PATENT CLAIM ANALYSIS

Application Number: 16021971
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-11
Patent Classification: ["706", "015000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. In one aspect, one of the systems includes an encoder neural network configured to receive the input sequence and generate encoded representations of the network inputs, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.

Claim (Index 29):
One or more computer storage media storing instructions that when executed by one or more computers cause the one or more computers to implement a sequence transduction neural network for transducing an input sequence having a respective network input at each of a plurality of input positions in an input order into an output sequence having a respective network output at each of a plurality of output positions in an output order, the sequence transduction neural network comprising:\n an encoder neural network configured to receive the input sequence and generate a respective encoded representation of each of the network inputs in the input sequence, the encoder neural network comprising a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the plurality of input positions and to generate a respective subnetwork output for each of the plurality of input positions, and each encoder subnetwork comprising:\n an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the plurality of input positions and, for each particular input position in the input order:\n apply an attention mechanism over the encoder subnetwork inputs at the input positions using one or more queries derived from the encoder subnetwork input at the particular input position to generate a respective output for the particular input position; and \n \n a decoder neural network configured to receive the encoded representations and generate the output sequence.

Metadata:
- Claim Count in Document: 80.0
- Percentile: 94.0
- Lexical Diversity: 2.65574
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15396091', '15237575', '15815686', '15970662', '15055476']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3259747610012201
- 35 USC 102 Novelty (BERT): 0.4804265215092106
- Combined Prediction Score: 0.3414199370520192
- Mean Citation Score: 196.154436
- Max Citation Score: 209.22903
- Similarity Product: 168.46041102030512

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

Dataset: test