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 4):
The method of  claim 3 , wherein each output sequence in the batch defines, for each of the operation blocks:\n a source for the first input hidden state for the operation block selected from one or more of: (i) outputs generated by one or more other components of the child convolutional neural network, (ii) an input image, or (iii) output hidden states of preceding operation blocks in the sequence of operation blocks within the first convolutional cell; a source for the second input hidden state for the operation block selected from one or more of: (i) outputs generated by one or more preceding convolutional cells in the sequence of convolutional cells, (ii) the input image, or (iii) output hidden states of preceding operation blocks in the sequence of operation blocks within the convolutional cell; an operation type for the first operation selected from a predetermined set of convolutional neural network operations; and an operation type for the second operation selected from the predetermined set of convolutional neural network operations.

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): 0.398183941041679
- 35 USC 102 Novelty (BERT): 0.4912645126485498
- Combined Prediction Score: 0.4074919982023661
- Mean Citation Score: 198.946934
- Max Citation Score: 215.07802
- Similarity Product: 149.12472060836794

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

Dataset: test