PATENT CLAIM ANALYSIS

Application Number: 16425900
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-12
Patent Classification: ["706", "015000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes obtaining training data for a dense image prediction task; and determining an architecture for a neural network configured to perform the dense image prediction task, comprising: searching a space of candidate architectures to identify one or more best performing architectures using the training data, wherein each candidate architecture in the space of candidate architectures comprises (i) the same first neural network backbone that is configured to receive an input image and to process the input image to generate a plurality of feature maps and (ii) a different dense prediction cell configured to process the plurality of feature maps and to generate an output for the dense image prediction task; and determining the architecture for the neural network based on the best performing candidate architectures.

Claim (Index 3):
The method of  claim 2 , wherein training the selected one or more candidate architectures comprises training each selected candidate architecture for a fixed number of iterations instead of to convergence.

Metadata:
- Claim Count in Document: 44.0
- Percentile: 100.0
- Lexical Diversity: 2.20548
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['16040067', '15352821', '15715400', '14793434', '15710821']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3830584136922819
- 35 USC 102 Novelty (BERT): 0.5122594858441751
- Combined Prediction Score: 0.3959785209074712
- Mean Citation Score: 199.572972
- Max Citation Score: 206.32176
- Similarity Product: 105.88755844717026

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

Dataset: test