Remote Event Guidance System for Simultaneous Expert Instruction
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Solution Overview
Problem
In remote events, synchronizing the needs of multiple local participants with a single remote expert is challenging, often disrupting the event or making individual problem-solving impossible.
Innovation Solution
A system that facilitates contextual participation by providing expert help through a communication network, enabling broadband access, wireless access, voice access, and media access, with functions for sensor capture, workflow alignment, anomaly detection, and AI-based guidance to align local and remote tasks, allowing for multimodal interaction and simultaneous instruction of multiple local users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single remote expert provides help to multiple local participants, then expert resources are optimized, but individual problem-solving becomes impossible and event synchronization becomes challenging
Solution Approach 1:
The system segments the expert's attention and guidance by creating separate communication channels for each local participant. Each participant receives personalized instructions through their own device while the expert manages multiple segmented interactions simultaneously, resolving the conflict between resource optimization and individual problem-solving capability.
Solution Approach 2:
The remote event system acts as an intermediary platform that facilitates communication between the expert and multiple local participants. The system mediates by routing individualized instructions from the expert to specific participants while maintaining overall event synchronization, enabling both efficient expert utilization and personalized problem-solving assistance.
2Reliability
If the expert provides individualized instructions to each local participant, then problem-solving effectiveness improves, but event synchronization becomes disrupted and system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where each local participant's progress and status are continuously monitored and reported back to the expert and the central system. This feedback loop enables the expert to provide individualized instructions while the system automatically adjusts to maintain event synchronization, ensuring both problem-solving effectiveness and event coherence.
Solution Approach 2:
The system dynamically adapts the level of individualization and synchronization based on real-time event progress and participant needs. The platform can flexibly adjust communication patterns, instruction delivery, and coordination mechanisms to maintain event stability while enabling effective individual problem-solving when necessary.
3Adaptability or versatility
If multiple communication channels are established for each local participant, then contextual participation improves, but device complexity and network requirements increase
Solution Approach 1:
The remote event system is designed as a universal multi-functional platform that handles multiple communication functions through a single integrated architecture. The system provides video conferencing, screen sharing, annotation capabilities, and individualized instruction delivery through one cohesive system, reducing overall complexity while enabling rich contextual participation for each participant.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, engaging in first communications between a device and a first user device, the first communications comprising a first visual representation sent from the first user device to the device of first actions performed by a first user; engaging in second communications between the device and a second user device, the second communications comprising a second visual representation sent from the second user device to the device of second actions performed by a second user, the second communications occurring substantially simultaneously with the first communications; making a first determination via machine learning, based at least in part upon the first visual representation, whether performance of a first task by the first user has been completed; making a second determination via the machine learning, based at least in part upon the second visual representation, whether performance of the first task by the second user has been completed; responsive to the first determination being that the performance of the first task by the first user has been completed, prompting an instructor to provide an indication of a next task to be performed by the first user; and responsive to the second determination being that the performance of the first task by the second user has not been completed, prompting the instructor to provide additional instructions to the second user to aid the second user in performing the first task. Other embodiments are disclosed.