PATENT CLAIM ANALYSIS

Application Number: 16040067
Application Type: Utility
Filing Date: 2018-07
Publication Date: 2019-01
Patent Classification: ["706", "016000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network having controller parameters and in accordance with current values of the controller parameters, a batch of output sequences. The method includes, for each output sequence in the batch: generating an instance of a child convolutional neural network (CNN) that includes multiple instances of a first convolutional cell having an architecture defined by the output sequence; training the instance of the child CNN to perform an image processing task; and evaluating a performance of the trained instance of the child CNN on the task to determine a performance metric for the trained instance of the child CNN; and using the performance metrics for the trained instances of the child CNN to adjust current values of the controller parameters of the controller neural network.

Claim (Index 22):
A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to perform operations comprising:\n generating, using a controller neural network having a plurality of controller parameters and in accordance with current values of the controller parameters, a batch of output sequences,\n each output sequence in the batch defining an architecture for a first convolutional cell configured to receive a cell input and to generate a cell output, and \n the first convolutional cell comprising a sequence of a predetermined number of operation blocks that each receive one or more respective input hidden states and generate a respective output hidden state; \n for each output sequence in the batch:\n generating an instance of a child convolutional neural network that includes multiple instances of the first convolutional cell having the architecture defined by the output sequence; \n training the instance of the child convolutional neural network to perform an image processing task; and \n evaluating a performance of the trained instance of the child convolutional neural network on the image processing task to determine a performance metric for the trained instance of the child convolutional neural network; and \n using the performance metrics for the trained instances of the child convolutional neural network to adjust the current values of the controller parameters of the controller neural network.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 95.0
- Lexical Diversity: 2.33803
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15352821', '14609775', '14313554', '15434643', '15151362']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3730572127691619
- 35 USC 102 Novelty (BERT): 0.4758677297035663
- Combined Prediction Score: 0.3833382644626024
- Mean Citation Score: 198.946934
- Max Citation Score: 215.07802
- Similarity Product: 172.27827581347347

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

Dataset: test