Personal Agent Intent Prediction for Interaction Efficiency
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Solution Overview
Problem
User interactions with personal agents, such as smart speakers, can be frustrating due to the need for additional information and repeated attempts to execute actions, leading to increased time and user dissatisfaction.
Innovation Solution
A system that predicts and presents likely future user intents based on user intent data and contextual data, allowing users to choose from suggested actions rather than having to figure out the correct command, thereby reducing interaction time and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the personal agent requests additional information to complete user actions, then the accuracy of action execution is improved, but the interaction time and user frustration increase
Solution Approach 1:
The system performs preliminary actions by predicting future user intents before the user actually needs to execute them. The intent prediction module analyzes historical data and contextual information to proactively determine what actions the user is likely to take next, allowing the personal agent to prepare responses in advance and reduce the time needed for information requests and user responses.
2Measurement precision
If the user provides detailed voice commands to complete actions, then the precision of intent recognition is improved, but the ease of operation deteriorates
Solution Approach 1:
The system enables self-service by having the personal agent autonomously determine and present predicted user intents without requiring the user to figure out the correct commands. The intent prediction module processes historical interactions and contextual data to automatically generate relevant action suggestions, allowing the user to simply select from predicted options rather than formulating precise voice commands.
3Ease of operation
If the personal agent provides multiple action options to the user, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The system extracts and presents only the most relevant predicted intents to the user rather than providing all possible actions. The intent prediction module filters and ranks potential actions based on historical data and contextual analysis, extracting only the top predictions that are most likely to be useful to the user, thereby simplifying the presented options without requiring complex processing of all possible actions.
Data Source
AI summary
This application relates to systems and methods for proactively predicting user intents on personal agents. In some examples, a user intent prediction system can include a computing device configured to obtain user intent data identifying a desired action by a user on a network-enabled tool. The computing device is further configured to obtain contextual data characterizing a user's interaction with the network-enabled tool. The computing device can then determine at least one predicted future intent of the user on the network-enabled tool based on the user intent data and the contextual data and present the at least one predicted future intent to the user.