Context-Aware Digital Assistant Selection in Interactive Interfaces
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Solution Overview
Problem
Users face inefficiencies in selecting a digital assistant with specific functions due to the need for manual selection from multiple options, affecting interaction efficiency and convenience.
Innovation Solution
A method and apparatus that provide a digital assistant list in an interactive interface based on interaction context and historical user operations, allowing quick recommendation of a target digital assistant through a predetermined mentioning symbol input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually select from multiple digital assistants, then they can choose the appropriate assistant, but the selection process is time-consuming and inefficient
Solution Approach 1:
The system performs preliminary action by proactively presenting a list of recommended digital assistants based on interaction context and historical data before the user needs to make a selection. The recommendation list is generated in advance based on analyzed patterns, eliminating the need for users to manually search through multiple options and significantly reducing selection time.
Solution Approach 2:
The system implements self-service by automatically analyzing interaction context and historical operations to generate personalized digital assistant recommendations without requiring user input or manual selection. The system serves itself by utilizing its own data to make intelligent recommendations, freeing the user from the manual selection process.
2Adaptability or versatility
If the system provides multiple digital assistant options, then users have choices, but the complexity of selection increases
Solution Approach 1:
The system applies segmentation by dividing the complex selection task into two parts: (1) the system automatically analyzes and segments the interaction context and historical data to identify relevant patterns, and (2) presents a simplified, pre-sorted list of recommendations. This segmentation transforms the complex multi-option selection into a simple confirmation or modification process for the user.
Solution Approach 2:
The recommendation list acts as an intermediary between the complex underlying system and the user. Instead of presenting raw complexity, the intermediary layer of automated recommendations filters and organizes the information, making the selection process simple while maintaining access to multiple digital assistant options.
3Measurement precision
If the system analyzes interaction context and historical data, then recommendations become more accurate, but processing requirements increase
Solution Approach 1:
The system applies partial action by analyzing only the most relevant portions of interaction context and historical data rather than processing everything in detail. It identifies and processes key patterns and features that are most predictive of user needs, achieving sufficient recommendation accuracy without the excessive processing resources that would be required to analyze all historical interactions in full detail.
Data Source
AI summary
Embodiments of the disclosure provide a solution for information processing. The method includes: in response to detecting a predetermined mentioning symbol input by a user in an interactive interface, providing a presentation of a digital assistant list in the interactive interface based on at least one of: interaction context information in the interactive interface or at least one historical interaction operation of the user for a digital assistant, the digital assistant list comprising at least one candidate digital assistant; in response to detecting a determination of a target digital assistant, receiving a request for the target digital assistant; and in response to receiving the request, providing, in the interactive interface, a response to the request via the target digital assistant.