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 16):
The method of  claim 15 , wherein, said at least one algorithm of the AI enhance module of the client device is defined by a training process of an artificial neural network module performed by the server; said weighted parameters are defined by said training process of said artificial neural network module performed by the server; said training process comprises:\n executing the first application in a training mode to generate a plurality of training raw images; encoding said training raw images into a plurality of training encoded images by using said encoder; decoding said training encoded images into a plurality of training decoded images by using a training decoder of the server; said artificial neural network module accepting said training decoded images and processing said training decoded images one by one by using at least one training algorithm in order to generate a plurality of training output images; said at least one training algorithm having a plurality of training weighted parameters; and using a compare module to compare the differences between the training output images and their corresponding training raw images one by one, so as to modify said training weighted parameters of said at least one training algorithm according to the differences between each said training output image and its corresponding training raw image; said training weighted parameters being modified to minimize the differences between the training output images and their corresponding training raw images; each time when the training weighted parameters being modified, the modified training weighted parameters being fed back to the at least one training algorithm for processing another said training decoded image in the step of said artificial neural network module accepting said training decoded images; wherein, after a predetermined amount of the training output images and their corresponding training raw images are compared, and a predetermined times of the training weighted parameters are modified, the training weighted parameters are applied to the at least one algorithm of said AI enhance module of the client device.

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): 0.2384029265827042
- 35 USC 102 Novelty (BERT): 0.5883347645792985
- Combined Prediction Score: 0.2733961103823636
- Mean Citation Score: 245.868822
- Max Citation Score: 533.9403
- Similarity Product: 399.96995249147415

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