Tele-Presence Robot Knowledge Partitioning for Bandwidth-Limited Tasks
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Solution Overview
Problem
Conversational tele-presence robots in geographically separated environments face limitations in information storage, processing speed, and network bandwidth, which hinder efficient task execution and decision-making, particularly due to congestion and communication failures.
Innovation Solution
A processor-implemented method for knowledge partitioning that dynamically queries information from multiple storage devices (on-board memory, edge, cloud, and web interface) based on task type and priority, generating an execution plan and validating tasks while partitioning and storing knowledge for seamless subsequent task execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If tele-presence robot stores all required information locally, then information retrieval speed improves, but device complexity and storage requirements increase
Solution Approach 1:
The patent segments information storage across multiple locations: local on-board storage for critical data, edge device storage for task-specific data, and cloud storage for comprehensive knowledge bases. This segmentation allows the robot to retrieve information from the most appropriate storage location based on task requirements, balancing speed and complexity.
Solution Approach 2:
The patent introduces an edge device as an intermediary between the robot and cloud storage. The edge device pre-loads and caches frequently accessed information, acting as a buffer that provides fast local access without requiring the robot to have all storage capacity locally.
2Measurement precision
If tele-presence robot processes all information locally, then decision-making accuracy improves, but processing speed decreases due to bandwidth constraints
Solution Approach 1:
The patent segments information processing across three levels: local processing for immediate decisions, edge processing for task-specific analysis, and cloud processing for complex decision-making. This segmentation allows critical time-sensitive operations to be processed locally while leveraging cloud computing power for non-time-critical complex tasks.
Solution Approach 2:
The patent implements local quality by enabling the robot to process information locally when speed is critical and to offload to edge/cloud when accuracy is more important than speed. Different processing locations provide different qualities of service based on task requirements.
3Quantity of substance
If tele-presence robot uses high bandwidth network, then information transfer capacity improves, but network congestion occurs during background processes
Solution Approach 1:
The patent implements preliminary action by pre-loading information to edge devices and cloud storage before it is needed by the robot. This allows information to be staged in advance, reducing the amount of real-time data transfer required during task execution and minimizing network congestion.
Solution Approach 2:
The edge device acts as an intermediary that buffers between the robot and the network. It caches information locally and serves the robot directly when possible, reducing network traffic during critical operations while maintaining high information transfer capacity when needed.
4Loss of information
If tele-presence robot queries all storage devices for every task, then information completeness improves, but time consumption increases
Solution Approach 1:
The patent implements dynamic querying where the robot adapts its information retrieval strategy based on task type, priority, and available time. For time-critical tasks, it queries only local storage; for comprehensive tasks, it progressively queries edge and cloud storage. This dynamic approach balances information completeness with time consumption.
Solution Approach 2:
The patent applies partial action by querying only the necessary portion of storage devices based on task requirements. For simple tasks, it queries only local storage; for complex tasks, it progressively queries additional storage layers. This avoids the time cost of querying all storage devices for every task while ensuring sufficient information is retrieved.
Data Source
AI summary
Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.