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 18):
The method of  claim 1 , wherein training each instance of the child convolutional neural network comprises training each instance until a particular amount of time has elapsed.

Metadata:
- Claim Count in Document: 59.0
- Percentile: 95.0
- Lexical Diversity: 2.33803
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15352821', '14609775', '14313554', '15434643', '15151362']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3944925204526647
- 35 USC 102 Novelty (BERT): 0.507077335472051
- Combined Prediction Score: 0.4057510019546034
- Mean Citation Score: 198.946934
- Max Citation Score: 215.07802
- Similarity Product: 121.2096785590553

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