Nvidia corporation (20240119612). IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS - 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
IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Michael Hemmer of Saarbruecken (DE)
IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119612 titled 'IDENTIFYING DUPLICATE OBJECTS USING CANONICAL FORMS IN CONTENT CREATION SYSTEMS AND APPLICATIONS
Simplified Explanation
The approaches presented in this patent application provide systems and methods for determining duplicate objects within an interaction environment. By using connectivity information for an object to map a set of three linearly independent vectors corresponding to a transform applied to the object, the system can identify duplicate or near-duplicate objects. These objects can then be represented by a common object with additional transforms applied, reducing redundancy in the environment.
- Mapping linearly independent vectors based on connectivity information
- Forming canonical forms of objects using these vectors
- Identifying duplicate or near-duplicate objects
- Deleting redundant objects and representing them with a common object
- Applying additional transforms to the common object
Potential Applications
This technology could be applied in digital asset management systems, content moderation platforms, and data deduplication tools.
Problems Solved
This technology solves the problem of identifying and managing duplicate or near-duplicate objects within an interaction environment, reducing redundancy and optimizing system performance.
Benefits
The benefits of this technology include improved efficiency in managing large datasets, reduced storage space requirements, and enhanced data organization and retrieval processes.
Potential Commercial Applications
Potential commercial applications of this technology include software development for content management systems, cloud storage platforms, and data analytics tools.
Possible Prior Art
One possible prior art for this technology could be the use of hashing algorithms to identify duplicate files in computer systems. Another could be the application of machine learning algorithms for data deduplication processes.
Unanswered Questions
How does this technology handle variations in object size or shape?
The patent application does not provide specific details on how the system accounts for variations in object characteristics when determining duplicates.
What is the computational overhead of implementing this technology?
The patent application does not discuss the potential computational costs or performance implications of integrating this system into existing environments.
Original Abstract Submitted
approaches presented herein provide systems and methods for determining duplicate objects within an interaction environment. connectivity information for an object may be used to map a set of three linearly independent vectors corresponding to a transform applied to the object. these three linearly independent vectors may be used to form canonical forms of first and second objects to determine whether the first object and the second object are duplicates or near-duplicates. copies of duplicate or near-duplicate objects may then be deleted from the interaction environment and represented by a common object to which one or more additional transforms are applied.
