EXHIBIT A

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UNITED STATES PATENT AND TRADEMARK OFFICE 

____________________ 

BEFORE THE PATENT TRIAL AND APPEAL BOARD 

____________________ 

ZTE Corporation, ZTE (USA), Inc., and ZTE (TX), Inc., 

Petitioner, 

v. 

WSOU Investments LLC D/B/A Brazos Licensing and Development, 

Patent Owner. 

U.S. Patent No. 9,294,060 

PCT Filing Date: May 25, 2010 

Issue Date: Mar. 22, 2016 

____________________ 

Case No. IPR2021-00697 

PETITION FOR INTER PARTES REVIEW 

OF U.S. PATENT NO. 9,294,060

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 TABLE OF CONTENTS 

I. INTRODUCTION ........................................................................................... 1

II. THE ’060 PATENT ......................................................................................... 2

A. ’060 Patent Summary ............................................................................ 2

B. ’060 Patent Prosecution History ............................................................ 6

III. OVERVIEW OF PRIOR ART ........................................................................ 6

A. Nilsson ................................................................................................... 6

B. Iser ......................................................................................................... 9

C. Vos ....................................................................................................... 11

D. Kontio .................................................................................................. 15

E. ETSI201.108 ........................................................................................ 16

IV. LEVEL OF ORDINARY SKILL IN THE ART ........................................... 19

V. CLAIM CONSTRUCTION .......................................................................... 19

VI. STATEMENT OF PRECISE RELIEF REQUESTED FOR EACH 

CLAIM CHALLENGED .............................................................................. 20

1. Claims for Which Review Is Requested ............................................. 20

2. Statutory Grounds................................................................................ 20

VII. GROUND 1: NILSSON AND ISER RENDER OBVIOUS CLAIMS 1, 

9, 10, AND 18 ................................................................................................ 22

A. POSITA Would Have Combined Nilsson and Iser ............................. 22

B. Independent Claim 1 ........................................................................... 23

1. [1preamble] “A method comprising:” ...................................... 23

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2. [1a] “generating an excitation signal from an audio 

signal, wherein in the audio signal comprises a plurality 

of frequency components;” ....................................................... 24

i. Nilsson discloses generating an excitation signal 

from an audio signal. ...................................................... 24

ii. Nilsson’s audio signal comprises a plurality of 

frequency components. ................................................... 25

3. [1b] “extracting a feature vector from the audio signal, 

wherein the feature vector comprises at least one 

frequency domain component feature and at least one 

time domain component feature;” ............................................. 26

i. Time-domain component feature ................................... 27

ii. Frequency-domain component feature ........................... 28

4. [1c] “determining at least one spectral shape parameter 

from the feature vector, wherein the at least one spectral 

shape parameter corresponds to a sub band signal 

comprising frequency components which belong to a 

further plurality of frequency components; and” ...................... 29

i. Nilsson discloses determining at least one spectral 

shape parameter from the feature vector. ....................... 30

ii. Nilsson’s spectral shape parameter corresponds to 

a sub band signal comprising frequency 

components which belong to a further plurality of 

frequency components. ................................................... 31

5. [1d] “generating the sub band signal by filtering the 

excitation signal through a filter bank and weighting the 

filtered excitation signal with the at least one spectral 

shape parameter,” ...................................................................... 33

i. Nilsson discloses filtering the excitation signal 

with a filter bank. ............................................................ 33

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ii. ’060 patent’s disclosure of weighting the filtered 

excitation signal with the at least one spectral 

shape parameter. ............................................................. 35

iii. Nilsson discloses weighting the filtered excitation 

signal with the at least one spectral shape 

parameter. ....................................................................... 35

6. [1e] “wherein the spectral shape parameter is a sub band 

energy level value and the sub band energy level value is 

attenuated when the power of the audio signal 

approaches an estimate of the level of noise in the audio 

signal.” ...................................................................................... 37

i. Nilsson discloses sub band energy level value. .............. 37

ii. Iser discloses that the sub band energy level value 

is attenuated when the power of the audio signal 

approaches an estimate of the level of noise in the 

audio signal. .................................................................... 37

iii. POSITA Would Have Combined Nilsson and Iser ........ 40

C. Independent Claim 10 ......................................................................... 41

1. [10preamble] “An apparatus comprising at least one 

processor and at least one memory including computer 

code, the at least one memory and the computer code 

configured to with the at least one processor cause the 

apparatus to at least:” ................................................................ 41

2. [10a] “generate an excitation signal from an audio signal, 

wherein in the audio signal comprises a plurality of 

frequency components;” ........................................................... 42

3. [10b] “extract a feature vector from the audio signal, 

wherein the feature vector comprises at least one 

frequency domain component feature and at least one 

time domain component feature;” ............................................. 42

4. [10c] “determine at least one spectral shape parameter 

from the feature vector, wherein the at least one spectral 

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shape parameter corresponds to a sub band signal 

comprising frequency components which belong to a 

further plurality of frequency components; and” ...................... 42

5. [10d] “generate the sub band signal by filtering the 

excitation signal through a filter bank and weighting the 

filtered excitation signal with the at least one spectral 

shape parameter,” ...................................................................... 43

6. [10e] “wherein the spectral shape parameter is a sub band 

energy level value and the sub band energy level value is 

attenuated when the power of the audio signal 

approaches an estimate of the level of noise in the audio 

signal.” ...................................................................................... 43

D. Dependent Claims 9 and 18 ................................................................ 43

1. [Claim 9] “The method as claimed in any of claim 1, 

further comprising combining the sub band signal with 

the audio signal to provide a bandwidth extended audio 

signal.” ...................................................................................... 43

2. [Claim 18] “The apparatus as claimed in claim 10, 

wherein the at least one memory and the computer code 

is configured to: combine the sub band signal with the 

audio signal to provide a bandwidth extended audio 

signal.” ...................................................................................... 45

VIII. GROUND 2: NILSSON, ISER, AND ETSI201.108 RENDER 

OBVIOUS CLAIMS 4, 5, 13, AND 14 ......................................................... 45

1. POSITA Would Have Combined Nilsson with Iser and 

ETSI201.108 .............................................................................. 45

2. [Claim 4] “The method as claimed in claim 1, wherein 

the frequency components of the sub band signal are 

distributed according to a psychoacoustic scale 

comprising a plurality of overlapping bands, and 

frequency characteristics of the filter bank correspond to 

a distribution of frequency components of the sub band 

signal.” ...................................................................................... 46

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i. Mel scale is a psychoacoustic scale. ............................... 46

ii. Nilsson discloses that the frequency components of 

the sub band signal are distributed according to 

Mel scale. ........................................................................ 47

iii. Mel scale includes a plurality of overlapping 

bands, and frequency characteristics of the Mel 

filter correspond to a distribution of frequency 

components of the signal. ............................................... 47

3. [Claim 5] “The method as claimed in claim 4, wherein 

the overlapping bands are distributed according to the 

mel scale, and wherein the sub band signal is masked 

using at least one of: a triangular masking function; and a 

trapezoidal masking function.” ................................................. 48

4. [Claim 13] “The apparatus as claimed in claim 10, 

wherein the frequency components of the sub band signal 

are distributed according to a psychoacoustic scale 

comprising a plurality of overlapping bands, and 

frequency characteristics of the filter bank correspond to 

a distribution of frequency components of the sub band 

signal.” ...................................................................................... 49

5. [Claim 14] “The apparatus as claimed in claim 13, 

wherein the overlapping bands are distributed according 

to the mel scale, and wherein the sub band signal is 

masked using at least one of a triangular masking 

function; and a trapezoidal masking function.” ........................ 49

IX. GROUND 3: NILSSON, ISER, AND VOS RENDER OBVIOUS 

CLAIMS 2, 3, 7, 11, 12, AND 16 ................................................................. 50

1. POSITA Would Have Combined Nilsson with Iser and 

Vos ............................................................................................. 50

2. [Claim 2a] “The method as claimed in claim 1, wherein 

generating the excitation signal comprises: generating a 

residual signal by filtering the audio signal with an 

inverse linear predictive filter;” ................................................ 50

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i. Vos’ whitening filter generates a residual signal. ........... 52

ii. Vos’ whitening filter is the inverse filter. ....................... 52

iii. Vos’ whitening filter is the linear predictive filter. ........ 52

3. [Claim 2b] “filtering the residual signal with a post filter 

stage comprising an auto regressive moving average filter 

based on the linear predictive filter; and” ................................. 53

4. [Claim 2c] “generating the excitation signal by up 

sampling and spectrally folding the output from the post 

filter stage.” ............................................................................... 55

5. [Claim 3] “The method as claimed in claim 2, wherein 

the post filter stage further comprises a spectral tilt filter 

and a harmonic filter.” .............................................................. 57

6. [Claim 7] “The method as claimed in claim 1, wherein 

the at least one frequency domain component feature of 

the feature vector comprises at least one of the following: 

a group of a plurality of energy levels of the audio signal, 

wherein each of the plurality energy levels corresponds 

to the energy of an overlapping band of the audio signal; 

a value representing a centroid of the frequency domain 

spectrum of the audio signal; and a value representing the 

degree of flatness of the frequency domain spectrum.” ........... 58

i. Energy levels .................................................................. 58

ii. Centroid .......................................................................... 59

iii. Degree of flatness ........................................................... 59

7. [Claim 11a] “The apparatus as claimed in claim 10, 

wherein the at least one memory and the computer code 

configured to with the at least one processor to cause the 

apparatus to at least perform generating the excitation 

signal is further configured to: generate a residual signal 

by filtering the audio signal with an inverse linear 

predictive filter;” ....................................................................... 60

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8. [Claim 11b] “filter the residual signal with a post filter 

stage comprising an auto regressive moving average filter 

based on the linear predictive filter; and” ................................. 60

9. [Claim 11c] “generate the excitation signal by up 

sampling and spectrally folding the output from the post 

filter stage.” ............................................................................... 60

10. [Claim 12] “The apparatus as claimed in claim 11, 

wherein the post filter stage further comprises a spectral 

tilt filter and a harmonic filter.” ................................................ 61

