PATENT CLAIM ANALYSIS

Application Number: 15983441
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-11
Patent Classification: ["706", "025000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a projection neural network. In one aspect, a projection neural network is configured to receive a projection network input and to generate a projection network output from the projection network input. The projection neural network includes a sequence of one or more projection layers. Each projection layer has multiple projection layer parameters, and is configured to receive a layer input, apply multiple projection layer functions to the layer input, and generate a layer output by applying the projection layer parameters for the projection layer to the projection function outputs.

Claim (Index 12):
A method of training the projection neural network of any one of  claims 1 - 11 , the method comprising:\n receiving a training input and a target output for the training input; processing the training input using the projection neural network in accordance with current values of the projection layer parameters to generate a projection network output for the training input; processing the training input using a trainer neural network having a plurality of trainer neural network parameters, wherein the trainer neural network is configured to process the training input in accordance with current values of the trainer neural network parameters to generate a trainer network output that is specific to the particular machine learning task; and determining a gradient with respect to the trainer neural network parameters of a loss function that depends on an error between the target output and the trainer network output; determining a gradient with respect to the projection layer parameters of a loss function that depends on an error between the trainer network output and the projection network output; and determining updates to the current values of the trainer network parameters and the projection layer parameters using the gradients.

Metadata:
- Claim Count in Document: 32.0
- Percentile: 93.0
- Lexical Diversity: 2.39583
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15151362', '14793434', '15257539', '14096234', '15174863']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3674110211786101
- 35 USC 102 Novelty (BERT): 0.4787538535417472
- Combined Prediction Score: 0.3785453044149238
- Mean Citation Score: 190.718064
- Max Citation Score: 209.2615
- Similarity Product: 159.7456303904653

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

Dataset: test