PATENT CLAIM ANALYSIS

Application Number: 15901722
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-10
Patent Classification: ["704", "002000"]

Abstract:
We introduce an attentional neural machine translation model for the task of machine translation that accomplishes the longstanding goal of natural language processing to take advantage of the hierarchical structure of language without a priori annotation. The model comprises a recurrent neural network grammar (RNNG) encoder with a novel attentional RNNG decoder and applies policy gradient reinforcement learning to induce unsupervised tree structures on both the source sequence and target sequence. When trained on character-level datasets with no explicit segmentation or parse annotation, the model learns a plausible segmentation and shallow parse, obtaining performance close to an attentional baseline.

Claim (Index 1):
An attentional neural machine translation system for translating a source sequence in a first language into a target sequence in a second language, comprising:\n an encoder that encodes tokens of the source sequence and phrase tree structures of the source sequence, wherein at least one of the phrase tree structures of the source sequence includes\n an encoder tree node that represents an encoder state when predicting a phrase type of said phrase tree structure of the source sequence, and \n an encoder compositional embedding that represents constituents of said phrase tree structure of the source sequence; and \n an attention-based decoder that outputs tokens of the target sequence and phrase tree structures of the target sequence, wherein a decoder embedding for a predicted phrase type of each of the phrase tree structures of the target sequence is a convex combination of encoder compositional embeddings scaled by attention weights.

Metadata:
- Claim Count in Document: 77.0
- Percentile: 88.0
- Lexical Diversity: 1.45205
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15817161', '15815686', '15817153', '15817165', '15408526']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2406564617440003
- 35 USC 102 Novelty (BERT): 0.5065058467878399
- Combined Prediction Score: 0.2672414002483843
- Mean Citation Score: 249.610352
- Max Citation Score: 276.25797
- Similarity Product: 215.0784769539177

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