PATENT CLAIM ANALYSIS

Application Number: 16017929
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-10
Patent Classification: ["382", "156000"]

Abstract:
In one embodiment of the present invention, a quality trainer and quality calculator collaborate to establish a consistent perceptual quality metric via machine learning. In a training phase, the quality trainer leverages machine intelligence techniques to create a perceptual quality model that combines objective metrics to optimally track a subjective metric assigned during viewings of training videos. Subsequently, the quality calculator applies the perceptual quality model to values for the objective metrics for a target video, thereby generating a perceptual quality score for the target video. In this fashion, the perceptual quality model judiciously fuses the objective metrics for the target video based on the visual feedback processed during the training phase. Since the contribution of each objective metric to the perceptual quality score is determined based on empirical data, the perceptual quality score is a more accurate assessment of observed video quality than conventional objective metrics.

Claim (Index 20):
The non-transitory computer-readable medium of  claim 12 , wherein a first subjective value for the perceptual video quality metric is a human-observed score for the visual quality of a reconstructed video that is derived from the first training video.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 94.0
- Lexical Diversity: 2.19444
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['14709230', '13193802', '15406617', '11199773', '10197334']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3728650770117158
- 35 USC 102 Novelty (BERT): 0.5513697678649022
- Combined Prediction Score: 0.3907155460970344
- Mean Citation Score: 245.42048
- Max Citation Score: 427.47266
- Similarity Product: 285.3063378638232

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