11. [Claim 16] “The apparatus as claimed in claim 10, 

wherein the at least one frequency domain component 

feature of the feature vector comprises at least one of the 

following: a group of a plurality of energy levels of the 

audio signal, wherein each of the plurality energy levels 

corresponds to the energy of an overlapping band of the 

audio signal; a value representing a centroid of the 

frequency domain spectrum of the audio signal; and; a 

value representing the degree of flatness of the frequency 

domain spectrum.” .................................................................... 61

X. GROUND 4: NILSSON, ISER, AND KONTIO RENDER OBVIOUS 

CLAIMS 6, 8, 15, AND 17 ........................................................................... 62

1. POSITA Would Have Combined Nilsson with Iser and 

Kontio ........................................................................................ 62

2. [Claim 6] “The method as claimed in claim 1, wherein 

determining at least one spectral shape parameter from 

the feature vector comprises: using a neural network to 

determine the at least one spectral shape from the feature 

vector, wherein the feature vector extracted from the 

audio signal forms an input target vector to the neural 

network, and wherein the neural network is trained to 

provide a sub band spectral shape parameter for the input 

target vector.” ............................................................................ 63

i. Kontio discloses using a neural network to 

determine the at least one spectral shape from the 

feature vector. ................................................................. 64

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ii. Kontio discloses that the feature vector extracted 

from the audio signal forms an input target vector 

to the neural network. ..................................................... 65

iii. Kontio discloses that the neural network is trained 

to provide a sub band spectral shape parameter for 

the input target vector. .................................................... 65

3. [Claim 8] “The method as claimed in claim 1, wherein 

the at least one time domain component feature of the 

feature vector comprises at least one of the following: a 

gradient index based on the sum of the gradient at points 

in the audio signal which result in a change in direction 

of the waveform of the audio signal; a ratio of the energy 

of a frame of the audio signal to the energy of a previous 

frame of the audio signal; and; and a voice activity 

detector indicating whether a frame of the audio signal is 

classified as active or inactive.” ................................................ 66

i. Gradient Index ................................................................ 66

ii. Energy ratio .................................................................... 67

iii. Voice activity detector .................................................... 67

4. [Claim 15] “The apparatus as claimed in claim 10, 

wherein the at least one memory and the computer code 

configured to with the at least one processor to cause the 

apparatus to at least perform determining at least one 

spectral shape parameter from the feature vector is 

further configured to: use a neural network to determine 

the at least one spectral shape from the feature vector, 

wherein the feature vector extracted from the audio 

signal forms an input target vector to the neural network, 

and wherein the neural network is trained to provide a 

sub band spectral shape parameter for the input target 

vector.” ...................................................................................... 68

5. [Claim 17] “The apparatus as claimed in claim 10, 

wherein the at least one time domain component feature 

of the feature vector comprises at least one of the 

following: a gradient index based on the sum of the 

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gradient at points in the audio signal which result in a 

change in direction of the waveform of the audio signal; 

a ratio of the energy of a frame of the audio signal to the 

energy of a previous frame of the audio signal; and; a 

voice activity detector indicating whether a frame of the 

audio signal is classified as active or inactive.” ....................... 68

XI. NON-INSTITUTION UNDER 35 U.S.C. §§ 314 OR 325 WOULD 

BE IMPROPER ............................................................................................. 70

A. Non-Institution Under 35 U.S.C. §314(a) Is Improper ....................... 70

B. Non-Institution Under 35 U.S.C. § 325 Is Improper .......................... 73

XII. MANDATORY NOTICES UNDER 37 C.F.R. § 42.8 ................................. 74

A. Real Party-in-Interest .......................................................................... 74

B. Related Matters .................................................................................... 74

C. Lead and Back-Up Counsel and Service Information ........................ 74

XIII. GROUNDS FOR STANDING ...................................................................... 75

XIV. CONCLUSION .............................................................................................. 76

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LIST OF EXHIBITS

Exhibit Description 

Ex-1001 U.S. Patent No. 9,294,060 (the ’060 patent) 

Ex-1002 Prosecution File History of U.S. Patent No. 9,294,060 

Ex-1003 Declaration of Nir Regev 

Ex-1004 Curriculum Vitae of Nir Regev 

Ex-1005 U.S. Patent No. 7,359,854 (“Nilsson”) 

Ex-1006 U.S. Patent No. 8,160,889 (“Iser”) 

Ex-1007 U.S. Patent App. Pub. No. 2006/0282262 (“Vos”)

Ex-1008 J. Kontio, L. Laaksonen and P. Alku, "Neural Network-Based 

Artificial Bandwidth Expansion of Speech," in IEEE Transactions 

on Audio, Speech, and Language Processing, vol. 15, no. 3, pp. 873-

881, March 2007, doi: 10.1109/TASL.2006.885934. (“Kontio”) 

Ex-1009 ETSI Standard, ETSI ES 201 108 V1.1.3 (2003-09), “Speech 

Processing, Transmission and Quality Aspects (STQ); Distributed 

speech recognition; Front-end feature extraction algorithm; 

Compression algorithms”, (“ETSI201.108”)

Ex-1010 Screen shot of ETSI (European Telecommunications Standards 

Institute) Work Programme showing the publication date of the 

ETSI-201.108

Ex-1011 U.S. Patent No. 6,711,536 B2 

Ex-1012 S. Umesh, L. Cohen and D. Nelson, "Frequency warping and the 

Mel scale," in IEEE Signal Processing Letters, vol. 9, no. 3, pp. 

104-107, March 2002, doi: 10.1109/97.995829.

Ex-1013 A. M. De Lima Araujo and F. Violaro, "Formant frequency 

estimation using a Mel-scale LPC algorithm," ITS'98 Proceedings. 

SBT/IEEE International Telecommunications Symposium (Cat. 

No.98EX202), Sao Paulo, Brazil, 1998, pp. 207-212 vol.1, doi: 

10.1109/ITS.1998.713118. 

Ex-1014 E. Karlsson and M. Hayes, "Least squares ARMA modeling of 

linear time-varying systems: Lattice filter structures and fast RLS 

algorithms," in IEEE Transactions on Acoustics, Speech, and Signal 

Processing, vol. 35, no. 7, pp. 994-1014, July 1987, doi: 

10.1109/TASSP.1987.1165246. 

Ex-1015 R. Badeau and B. David, "Weighted maximum likelihood 

autoregressive and moving average spectrum modeling," 2008 IEEE 

International Conference on Acoustics, Speech and Signal 

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Processing, Las Vegas, NV, USA, 2008, pp. 3761-3764, doi: 

10.1109/ICASSP.2008.4518471. 

Ex-1016 Bernd Iser, Gerhard Schmidt, “Neural Networks Versus Codebooks 

in an Application for Bandwidth Extension of Speech Signals,”, 

Neural Networks Versus Codebooks in an Application for 

Bandwidth Extension of Speech Signals (“Iser-2”) 

Ex-1017 Kornagel, Ulrich, “Techniques for artificial bandwidth extension of 

telephone speech”, Signal Processing 86.6, pp. 1296-1306 (2006) 

(“Kornagel”)

Ex-1018 Iser Bernd, Gerhard Schmidt, and Wolfgang Minker, Bandwidth 

extension of speech signals. Vol. 13, Springer Science & Business 

Media, page 15, and pages 77-94, 2008 (“Iser-3”)

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I. INTRODUCTION 

ZTE Corporation, ZTE (USA), Inc., and ZTE (TX), Inc., (collectively 

“ZTE”) requests inter partes review of claims 1-18 of U.S. Patent No. 9,294,060 

(Ex-1001), assigned to WSOU Investments LLC (“WSOU”)1

. 

The ’060 patent describes a method for extending the bandwidth of an audio 

signal, but the disclosed methods for bandwidth extension were known in the prior 

art. 

’060 Patent, Claim 1 and Figure 4 (Annotated) 

Nilsson (Ex-1005) and Vos (Ex-1007) disclose methods for extending the 

bandwidth of an audio signal like those claimed in ’060 patent. Alone or when 

combined in routine, predictable ways, Nilsson, Vos, Iser, Kontio, and 

1

 WSOU asserts the ’060 patent against ZTE in WSOU Investments LLC v. ZTE 

Corporation et al., 6:20-cv-00493-ADA (W.D. Tex.) (“Litigation”). 

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ETSI201.108 —none considered by the USPTO for ’060 patent—render obvious 

all features of the challenged claims. 

Nilsson, FIG. 5 (Top), and FIG. 7 (Bottom) (Annotated) 

II. THE ’060 PATENT 

A. ’060 Patent Summary 

The ’060 patent describes a method “for extending the bandwidth of an 

audio signal.” Ex-1001, Abstract. 

Generating artificial high 

band signal (4-8 kHz) Low band signal 

(0-4 kHz)

Input signal 

e.g., 0-4 kHz 

Bandwidth 

extended signal 

(0-8 kHz)

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’060 Patent, FIG. 1 (annotated) 

As shown above in annotated FIG. 1, ’060 patent discloses an electronic 

device 10 including a processor 21 and program code 23 stored in memory 22. The 

program code 23 “may comprise an audio decoding code or speech decoding 

code.” Ex-1001, 7:28-29. “The processor 21 may execute the decoding program 

code stored in the memory 22,” (Id., 4:62-63), and functions as an audio 

“bandwidth extender.” Id., 8:11-14.

Bandwidth 

extender 

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’060 Patent, FIG. 4 (annotated) 

As shown above in annotated FIG. 4, the bandwidth extender includes a 

frame collector 403 “whereby the input audio signal (otherwise known as the audio 

sample stream) is partitioned and collated into a continual series of audio frames.” 

Id., 9:36-39. 

The bandwidth extender includes a time to frequency transformer 405

“whereby the time based audio signal frame 404 of 96 samples can be transformed 

to the frequency domain.” Id., 10:6-9. 

The bandwidth extender includes a feature extractor 407 whereby 

“frequency domain and time domain features [are] extracted for each frame of the 

input audio signal and frequency domain signal.” Id., 10:51-54. 

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The bandwidth extender includes a neural network processor 409 that can

“generate the spectral shape parameters for the artificial high band signal 431.” 

