Google llc (20240104435). USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO simplified abstract
Contents
- 1 USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO - 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
USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO
Organization Name
Inventor(s)
King Hong Thomas Leung of Saratoga CA (US)
Dmitrii Tochilkin of Zürich (CH)
USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104435 titled 'USING MACHINE LEARNING TO DETECT WHICH PART OF THE SCREEN INCLUDES EMBEDDED FRAMES OF AN UPLOADED VIDEO
Simplified Explanation
The abstract describes a system and methods for using a trained machine learning model to identify constituent images within composite images.
- Trained machine learning model used to identify constituent images within composite images
- Input data provided to the model to determine if an image is a composite image with constituent images
- Output of the model used to identify the constituent images within the composite image, including spatial areas and corresponding frames of embedded videos
Potential Applications
The technology can be applied in various fields such as image recognition, video analysis, and content creation.
Problems Solved
This technology solves the problem of manually identifying constituent images within composite images, saving time and effort in image analysis tasks.
Benefits
The benefits of this technology include improved accuracy in identifying constituent images, increased efficiency in image analysis processes, and enhanced capabilities in content creation.
Potential Commercial Applications
The technology can be used in industries such as media and entertainment, advertising, surveillance, and digital forensics.
Possible Prior Art
Prior art may include image recognition algorithms, video analysis software, and content creation tools that involve identifying and manipulating images within composite images.
Unanswered Questions
How does the accuracy of the machine learning model compare to manual identification of constituent images within composite images?
The article does not provide information on the accuracy rates of the machine learning model compared to human identification methods.
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.
Original Abstract Submitted
a system and methods are disclosed for using a trained machine learning model to identify constituent images within composite images. a method may include providing data identifying a first image as input to a machine learning model trained using training data identifying a plurality of composite images that each include one or more constituent images, and determining, using one or more outputs of the trained machine learning model, that the first image is a composite image that includes a first constituent image, wherein at least a portion of the first constituent image is in a spatial area of the first image, and wherein the first constituent image corresponds to a frame of a video embedded into the first image.
