PATENT CLAIM ANALYSIS

Application Number: 16442365
Application Type: Utility
Filing Date: 2019-06
Publication Date: 2019-10
Patent Classification: ["382", "156000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using an image processing neural network system. One of the systems includes a domain transformation neural network implemented by one or more computers, wherein the domain transformation neural network is configured to: receive an input image from a source domain; and process a network input comprising the input image from the source domain to generate a transformed image that is a transformation of the input image from the source domain to a target domain that is different from the source domain.

Claim (Index 19):
The method of  claim 17 , wherein updating current values of parameters of the discriminator neural network and current values of parameters of a task neural network while holding values of parameters of the domain transformation neural network fixed comprises:\n determining an update for the current values of the parameters of the discriminator neural network by performing a neural network training technique to maximize a domain loss term of a loss function with respect to the parameters of the discriminator neural network, wherein the domain loss term (i) penalizes the discriminator neural network for incorrectly identifying target domain images as not being from the target domain and for identifying transformed images as being from the target domain while (ii) penalizing the domain transformation neural network for generating transformed images that are identified as not being from the target domain by the discriminator neural network; and determining an update for the current values of the parameters of the task neural network by performing the neural network training technique to minimize task-specific loss term of the loss function with respect to the parameters of the task neural network, wherein the task-specific loss term penalizes (i) the task neural network for characterizing transformed images differently from the known task output for the corresponding source domain images while (ii) penalizing the domain transformation neural network for generating transformed images that are characterized differently by the task neural network from the known task output for the corresponding source domain images.

Metadata:
- Claim Count in Document: 18.0
- Percentile: 100.0
- Lexical Diversity: 2.01923
- Patent Class: 382.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15633288', '15166164', '15705151', '15179403', '15850007']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3673486569711532
- 35 USC 102 Novelty (BERT): 0.4982835368657664
- Combined Prediction Score: 0.3804421449606145
- Mean Citation Score: 246.03973800000003
- Max Citation Score: 255.61539
- Similarity Product: 193.5870469506919

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