AI Workspace Coordination With Real-Time Actor Feedback
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Solution Overview
Problem
The coordination of actors, including humans and machines, in work environments is challenging due to their varying characteristics and capabilities, leading to inefficient and costly attempts at achieving objectives such as effective task performance and resource management.
Innovation Solution
A system that accesses and analyzes information about actors and their activities using sensors and machine learning algorithms to provide feedback on optimal configurations and assignments, utilizing automated artificial intelligence for improved coordination and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods are used to coordinate actors and activities, then implementation is simple, but coordination efficiency and effectiveness deteriorate
Solution Approach 1:
The system continuously monitors actor activities through sensors and provides real-time feedback to optimize coordination. The feedback loop captures activity data, analyzes it through AI algorithms, and adjusts actor assignments and workspace configurations dynamically, resolving the contradiction by implementing intelligent feedback mechanisms that improve productivity without requiring overly complex manual coordination systems.
Solution Approach 2:
The patent replaces conventional mechanical coordination methods with automated sensing, data processing, and AI-based decision-making systems. Sensors, processors, and machine learning algorithms substitute for manual planning and coordination mechanisms, achieving superior coordination efficiency while maintaining manageable system complexity through modular architecture.
2Reliability
If automated sensing and AI analysis are implemented, then coordination effectiveness improves, but implementation cost and complexity increase
Solution Approach 1:
The system employs multi-functional sensors and processing units that can detect various actor types (human, robot, cobot) and activities simultaneously. The AI analysis engine handles multiple coordination tasks including activity recognition, actor matching, and workspace optimization through a single integrated platform, reducing implementation complexity while maintaining high coordination effectiveness.
Solution Approach 2:
The patent introduces an intermediary layer of activity recognition algorithms and AI processing that mediates between raw sensor data and coordination decisions. This intermediary processing layer simplifies the overall system architecture by standardizing data formats and decision-making protocols, making the system easier to implement while achieving reliable coordination results.
3Measurement precision
If real-time monitoring and analysis are performed, then activity coordination accuracy improves, but computational resource consumption increases
Solution Approach 1:
The system applies partial monitoring and analysis to different actor types and activities based on their specific requirements. Not all actors require the same level of monitoring intensity, and the AI system dynamically adjusts analysis depth to achieve sufficient accuracy without unnecessary computational overhead, balancing precision with energy consumption.
Solution Approach 2:
The patent implements preliminary classification and filtering of activity data before detailed AI analysis. Sensors first identify basic activity types and actor categories, then only relevant data undergoes comprehensive AI processing. This preliminary action reduces the volume of data requiring intensive computational resources while maintaining accurate activity recognition for coordination purposes.
Data Source
AI summary
Workspace coordination systems and methods are presented. A method can comprise: accessing in real time respective information associated with a first actor and a second actor, including sensed activity information; analyzing the information, including analyzing activity of the first actor with respect to a second actor; and forwarding respective feedback based on the results of the analysis. The feedback can includes an individual objective specific to one of either the first actor or the second actor. The feedback includes collective objective with respect to the first actor or the second actor. The analyzing can include automated artificial intelligence analysis. Sensed activity information can be associated with a grid within the activity space. It is appreciated there can be various combinations of actors (e.g., human and device, device and device, human and human, etc.). The feedback can be a configuration layout suggestion. The feedback can be a suggested assignment of a type of actor to an activity.