Delegated Decision Tree Evaluation for Latency Reduction
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Solution Overview
Problem
Automated service agents face performance issues due to network conditions like latency and bandwidth when executed on a computing device separate from the client device, leading to delayed responses to user inputs.
Innovation Solution
Delegated decision tree evaluation, where a client device receives and caches decision tree nodes from a remote computing device, allowing local evaluation of user inputs and immediate response generation, while pruning and updating nodes in the background to mitigate network impact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automated service agent is executed on a remote computing device, then centralized control and decision-making are maintained, but network latency and bandwidth issues cause delayed responses to user inputs
Solution Approach 1:
The decision tree is segmented into multiple nodes that are distributed between the remote computing device and the client device. The client device receives and caches portions of the decision tree nodes, allowing local evaluation of user inputs against cached nodes while the remote device maintains authority over the complete decision tree structure and can provide additional nodes as needed.
Solution Approach 2:
The client device performs preliminary actions by caching decision tree nodes in advance before they are needed for evaluation. This allows the client device to immediately evaluate user inputs against cached nodes without waiting for network communication, and only communicates with the remote device when additional nodes are needed or when the interaction completes.
2Speed
If decision tree nodes are cached locally on client device, then immediate local evaluation is possible, but memory resources are consumed and nodes must be managed
Solution Approach 1:
The client device discards cached decision tree nodes when they are no longer needed for the current interaction path, and recovers memory resources for future use. The system manages the cache by removing nodes from branches that are not pursued during interaction, allowing memory to be reused for nodes that may be needed in future interactions.
Data Source
AI summary
Disclosed are various embodiments for delegated evaluation of a decision tree. A client device receives an expression and a plurality of responses from a second computing device, the expression providing an evaluation of a user input and each of the plurality of responses representing a potential response for the first computing device to provide to the user input. The client device then evaluates the user input with the expression received from the second computing device to determine a user intent. The client device then selects a response to the user intent from the plurality of responses and renders the response within a user interface.