PATENT CLAIM ANALYSIS

Application Number: 15889748
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-06
Patent Classification: ["704", "207000"]

Abstract:
An autocorrelation calculating part calculates autocorrelation Ro(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 Ro(i) by a coefficient wo(i). Here, it is assumed that a case where, for at least part of each order i, the coefficient wo(i) corresponding to each order i monotonically increases as a value having negative correlation with a fundamental frequency of an input signal in a current frame or a past frame increases and a case where the coefficient wo(i) monotonically decreases as a value having positive correlation with a pitch gain in a current frame or a past frame increases, are included.

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 o (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 one coefficient table among coefficient tables t0, t1 and t2 using a value having positive correlation with a fundamental frequency based on an input time series signal in the current frame or a past frame and a value having positive correlation with intensity of periodicity or a pitch gain assuming that a coefficient w t0 (i) is stored in the coefficient table t0, a coefficient w t1 (i) is stored in the coefficient table t1, and a coefficient w t2 (i) is stored in the coefficient table t2, for at least part of i other than i=0, w t0 (i)<w t1 (i)\u2264w t2 (i), for at least part of each i among other i other than i=0, w t0 (i)\u2264w o (i)<w t2 (i), and for the remaining each i other than i=0, w t0 (i)\u2264w t1 (i)\u2264w t2 (i), and in the coefficient determining step, a coefficient table is selected and a coefficient stored in the selected coefficient table is acquired so as to comprise a case where, in at least two ranges among three ranges constituting a possible range of the value having positive correlation with the fundamental frequency, a coefficient determined when the value having positive correlation with the intensity of periodicity or the pitch gain is small is greater than a coefficient determined when the value having positive correlation with the intensity of periodicity or the pitch gain is great, and a case where, in at least two ranges among three ranges constituting a possible range of the value having positive correlation with the intensity of periodicity or the pitch gain, a coefficient determined when the value having positive correlation with the fundamental frequency is small is greater than a coefficient determined when the value having positive correlation with the fundamental frequency is great.

Metadata:
- Claim Count in Document: 9.0
- Percentile: 88.0
- Lexical Diversity: 2.0597
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: True
- Related Applications: ['15889775', '15889684', '15112318', '15112534', '11760095']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3301292998416407
- 35 USC 102 Novelty (BERT): 0.6213708167081331
- Combined Prediction Score: 0.3592534515282899
- Mean Citation Score: 421.611298
- Max Citation Score: 559.8747
- Similarity Product: 555.0290113179087

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