Id., 14:65-67. 

The bandwidth extender includes a band energy smoother 411 that can 

“filter the energy level for each sub band over current and past values,” (Id., 17:34-

36) and an energy to gain converter 413, “whereby an energy level associated with 

a particular sub band of the filter bank can [] be converted to a suitable gain 

factor.” Id., 19:1-5. 

The bandwidth extender includes an excitation signal generator 417 that 

generates excitation signal using “the time domain frames” and an up-sampler 419

that up-sample the output excitation signal to generate high band excitation signal. 

Id., 18:51-56. 

The bandwidth extender includes a filter bank 421 and a band weighting and 

summing processor 415 whereby “each sub band from the filter bank 421 can [ ] be 

individually weighted by a corresponding sub band gain factor,” and the weighted 

sub band signals are summed together to form the artificially generated high band 

signal 431. Id., 18:56-67. 

“The artificially generated high band signal 431 [] then be passed to an input 

of a summer 427 in which the signal 431 is combined with an up sampled input 

audio signal 433 to produce the bandwidth extended signal 435.” Id., 26:44-48. 

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B. ’060 Patent Prosecution History 

The prosecution history of ’060 patent was surprisingly short. In the first 

Office Action (dated June 25, 2015), the Examiner allowed dependent claims 2-5, 

7, 14-17, and 19. Ex-1002, pages 230-231. In response, Applicant amended 

independent claims to incorporate the subject matter of allowable claim 7. Ex1002, pages 241-249. 

However, in allowing the claims, the Examiner did not consider any of the 

references set forth in this Petition. Claims 1-18 of ’060 patent are unpatentable 

over the references set forth in this Petition. 

III. OVERVIEW OF PRIOR ART 

A. Nilsson 

Like ’060 patent, Nilsson is directed to a bandwidth extension. As shown 

below in annotated FIG. 5, Nilsson discloses a signal decoder that “receives a 

narrow-band acoustic signal aNB” and generates “the wide-band acoustic signal aWB

[that] has a wider spectrum AWB than the spectrum ANB of the narrow-band 

acoustic signal aNB.” Ex-1005, 5:54-61; 4:24-36. 

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Nilsson, FIG. 5 (annotated) 

Nilsson’s decoder includes a feature extraction unit 101 that “receives the 

narrow-band acoustic signal aNB and produces in response thereto at least one 

essential feature Z(r, c) that describes particular properties of the received narrowband acoustic signal aNB.” Id., 6:25-28. 

As shown below in annotated FIG. 7, Nilsson uses a transform unit 101c

included in feature extraction unit 101 and “computes a corresponding spectrum 

SW by means of a fast Fourier transform.” Id., 6:56-58. 

Nilsson, FIG. 7 (annotated)

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The extracted essential feature Z(r, c) includes time domain component 

feature such as “[t]he degree of voicing r, which represents one such essential 

feature zNB(r, c),” (Id., 6:25-32), and frequency domain component feature such 

as “the spectral envelope c.” Id., 6:15-21. 

Nilsson’s decoder includes a high-band shape estimator 104b “to create a 

combination of the high-band shape and energy-ratio.” Id., 10:31-38. 

Nilsson’s decoder includes an energy-ratio estimator 104a that “receives the 

first component c0 in the vector of linear frequency cepstral coefficients c and 

produces, on basis thereof, plus on basis of the narrow-band shape x and the degree 

of voicing r an estimated energy-ratio ĝ between the high-band and the narrowband.” Id., 7:15-21. 

Nilsson’s decoder includes an excitation extension unit 105 that “receives 

the narrow-band acoustic signal aNB and, on basis thereof, produces an extended 

excitation signal EWB.” Id., 10:51-53. 

Nilsson’s decoder includes a high band shape estimator 106a in the wideband filter 106 that performs frequency shaping of the excitation signal and a highpass filter 107 that produces “a high-pass filtered signal HP(y0).” Id., 11:21-59. 

Nilsson’s decoder includes an “adder 108 [that] receives the low-pass 

filtered signal LP(aNB-u), receives the high-pass filtered signal HP(y0) and adds the 

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received signals together and thus forms the wide-band acoustic signal aWB.” Id., 

12:17-21. 

B. Iser 

Like ’060 patent, Iser discloses “a bandwidth extension system for providing 

an acoustic signal with extended bandwidth.” Ex-1006, 2:19-24. 

As shown below in annotated FIG. 1, Iser’s bandwidth extension system 

includes a weight determination module 107 that determines “weighting factors for 

the other signal component,” (Id., 9:39-41), and a weighted summation filter 108 

which performs “weighted sum of the received acoustic signal x(n) at time n and at 

time n−1.” Id., 9:38-39. 

Iser, FIG. 1 (annotated) 

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In applying the weighting factors, Iser compares the power of the input 

audio signal and the estimated noise power. “FIG. 4 is a representation of an input 

speech signal, such as the received acoustic signal x(n).” Id., 7:8-10. 

Iser, FIG. 4 

As shown below in annotated FIG. 5, Iser estimates “a short time power and 

a noise power that correspond to the received acoustic signal x(n) of FIG. 4. Line 

502 in FIG. 5 represents the estimated short time power x(n) of the received 

acoustic signal x(n). Line 504 in FIG. 5 represents the noise power estimation b(n). 

The short time power estimation may be used to determine different factors for 

weighting the signal components.” Id., 7:8-18. 

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Iser, FIG. 5 (annotated) 

Based on the above estimation of signal power and noise power, Iser

determines “signal-to-noise ratio of the received acoustic signal” and applies a 

weighting factor as “a function of an estimated signal-to-noise ratio of the received 

acoustic signal x(n).” Id., 7:20-32. That is, “[i]f the received acoustic signal x(n) 

contains speech passages with a low signal-to-noise ratio, then this weighting 

factor may be used to damp the upper bandwidth extension signal yhigh(n).” Id., 

7:20-32. In the above annotated FIG. 5, point A indicates a low signal-to-noise 

ratio (i.e., signal power approaches the noise power estimation) and point B 

indicates a high signal-to-noise ratio. 

C. Vos 

Like ’060 patent, Vos is directed to bandwidth extension. Ex-1007, [0011]. 

A 

B 

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As shown below in annotated FIG. 4, Vos starts with generating a 

narrowband signal S20 using a wideband signal. 

Vos, FIG. 4 (annotated) 

Using the generated narrowband signal, Vos generates an encoded 

narrowband excitation signal. As shown below in annotated FIG. 6, a “narrowband 

encoder A122 generates a residual signal by passing narrowband signal S20 

through a whitening filter 260 (also called an analysis or prediction error filter) that 

is configured according to the set of filter coefficients.” Id., [0118]. 

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Vos, FIG. 6 (annotated) 

Further, using the encoded narrowband excitation signal, Vos generates 

highband excitation signal. As shown below in annotated FIG. 11, a “[h]ighband 

excitation generator A300 is configured to generate highband excitation signal 

S120 by extending the spectrum of narrowband excitation signal S80 into the 

highband frequency range.” Id., [0139]. 

Vos, FIG. 11 (annotated) 

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Vos discloses extracting a feature vector including frequency domain feature 

components, such as spectral tilt, centroid, energy envelope. Id., [0130], [0255], 

[0218]. Vos also discloses extracting a feature vector including time domain 

feature components, such as voice activity, time-domain envelope. Id., [0131], 

[0155]. 

Vos discloses determining spectral shape parameter (e.g., gain factor) using 

the extracted feature vector. As shown below in annotated FIG. 33a, Vos discloses 

“a highband gain factor calculator A230 that is configured to calculate a series of 

gain factors.” Id., [0216]-[0217]. 

Vos, FIG. 33a (annotated) 

As shown below in annotated FIG. 38, Vos discloses a “Highband synthesis 

filter B200 [that] is configured to produce a synthesized highband signal according 

to highband excitation signal S120 and the set of filter coefficients.” Id., [0181]. 

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Vos, FIG. 38 (annotated) 

D. Kontio 

Like ’060 patent, Kontio is directed to bandwidth extension. Kontio’s 

method is neural network-based artificial bandwidth expansion of speech. As 

shown below in annotated FIG. 1, in Kontio’s method “the narrowband input 

signal, denoted by Snb, is treated in time-domain frames. New frequency 

components are produced into the highband using spectral folding, which can be 

achieved, for example, by zero-insertion in the time-domain. The frame is then 

transformed into the frequency domain with FFT. A set of features is calculated 

from the narrowband signal and given as an input to a neural network which 

transforms them into shaping parameters.” Ex-1008, page 874, 2:18-25. 

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Kontio, FIG. 1 (annotated) 

Kontio also discloses extracting feature vector such as gradient index, 

differential energy ratio, frame energy ratio, and subband power levels. Id., page 

875, 1:35-2:19. For the subband power level estimation, Kontio selects “the 

average spectral magnitudes of four spectral subbands (0.3–1.0 kHz, 1.0–1.7 kHz, 

1.7–2.5 kHz, and 2.5–3.4 kHz).” Id., page 875, 2:12-15. Kontio also discloses that 

the neural network is trained to provide shaping parameters for shaping highband 

signal. Id., Sections II-B and II-C. 

E. ETSI201.108 

ETSI201.108 was released by European Telecommunications Standards 

Institute (ETSI) in 2003 and publicly available on the ETSI server from September 

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23, 2003. Ex-10102

. No particular indica is per se sufficient for satisfying the 

“reasonable likelihood” publicly available standard, but for a given form of 

technology, screenshots from internet archive sources, and evidence of printed 

version release dates are considered among the examples of sufficient indica 

evidencing public accessibility. Hulu, LLC v. Sound View Innovations, LLC, Case 

IPR2018-013039, Paper 29 (P.T.A.B. Dec. 20, 2019) (precedential). For instance, 

the ETSI website demonstrates that ETSI201.108 was published on September 23, 

2003—a screen shot of the ETSI website showing the publication date is submitted 

as Ex-1010. 

ETSI201.108 discloses “the algorithm for front-end feature extraction to 

create Mel-Cepstrum parameters.” ETSI201.108, page 5, line 4. 

