PATENT CLAIM ANALYSIS

Application Number: 15900166
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-12
Patent Classification: ["704", "009000"]

Abstract:
The present disclosure discloses a method and apparatus for generating a parallel text in the same language. The method comprises: acquiring a source segmented word sequence and a pre-trained word vector table; determining a source word vector sequence corresponding to the source segmented word sequence, according to the word vector table; importing the source word vector sequence into a first pre-trained recurrent neural network model, to generate an intermediate vector of a preset dimension for characterizing semantics of the source segmented word sequence; importing the intermediate vector into a second pre-trained recurrent neural network model, to generate a target word vector sequence corresponding to the intermediate vector; and determining a target segmented word sequence corresponding to the target word vector sequence according to the word vector table, and determining the target segmented word sequence as a parallel text in the same language corresponding to the source segmented word sequence.

Claim (Index 4):
The method according to  claim 1 , wherein before the acquiring a source segmented word sequence and a pre-trained word vector table, the method further comprises a training step, the training step comprising:\n acquiring at least one pair of parallel segmented word sequences in the same language, wherein each pair of parallel segmented word sequences in the same language comprises a first segmented word sequence and a second segmented word sequence having the same language and same semantics; acquiring a preset word vector table, a first preset recurrent neural network model and a second preset recurrent neural network model; for each pair of parallel segmented word sequences in the same language in the at least one pair of parallel segmented word sequences in the same language, determining a first segmented word vector sequence corresponding to the first segmented word sequence of the pair of parallel segmented word sequences in the same language according to the preset word vector table, importing the first segmented word vector sequence into the first preset recurrent neural network model to obtain the vector of the preset dimension corresponding to the first segmented word vector sequence, importing the obtained vector into the second preset recurrent neural network model to obtain a second segmented word vector sequence corresponding to the obtained vector, determining a word sequence corresponding to the second segmented word vector sequence according to the preset word vector table, and adjusting the preset word vector table, the first preset recurrent neural network model, and the second preset recurrent neural network model according to difference information between the obtained word sequence and the second segmented word sequence of the pair of parallel segmented word sequences in the same language; and defining the preset word vector table, the first preset recurrent neural network model and the second preset recurrent neural network model respectively as the word vector table, the first recurrent neural network model and the second recurrent neural network model obtained by training.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 88.0
- Lexical Diversity: 3.13725
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15900176', '15426727', '14995042', '15407713', '15458887']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2735678737507036
- 35 USC 102 Novelty (BERT): 0.490969295979058
- Combined Prediction Score: 0.295308015973539
- Mean Citation Score: 238.38446000000005
- Max Citation Score: 255.3363
- Similarity Product: 203.80344115698333

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

Dataset: test