Video Workflow Step Extraction for Query-Based Instructions
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Solution Overview
Problem
Existing online instructional videos are not optimized for user consumption based on individual circumstances and user needs, making it difficult for users to follow the pace of the video and requiring frequent pausing and rewinding due to noise or environmental factors.
Innovation Solution
A system that utilizes a processor to parse video data, identify discrete workflow steps, and present them in text and image form, using large language models and natural language processing to generate tailored step-by-step instructions based on user queries, allowing for dynamic presentation of relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users watch online instructional videos, then they can learn complex tasks, but they must follow the video's fixed pace and cannot easily pause or rewind in noisy environments
Solution Approach 1:
The video is segmented into discrete workflow steps, each representing a distinct actionable unit. This segmentation allows users to consume instructions in manageable chunks, pause between steps, and revisit specific steps without rewinding entire video segments, directly reducing time loss while maintaining ease of operation
Solution Approach 2:
The system performs preliminary actions by pre-processing the video into structured workflow steps with associated text descriptions and images before user interaction. This preparation enables users to immediately access specific steps without needing to rewind or fast-forward through video content, eliminating time loss from navigation
2Adaptability or versatility
If instructional videos are made available online, then users can access learning content, but the content is not optimized for individual user circumstances and needs
Solution Approach 1:
The system dynamically adapts content delivery based on user needs by allowing selective presentation of workflow steps. Users can request specific steps or ranges of steps, and the system adjusts the delivered content accordingly, transforming static video content into a dynamic, user-adaptive learning experience without requiring complex additional hardware
3Loss of information
If videos are used to teach complex tasks, then users can observe demonstrations, but audio may not be audible in noisy environments
Solution Approach 1:
The system extracts the essential instructional information from the video's audio and visual components, separating it into text descriptions and key images. This extraction eliminates dependency on audible audio while preserving the instructional content, preventing information loss in noisy environments through a relatively simple implementation approach
Data Source
AI summary
In one aspect, a device includes a processor system and storage accessible to the processor system. The storage includes instructions executable by the processor system to, responsive to a user query, parse data related to a source video to identify discrete steps that conform to the user query. The discrete steps are steps in a workflow indicated in the source video. Based on identifying the discrete steps, the instructions are then executable to present, on a display, text and images that indicate the discrete steps. The text and images are different from the source video itself but are derived from the source video. In one particular example, the instructions may even be executable to use a large language model (LLM) to execute retrieval-augmented generation (RAG) to present, on the display, the text and images in conformance with the user query.