Deep Link Tutorial Generation for Digital Image Editing
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Solution Overview
Problem
Conventional digital content systems are inefficient in generating and replaying animated tutorials, inflexible across different learner devices, and require significant computing resources, leading to inefficiencies and incompatibility with various platforms.
Innovation Solution
A tutorial generation system that analyzes modifications to digital images on educator devices, automatically generates animated tutorials, and publishes them across multiple platforms, allowing learners to access and interact with the tutorials directly within the digital image editing application through deep links and social media posts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional digital content systems are used to generate and distribute digital video tutorials, then tutorials can be created and shared across platforms, but the systems suffer from inefficiencies in generating and replaying animated tutorials, requiring significant computing resources
Solution Approach 1:
The system uses deep links as simplified copies or references to full tutorials, allowing learners to access tutorial functionality without downloading or processing complete video files. The deep link contains essential parameters (tutorial ID, device ID, platform type) that enable the system to retrieve and adapt the full tutorial content on-demand, reducing the computing resources required at the learner device while maintaining tutorial accessibility.
Solution Approach 2:
The patent extracts the essential tutorial access mechanism from the complete video tutorial package. Instead of requiring learners to process entire video files, the system separates the access key (deep link) from the full content, allowing efficient sharing and distribution while deferring the resource-intensive tutorial delivery to when and where it is actually needed.
2Adaptability or versatility
If conventional systems distribute digital video tutorials, then tutorials can be accessed by learners, but the systems lack flexibility in adapting to different learner devices and platforms
Solution Approach 1:
The deep link structure is designed to be dynamic and adaptive, incorporating device-specific parameters (device ID, platform type) that allow the same link format to function across different devices. When a learner clicks the deep link, the system dynamically determines the appropriate tutorial delivery method based on the learner's device characteristics, enabling seamless cross-platform compatibility without requiring separate tutorial versions for each platform.
Solution Approach 2:
The deep link serves multiple functions simultaneously: it acts as a sharing mechanism on social media, an access key for tutorials, a device identifier, and a platform adapter. This multi-functional design allows a single link structure to handle diverse device types and platforms, simplifying the learner experience while maintaining broad compatibility across different devices and social media platforms.
3Ease of manufacture
If conventional systems use recorded digital videos for tutorials, then tutorials can be created, but the systems have friction between watching tutorials and implementing changes
Solution Approach 1:
The system merges the tutorial viewing experience with the image editing application environment. By launching the tutorial directly within the educator's image editing application when the deep link is activated, learners can watch the tutorial and immediately apply changes to their own images in the same application interface, eliminating the friction of switching between separate video player and editing application windows.
Solution Approach 2:
The deep link acts as an intermediary that bridges the social media sharing platform and the image editing application. It carries the necessary information to both retrieve the appropriate tutorial content and launch it within the correct application context, creating a seamless transition from social media share to in-app tutorial playback without requiring manual intervention from the learner.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating and providing an animated tutorial based on determining modifications made to a digital image. For example, the disclosed systems can determine modifications made to a digital image based on comparing an initial state of a digital image with a modified state of the digital image and/or based on analyzing an action history within a digital image editing application. The disclosed systems can generate an animated tutorial based on the determined modifications and can further generate a deep link that references the animated tutorial. In addition, the disclosed systems can provide the animated tutorial to a social networking system together with the deep link to cause devices to execute a digital image editing application and present the animated tutorial upon selection of the deep link.