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 15):
A computer readable storage medium storing a computer program, wherein the program, when executed by a processor, implements a method comprising:\n acquiring a source segmented word sequence and a pre-trained word vector table, wherein the word vector table characterizes a correlation between a word and a word vector; 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, wherein the first recurrent neural network model characterizes a correlation between a word vector sequence and a vector of the preset dimension; 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, wherein the second recurrent neural network model characterizes a correlation between the vector of the preset dimension and the word vector sequence; 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.

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): 0.2735317422272362
- 35 USC 102 Novelty (BERT): 0.4911797203179389
- Combined Prediction Score: 0.2952965400363064
- Mean Citation Score: 238.38446000000005
- Max Citation Score: 255.3363
- Similarity Product: 203.2099825234294

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