Video Process Capture to Generate Structured Business Documents
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Solution Overview
Problem
The conversion of video recordings into detailed documents for knowledge transfer is time-consuming, and extracting implicit knowledge from these recordings is challenging.
Innovation Solution
A computer-implemented method that processes time-aligned video frames using a machine learning model to extract control data and convert audio content to text, generating comprehensive process documents that outline business processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual conversion of video recordings into detailed documents is performed, then comprehensive process documentation is achieved, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical processes (human analysts watching videos and extracting information) with automated computer-based systems including machine learning models, optical character recognition (OCR), and natural language processing to automatically extract process information from video recordings, thereby reducing time consumption while maintaining comprehensive documentation
Solution Approach 2:
The system enables the video recordings themselves to provide the necessary information through automated analysis. The videos contain embedded process information that the system extracts automatically through AI-driven techniques, eliminating the need for external manual intervention and significantly reducing the time required for documentation
2Loss of information
If detailed analysis of video recordings is performed to extract implicit knowledge, then tacit knowledge is captured, but processing complexity increases
Solution Approach 1:
The patent replaces complex manual analysis processes with automated machine learning models and AI algorithms that can identify and extract implicit knowledge patterns from video data, reducing processing complexity while maintaining comprehensive tacit knowledge capture
Solution Approach 2:
The system introduces intermediate processing layers including automated speech-to-text conversion, OCR for text extraction, and machine learning models that serve as mediators between the raw video data and the final extracted knowledge, simplifying the overall processing complexity by breaking down the complex task into manageable automated steps
3Productivity
If automated processing is implemented to reduce time consumption, then processing speed increases, but extraction precision may deteriorate
Solution Approach 1:
The patent employs sophisticated automated systems including trained machine learning models, deep learning algorithms, and multiple verification layers that maintain high extraction precision while achieving rapid processing speeds, overcoming the traditional trade-off between automation and accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where extracted information is validated and refined through multiple processing passes, with the machine learning models continuously improving their extraction accuracy based on feedback from the video data, ensuring high precision is maintained even as processing speed increases
Data Source
AI summary
A computer-implemented method for generating content from video is described. In an example, video content may be extracted from source media that includes captured process steps involving a business process performed via an application. Further, time-aligned video frames may be extracted from the video content. Each frame represents an image at a different time. Furthermore, the time-aligned video frames may be processed to extract control data representing the captured process steps related to the business process. Based on the extracted control data, the content may be generated in a desired format to perform the business process.