View source for ObviousFuture GmbH (20240320502). ARTIFICIAL NEURAL NETWORK BASED SEARCH ENGINE CIRCUITRY simplified abstract

=ARTIFICIAL NEURAL NETWORK BASED SEARCH ENGINE CIRCUITRY=


==Organization Name==

[[:Category:ObviousFuture GmbH|ObviousFuture GmbH]]


[[Category:ObviousFuture GmbH]]

==Inventor(s)==

[[:Category:Eduard Weinwurm of Vienna (AT)|Eduard Weinwurm of Vienna (AT)]][[Category:Eduard Weinwurm of Vienna (AT)]]

==ARTIFICIAL NEURAL NETWORK BASED SEARCH ENGINE CIRCUITRY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240320502 titled 'ARTIFICIAL NEURAL NETWORK BASED SEARCH ENGINE CIRCUITRY

'''Simplified Explanation''':
The patent application describes a method and apparatus for characterizing digital content using artificial neural network techniques. This involves processing computer data sets to generate multi-dimensional embedding vectors in a latent space, grouping these vectors into intervals based on movement metrics, selecting representative vectors for each group, and identifying selected intervals based on similarity measures for query inputs.

* The method involves processing computer data sets to generate multi-dimensional embedding vectors in a latent space.
* These vectors are grouped into intervals based on movement metrics associated with them.
* Representative vectors are selected for each group to represent the data sets.
* Selected intervals are identified based on similarity measures between the representative vectors and a search vector derived from query inputs.
* A transformation model is provided to transform the embedding vectors and representative vectors from a first latent space to a different second latent space based on different embedding models.

'''Key Features and Innovation''':
* Characterizing digital content using artificial neural network techniques.
* Generating multi-dimensional embedding vectors in a latent space.
* Grouping vectors into intervals based on movement metrics.
* Selecting representative vectors for each group.
* Identifying selected intervals based on similarity measures for query inputs.
* Providing a transformation model to transform vectors between different latent spaces.

'''Potential Applications''':
This technology can be applied in various fields such as:
* Content recommendation systems.
* Video and audio analysis.
* Text classification and sentiment analysis.
* Image recognition and processing.

'''Problems Solved''':
* Efficiently characterizing and organizing digital content.
* Improving search and retrieval processes.
* Enhancing content recommendation accuracy.
* Facilitating data analysis and classification tasks.

'''Benefits''':
* Improved content organization and categorization.
* Enhanced search and retrieval efficiency.
* More accurate content recommendations.
* Streamlined data analysis processes.

'''Commercial Applications''':
Title: "Artificial Neural Network-based Digital Content Characterization Technology"
This technology can be commercially utilized in:
* Media and entertainment industries for content recommendation.
* E-commerce platforms for personalized product recommendations.
* Marketing companies for targeted advertising campaigns.
* Research institutions for data analysis and pattern recognition.

'''Prior Art''':
Readers interested in prior art related to this technology can explore research papers, patents, and publications in the fields of artificial intelligence, machine learning, and digital content analysis.

'''Frequently Updated Research''':
Researchers are constantly exploring advancements in artificial neural network techniques for characterizing digital content, with a focus on improving accuracy, efficiency, and scalability.

'''Questions about Artificial Neural Network-based Digital Content Characterization Technology''':
1. How does this technology improve content recommendation systems?
2. What are the potential challenges in implementing this technology in real-world applications?


==Original Abstract Submitted==

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.



[[Category:G06N3/091]]
[[Category:G06N3/045]]
[[Category:CPC_G06N3/091]]