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 12):
A non-transitory computer-readable medium including instructions that, when executed by a processor, cause the processor to perform the steps of:\n selecting a set of objective metrics that includes an anti-noise signal-to-noise ratio; for each training video included in a set of training videos, receiving a subjective value for a perceptual video quality metric and a set of objective values for the set of objective metrics, wherein the subjective value and the set of objective values describe the training video; deriving a composite relationship based on a correlation between the subjective value, the set of objective values, and a measure of pixel motion within at least one of the set of training videos, wherein the composite relationship specifies a level of contribution for at least one of the set of objective metrics to the perceptual video quality metric; for a target video, calculating a first set of values for the set of objective metrics; and applying the composite relationship to the first set of values to generate an output value for the perceptual video quality metric.

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): 0.3674924959916146
- 35 USC 102 Novelty (BERT): 0.5616693148428225
- Combined Prediction Score: 0.3869101778767354
- Mean Citation Score: 245.42048
- Max Citation Score: 427.47266
- Similarity Product: 388.62869375841257

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