PATENT CLAIM ANALYSIS

Application Number: 16376877
Application Type: Utility
Filing Date: 2019-04
Publication Date: 2020-01
Patent Classification: ["709", "203000"]

Abstract:
A method for enhancing quality of media transmitted via network uses an AI enhancing model built-in the client device to enhance the quality of video streams received from network. The AI enhance module is pre-trained by using a neural network in the server to analyze differences between the decoded images and the raw images that are generated by the server. Wherein, the AI enhance module enhances decoded images by using algorithms which are defined by analyzing differences between the decoded images and the raw images that are generated by the server. Such that, the enhanced images are visually more similar to the raw images than the decoded images do.

Claim (Index 29):
The method of  claim 28 , wherein the discriminator of the compare module is trained by the following:\n the training raw images comprise n channels, wherein n is an integer greater than two; the training decoded images comprise m channels, wherein m is an integer greater than two; in the step of said artificial neural network module accepting said training decoded images, said artificial neural network module processes said m channels of training decoded images to generate n channels of training output images; said n channels of training output images are summed with the m channels of training decoded images to generate m+n channels of simulated false samples; said n channels of training raw images are summed with the m channels of training decoded images to generate m+n channels of simulated true samples; in the step of using a compare module to compare, the m+n channels of simulated false samples and the m+n channels of simulated true samples are fed to the discriminator of the compare module for training an ability of the discriminator of the compare module to detect and recognize the simulated false samples and simulated true samples.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 100.0
- Lexical Diversity: 1.94915
- Patent Class: 709.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16027022', '15417056', '15871945', '15703896', '16254448']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2365081854332901
- 35 USC 102 Novelty (BERT): 0.5946149688065655
- Combined Prediction Score: 0.2723188637706177
- Mean Citation Score: 245.868822
- Max Citation Score: 533.9403
- Similarity Product: 427.53131777436727

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

Dataset: test