Video Processing System for Action-Based Recipe Retrieval
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Solution Overview
Problem
Users face difficulties in identifying and accessing relevant video content for specific steps or recipe stages while learning a task, as existing video processing technologies lack efficient methods to extract and match actions within videos, making it hard for learners to find instructional material that corresponds to their current challenges.
Innovation Solution
A system and method that captures user video performance, generates machine representations of the actions, compares them to a corpus of pre-existing representations, and facilitates playback of similar videos, using frame and sequence feature extractors, machine learning agents, and similarity search functions to identify and present relevant video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video content is stored in a comprehensive corpus for learners to search, then the quantity and variety of instructional material increases, but the difficulty of identifying and retrieving relevant video segments increases
Solution Approach 1:
The system performs preliminary action by automatically generating machine representations (captions, tags, metadata) for all video content in advance. This preprocessing enables efficient retrieval without requiring learners to manually search through comprehensive video corpora, resolving the contradiction between having abundant content and making it easily searchable.
Solution Approach 2:
The system introduces machine representations (captions, tags, metadata) as intermediaries between the video content and the learner's search query. These intermediaries enable automatic matching and retrieval of relevant video segments without requiring the learner to directly navigate or identify content within the comprehensive corpus.
2Ease of operation
If learners manually search for video content using text-based search, then they can find relevant material, but they must be sufficiently well-versed in the task to adequately define or describe the step or recipe stage
Solution Approach 1:
The system enables self-service by automatically generating machine representations of video content without requiring learner input. The system captures video of the learner performing tasks, automatically processes it to create machine representations, and uses these for retrieval, eliminating the need for learners to precisely define or describe task steps.
Solution Approach 2:
The system replaces the mechanical search process (where learners manually type and search for terms) with an automated machine learning system. The machine learning agent automatically generates and matches machine representations, substituting the manual mechanical search operation with an intelligent automated process that does not require learner expertise.
3Speed
If the system generates machine representations from captured video in real-time, then it can provide immediate feedback, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively processing only the necessary portions of video content. Rather than analyzing every frame in full detail, the system processes key moments and extracts essential machine representations, reducing computational overhead while maintaining the ability to provide timely feedback.
Solution Approach 2:
The system performs preliminary processing of video content to generate machine representations in advance, before they are needed for retrieval or comparison. This preprocessing allows the system to have ready-made representations available, reducing the computational burden during real-time operations and enabling faster response times.
Data Source
AI summary
A method of assisting a user in preparation of food. The method comprises capturing a video of the user performing one or more steps in the preparation; generating, from the captured video, a machine representation of the performed one or more steps; comparing the generated machine representation to a corpus of one or more pre-existing machine representations, the one or more pre-existing machine representations corresponding to one or more pre-existing videos; based on the comparison, identifying at least one pre-existing machine representation in the corpus which has a similarity relationship with the generated machine representation; and facilitating playback to the user of at least one pre-existing video corresponding to the identified at least one pre-existing machine representation.