Google llc (20240134927). Systems and Methods for Automatically Creating Content Modification Scheme simplified abstract
Contents
- 1 Systems and Methods for Automatically Creating Content Modification Scheme
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 Systems and Methods for Automatically Creating Content Modification Scheme - 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 How does this technology handle privacy concerns related to extracting and associating content characteristics?
- 1.11 What are the potential limitations or challenges in implementing this technology on a large scale?
- 1.12 Original Abstract Submitted
Systems and Methods for Automatically Creating Content Modification Scheme
Organization Name
Inventor(s)
Guannan Zhang of Shanghai (CN)
Systems and Methods for Automatically Creating Content Modification Scheme - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240134927 titled 'Systems and Methods for Automatically Creating Content Modification Scheme
Simplified Explanation
The patent application describes systems and methods for automatically associating content characteristics to third-party content. This involves receiving a URL from a content provider, rendering the content to create an object tree, identifying a node representing a content slot, finding a node near the content slot with a content characteristic, extracting and associating that characteristic with the content slot, and storing it.
- Explanation of the patent:
- URL from content provider received
- Content rendered to create object tree
- Node representing content slot determined
- Node near content slot with content characteristic identified
- Characteristic extracted and associated with content slot
- Characteristic stored
Potential Applications
The technology described in this patent application could be applied in various industries such as digital marketing, content management systems, and online advertising platforms.
Problems Solved
This technology solves the problem of efficiently associating content characteristics to third-party content, which can help improve content targeting and personalization.
Benefits
The benefits of this technology include enhanced content relevance, improved user engagement, and more effective content delivery strategies.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of advanced content management systems for digital marketing agencies.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning algorithms to automatically tag and categorize content based on its characteristics.
Unanswered Questions
The patent application does not provide details on how privacy concerns related to extracting and associating content characteristics are addressed. It would be important to understand how user data is handled and protected in this process.
What are the potential limitations or challenges in implementing this technology on a large scale?
The patent application does not discuss potential limitations or challenges in implementing this technology on a large scale. It would be crucial to consider scalability, data processing speed, and system integration issues when deploying this technology in real-world applications.
Original Abstract Submitted
systems and methods for automatically associating content characteristics to a third-party content are provided. a uniform resource locator identifying a resource is received from a content provider. the content is rendered to produce an object tree. a first node of the object tree is determined, where the first node represents a content slot. a second node of the object tree proximate to the first node is identified. the second node has a content characteristic, which is extracted. the extracted content characteristic is associated with the first node and stored.
