PATENT CLAIM ANALYSIS

Application Number: 16051607
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2019-02
Patent Classification: ["704", "231000"]

Abstract:
An example method includes: receiving a test phrase; comparing feature vectors of the test phrase to contents of a first database to generate a first score; comparing the feature vectors of the test phrase to contents of a second database to generate a second score; comparing feature vectors of the contents of the second database to the contents of the first database to generate a third score; comparing the feature vectors of the contents of the second database to a model of the test phrase to generate a fourth score; determining a first difference score based on a difference between the first and second scores; determining a second difference score based on a difference between the third and fourth scores; and generating a difference confidence score based on a lesser of the first and second difference scores.

Claim (Index 5):
The method of  claim 1 , wherein the contents of the first database and the contents of the second database include models of phrases, and the models are formed according to an algorithmic technique selected from the group consisting of: Hidden Markov models (HMMs), Gaussian mixtures models (GMMs), convolution neural networks (CNNs), deep neural networks (DNNs), recursive neural networks (RNNs), and lattice decoding.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 4.05556
- Patent Class: 704.0
- Transitional Phrase Type: closed
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['13717645', '11355082', '14870771', '11517369', '13692775']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2847642196855904
- 35 USC 102 Novelty (BERT): 0.4884757098044509
- Combined Prediction Score: 0.3051353686974765
- Mean Citation Score: 203.045756
- Max Citation Score: 206.74944
- Similarity Product: 152.73751273475648

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