Instructional Video Analysis via Action Graphs for Troubleshooting
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Solution Overview
Problem
Existing video processing technologies that perform video segmentation and indexing based on metadata analysis are unable to provide interactive help to users who need assistance with instructional videos, such as troubleshooting steps or understanding missed or misinterpreted content.
Innovation Solution
A method that analyzes instructional video data to form units of work by grouping video frames based on logical activities, producing action graphs to show interdependencies among activities within and across units, and creating a critical path graph, which is used to provide interactive troubleshooting assistance using a knowledge base.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video segmentation and indexing is performed based on metadata analysis, then video processing efficiency is improved, but the ability to provide interactive troubleshooting assistance deteriorates
Solution Approach 1:
The instructional video is segmented into discrete units of work, where each unit represents a logical grouping of activities. This segmentation enables both efficient processing through structured organization and interactive assistance by creating manageable, queryable segments that can be independently analyzed and referenced during troubleshooting.
Solution Approach 2:
Action graphs serve as intermediary structures that connect video content with user queries. These graphs represent interdependencies among activities and enable the system to provide interactive troubleshooting assistance by mediating between the segmented video content and user needs, bridging the gap between efficient processing and adaptive assistance.
2Adaptability or versatility
If video frames are grouped into units of work with action graphs, then troubleshooting capability is improved, but system complexity increases
Solution Approach 1:
The system divides the instructional video into discrete units of work, each with its own action graph. This segmentation manages complexity by breaking down the overall system into smaller, more manageable components that can be processed and queried independently, while still maintaining the ability to provide comprehensive troubleshooting assistance.
Solution Approach 2:
The patent transforms video content into structured parameters including units of work, action graphs, and critical path graphs. This parameter transformation organizes complex video data into standardized formats that are easier to process and query, improving troubleshooting capability while managing system complexity through consistent data representation.
Data Source
AI summary
A method, a system, and a computer program product are provided for analyzing an instructional video. Video data of an instructional video is analyzed to form multiple units of work. Each unit of work is a respective grouping of video frames of the instructional video based on a respective logical combination of activities associated therewith. Each unit of work is analyzed to produce a respective action graph of activities included in the unit of work, the respective action graph indicating interdependencies among the activities included therein. Interdependencies among activities across the units of work are determined to form a critical path graph. A received query is processed to provide troubleshooting assistance with respect to the instructional video based on the units of work, the action graphs, the critical path graph, and a knowledge base including information related to a subject matter of the instructional video.