Hair Pattern Embedding for Content Filtering
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Solution Overview
Problem
The vast amount of accessible content online makes it difficult for users to find and access relevant content, as identifying proper keywords or queries and browsing search results can be time-consuming and challenging due to the sheer volume of information.
Innovation Solution
A system and method that determine hair patterns in content items using embedding vectors, allowing for the elimination of pre-processing steps like image segmentation, and utilize machine learning models to associate these patterns with content items for efficient searching, filtering, and indexing, enabling personalized recommendations based on user history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to determine hair patterns, then measurement precision can be achieved, but device complexity and processing time increase due to required pre-processing steps
Solution Approach 1:
The patent extracts and removes the pre-processing steps (image segmentation, background subtraction, object detection) from the traditional hair pattern determination pipeline. By using embedding vectors that directly encode hair pattern information from raw images, the invention eliminates the need for these separate pre-processing modules, thereby reducing device complexity while maintaining measurement precision through the learned representations in the embedding space.
Solution Approach 2:
The patent replaces the mechanical/image processing system with a data-driven machine learning approach. Instead of using traditional computer vision algorithms that require manual pre-processing steps, the invention uses trained models that automatically learn hair pattern features from embedding vectors, substituting the mechanical processing pipeline with an intelligent system that achieves the same or better precision with less complexity.
2Loss of information
If comprehensive content analysis is performed to improve search relevance, then information completeness increases, but loss of time increases due to extensive browsing required
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing embedding vectors for all content items in the database. These embedding vectors contain pre-extracted hair pattern information that can be quickly queried and compared during search operations. This preliminary processing eliminates the need for real-time image analysis during user searches, significantly reducing search time while maintaining complete content relevance through the rich embedding representations.
Solution Approach 2:
The patent creates compressed representations (embedding vectors) of the original images that capture essential hair pattern information. Instead of requiring users to browse through full-resolution images or perform complex image analysis during search, the system uses these compact embedding copies to rapidly retrieve and rank relevant content, reducing both search time and information loss.
3Measurement precision
If detailed content processing is implemented to improve filtering accuracy, then measurement precision improves, but productivity decreases due to increased processing requirements
Solution Approach 1:
The patent changes the parameter representation from raw pixel data to embedding vectors that capture essential hair pattern features in a compressed form. This parameter transformation enables efficient comparison and filtering operations on the embedding representations rather than processing full images, thereby maintaining high filtering accuracy through the rich semantic information in embeddings while dramatically increasing processing throughput and system productivity.
Data Source
AI summary
Described are systems and methods to determine hair patterns presented in content items. The determined hair patterns may be associated with the content items to facilitate indexing, filtering, etc. of the content items based on the determined hair patterns. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a binary representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more hair patterns presented in each content item while eliminating the need for performing image pre-processing prior to determination of the hair pattern(s) presented in the content item.