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 20):
The method of  claim 16 , wherein:\n a color coding format of the training raw images is either RGB or YUV444, another color coding format of the training decoded images is YUV420; in the step of said artificial neural network module accepting said training decoded images, said artificial neural network module includes a first neural network and a second neural network; the second neural network is a Convolutional Neural Network (CNN); the first neural network accepts and processes the training decoded images and generates a plurality of first output X2 images that have the same coding format with the training raw images; the second neural network accepts and processes the first output X2 images and generates a plurality of second output images; the first output X2 images and the second output images are summed to form the training output images; in the step of using a compare module to compare, the compare module includes a first comparator and a second comparator; the first comparator compares the differences between the first output X2 images and their corresponding training raw images in order to train the first neural network; the second comparator compares the differences between the training output images and their corresponding training raw images in order to train the second neural network.

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): 0.2356107864916322
- 35 USC 102 Novelty (BERT): 0.5975907841403609
- Combined Prediction Score: 0.271808786256505
- Mean Citation Score: 245.868822
- Max Citation Score: 533.9403
- Similarity Product: 440.6380037483453

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