Third-Party Server Reliability Metrics for Accurate Device Status
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Solution Overview
Problem
Third-party servers providing device status updates to automated assistants are unreliable, leading to inaccurate status information and wasteful network and computational resource usage due to inconsequential transactions.
Innovation Solution
Implementing metrics to characterize the reliability of third-party servers, allowing automated assistants to proactively request device statuses based on server reliability, reducing unnecessary queries and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated assistant frequently queries the third party server for device status updates, then the accuracy of status information is improved, but the network bandwidth and processing resources are wasted
Solution Approach 1:
The system changes the parameter of query frequency based on the reliability metric of the third-party server. When reliability is low, query frequency increases to ensure accuracy; when reliability is high, query frequency decreases to conserve resources. This dynamic parameter adjustment resolves the contradiction between accuracy and resource consumption.
Solution Approach 2:
The system implements feedback by monitoring the reliability of third-party servers and adjusting query behavior accordingly. The automated assistant receives feedback about server reliability and modifies its status update retrieval strategy, increasing queries when servers are unreliable and decreasing them when servers are reliable, thus balancing accuracy with resource efficiency.
2Device complexity
If the automated assistant relies on third party server status updates, then the system complexity is reduced, but the reliability of status information deteriorates
Solution Approach 1:
The system introduces an intermediary reliability assessment mechanism between the automated assistant and third-party servers. This intermediary layer evaluates server reliability and mediates the interaction by adjusting query frequency, allowing the system to maintain simplicity while compensating for server unreliability through intelligent query management.
Solution Approach 2:
The system performs preliminary assessment of third-party server reliability before relying on their status updates. By evaluating server trustworthiness in advance, the automated assistant can determine appropriate query strategies, ensuring reliability is maintained while keeping the overall system architecture simple.
3Measurement precision
If the automated assistant proactively requests device statuses from unreliable servers, then the accuracy of status information is improved, but the processing resources are wasted
Solution Approach 1:
The system dynamically changes the parameter of query proactiveness based on server reliability metrics. For unreliable servers, the automated assistant proactively requests status updates more frequently; for reliable servers, it reduces proactive querying. This parameter adjustment resolves the contradiction between accuracy and processing resource consumption.
Solution Approach 2:
The system applies partial action by selectively increasing query frequency only for servers with low reliability metrics, rather than uniformly increasing queries for all servers. This targeted approach improves status accuracy for problematic servers while avoiding unnecessary processing resource waste on reliable servers.
Data Source
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AI summary
Implementations herein relate to information describing one or more internal states of a technical system. Implementations herein are provided for characterizing reliability of various different third party servers, at least when reporting third party device statuses, as well as adapting protocols for device ecosystems affected by such reliability. Latency can affect accuracy of device states represented by assistant devices. Certain servers can be characterized as especially delayed when reporting an updated device state in response to a user request, and, as a result, the third party server can be correlated to a metric that characterizes the relative latency of the third party server. When the metric fails to satisfy a particular threshold, a server and/or client associated with the "ecosystem" of third party devices can affirmatively operate to retrieve device state updates, rather than passively await updates from a corresponding third party server.