PATENT CLAIM ANALYSIS

Application Number: 15741781
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-01
Patent Classification: ["382", "156000"]

Abstract:
An image upscaling system includes at least two convolutional neural network modules and at least one synthesizer. The convolutional neural network module and the synthesizer are alternately connected to one another. The first convolutional neural network module receive an input image and the corresponding supplemental image, generate a first number of the feature images, and output them to the next synthesizer connected thereto. Other convolutional neural network modules each may receive the output image from the previous synthesizer and the corresponding supplemental image, generate a second number of feature images, and output them to the next synthesizer connected thereto, or output them from the image upscaling system. The synthesizer may synthesize every n*n feature images in the received feature image into one feature image and output the resultant third number of feature images to the next convolutional neural network module connected thereto or output them from the image upscaling system.

Claim (Index 7):
A method for training the image upscaling system according to  claim 1 , comprising:\n constructing a first training set comprising an original image and at least one down-converted image of the original image, wherein the down-converted image has a lower resolution than the original image; constructing a second training set comprising the original image, a magnification factor, and a first degraded image of the original image based on the magnification factor, wherein the first degraded image has the same resolution as the resolution of the original image; training the convolutional neural network system with the second training set, taking the original image and the first degraded image as input, and taking the magnification factor as output; acquiring a parameter of the image upscaling system with the trained convolutional neural network system and the first training set; constructing a new training set comprising the original image, the magnification factor, and a second degraded image of the original image based on the magnification factor, based on the image upscaling system with the acquired parameter, wherein the second degraded image degraded image has a resolution the same as the original image; training the convolutional neural network system with the new training set, taking the original image and the second degraded image as input, and taking the magnification factor as output; acquiring again a parameter of the image upscaling system with the trained convolutional neural network system and the first training set; and repeatedly performing the step of constructing a new training set, training the image upscaling system and acquiring a parameter of the image upscaling system.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 86.0
- Lexical Diversity: 2.84211
- Patent Class: 382.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15821095', '15745091', '15784039', '15842402', '15518404']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3114878491812217
- 35 USC 102 Novelty (BERT): 0.5261449491599195
- Combined Prediction Score: 0.3329535591790915
- Mean Citation Score: 270.06995
- Max Citation Score: 328.58008
- Similarity Product: 244.3383398883533

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 0
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test