ANN Search Engine Circuitry Using Representative Media Vectors
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Solution Overview
Problem
Existing artificial neural networks (ANNs) face challenges in efficiently processing and searching large media data sets due to high computational complexity and resource requirements, particularly when dealing with diverse forms of digital content such as video, audio, and text, which are difficult to group and compare effectively.
Innovation Solution
The system processes data sets to generate multi-dimensional embedding vectors, groups them into intervals based on movement metrics, and selects representative vectors (RVs) for each group, transforming these vectors between latent spaces using different embedding models to facilitate efficient searching based on similarity measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ANNs process large media data sets frame-by-frame or word-by-word, then comprehensive analysis is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments media data sets into meaningful intervals or chunks rather than processing frame-by-frame or word-by-word. Each interval is represented by a combined vector that aggregates information from multiple frames or words, reducing the total number of vectors to process while preserving essential content characteristics.
Solution Approach 2:
Multiple frame vectors or word vectors within each interval are merged into a single combined vector that represents the entire interval. This combining process reduces dimensionality and computational load while maintaining the essential information needed for accurate search and analysis.
2Loss of information
If ANNs process every frame or word in media data sets, then complete content coverage is achieved, but processing speed decreases
Solution Approach 1:
The patent divides continuous media streams into discrete intervals, processing one interval at a time rather than every individual frame or word. This segmentation enables batch processing and reduces the total number of operations required while maintaining content coverage through representative combined vectors.
Solution Approach 2:
The system performs preliminary processing by pre-computing combined vectors for each interval and storing them for quick retrieval. This preliminary action eliminates the need to re-process raw frames or words during search operations, significantly improving processing speed for subsequent queries.
3Quantity of substance
If dimensionality reduction is applied layer by layer, then resource requirements decrease, but computational complexity increases
Solution Approach 1:
The patent transitions from temporal dimension (frame-by-frame processing) to spatial dimension (interval-based combined vectors). By organizing data into intervals and representing each interval with a single combined vector, the system reduces dimensionality in a way that simplifies rather than complicates computational operations.
Data Source
AI summary
Method and apparatus for characterizing digital content using artificial neural network (ANN) techniques. In some embodiments, computer data sets (such as video, audio, text, etc.) are processed to generate a corresponding sequence of multi-dimensional embedding vectors in a latent space. The embedding vectors are grouped into intervals (segments) of the data sets based on movement metrics associated with the embedding vectors. A representative vector (RV) is selected for each group. Thereafter, in response to a query input, selected intervals among the various computer data sets are identified and output based on a similarity measure between the RVs and a search vector derived from the query input. Further embodiments provide a transformation model that transforms the embedding vectors and/or the RVs from a first latent space based on a first embedding model to a different, second latent space based on a second embedding model.