2 See also the Internet Archive stored document at 

https://web.archive.org/web/20031215120853/https://www.etsi.org/. 

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ETSI201.108, FIG. 4.1 (annotated) 

As shown above in annotated FIG. 4.1, an input speech signal goes through 

an analog-to-digital conversion step and an offset compensation step, and then “is 

divided into overlapping frames of N samples” in a framing step. Ex-1009, page 

9, line 24. 

After the framing, the signal is divided into two paths. The first path is for 

logarithmic frame energy measure (logE). In the second path, the signal goes 

through a pre-emphasis step by a pre-emphasis filter and a windowing step by 

Hamming window, and then converted to frequency domain signal by fast Fourier 

transform (FFT). 

The useful band of the converted frequency band “is divided into 23 

channels equidistant in mel frequency domain. Each channel has triangularshaped frequency window. Consecutive channels are half-overlapping.” Id., page 

10, line 25-27. “The output of mel filtering is subjected to a logarithm function 

(natural logarithm)” at step LOG and then 13 Mel-frequency cepstral coefficients 

(MFCC) are calculated from the output of the Non-linear Transformation block” at 

step DCT. Id., page 10, line 13-20. 

The final feature vector consists of 14 coefficients: the log-energy 

coefficient and the 13 Mel-frequency cepstral coefficients. The feature vectors are 

compressed and formatted as bitstream for further transmission. Thus, Mel 

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frequency cepstral coefficients (MFCC) are “cepstral coefficients calculated from 

the mel-frequency warped Fourier transform representation of the log 

magnitude spectrum.” Id., page 6, lines 23-26. 

IV. LEVEL OF ORDINARY SKILL IN THE ART 

The level of ordinary skill in the art may be reflected by the prior art of 

record. See Okajima v. Bourdeau, 261 F.3d 1350, 1355 (Fed. Cir. 2001). A person 

of ordinary skill in the art (“POSITA”) for ’060 patent would at least have a 

bachelor’s degree in electrical engineering, computer engineering, or a related 

engineering discipline and two or more years of industry experience in the field of 

signal processing, or equivalent experience, education, or both. Ex-1004. The 

person would also have knowledge or familiarity with signal processing. Id.

V. CLAIM CONSTRUCTION 

Only claim terms “in controversy” need be construed in IPR “and only to the 

extent necessary to resolve the controversy.” Nidec Motor Corp. v. Zhongshan 

Broad Ocean Motor Co., 868 F.3d 1013, 1017 (Fed. Cir. 2017) (citation omitted), 

cert. denied, 138 S. Ct. 1695 (2018). No claim terms need to be construed by the 

Board at this time. 

In the District Court, Petitioner and Patent Owner have offered the following 

terms for construction, see Litigation, Dkt. 61, p. 5, after the number of terms have 

been narrowed: 

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Claim Term Petitioner’s Proposed 

Construction 

Patent Owner’s 

Proposed 

Construction 

1 and 10 “extracting a feature 

vector” 

Indefinite Plain and 

Ordinary Meaning

1 and 10 “the level value is 

attenuated” 

Indefinite Plain and 

Ordinary Meaning

1 and 10 “spectral shape 

parameter” 

A sub band energy 

level value or a sub 

band gain factor 

based on the sub 

band energy level 

value 

Plain and 

Ordinary Meaning 

For the purpose of this Inter Partes Review, Petitioner adopts Patent 

Owner’s constructions for the above terms. Nevertheless, the evidence presented 

in this Petition is sufficient to invalidate the claims without addressing the 

indefinite issue. 

VI. STATEMENT OF PRECISE RELIEF REQUESTED FOR EACH 

CLAIM CHALLENGED 

1. Claims for Which Review Is Requested 

Petitioners respectfully request review under 35 U.S.C. § 311 of claims 1-18 

of ’060 patent and cancellation of those claims as unpatentable. 

2. Statutory Grounds 

Each asserted reference identified in the table below issued, published, 

and/or was filed before May 25, 2010, the earliest purported priority date of ’060 

patent. Thus, each asserted reference is prior art under at least one of pre-AIA 35 

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U.S.C. §§ 102(a), (b), and/or (e).

Prior Art References Exhibit 

Nilsson, U.S. Patent No. 7,359,854, filed on Apr. 10, 2002, issued 

on Apr. 15, 2008. 

Ex-1005 

Iser, U.S. Patent No. 8,160,889, filed on Jan. 17, 2008, issued on 

Apr. 17, 2012. 

Ex-1006 

Vos, U.S. Patent App. Pub. No. 2006/0282262, filed on Apr. 21, 

2006, published on Dec. 14, 2006. 

Ex-1007 

Kontio, J. Kontio, L. Laaksonen and P. Alku, "Neural NetworkBased Artificial Bandwidth Expansion of Speech," in IEEE 

Transactions on Audio, Speech, and Language Processing, vol. 

15, no. 3, pp. 873-881, March 2007. 

Ex-1008 

ETSI201.108, ETSI ES 201 108 V1.1.3 (2003-09), ETSI Standard, 

Speech Processing, Transmission and Quality Aspects (STQ); 

Distributed speech recognition; Front-end feature extraction 

algorithm; Compression algorithms, published in 2003. 

Ex-1009

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Claims 1-18 of ’060 patent are unpatentable under the following grounds 

based on pre-AIA 35 U.S.C. § 103: 

Grounds Grounds of Unpatentability

1 Nilsson and Iser render obvious claims 1, 9, 10, and 18. 

2 Nilsson, Iser, and ETSI201.108 render obvious claims 4, 5, 13, and 14 

3 Nilsson, Iser, and Vos render obvious claims 2, 3, 7, 11, 12, and 16 

4 Nilsson, Iser, and Kontio render obvious claims 6, 8, 15, and 17. 

VII. GROUND 1: NILSSON AND ISER RENDER OBVIOUS CLAIMS 1, 9, 

10, AND 18 

A. POSITA Would Have Combined Nilsson and Iser

Both Nilsson and Iser are directed to a method for bandwidth extension to 

generate bandwidth extended audio signal using a narrow-band audio signal in a 

frequency range of 0-4 kHz. Ex-1003, ¶¶76-137. 

Each reference includes similar modules with similar functions, for instance, 

both Nilsson and Iser disclose adjusting the high-band (e.g., 4-8 kHz) signal 

components. While Nilsson discloses a broad concept of adjusting the high-band 

energy, Iser discloses a specific way of adjusting, that is, by estimating signal 

power and noise power in the audio signal and determining a signal-to-noise ratio 

based on the power estimation and applying a weight factor corresponding to the 

signal-to-noise ratio. POSITA would have motivated to combine Nilsson and Iser

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by adopting the specific method of adjusting the high-band energy, as taught by 

Iser, in Nilsson’s system, as discussed in detail below. 

B. Independent Claim 1 

1. [1preamble] “A method comprising:” 

Nilsson discloses a method for “extending the spectrum of a received 

narrow-band acoustic signal (aNB). A wide-band acoustic signal (AWB) is 

produced by extracting at least one essential attribute (zNB) from the narrow-band 

acoustic signal (aNB). Parameters, e.g., representing signal energies, with respect to 

wide-band frequency components outside the spectrum (ANB) of the narrow-band 

acoustic signal (aNB), are estimated based on the at least one essential attribute 

(zNB).” Ex-1005, Abstract. Ex-1003, ¶¶78-79. 

As shown below in annotated FIG. 5, Nilsson’s method is performed by a 

“signal decoder” that “receives a narrow-band acoustic signal aNB” to generate 

bandwidth extended signal aWB. Id., 5:54-61. Ex-1003, ¶79. 

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Nilsson, FIG. 5 (annotated) 

2. [1a] “generating an excitation signal from an audio signal, 

wherein in the audio signal comprises a plurality of 

frequency components;” 

Nilsson discloses generating an excitation signal from an audio signal, as 

claimed, (Nilsson’s “excitation extension unit 105” generates “excitation signal 

EWB” from the “narrow-band acoustic signal aNB”), wherein the audio signal 

comprises a plurality of frequency components. Ex-1003, ¶¶80-85. 

i. Nilsson discloses generating an excitation signal 

from an audio signal. 

As shown below in annotated FIG. 5, Nilsson’s decoder includes an 

“excitation extension unit 105 [that] receives the narrow-band acoustic signal aNB

and, on basis thereof, produces an extended excitation signal EWB.” Ex-1005,

10:51-53. 

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Nilsson, FIG. 5 (annotated)

Specifically, “the extended excitation signal EWB is generated by means of 

spectral folding of a corresponding excitation signal ENB for the narrow-band 

acoustic signal aNB around a particular frequency.” Id., 10:57-11:2. 

ii. Nilsson’s audio signal comprises a plurality of 

frequency components. 

The ’060 patent fails to define “a plurality of frequency components” of the 

audio signal. The ’060 patent merely discloses using the low-band audio signal 

with “effective frequency range from 250 to 3500 Hz” to generate the excitation 

signal. Ex-1001, 11:9-10. In fact, it is well-known in the art that any audio signal 

naturally comprises a plurality of frequency components. Ex-1003, ¶83. 

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Similarly, Nilsson’s narrowband signal includes a plurality of frequency 

components. In generating the excitation signal, Nilsson uses a narrowband signal 

aNB which is a frequency band that covers “channel bandwidth of today's public 

switched telephony networks (PSTNs)”, for example, “between 0.3 kHz and 3.4 

kHz” (Ex-1005, 1:16-21) or “between 0 kHz and 4 kHz.” Ex-1005, 1:55-58. Ex1003, ¶84. 

 Therefore, Nilsson discloses generating an excitation signal from an audio 

signal, wherein in the audio signal comprises a plurality of frequency components. 

Ex-1003, ¶85. 

3. [1b] “extracting a feature vector from the audio signal, 

wherein the feature vector comprises at least one frequency 

domain component feature and at least one time domain 

component feature;” 

Nilsson discloses extracting a feature vector from the audio signal, as 

claimed, (Nilsson’s “feature extraction unit 101” extracts the “essential feature 

zNB(r, c)” from “narrow-band acoustic signal aNB”), wherein the feature vector 

comprises at least one frequency domain component feature, as claimed, (e.g., 

“spectral envelope c” of Nilsson) and at least one time domain component feature, 

as claimed, (e.g., “degree of voicing r” of Nilsson). Ex-1003, ¶¶86-94. 

