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 19):
The method of  claim 1 , further comprising:\n generating an architecture for the first convolutional cell using the adjusted values of the controller parameters; and generating a computationally-efficient architecture of a convolutional neural network that includes fewer instances of the first convolutional cell than the child convolutional neural network instances, wherein the instances of the convolutional cell have the generated architecture.

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): 0.3686604431941822
- 35 USC 102 Novelty (BERT): 0.4951640894915494
- Combined Prediction Score: 0.3813108078239189
- Mean Citation Score: 198.946934
- Max Citation Score: 215.07802
- Similarity Product: 138.18373990685706

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