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 19):
A recurrent neural network grammar (abbreviated RNNG)-based machine translation system for translating a source sequence in a first language into a target sequence in a second language, comprising:\n an RNNG encoder that encodes tokens of the source sequence and phrase tree structures of the source sequence by embedding character-based token constituents of each phrase tree structure in an encoder compositional vector; and an RNNG attention-based decoder that outputs tokens of the target sequence and phrase tree structures of the target sequence categorized by a phrase type, with a vector representing the phrase type calculated by attending over encoder compositional vectors, with the attention conditioned on a comparison between a current RNNG decoder state and RNNG encoder states during the encoding.

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): 0.2408041379835206
- 35 USC 102 Novelty (BERT): 0.5054556271996441
- Combined Prediction Score: 0.2672692869051329
- Mean Citation Score: 249.610352
- Max Citation Score: 276.25797
- Similarity Product: 218.98315368586407

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