Samsung electronics co., ltd. (20240126990). DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION simplified abstract
Contents
- 1 DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION
Organization Name
Inventor(s)
Sunil Bharitkar of Stevenson Ranch CA (US)
DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240126990 titled 'DEEP LEARNING FOR MULTIMEDIA CLASSIFICATION
Simplified Explanation
The computer-implemented method described in the abstract utilizes text information from a media content item's title and a trainable model to improve accuracy in classifying the media content item. The trainable model uses text to numeric-vector embeddings for classification, jointly optimizing word embedding model parameters or latent semantic analysis dimensions using the text information, and a classifier model to maximize accuracy.
- Text information from a media content item's title is used in conjunction with a trainable model to improve classification accuracy.
- The trainable model utilizes text to numeric-vector embeddings for classification of the media content item.
- Word embedding model parameters or latent semantic analysis dimensions are jointly optimized using the text information.
- A classifier model is employed to maximize the accuracy of classifying the media content item.
Potential Applications
This technology could be applied in various industries such as media and entertainment, e-commerce, and online advertising for more accurate content classification and recommendation systems.
Problems Solved
This technology addresses the challenge of accurately classifying media content items based on text information, improving the overall efficiency and effectiveness of classification processes.
Benefits
The benefits of this technology include enhanced accuracy in classifying media content items, leading to better recommendations, improved user experience, and increased efficiency in content management systems.
Potential Commercial Applications
Potential commercial applications of this technology include content recommendation systems, targeted advertising platforms, and media content classification tools.
Possible Prior Art
One possible prior art for this technology could be the use of text embeddings and trainable models in natural language processing tasks such as sentiment analysis and text classification.
Unanswered Questions
=== How does this technology compare to existing content classification methods in terms of accuracy and efficiency? This article does not provide a direct comparison to existing content classification methods, leaving the reader to wonder about the technology's performance relative to current solutions.
=== What are the potential limitations or challenges of implementing this technology in real-world applications? The article does not address any potential limitations or challenges that may arise when implementing this technology in practical settings, leaving room for speculation on its feasibility and scalability.
Original Abstract Submitted
one embodiment provides a computer-implemented method that includes utilizing text information obtained from a title of a media content item and a trainable model for improving accuracy for classification of the media content item. the trainable model is utilized using a sequence of text to numeric-vector embeddings for classification of the media content item. at least one of a word embedding model parameter or a latent semantic analysis dimension is jointly optimized using the text information, and a classifier model for maximizing accuracy of the classification of the media content item.