As shown below in annotated FIG. 5, Nilsson’s decoder includes “feature 

extraction unit 101.” Ex-1005, 4:45-46. 

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Nilsson, FIG. 5 (annotated) 

Nilsson discloses that “[t]he narrow-band acoustic signal aNB is fed in 

parallel to the feature extraction unit 101, the excitation extension unit 105 and the 

up-sampler 102.” Id., 4:55-58. “The feature extraction unit 101 receives the 

narrow-band acoustic signal aNB and produces in response thereto at least one 

essential feature zNB(r, c) that describes particular properties of the received 

narrow-band acoustic signal aNB.” Id., 6:25-28. 

i. Time-domain component feature 

Nilsson discloses that the essential feature zNB(r, c) includes “[t]he degree of 

voicing r which represents one such essential feature zNB(r, c).” Id., 6:28-32. The 

degree of voicing r of Nilsson is a time-domain feature because it “is determined 

by localising a maximum of a normalised autocorrelation function,” using sampled 

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“narrow-band acoustic segment having a duration of Tf (e.g. 20 ms),” without 

converting the narrowband time-domain signal into frequency-domain signal. Id., 

6:30-46. Ex-1003, ¶89. 

ii. Frequency-domain component feature 

Nilsson’s essential feature zNB(r, c) includes a “spectral envelope c.” Id.,

6:46-51. The spectral envelop c is a frequency-domain component feature because 

spectral envelop c is determined by converting the time-domain narrowband input 

signal into a frequency-domain signal by Fourier transform. As shown below in 

annotated FIG. 7, Nilsson discloses “a part of the feature extraction unit 101, which 

is utilised for determining the spectral envelope c.” Id., 6:48-51. Ex-1003, ¶90. 

Nilsson, FIG. 7 (annotated) 

 The part of the feature extraction unit 101 which is utilized for determining 

the spectral envelope c includes “[a] segmenting unit 101a [that] separates a 

segment s of the narrow-band acoustic signal aNB that has a duration of Tf=20 ms,” 

and “a transform unit 101c [that] computes a corresponding spectrum SW by 

means of a fast Fourier transform, i.e. Sw=FFT(w·s),” that converts a timedomain signal into a frequency-domain signal. Id., 6:56-58. 

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Nilsson discloses that the spectral envelope c is “represented by LFCCs 

[linear frequency cepstral coefficients].” Id., 6:47, 5:29-30. Alternatively, the 

spectral envelope c is represented by “Line Spectral Frequencies (LSF), Mel 

Frequency Spectral Coefficients (MFCC), and Linear Prediction Coefficients 

(LPC).” Id., 8:45-50. 

Among these alternative representations of the spectral envelope c, at least 

Mel frequency cepstral coefficients (MFCC) are frequency domain component 

feature because it is well-known in the art that Mel frequency cepstral coefficients 

are “cepstral coefficients calculated from the mel-frequency warped Fourier 

transform representation of the log magnitude spectrum.” See ETSI201.108, 

page 6, lines 23-26. See also above Section III.E. Ex-1003, ¶93. 

Therefore, Nilsson discloses extracting a feature vector from the audio 

signal, wherein the feature vector comprises at least one frequency domain 

component feature and at least one time domain component feature. Ex-1003, ¶94. 

4. [1c] “determining at least one spectral shape parameter 

from the feature vector, wherein the at least one spectral 

shape parameter corresponds to a sub band signal 

comprising frequency components which belong to a 

further plurality of frequency components; and” 

Nilsson discloses determining at least one spectral shape parameter from the 

feature vector, as claimed, (Nilsson discloses determining energy-ratio ĝ from the 

essential feature zNB(r, c)), wherein the at least one spectral shape parameter 

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corresponds to a sub band signal comprising frequency components which belong 

to a further plurality of frequency components. Ex-1003, ¶¶95-104. 

i. Nilsson discloses determining at least one 

spectral shape parameter from the feature 

vector. 

The ’060 patent discloses that “[t]he spectral shape parameter may be a sub 

band gain factor based on the sub band energy level value.” Ex-1001, 5:5-8 

(emphasis added). 

Nilsson similarly discloses determining energy-ratio ĝ from the essential 

feature zNB(r, c). It is well-known in the art that a gain factor indicates a relative 

change of signal parameters (e.g., power, current, voltage, energy, etc.) and is 

usually expressed as a linear ratio of the parameters or a logarithm difference of 

the parameters as in Nilsson. Ex-1003, ¶97. Therefore, the energy-ratio ĝ of 

Nilsson is or corresponds to the gain factor. Ex-1003, ¶97. 

As shown below in annotated FIG. 5, Nilsson’s decoder includes an “energyratio estimator 104a, which is included in the wide-band envelope estimator 104, 

receives the first component c0 in the vector of linear frequency cepstral 

coefficients c and produces, on basis thereof, plus on basis of the narrow-band 

shape x and the degree of voicing r an estimated energy-ratio ĝ between the highband and the narrow-band.” Ex-1005, 7:15-27. 

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Nilsson, FIG. 5 (annotated) 

As discussed above, Nilsson’s energy-ratio ĝ is determined by computing 

linear frequency cepstral coefficients (LFCC), which is merely one type of 

representation of spectral envelope c. Nilsson also discloses that “[o]ther 

parameters than LFCCs can be used as alternative representations of the narrowband spectral envelope x. Line Spectral Frequencies (LSF), Mel Frequency 

Spectral Coefficients (MFCC), and Linear Prediction Coefficients (LPC) constitute 

such alternatives.” Id., 8:45-50. Therefore, Nilsson discloses determining energyratio ĝ by computing Mel Frequency Spectral Coefficients (MFCC), and thus, all 

inherent features associated with MFCC. Ex-1003, ¶99. 

ii. Nilsson’s spectral shape parameter corresponds 

to a sub band signal comprising frequency 

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components which belong to a further plurality 

of frequency components. 

The ’060 patent fails to define the term of “belong to a further plurality of 

frequency components.” 

Regarding the “sub band signal”, ’060 patent discloses that “[t]he apparatus 

may further comprise a signal combiner configured to combine the sub band signal 

with the audio signal to provide a bandwidth extended audio signal.” Ex-1001, 

6:33-36. Therefore, the claimed “sub band signal” is the high-band signal. Ex1003, ¶101. 

As discussed above, Nilsson discloses determining the spectral shape 

parameter for the high-band signal. Ex-1003, ¶102. 

Nilsson also discloses that the narrow-band shape is “modelled by the 

cepstral coefficients.” Ex-1005, 6:7-8. As discussed above, the cepstral coefficient 

can be represented by linear frequency cepstral coefficients (LFCC), or 

alternatively, by Mel Frequency Spectral Coefficients (MFCC). See Id., 8:45-50. It 

is well-known in the art that in computing MFCC, the frequency band is divided 

into a plurality of channels and each channel including a plurality of frequency 

component. See Ex-1009, see also Section III.E. Thus, Nilsson’s cepstral 

coefficients correspond to a plurality of frequency components, which belong to a 

further plurality of frequency components. Ex-1003, ¶103. Moreover, the term 

“sub bands” is well-known in the art. See Ex-1008, see also Section III.D. 

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Therefore, Nilsson discloses determining at least one spectral shape 

parameter from the feature vector, wherein the at least one spectral shape 

parameter corresponds to a sub band signal comprising frequency components 

which belong to a further plurality of frequency components. Ex-1003, ¶104. 

5. [1d] “generating the sub band signal by filtering the 

excitation signal through a filter bank and weighting the 

filtered excitation signal with the at least one spectral shape 

parameter,” 

As discussed above in claim element [1c], the claimed “sub band signal” is 

the high-band signal. 

Nilsson discloses generating the high-band signal by filtering the excitation 

signal through a filter bank and weighting the filtered excitation signal with the at 

least one spectral shape parameter, as claimed, (Nilsson discloses generating highband signal by filtering the wide-band excitation spectrum EWB through the wideband filter 106 and multiplying the filtered excitation signal with the smoothed 

energy ratio ĝ). Ex-1003, ¶¶106-113. 

i. Nilsson discloses filtering the excitation signal 

with a filter bank. 

The ‘060 patent does not define the “filter bank.” 

Nevertheless, Nilsson discloses generating the high band signal by filtering 

the wide-band excitation spectrum EWB through the wide-band filter 106. For 

instance, as shown below in annotated FIG. 5, Nilsson discloses that the “high 

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band shape estimator 106a in the wide-band filter 106 [] receives the wide-band 

excitation spectrum EWB from the excitation extension unit 105” to filter the 

excitation signal. Ex-1005, 11:21-25. 

Nilsson, FIG. 5 (annotated) 

Also, filtering an excitation signal with a filter bank is well-known in the art. 

See e.g., Ex-1007 (in which Vos uses a synthesis filter 330 to filter an excitation 

signal, which can be implemented as a filter bank). Ex-1003 ¶109. Further, the 

wide-band filter 106 of Nilsson can be implemented as a filter bank. Therefore, the 

wide-band filter 106 of Nilsson corresponds to the claimed filter bank. Ex-1003 

¶109. 

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ii. ’060 patent’s disclosure of weighting the filtered 

excitation signal with the at least one spectral 

shape parameter. 

The ’060 patent only discloses weighting the filtered excitation signal with 

the gain factor. Specifically, ’060 patent discloses “inputting the time domain 

frames into an excitation signal generator 417 up-sampling the output of the 

excitation signal generator 417 in the up-sampler 419 filtering an up-sampled 

excitation signal through the filter bank 421 and then weighting each sub band 

signal with a gain factor derived from the corresponding mel band energy levels.” 

Ex-1001, 18:51-58 (emphasis added). 

Therefore, “the at least one spectral shape parameter” according to claim 

element [1d] is the gain factor. Ex-1003 ¶111. 

iii. Nilsson discloses weighting the filtered 

excitation signal with the at least one spectral 

shape parameter. 

