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 23):
The system of  claim 19 , wherein the comparison is performed using at least one of an inner product, a bilinear function, and a single layer neural network.

Metadata:
- Claim Count in Document: 77.0
- Percentile: 88.0
- Lexical Diversity: 1.45205
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15817161', '15815686', '15817153', '15817165', '15408526']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2612424500362361
- 35 USC 102 Novelty (BERT): 0.5209062159271239
- Combined Prediction Score: 0.2872088266253249
- Mean Citation Score: 249.610352
- Max Citation Score: 276.25797
- Similarity Product: 170.04395244667768

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