Third-Party Server State Reporting Reliability for Adaptive Queries
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Solution Overview
Problem
Inaccurate device status reporting by third-party servers leads to inefficient resource consumption and network traffic due to users relying on outdated or misleading information, causing unnecessary commands to be sent to devices.
Innovation Solution
Implementing metrics to assess the reliability of third-party servers in reporting device statuses, allowing automated assistants to proactively request updates when necessary and bypass queries when servers are reliable, thereby optimizing network and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated assistant frequently queries third-party servers for device status updates, then the accuracy of device state information is improved, but network traffic and computational resources are wasted when servers are reliable
Solution Approach 1:
The system dynamically adjusts the querying strategy based on the reliability metric of third-party servers. When reliability is high, the system reduces querying frequency to save resources. When reliability is low, the system increases querying frequency to ensure accurate device state information. This dynamic adaptation resolves the contradiction by making the querying behavior flexible rather than static.
Solution Approach 2:
The system changes the parameter of querying frequency based on the reliability metric. By monitoring server reliability over time and adjusting the querying interval accordingly, the system optimizes the balance between information accuracy and resource consumption. This parameter adjustment directly addresses the technical contradiction.
2Loss of energy
If the automated assistant relies on third-party server status updates, then network traffic is reduced, but the accuracy and timeliness of device state information deteriorates
Solution Approach 1:
The system implements a feedback mechanism by monitoring the reliability metric of third-party servers and using this information to adjust querying behavior. The reliability metric serves as feedback that informs whether to trust server-provided status updates or to actively query for updates. This feedback loop ensures that the system maintains accurate device state information while minimizing unnecessary network traffic.
Solution Approach 2:
The system performs preliminary assessment of server reliability before deciding whether to query for device status. By evaluating the reliability metric in advance, the system can make informed decisions about whether to trust server updates or to conduct active queries, thereby avoiding unnecessary network traffic while ensuring information accuracy.
3Productivity
If the automated assistant sends commands based on potentially inaccurate status data, then user responsiveness is improved, but resource consumption increases due to redundant operations
Solution Approach 1:
The system uses the reliability metric as feedback to determine whether to send commands based on current status data. When reliability is high, the system can confidently act on status information without excessive verification. When reliability is low, the system increases querying before sending commands, ensuring that user-responsive actions are based on accurate information, thereby avoiding redundant operations and resource waste.
Data Source
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.