Nilsson discloses weighting the filtered excitation signal with energy-ratio ĝ

(the claimed spectral shape parameter). As shown below in annotated FIG. 5, 

Nilsson discloses that the “multiplier 106b receives the high-band envelope 

spectrum SY from the high band shape estimator 106a and receives the temporally 

smoothed energy ratio estimate ĝsmooth from the energy ratio estimator 104a. On 

basis of the received signals SY and ĝsmooth the multiplier 106b generates a highband energy y0.” Ex-1005, 11:21-44. 

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Nilsson, FIG. 5 (annotated) 

Because the multiplier 106b multiples the filtered excitation signal EWB with 

the smoothed energy ratio ĝsmooth and the high-band envelope spectrum SY which is 

generated based on energy-ratio ĝ, Nilsson discloses weighting the filtered 

excitation signal with the energy-ratio ĝ. “The high-pass filter 107 receives the 

high-band energy signal y0 from the high-band shape reconstruction unit 106 and 

produces in response thereto a high-pass filtered signal HP(y0).” Id., 11:56-59. Ex1003 ¶113. 

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6. [1e] “wherein the spectral shape parameter is a sub band 

energy level value and the sub band energy level value is 

attenuated when the power of the audio signal approaches 

an estimate of the level of noise in the audio signal.” 

As discussed above, the “spectral shape parameter” according to Claim [1d] 

is the “gain factor” because ’060 patent only discloses weighting the filtered 

excitation signal with the gain factor. Ex-1003, ¶¶114-126. 

Nevertheless, Nilsson discloses the sub band energy level value, and Iser

discloses that the sub band energy level value is attenuated when the power of the 

audio signal approaches an estimate of the level of noise in the audio signal. 

i. Nilsson discloses sub band energy level value. 

As discussed above, the “sub band signal” is the high-band signal. Thus, the 

claimed sub band energy is the high-band energy. 

Nilsson discloses the high-band energy. For instance, Nilsson discloses that 

“[t]he high-band energy y0 is determined by computing a first LFCC using only a 

high-band part of the spectrum between fNu and fWu (where e.g. fNu=3,3 kHz and 

fWu=8,0 kHz).” Ex-1005, 11:44-55. Also, as discussed above, Nilsson disclose 

that Mel Frequency Spectral Coefficients (MFCC) is used as an alternative of 

LFCC. Id., 8:45-50. Therefore, Nilsson inherently discloses computing Mel band 

energy level. Ex-1003, ¶118. 

ii. Iser discloses that the sub band energy level 

value is attenuated when the power of the audio 

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signal approaches an estimate of the level of 

noise in the audio signal. 

In the event that Patent Owner argues that Nilsson does not explicitly 

disclose that “the sub band energy level value is attenuated when the power of the 

audio signal approaches an estimate of the level of noise in the audio signal,” Iser

discloses it. Ex-1003, ¶119. 

Iser discloses estimating power of the input audio signal and the noise 

power, and based on the estimation, applying “weighting factors to the signal 

component.” For example, FIG. 4 shown below is “a representation of an input 

speech signal, such as the received acoustic signal x(n).” Ex-1006, 7:8-10. 

Iser, FIG. 4 

As shown below in annotated FIG. 5, Iser performs “a short time power 

estimation and a noise power estimation that correspond to the received acoustic 

signal x(n) of FIG. 4. Line 502 in FIG. 5 represents the estimated short time power 

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x(n) of the received acoustic signal x(n). Line 504 in FIG. 5 represents the noise 

power estimation b(n). The short time power estimation may be used to determine 

different factors for weighting the signal components.” Id., 7:8-18. 

Iser, FIG. 5 (annotated) 

Based on the estimated signal power and noise power, Iser further estimates 

“signal-to-noise ratio of the received acoustic signal”. As shown above in 

annotated FIG. 5, when the power of the audio signal approaches noise power 

estimation, the low signal-to-noise ratio is low, for example, at point A. On the 

other hand, when the power of the audio signal is far away from noise power 

estimation, the signal-to-noise ratio is high, for example, at point B. 

Iser further discloses that a “first weighting factor gsnr(n) that may be 

applied is a function of an estimated signal-to-noise ratio of the received acoustic 

signal x(n)” and “[i]f the received acoustic signal x(n) contains speech passages 

A 

B 

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with a low signal-to-noise ratio, then this weighting factor may be used to damp

the upper bandwidth extension signal yhigh(n).” Id., 7:20-32. 

If the power of the audio signal approaches an estimate of the level of noise 

in the audio signal, the signal-to-noise ratio is low, and the upper bandwidth 

extension signal yhigh(n) is dampled. Therefore, Iser discloses that the sub band 

energy level value is attenuated when the power of the audio signal approaches an 

estimate of the level of noise in the audio signal. Ex-1003, ¶124. 

iii. POSITA Would Have Combined Nilsson and 

Iser 

As discussed above, both Nilsson and Iser are directed to a method for 

bandwidth extension to generate bandwidth extended signal using a narrow-band 

audio signal. Also, both Nilsson and Iser disclose adjusting the generated high 

band signal components. 

While Nilsson discloses a broad concept of adjusting the high band energy, 

Iser discloses a specific method for adjusting, that is, by estimating signal power 

and noise power in the audio signal and determining a signal-to-noise ratio based 

on the power estimation and applying a weight factor corresponding to the signalto-noise ratio. 

Therefore, POSITA would have been motivated to apply the teaching of Iser

to Nilsson’s system to estimate signal power and noise power, to determine signalto-noise ratio, and to damp the sub band energy level value of Nilsson so that the 

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sub band energy level value is attenuated when the power of the audio signal 

approaches an estimate of the level of noise in the audio signal, as taught by Iser. 

Ex-1003, ¶126. By doing so, Nilsson’s system may obtain “a more natural output 

signal,” as described by Iser. Ex-1006, 8:6-7, 8:58-59. Also, by doing so, 

Nilsson’s system may achieve its purpose, i.e., to “accurately describe [] the 

particular wide-band frequency component.” Ex-1005, Claim 1. Ex-1003, ¶126. 

C. Independent Claim 10 

1. [10preamble] “An apparatus comprising at least one 

processor and at least one memory including computer 

code, the at least one memory and the computer code 

configured to with the at least one processor cause the 

apparatus to at least:” 

Nilsson discloses an apparatus comprising at least one processor, as claimed, 

(“computer” of Nilsson) and at least one memory including computer code, as 

claimed, (Nilsson’s “memory of a computer, comprising software”).

For instance, Nilsson discloses “the object is achieved by a computer 

program directly loadable into the internal memory of a computer, comprising 

software for performing the method described in the above paragraph when said 

program is run on a computer.” Ex-1005, 3:25-29. 

“The signal decoder receives a narrow-band acoustic signal aNB, either via a 

communication link (e.g. in PSTN) or from a storage medium (e.g. a digital 

memory).” Id., 4:53-55. 

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2. [10a] “generate an excitation signal from an audio signal, 

wherein in the audio signal comprises a plurality of 

frequency components;” 

As explained in Claim [1a], Nilsson discloses generating an excitation signal 

from an audio signal, wherein in the audio signal comprises a plurality of 

frequency components. See Section VII.B.2. 

3. [10b] “extract a feature vector from the audio signal, 

wherein the feature vector comprises at least one frequency 

domain component feature and at least one time domain 

component feature;” 

As explained in Claim [1b], Nilsson discloses extracting a feature vector 

from the audio signal, wherein the feature vector comprises at least one frequency 

domain component feature and at least one time domain component feature. See

Section VII.B.3. 

4. [10c] “determine at least one spectral shape parameter from 

the feature vector, wherein the at least one spectral shape 

parameter corresponds to a sub band signal comprising 

frequency components which belong to a further plurality 

of frequency components; and” 

As explained in Claim [1c], Nilsson discloses determining at least one 

spectral shape parameter from the feature vector, wherein the at least one spectral 

shape parameter corresponds to a sub band signal comprising frequency 

components which belong to a further plurality of frequency components. See

Section VII.B.4. 

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5. [10d] “generate the sub band signal by filtering the 

excitation signal through a filter bank and weighting the 

filtered excitation signal with the at least one spectral shape 

parameter,” 

As explained in Claim [1d], Nilsson discloses generating the sub band signal 

by filtering the excitation signal through a filter bank and weighting the filtered 

excitation signal with the at least one spectral shape parameter. See Section 

VII.B.5. 

6. [10e] “wherein the spectral shape parameter is a sub band 

energy level value and the sub band energy level value is 

attenuated when the power of the audio signal approaches 

an estimate of the level of noise in the audio signal.” 

As explained in Claim [1e], the combination of Nilsson and Iser discloses 

that the spectral shape parameter is a sub band energy level value and the sub band 

energy level value is attenuated when the power of the audio signal approaches an 

estimate of the level of noise in the audio signal. See Section VII.B.6. 

D. Dependent Claims 9 and 18 

1. [Claim 9] “The method as claimed in any of claim 1, further 

comprising combining the sub band signal with the audio 

signal to provide a bandwidth extended audio signal.” 

Nilsson discloses combining the sub band signal with the audio signal to 

provide a bandwidth extended audio signal, as claimed, (Nilsson’s adder 108 

combines the “low-pass filtered signal LP(aNB-u)” and the “high-pass filtered 

signal HP(y0)” to form the “wide-band acoustic signal aWB”). Ex-1003, ¶¶135-137. 

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As shown below in annotated FIG. 5, Nilsson discloses that “the adder 108 

receives the low-pass filtered signal LP(aNB-u), receives the high-pass filtered 

signal HP(y0) and adds the received signals together and thus forms the wide-band 

acoustic signal aWB, which is delivered on the signal decoder's output.” Ex-1005, 

12:17-21. 

Nilsson, FIG. 5 (annotated) 

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2. [Claim 18] “The apparatus as claimed in claim 10, wherein 

the at least one memory and the computer code is 

configured to: combine the sub band signal with the audio 

signal to provide a bandwidth extended audio signal.” 

As explained in Claim 9, Nilsson discloses combining the sub band signal 

with the audio signal to provide a bandwidth extended audio signal. See Section 

VII.D.1. 

