Dynamic Onboarding Tutorial Generation via Contextual Data
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Solution Overview
Problem
Existing application onboarding tutorials are not personalized to individual users, providing the same information to all users, which can lead to reduced effectiveness in user understanding and retention of application features.
Innovation Solution
An application executing on a computing device determines contextual information and uses it to dynamically generate a customized onboarding tutorial by selecting appropriate template graphical user interfaces and populating them with relevant content, ensuring the tutorial is tailored to the user's interests and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a standardized onboarding tutorial is provided to all users, then the implementation complexity is low, but the user understanding and retention effectiveness deteriorates
Solution Approach 1:
The patent applies local quality by customizing different portions of the tutorial content based on user-specific contextual information. The system selects and personalizes specific tutorial modules (such as travel, food, entertainment categories) according to individual user profiles, device settings, and contextual data, while maintaining a standardized framework for tutorial delivery. This allows personalized content adaptation without redesigning the entire tutorial system.
Solution Approach 2:
The patent implements preliminary action by collecting and analyzing user contextual information (device settings, app preferences, user profile data) before generating the personalized tutorial. The system prepares user-specific content selections and customizes tutorial pathways in advance based on pre-collected data, enabling personalized tutorials to be delivered immediately when users start the application without requiring real-time processing during the onboarding experience.
2Reliability
If a personalized onboarding tutorial is generated for each user, then the user retention effectiveness improves, but the device complexity increases
Solution Approach 1:
The patent applies universality by creating a multi-functional tutorial generation system that handles multiple user types, device configurations, and content categories through a single unified framework. The system uses a standardized set of template graphical user interfaces that can be dynamically populated with different content based on user context, allowing one system to serve multiple personalization needs without requiring separate tutorial systems for different user scenarios.
Solution Approach 2:
The patent implements copying by using template graphical user interfaces that serve as reusable patterns for tutorial content. Instead of creating entirely custom tutorials for each user, the system copies and populates standardized template structures with user-specific content, reducing the complexity of generating personalized tutorials while maintaining the effectiveness of customization.
3Measurement precision
If comprehensive contextual information is collected for personalization, then the tutorial customization accuracy improves, but the processing time and energy consumption increase
Solution Approach 1:
The patent applies extraction by selectively pulling out only the most relevant contextual information needed for tutorial personalization from the available user data. The system identifies and extracts specific parameters (such as travel preferences, food categories, entertainment interests) that have the highest impact on tutorial effectiveness, rather than processing all available user data. This reduces processing time while maintaining high customization accuracy by focusing on the most influential factors.
4Duration of action of moving object
If the same tutorial is displayed to all users, then the display duration is consistent, but the user experience quality deteriorates
Solution Approach 1:
The patent implements dynamics by making the tutorial display duration and content flow adaptive to user interactions and comprehension levels. The system dynamically adjusts the tutorial based on user responses, allowing users to progress through relevant content at their own pace while the system monitors engagement and modifies the tutorial pathway accordingly. This creates variable display durations that are optimized for each user's learning needs rather than enforcing a fixed timeline.
Data Source
AI summary
A method includes determining whether an application has previously been executed by a computing device. The method includes, responsive to determining that the application has not previously been executed by the computing device, determining, by the application, contextual information associated with the computing device. The method also includes determining, based at least in part on the contextual information, content to include in at least one template graphic user interface of a plurality of template graphical user interfaces for an onboarding tutorial of the application. At least one template graphical user interface is associated with at least one feature of the application. The method also includes generating, based on the at least one template graphical user interface and the content, at least a first graphical user interface of the onboarding tutorial. The method further includes outputting an indication of the first graphical user interface of the onboarding tutorial.