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 17):
The apparatus of  claim 13 , wherein the operations further comprises:\n randomly initializing a first CNN; iteratively updating weights of the first CNN based on an inputted raw image and predetermined label ground truth annotations so that category label probabilities output from the first CNN approaches the predetermined label ground truth annotations; randomly initializing a second CNN; correcting said category label probabilities to determine classification errors of the category label probabilities; and updating the second CNN by back-propagating the classification errors; concatenating the updated first CNN and the updated second CNN; classifying each pixel in the raw image into one of a plurality of general object categories to obtain a classification error; and back-propagating the classification error through the concatenated CNN to update weights of the concatenated CNN until the classification error is less than a predetermined threshold.

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): 0.3658131339534007
- 35 USC 102 Novelty (BERT): 0.5176418096180775
- Combined Prediction Score: 0.3809960015198684
- Mean Citation Score: 250.145576
- Max Citation Score: 307.68814
- Similarity Product: 215.3212285314536

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

Dataset: test