In-App Guided Edits Using Multi-Task Summarization for Coachmarks
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Solution Overview
Problem
Conventional digital tutorial systems face issues with accuracy, efficiency, and flexibility due to inadequate context provision, excessive resource consumption, and inflexibility in adapting to different user interfaces and devices.
Innovation Solution
The implementation of a multi-task machine learning model generates concise in-app guided tutorials by creating coachmarks and concise instructions directly within the user interface, providing context and reducing the need for users to switch between applications, and adapting to various devices and screen sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional digital tutorial systems provide lengthy text descriptions with supplementary images and videos, then users receive comprehensive instructions, but the system consumes excessive computer resources including memory, processing power, and computing time
Solution Approach 1:
The system extracts only the essential elements needed for tutorial delivery: instead of providing complete lengthy text descriptions with all supplementary materials, it generates concise text descriptions and selectively extracts only the necessary user interface elements to highlight. This extraction approach maintains instructional effectiveness while dramatically reducing memory, processing power, and computing time requirements.
Solution Approach 2:
The system changes the parameter of instruction length from lengthy and comprehensive to concise and targeted. By transforming the density and scope of instructional content, the system reduces resource consumption while maintaining the essential information needed for users to complete tasks.
2Reliability
If conventional digital tutorial systems separate workspace and learning space requiring users to switch between applications, then tutorials can be delivered, but user efficiency significantly decreases due to frequent context switching
Solution Approach 1:
The system merges the workspace and learning space by integrating tutorial instructions directly into the application interface. Coachmarks are overlaid on the actual user interface elements within the workspace application, allowing users to receive guidance without leaving their work context. This combination eliminates the need to switch between separate tutorial and workspace applications.
Solution Approach 2:
The system introduces coachmarks as an intermediary element that bridges the tutorial content and the workspace. These visual overlays serve as a mediator that delivers instructional information directly within the application context, allowing users to learn and work simultaneously without context switching.
3Ease of manufacture
If conventional digital tutorial systems use fixed manuals and tutorials that do not account for particular context, then implementation is simple, but the system lacks flexibility to adapt to different users' individual devices and interfaces
Solution Approach 1:
The system transitions from static fixed manuals to dynamic adaptive tutorials. The coachmark generation process dynamically identifies and highlights the specific user interface elements relevant to the current task and device context. This dynamic adaptation allows the same tutorial to effectively guide users across different devices and interface variations without requiring separate fixed manuals for each scenario.
4Device complexity
If conventional digital tutorial systems provide instructions without context indicating which user interface element to apply to, then instruction generation is straightforward, but users execute incorrect operations due to ambiguity and misinterpretation
Solution Approach 1:
The system applies local quality by providing context-specific highlighting for each individual instruction. Instead of generic instructions, the system identifies and highlights the specific user interface element that corresponds to each step. This localized contextual information ensures users apply instructions to the correct elements, eliminating ambiguity and misinterpretation while maintaining manageable instruction generation complexity.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating coachmarks and concise instructions based on operation descriptions for performing application operations. For example, the disclosed systems can utilize a multi-task summarization neural network to analyze an operation description and generate a coachmark and a concise instruction corresponding to the operation description. In addition, the disclosed systems can provide a coachmark and a concise instruction for display within a user interface to, directly within a client application, guide a user to perform an operation by interacting with a particular user interface element.