Context-Specific Schema Files for Automated Assistant Latency Reduction
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Solution Overview
Problem
Automated assistants experience latency and inefficiency when searching for suitable applications to invoke, leading to wasted computational and network resources due to incorrect application invocations and the need to repeatedly access network content for responses.
Innovation Solution
The implementation of context-specific schema files that allow automated assistants to facilitate human-to-computer dialogs by identifying entry points and providing responsive content, reducing the need for real-time application invocation and network access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If an automated assistant is pre-loaded with extensive responses, then user query response speed is improved, but device operational efficiency deteriorates due to memory and processing overhead
Solution Approach 1:
The patent segments the response database into context-specific schema files organized by application domain (e.g., ride-sharing, food delivery, messaging). Each schema file contains only the responses relevant to a specific context, allowing the automated assistant to load only the necessary subset of responses into memory based on the detected user context, thereby reducing memory overhead while maintaining fast response capabilities.
Solution Approach 2:
The patent implements dynamic loading of schema files based on detected user context. The system transitions from a static pre-loaded approach to a dynamic on-demand approach where schema files are loaded into memory only when their corresponding context is detected, and unloaded when no longer needed. This dynamic behavior optimizes the balance between response speed and device efficiency.
2Reliability
If the automated assistant repeatedly accesses network content for each query, then response accuracy is improved, but network bandwidth and processing resources are wasted
Solution Approach 1:
The patent performs preliminary actions by pre-processing and organizing response data into context-specific schema files that are stored locally on the device. This preliminary organization allows the automated assistant to quickly retrieve accurate responses from local storage without needing to access network content for each query, thereby maintaining response accuracy while eliminating repeated network access.
Solution Approach 2:
The patent creates local copies of response data in the form of schema files that are downloaded once and stored on the device. These local copies enable the automated assistant to serve responses without repeated network access, significantly reducing network resource consumption while maintaining the accuracy of the original network-based response database.
3Adaptability or versatility
If the automated assistant invokes applications in real-time, then application functionality is maximized, but latency increases due to application search and invocation overhead
Solution Approach 1:
The patent performs preliminary actions by pre-identifying and loading the appropriate schema file into memory based on the detected user context before the user actually submits their query. This preliminary preparation ensures that when the user queries the automated assistant, the relevant responses are already available in memory, eliminating the need for real-time application search and invocation, thereby reducing latency while maintaining full application functionality.
Data Source
AI summary
Methods, apparatus, systems, and computer-readable media are provided for providing context specific schema files that allow an automated assistant to broker human-to-computer dialogs between a user and an application that is separate from the automated assistant. The context specific schema file can provide the automated assistant with sufficient data to be responsive to user queries without necessarily communicating with a remote device, such as a server. Multiple different context specific schema files can be made available to the automated assistant according to a context in which a user is interacting with the automated assistant. In this way, latency otherwise exhibited by the automated assistant can be mitigated by providing the automated assistant with the information needed to respond to a user without continually retrieving the information over a network.