PATENT CLAIM ANALYSIS

Application Number: 15864142
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-05
Patent Classification: ["382", "157000"]

Abstract:
Disclosed is a method for generating a semantic image labeling model, comprising: forming a first CNN and a second CNN, respectively; randomly initializing the first CNN; inputting a raw image and predetermined label ground truth annotations to the first CNN to iteratively update weights thereof so that a category label probability for the image, which is output from the first CNN, approaches the predetermined label ground truth annotations; randomly initializing the second CNN; inputting the category label probability to the second CNN to correct the input category label probability so as to determine classification errors of the category label probabilities; updating the second CNN by back-propagating the classification errors; concatenating the updated first and second CNNs; classifying each pixel in the raw image into one of general object categories; and back-propagating classification errors through the concatenated CNN to update weights thereof until the classification errors less than a predetermined threshold.

Claim (Index 14):
The apparatus of  claim 13 , wherein the determining contextual information for each pixel in spatial domain from the category label probabilities comprises determining the contextual information for each pixel in spatial domain from the category label probabilities.

Metadata:
- Claim Count in Document: 16.0
- Percentile: 86.0
- Lexical Diversity: 2.24658
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15081337', '15718554', '14724660', '13251459', '14463806']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3661703732284361
- 35 USC 102 Novelty (BERT): 0.5146289394035745
- Combined Prediction Score: 0.3810162298459499
- Mean Citation Score: 250.145576
- Max Citation Score: 307.68814
- Similarity Product: 233.9199549443329

Labels:
- Claim Label 101: 1
- Claim Label 102: 0
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 1

Dataset: test