VIII. GROUND 2: NILSSON, ISER, AND ETSI201.108 RENDER OBVIOUS 

CLAIMS 4, 5, 13, AND 14 

1. POSITA Would Have Combined Nilsson with Iser and 

ETSI201.108

Both Nilsson and Iser are directed to a method for bandwidth extension to 

generate bandwidth extended audio signal using a narrow-band audio signal in a 

frequency range of 0-4 kHz. Ex-1003, ¶¶138-152. Each reference includes similar 

module with similar functions, for instance, both Nilsson and Iser disclose 

adjusting the high-band (e.g., 4-8 kHz) signal components. Ex-1003, ¶¶138-139. 

Thus, POSITA would have sought to combine Nilsson with Iser due to the 

compatible components. Ex-1003, ¶¶138-152. 

Second, Nilsson discloses using Mel Frequency Spectral Coefficients 

(MFCC) to determine the spectral shape parameter for shaping the high-band 

signal. And, ETSI201.108 discloses the well-known algorithms of computing 

MFCC. POSITA would have likewise sought standards like ETSI201.108 for 

combining with Nilsson for these claimed limitations. Ex-1003, ¶139. 

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2. [Claim 4] “The method as claimed in claim 1, wherein the 

frequency components of the sub band signal are 

distributed according to a psychoacoustic scale comprising 

a plurality of overlapping bands, and frequency 

characteristics of the filter bank correspond to a 

distribution of frequency components of the sub band 

signal.” 

The ’060 patent fails to define the “psychoacoustic scale.” It appears that 

the claimed “psychoacoustic scale” is the psychoacoustically derived “Mel scale”. 

Ex-1001, 11:3-6. The ’060 patent also fails to define the “frequency characteristics 

of the filter bank.” Regardless of this indefiniteness, this claim is unpatentable over 

Nilsson in view of Iser and ETSI201.108. Ex-1003, ¶¶140-147. 

Nilsson discloses that the frequency components of the sub band signal are 

distributed according to Mel scale, and ETSI201.108 discloses the well-known 

features of the Mel scale, e.g., a plurality of overlapping bands, and frequency 

characteristics of the Mel filter. Ex-1003, ¶141. 

i. Mel scale is a psychoacoustic scale. 

It is well-known in the art that Mel scale was derived historically from 

hearing-based psychoacoustic experiments that gave a perceptual measure of pitch, 

and therefore, Mel scale is a psychoacoustic scale. Ex-1012, Ex-1013. Ex-1003, 

¶142. 

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ii. Nilsson discloses that the frequency components 

of the sub band signal are distributed according 

to Mel scale. 

As discussed above, Nilsson discloses using Mel scale in representing the 

narrow-band spectral envelop c. For instance, Nilsson discloses that “[o]ther 

parameters than LFCCs can be used as alternative representations of the narrowband spectral envelope x. Line Spectral Frequencies (LSF), Mel Frequency 

Spectral Coefficients (MFCC), and Linear Prediction Coefficients (LPC) 

constitute such alternatives.” Ex-1005, 8:46-54. Ex-1003, ¶143. 

iii. Mel scale includes a plurality of overlapping 

bands, and frequency characteristics of the Mel 

filter correspond to a distribution of frequency 

components of the signal. 

As discussed above, Nilsson discloses using Mel Frequency Spectral 

Coefficients (MFCC) in Mel scale to determine the spectral shape parameter. 

Therefore, Nilsson discloses all the inherent feature of Mel scale. 

 In the event that Patent Owner argues that Nilsson does not explicitly 

disclose the detailed feature of Mel scale, ETSI201.108 discloses it. For instance, 

ETSI201.108 discloses that Mel frequency cepstral coefficients are derived by a 

process in which an input signal “is divided into overlapping frames of N 

samples.” Ex-1009, page 9, line 24. Ex-1003, ¶145. 

ETSI201.108 also discloses the overlapping feature of Mel filter, which 

corresponds to the overlapping feature of the frames. For instance, ETSI201.108

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discloses that “[t]he useful frequency band lies between 64 Hz and half of the 

actual sampling frequency. This band is divided into 23 channels equidistant in mel 

frequency domain. Each channel has triangular-shaped frequency window. 

Consecutive channels are half-overlapping.” Ex-1009, page 10, line 25-27. 

Therefore, in Mel scale, the frequency characteristics of the Mel filter correspond 

to a distribution of frequency components of the sub band signal. Ex-1003, ¶146. 

Therefore, the combination of Nilsson and ETSI201.108 discloses that the 

frequency components of the sub band signal are distributed according to a 

psychoacoustic scale comprising a plurality of overlapping bands, and frequency 

characteristics of the filter bank correspond to a distribution of frequency 

components of the sub band signal. Ex-1003, ¶147. 

3. [Claim 5] “The method as claimed in claim 4, wherein the 

overlapping bands are distributed according to the mel 

scale, and wherein the sub band signal is masked using at 

least one of: a triangular masking function; and a 

trapezoidal masking function.” 

As discussed above, Nilsson discloses the “Mel Frequency Spectral 

Coefficients (MFCC)” in which overlapping bands are distributed according to the 

Mel scale. Therefore, Nilsson discloses all the inherent feature of Mel scale, 

including the claimed feature. Ex-1003, ¶¶148-150. 

In addition, ETSI201.108 discloses this well-known feature of Mel scale. 

For instance, ETSI201.108 discloses that “Mel Frequency Spectral Coefficients 

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(MFCC)” are derived by Mel filtering using “triangular-shaped frequency 

window.” Ex-1009, page 10, line 25-27. Ex-1003, ¶149. 

Therefore, the combination of Nilsson and ETSI201.108 discloses claim 5. 

Ex-1003, ¶150. 

4. [Claim 13] “The apparatus as claimed in claim 10, wherein 

the frequency components of the sub band signal are 

distributed according to a psychoacoustic scale comprising 

a plurality of overlapping bands, and frequency 

characteristics of the filter bank correspond to a 

distribution of frequency components of the sub band 

signal.” 

As explained in Claim 4, the combination of Nilsson and ETSI201.108 

discloses that the frequency components of the sub band signal are distributed 

according to a psychoacoustic scale comprising a plurality of overlapping bands, 

and frequency characteristics of the filter bank correspond to a distribution of 

frequency components of the sub band signal. See Section VIII.2; Ex-1003, ¶151. 

5. [Claim 14] “The apparatus as claimed in claim 13, wherein 

the overlapping bands are distributed according to the mel 

scale, and wherein the sub band signal is masked using at 

least one of a triangular masking function; and a 

trapezoidal masking function.” 

As explained in Claim 5, the combination of Nilsson and ETSI201.108 

discloses that the overlapping bands are distributed according to the mel scale, and 

wherein the sub band signal is masked using at least one of a triangular masking 

function; and a trapezoidal masking function. See Section VIII.3; Ex-1003, ¶152. 

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IX. GROUND 3: NILSSON, ISER, AND VOS RENDER OBVIOUS 

CLAIMS 2, 3, 7, 11, 12, AND 16 

1. POSITA Would Have Combined Nilsson with Iser and Vos 

Nilsson, Iser, and Vos all are directed to a method for bandwidth extension 

to generate bandwidth extended audio signal using a narrow-band audio signal in a 

frequency range of 0-4 kHz. Ex-1003, ¶¶153-187. Each reference includes similar 

module with similar functions, for instance, Nilsson, Iser, and Vos disclose 

adjusting the high-band (e.g., 4-8 kHz) signal components. Thus, POSITA would 

have sought to combine Nilsson with Iser and Vos due to the compatible 

components. Ex-1003, ¶¶153-154. 

Second, both Nilsson and Vos are directed to generating excitation signal, up 

sampling the excitation signal, filtering, weighting the excitation signal. Vos

discloses many different types of filters for generating and processing the 

excitation signal. POSITA would have implemented these filters in Nilsson’s 

system due to the advantages provided by the filters. The advantages of these 

various filters are well-known in the art and also mentioned by Vos. Ex-1003, 

¶154. 

2. [Claim 2a] “The method as claimed in claim 1, wherein 

generating the excitation signal comprises: generating a 

residual signal by filtering the audio signal with an inverse 

linear predictive filter;” 

The ’060 patent discloses that “[o]nce the LPC filter coefficients have been 

determined within the excitation signal generator 417, the input audio signal frame 

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404 can in some embodiments be filtered by the LP analysis filter in order to 

produce a LP residual signal.” Ex-1001, 22:54-57. Therefore, the inverse linear 

predictive filter is the LP analysis filter. Ex-1003, ¶¶155-163. 

As discussed above, Nilsson discloses generating the excitation signal. In the 

event that Patent Owner argues that Nilsson does not explicitly disclose generating 

a residual signal by filtering the audio signal with an inverse linear predictive filter, 

then Vos further discloses this claim element. Ex-1003, ¶156. 

Vos discloses generating a residual signal by filtering a narrowband signal 

with a whitening filter implemented with a linear prediction coding (LPC) analysis 

module, indicating the whitening filter is the inverse linear predictive filter. As 

shown below in annotated FIG. 6, Vos discloses a narrowband encoder A122 that 

generates encoded narrowband excitation signal S50 by filtering the narrowband 

signal 20 with a whitening filter 260. 

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Vos, FIG. 6a (annotated) 

i. Vos’ whitening filter generates a residual signal. 

Vos discloses that the “narrowband encoder A122 generates a residual 

signal by passing narrowband signal S20 through a whitening filter 260 (also 

called an analysis or prediction error filter) that is configured according to the set 

of filter coefficients.” Ex-1007, [0118]. 

ii. Vos’ whitening filter is the inverse filter. 

Vos discloses that the whitening filter 260 “removes the spectral envelope

to spectrally flatten the signal. The resulting whitened signal (also called a 

residual) has less energy and thus less variance and is easier to encode than the 

original speech signal,” and “[t]he peaks that characterize this spectral envelope 

represent resonances of the vocal tract and are called formants.” Id., [0113]. 

