Context-Aware Action Suggestion for Touchscreen Content
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Solution Overview
Problem
Users face cumbersome processes when trying to perform actions related to content on their devices, such as making reservations or obtaining directions, as they need to manually select, copy, and paste content, which is particularly cumbersome on touchscreen devices due to limitations in touch input.
Innovation Solution
A computer-implemented method and system that predicts the user's intended purpose for selecting content and provides associated actions for selection, using a processor to identify a referent entity from user-selectable content and display relevant actions based on context, such as location, past actions, or search queries, allowing users to achieve their intended purpose with fewer input actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection and copying of content is used, then user control over content is maintained, but the number of input actions increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically identifying referent entities and generating relevant action suggestions before the user needs to execute any manual operations. This pre-processing of content analysis and action generation reduces the subsequent user input required
Solution Approach 2:
The system acts as an intermediary between the user and the content by automatically interpreting user selections, identifying referent entities, and presenting curated action options. This intermediary layer translates manual content selection into intelligent action recommendations, reducing the gap between user intent and system response
2Extent of automation
If automated action suggestions are provided, then the number of input actions is reduced, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of action identification into distinct modules: content selection, referent entity identification, context analysis, and action suggestion generation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable despite the automation complexity
Solution Approach 2:
The system performs self-service by automatically analyzing content and context to generate action suggestions without requiring extensive manual configuration or complex external processing. The system serves itself by using its own resources (processed content and context data) to generate intelligent recommendations
3Measurement precision
If context-based action identification is used, then relevant actions are accurately identified, but the processing time increases
Solution Approach 1:
The system applies partial action by focusing context analysis on the most relevant factors rather than exhaustively analyzing all possible context elements. This selective approach maintains sufficient accuracy for identifying relevant actions while reducing unnecessary processing time
Data Source
AI summary
Computer-implemented methods for proposing actions to a user to select based on the user's predicted purpose for selecting content are provided. In one aspect, a method includes receiving an identifier of a referent entity associated with user-selectable content, identifying, based on a prediction of a purpose in selecting the content, at least one action to be executed that is associated with the entity, and providing, for display, at least one identifier of the at least one action to the device for selection by a user. Systems, graphical user interfaces, and machine-readable media are also provided.