Latency Prediction Model for Automated Assistant Response Optimization
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Solution Overview
Problem
Automated assistants experience latency issues in interpreting and responding to user requests, leading to repeated user input and increased computational resources due to the complexity of user requests and the pipeline of components involved in processing spoken utterances.
Innovation Solution
Implementing a latency prediction model on a client device to determine if pre-cached content can be rendered before obtaining responsive content, using a machine learning model trained on user interactions to predict latency and select appropriate pre-cached content segments based on predicted latency thresholds, thereby reducing wait times and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a pipeline of components (ASR engine, NLU engine, fulfillment engine) is used to process user requests, then the automated assistant can interpret and respond to user requests, but latency increases due to the complexity and multiple processing stages
Solution Approach 1:
The system performs preliminary actions by predicting latency before the complete request processing pipeline executes. The latency prediction model analyzes the request and estimates processing time in advance, allowing the system to prepare pre-cached content or optimize the processing pipeline proactively rather than reactively, thereby reducing overall latency.
Solution Approach 2:
The system dynamically adjusts the processing pipeline based on predicted latency. When latency is predicted to be high, the system can optimize the pipeline by selecting alternative fulfillment paths, using pre-cached content, or adjusting processing priorities. This dynamic adaptation allows the system to maintain responsiveness while handling complex requests efficiently.
2Reliability
If the automated assistant waits for complete processing of user requests before rendering content, then content accuracy is ensured, but user experience deteriorates due to repeated input and extended wait times
Solution Approach 1:
The system performs preliminary content preparation by rendering pre-cached content before the complete request processing finishes. The latency prediction model enables the system to anticipate when accurate content will be ready and prepare alternative content in the meantime, ensuring both accuracy and improved user experience.
Solution Approach 2:
The system uses feedback from the latency prediction model to dynamically adjust content rendering strategies. By continuously monitoring actual processing time against predictions, the system optimizes when to render pre-cached content versus waiting for complete processing, balancing accuracy and user experience.
3Adaptability or versatility
If the system processes complex user requests through multiple pipeline components, then comprehensive response capability is achieved, but computational resources are consumed and latency is exacerbated
Solution Approach 1:
The system performs preliminary analysis using the latency prediction model to assess computational resource requirements before executing the full processing pipeline. This allows the system to optimize resource allocation by preparing content in advance or selecting more efficient fulfillment paths for complex requests, reducing overall computational consumption.
Solution Approach 2:
The system dynamically adjusts computational resource allocation based on predicted latency and request complexity. When requests are predicted to be complex and latency-prone, the system can optimize the processing pipeline by selecting alternative fulfillment paths or adjusting processing priorities, thereby reducing unnecessary computational resource consumption while maintaining versatility.
Data Source
AI summary
Implementations described herein relate to reducing latency in automated assistant interactions. In some implementations, a client device can receive audio data that captures a spoken utterance of a user. The audio data can be processed to determine an assistant command to be performed by an automated assistant. The assistant command can be processed, using a latency prediction model, to generate a predicted latency to fulfill the assistant command. Further, the client device (or the automated assistant) can determine, based on the predicted latency, whether to audibly render pre-cached content for presentation to the user prior to audibly rendering content that is responsive to the spoken utterance. The pre-cached content can be tailored to the assistant command and audibly rendered for presentation to the user while the content is being obtained, and the content can be audibly rendered for presentation to the user subsequent to the pre-cached content.