PATENT CLAIM ANALYSIS

Application Number: 15876534
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2018-07
Patent Classification: ["704", "233000"]

Abstract:
Disclosed herein are methods of diarizing audio data using first-pass blind diarization and second-pass blind diarization that generate speaker statistical models, wherein the first pass-blind diarization is on a per-frame basis and the second pass-blind diarization is on a per-word basis, and methods of creating acoustic signatures for a common speaker based only on the statistical models of the speakers in each audio session.

Claim (Index 15):
A system of blind diarization comprising:\n a communication interface of a computer receiving audio data over a network on a frame by frame basis, wherein the audio data comprises speaker input data and wherein the computer comprises at least one processor in data communication with computerized memory implementing computer readable commands configured to: represent segments of the audio data according to respective feature vectors; cluster respective segments of the audio data according to the respective feature vectors, such that agglomerative clusters of similar feature vectors are gathered as super segments of the audio data; build respective voiceprint models for speaker inputs included in the audio data from the super segments according to a size of respective agglomerative clusters; create a background model from a diagonal Gaussian distribution that includes all segments associated with those feature vectors not representing a speaker; wherein the computer readable commands build respective voiceprint models by configuring the computer to:\n train a diagonal Gaussian distribution for each of the agglomerative clusters of super segments; \n assign a weighting value to each diagonal Gaussian distribution, wherein the weighting value is proportional to a total number of super-segments in the agglomerative cluster composing the respective diagonal Gaussian distribution; \n merge the diagonal Gaussian distributions, wherein the respective diagonal Gaussian distributions are included in a merged Gaussian distribution according to the respective weighting values; \n utilize the respectively merged Gaussian distributions as respective voice print models and use the voice print models with the background model to label the segments of audio data with an identification of a speaker source of speaker input data or a different identification as background data; iteratively refining each voiceprint model on an audio segment by audio segment basis by calculating a log likelihood of the presence of the respective segments as fitting within either the voice print model or the background model; within each iteration, reassigning the segments of the audio data as fitting either one of the voiceprint models or the background model and repeating the step of utilizing the voice print models and the background models to label the segments; verify the voiceprint model when a comparison to sample agent models stored in a memory indicates a match at a threshold quality; and decoding the segments identified as a speaker segment in accordance with the voiceprint model.

Metadata:
- Claim Count in Document: 30.0
- Percentile: 86.0
- Lexical Diversity: 1.55814
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15006575', '15006572', '15254326', '14319860', '15839190']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3263316449981391
- 35 USC 102 Novelty (BERT): 0.6087343606837136
- Combined Prediction Score: 0.3545719165666965
- Mean Citation Score: 471.088296
- Max Citation Score: 580.3118
- Similarity Product: 474.9213116638183

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

Dataset: test