efficiency improves, but system complexity increases
Solution Approach 1:
The patent segments the actor selection system into distinct functional modules including activity information collection, deep learning neural network analysis, actor candidate identification, and assignment generation, allowing complex AI processing to be managed through modular components
Solution Approach 2:
The system introduces an intermediary AI analysis layer that processes activity information and generates actor recommendations, mediating between raw data collection and final selection decisions to manage complexity through structured intermediate processing
3Loss of time
If real-time activity analytics are performed, then actor replacement decisions are made quickly, but computational resources increase
Solution Approach 1:
The system performs partial real-time analytics by analyzing only the most critical activity information parameters needed for actor replacement decisions, rather than processing all available data, thereby reducing computational energy consumption while maintaining quick decision-making
Solution Approach 2:
The system performs preliminary analysis of activity information patterns to identify when actor replacement is actually needed, avoiding continuous full-scale computational analysis and reducing energy consumption by activating intensive processing only when necessary
Data Source
AI summary
In one embodiment a method comprises: accessing information associated with a first actor, including sensed activity information associated with an activity space; analyzing the activity information, including analyzing activity of the first actor with respect to a plurality of other actors; and forwarding feedback on the results of the analysis, wherein the results includes identification of a second actor as a replacement actor to replace the first actor, wherein the second actor is one of the plurality of other actors. The activity space can include an activity space associated with performance of a task. The analyzing can comprise: comparing information associated with activity of the first actor within the activity space with anticipated activity of the respective ones of the plurality of the actors within the activity space; and analyzing/comparing deviations between the activity of the first actor and the anticipated activity of the second actor.