18206901. IDENTIFYING INSTANCES OF DIGITAL CONTENT simplified abstract (Adobe Inc.)
Contents
IDENTIFYING INSTANCES OF DIGITAL CONTENT
Organization Name
Inventor(s)
Jennifer Jiaying Qian of New York NY (US)
Mateus De Araujo Lopes of Naperville IL (US)
IDENTIFYING INSTANCES OF DIGITAL CONTENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18206901 titled 'IDENTIFYING INSTANCES OF DIGITAL CONTENT
Simplified Explanation: The patent application describes a system for identifying instances of digital content by analyzing attributes and keywords associated with the content.
- **Machine-learning model:** Utilized to determine semantically similar keywords.
- **Compilation of matchable keywords:** Includes original keywords and additional similar keywords.
- **Identification of candidate instances:** Based on content keywords and matchable keywords.
- **Generation of indication:** For display in a user interface.
Key Features and Innovation:
- Utilization of machine-learning model for determining semantically similar keywords.
- Compilation of matchable keywords to enhance content identification.
- Generation of indications for display in a user interface.
Potential Applications: This technology can be applied in content management systems, digital marketing, and online advertising platforms.
Problems Solved:
- Efficient identification of digital content instances.
- Improved accuracy in matching keywords.
Benefits:
- Enhanced content organization and categorization.
- Streamlined content identification process.
Commercial Applications: Potential commercial applications include digital asset management systems, content recommendation engines, and online search platforms.
Prior Art: Readers can explore prior art related to machine-learning models for keyword classification and content identification systems.
Frequently Updated Research: Stay updated on advancements in machine-learning models for keyword classification and content identification systems.
Questions about the Technology: 1. How does the system determine semantically similar keywords? 2. What are the potential limitations of using machine-learning models in content identification systems?
Original Abstract Submitted
In implementations of systems for identifying instances of digital content, a computing device implements a content system to receive input data describing attributes of an entity segment and keywords that are associated with the attributes of the entity segment. The content system determines additional keywords that are semantically similar to the keywords using a machine-learning model trained on training data to classify semantically similar keywords. A set of matchable keywords is compiled that includes the keywords and the additional keywords. The content system identifies candidate instances of digital content based on content keywords assigned to the candidate instances of digital content and the set of matchable keywords. An indication of an instance of digital content is generated for display in a user interface based on the candidate instances of digital content.
