PATENT CLAIM ANALYSIS

Application Number: 15924963
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-07
Patent Classification: ["704", "500000"]

Abstract:
An autocorrelation calculating part calculates autocorrelation R o (i) from an input signal. A predictive coefficient calculating part performs linear predictive analysis using modified autocorrelation R′ o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient w o (i). Here, a case is comprised where, for at least part of each order i, the coefficient w o (i) corresponding to each order i monotonically decreases as a value having positive correlation with a pitch gain in an input signal of a current frame or a past frame increases.

Claim (Index 2):
A linear predictive analysis method for obtaining a coefficient which can be converted into a linear predictive coefficient corresponding to an input time series signal for each frame which is a predetermined time interval, the linear predictive analysis method comprising:\n an autocorrelation calculating step of calculating autocorrelation R o (i) between an input time series signal X o (n) of a current frame and an input time series signal X o (n\u2212i) i sample before the input time series signal X o (n) or an input time series signal X(n+i) i sample after the input time series signal X o (n) for each of at least i=0, 1, . . . , P max ; and a predictive coefficient calculating step of obtaining a coefficient which can be converted into linear predictive coefficients from the first-order to the P max -order using modified autocorrelation R\u2032 o (i) obtained by multiplying the autocorrelation R o (i) by a coefficient for each corresponding i, wherein the linear predictive analysis method further comprises a coefficient determining step of acquiring the coefficient from at least one of coefficient tables t 0  and t 2  using a value having positive correlation with intensity of periodicity of an input time series signal of the current frame or a past frame or a pitch gain based on the input time series signal assuming that a coefficient w t0 (i) is stored in the coefficient table t 0  and a coefficient w t2 (i) is stored in the coefficient table t 2 , assuming that, according to the value having positive correlation with the intensity of the periodicity or the pitch gain, a case is classified into any of a case where the intensity of the periodicity or the pitch gain is high, a case where the intensity of the periodicity or the pitch gain is medium, and a case where the intensity of the periodicity or the pitch gain is low, a coefficient table from which a coefficient is acquired in the coefficient determining step when the intensity of the periodicity or the pitch gain is high is set as a coefficient table t 0  and a coefficient table from which a coefficient is acquired in the coefficient determining step when the intensity of the periodicity or the pitch gain is low is set as a coefficient table t 2 , for at least part of i other than i=0, w t0 (i)<w t2 (i) and for the remaining each i other than i=0, w t0 (i)\u2264w t2 (i), the coefficient determining step determines, when the intensity of the periodicity or the pitch gain is medium, for at least part of i other than i=0, a coefficient w o (i) which satisfies w o (i)=\u03b2\u2032\u00d7w t0 (i)+(1\u2212\u03b2\u2032)\u00d7w t2 (i) (0\u2264\u03b2\u2032\u22641).

Metadata:
- Claim Count in Document: 9.0
- Percentile: 90.0
- Lexical Diversity: 1.78333
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15889748', '15889775', '15924887', '15112534', '15112318']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3583175350662668
- 35 USC 102 Novelty (BERT): 0.5699325815558157
- Combined Prediction Score: 0.3794790397152218
- Mean Citation Score: 436.803906
- Max Citation Score: 453.8736
- Similarity Product: 449.92743668632505

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

Dataset: test