PATENT CLAIM ANALYSIS

Application Number: 15980208
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-11
Patent Classification: ["704", "232000"]

Abstract:
A method and apparatus of building an acoustic feature extracting model, and an acoustic feature extracting method and apparatus. The method of building an acoustic feature extracting model comprises: considering first acoustic features extracted respectively from speech data corresponding to user identifiers as training data; using the training data to train a deep neural network to obtain an acoustic feature extracting model; wherein a target of training the deep neural network is to maximize similarity between the same user's second acoustic features and minimize similarity between different users' second acoustic features. The acoustic feature extracting model according to the present disclosure can self-learn optimal acoustic features that achieves a training target. As compared with a conventional acoustic feature extracting manner with a preset feature type and transformation manner, the acoustic feature extracting manner of the present disclosure achieves better flexibility and higher accuracy.

Claim (Index 13):
The device according to  claim 8 , wherein the operation further comprises:\n extracting first acoustic features of to-be-processed speech data; inputting the first acoustic features into the acoustic feature extracting model, to obtain second acoustic features of the to-be-processed speech data.

Metadata:
- Claim Count in Document: 20.0
- Percentile: 93.0
- Lexical Diversity: 2.13889
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: True
- Related Applications: ['15621162', '15501292', '15042309', '15709232', '15262748']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2770868571486419
- 35 USC 102 Novelty (BERT): 0.5101716888622958
- Combined Prediction Score: 0.3003953403200073
- Mean Citation Score: 262.172234
- Max Citation Score: 283.60104
- Similarity Product: 223.4508401642132

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

Dataset: test