Because whitening filter 260 generates the residual signal by removing the 

formants, the whitening filter 260 is the claimed inverse filter. Ex-1003, ¶160. 

iii. Vos’ whitening filter is the linear predictive 

filter. 

As shown above in annotated FIG. 6a, Vos discloses that “a linear prediction 

coding (LPC) analysis module 210 encodes the spectral envelope of narrowband 

signal S20 as a set of linear prediction (LP) coefficients (e.g., coefficients of an 

all-pole filter 1/A(z)),” and that “LPC analysis module 210 is configured to 

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calculate a set of ten LP filter coefficients to characterize the formant structure of 

each 20-millisecond frame.” Ex-1007, [0114]. 

In particular, as shown above in annotated FIG. 6a the linear prediction filter 

coefficients are transformed into a set of corresponding “line spectral frequencies 

(LSFs)” by LP filter coef.-to-LSF transform 220, quantized by “quantizer 230”, 

dequantized by “inverse quantizer 240” transformed to corresponding set of LP 

filter coefficients by LSF-to-LP filter coefficient transform 250, and “this set of 

coefficients is used to configure whitening filter 260 to generate the residual 

signal that is quantized by quantizer 270,” to form encoded narrowband excitation 

signal. Id., [0116]-[0119]. 

Therefore, Vos discloses generating a residual signal by filtering the audio 

signal with the inverse linear predictive filter. 

3. [Claim 2b] “filtering the residual signal with a post filter 

stage comprising an auto regressive moving average filter 

based on the linear predictive filter; and” 

The ’060 patent fails to define the “post filter stage.” 

Nevertheless, Vos discloses filtering the residual signal with a post filter 

stage comprising an auto regressive moving average filter, as claimed, (syntheses 

filter A220 in FIG. 10 of Vos) based on the linear predictive filter. Ex-1003, ¶¶164-

169. 

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As discussed above, the residual signal generated by the whitening filter 260 

is quantized by quantizer 270 to form encoded narrowband excitation signal. As 

shown below in annotated FIG. 10, the formed encoded narrowband excitation 

signal is fed to a highband excitation generator A300 included in a highband 

encoder A200 to derive a highband excitation signal S120. The highband 

excitation signal is then sent a synthesis filter A220. Ex-1007, [0132], [0134]. 

Vos, FIG. 10 (annotated) 

In particular, Vos discloses that the synthesis filter A220 is “configured to 

produce a synthesized highband signal S130 according to highband excitation 

signal S120 and the encoded spectral envelope (e.g., the set of LP filter 

coefficients) produced by analysis module A210. Synthesis filter A220 is typically 

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implemented as an IIR filter, although FIR implementations may also be used. In a 

particular example, synthesis filter A220 is implemented as a sixth-order linear

autoregressive filter.” Id., [0134]. 

Therefore, the synthesis filter A220 of Vos is an autoregressive filter. Also, 

it is well known in the art that autoregressive model and moving average model are 

usually combined to form auto regressive moving average (ARMA) model and 

ARMA filter is well-known in the art. Ex-1014, Ex-1015. Ex-1003, ¶168. In this 

regard, ARMA filter can be said as a type of autoregressive filter. Because Vos

discloses filtering the residual signal with auto regressive filter, POSITA would 

have understood that Vos also discloses filtering the residual signal with the auto 

regressive moving average filter. Ex-1003, ¶168. 

Therefore, Vos discloses filtering the residual signal with a post filter stage 

comprising an auto regressive moving average filter based on the linear predictive 

filter. Ex-1003, ¶169. 

4. [Claim 2c] “generating the excitation signal by up sampling 

and spectrally folding the output from the post filter stage.” 

Vos generates highband excitation signal by spectral folding operation or 

spectrally translating narrowband excitation signal S80 into the highband, as 

claimed, (e.g., via upsampling followed by multiplication with a constantfrequency cosine signal). Ex-1003, ¶¶170-173. 

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As shown above in annotated FIG. 10, the encoded narrowband excitation 

signal is fed to a highband excitation generator A300 included in a highband 

encoder A200 to derive a highband excitation signal S120. Ex-1007, [0132]. Vos

further shows the details of the highband excitation generator A300 in FIG. 11. As 

shown below in annotated FIG. 11, the highband excitation generator A300 

includes a spectrum extender A400 that “is configured to produce a harmonically 

extended signal S160 based on narrowband excitation signal S80.” Ex-1007, 

[0139]. 

Vos, FIG. 11 (annotated) 

For example, “spectrum extender A400 is configured to perform a spectral 

folding operation (also called mirroring) on narrowband excitation signal S80 to 

produce harmonically extended signal S160. Spectral folding may be performed by 

zero-stuffing excitation signal S80 and then applying a highpass filter to retain the 

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alias. In another example, spectrum extender A400 is configured to produce 

harmonically extended signal S160 by spectrally translating narrowband 

excitation signal S80 into the highband (e.g., via upsampling followed by 

multiplication with a constant-frequency cosine signal).” Id., [0140]. 

Therefore, Vos discloses generating the excitation signal by up sampling and 

thus, spectrally folding the output from the post filter stage. Ex-1003, ¶173. 

5. [Claim 3] “The method as claimed in claim 2, wherein the 

post filter stage further comprises a spectral tilt filter and a 

harmonic filter.” 

Vos discloses that the post filter stage further comprises a spectral tilt filter, 

as claimed, (spectral flattener in FIG. 12 of Vos) and a harmonic filter, as claimed, 

(the spectral flattener of Vos). Ex-1003, ¶¶174-177. 

As shown below in annotated FIG. 12, Vos discloses a spectral flattener 540 

included in spectral extender A402 and performs an adaptive whitening operation 

on the down-sampled signal. 

Vos, FIG. 12 (annotated) 

For example, Vos discloses that “spectral flattener 540 includes an LPC 

[linear prediction coding] analysis module configured to calculate a set of four 

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filter coefficients from the downsampled signal and a fourth-order analysis filter 

configured to whiten the signal according to those coefficients.” Ex-1007, [0152]. 

Because the spectral flattener 540 performs whitening operation and flattens 

the spectral of the extended signal, the spectral flattener 540 is the spectral tilt 

filter. Also, because the spectral flattener 540 removes the harmonics, the spectral 

flattener 540 of Vos is the harmonic filter. Ex-1003, ¶177. 

6. [Claim 7] “The method as claimed in claim 1, wherein the at 

least one frequency domain component feature of the 

feature vector comprises at least one of the following: a 

group of a plurality of energy levels of the audio signal, 

wherein each of the plurality energy levels corresponds to 

the energy of an overlapping band of the audio signal; a 

value representing a centroid of the frequency domain 

spectrum of the audio signal; and a value representing the 

degree of flatness of the frequency domain spectrum.” 

Nilsson and Vos disclose the elements of claim 7. Ex-1003, ¶¶178-183. 

i. Energy levels 

Nilsson discloses determining a group of a plurality of energy levels of the 

audio signal, wherein each of the plurality energy levels corresponds to the energy 

of an overlapping band of the audio signal. 

As discussed above, Nilsson discloses a plurality of overlapping segments of 

an audio signal. Also as discussed above, Nilsson discloses determining c which is 

a vector of linear frequency cepstral coefficients. Nilsson further discloses that “[a] 

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first component c0 of the vector c constitutes the log energy of the narrow-band 

acoustic segment s.” Ex-1005, 7:7-9. 

Because Nilsson discloses calculating log energy of narrow-band acoustic 

segments, and the adjacent segments overlap, Nilsson discloses determining a 

group of a plurality of energy levels of the audio signal, wherein each of the 

plurality energy levels corresponds to the energy of an overlapping band of the 

audio signal. Ex-1003, ¶181. 

ii. Centroid 

In the event that the Patent Owner argues that Nilsson does not disclose this 

claim element, Vos explicitly discloses it. For instance, Vos discloses that “[t]he 

quantization maps each input value to a value that represents the corresponding 

Voronoi region (typically, the centroid), shown here as a point.” Ex-1007, [0255]. 

Ex-1003, ¶182. 

iii. Degree of flatness 

In the event that the Patent Owner argues that Nilsson does not disclose this 

claim element, Vos explicitly discloses it. For instance, Vos discloses that 

“narrowband encoder A120 produces values for spectral tilt and speech mode 

parameters for each frame. Spectral tilt relates to the shape of the spectral envelope 

over the passband and is typically represented by the quantized first reflection 

coefficient. For most voiced sounds, the spectral energy decreases with increasing 

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frequency, such that the first reflection coefficient is negative and may approach 

−1. Most unvoiced sounds have a spectrum that is either flat, such that the first 

reflection coefficient is close to zero, or has more energy at high frequencies, such 

that the first reflection coefficient is positive and may approach +1.” Ex-1007, 

[0130]. Ex-1003, ¶183. 

7. [Claim 11a] “The apparatus as claimed in claim 10, wherein 

the at least one memory and the computer code configured 

to with the at least one processor to cause the apparatus to 

at least perform generating the excitation signal is further 

configured to: generate a residual signal by filtering the 

audio signal with an inverse linear predictive filter;” 

As explained in Claim [2a], Vos discloses generating a residual signal by 

filtering the audio signal with an inverse linear predictive filter. See Section IX.2; 

Ex-1003, ¶183. 

8. [Claim 11b] “filter the residual signal with a post filter stage 

comprising an auto regressive moving average filter based 

on the linear predictive filter; and” 

As explained in Claim [2b], Vos discloses filtering the residual signal with a 

post filter stage comprising an auto regressive moving average filter based on the 

linear predictive filter. See Section IX.3; Ex-1003, ¶¶184. 

9. [Claim 11c] “generate the excitation signal by up sampling 

and spectrally folding the output from the post filter stage.” 

As explained in Claim [2c], Vos discloses generating the excitation signal by 

up sampling and spectrally folding the output from the post filter stage. See Section 

IX.4; Ex-1003, ¶¶185-186. 

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10. [Claim 12] “The apparatus as claimed in claim 11, wherein 

the post filter stage further comprises a spectral tilt filter 

and a harmonic filter.” 

As explained in Claim 3, Vos discloses that the post filter stage further 

comprises a spectral tilt filter and a harmonic filter. See Section IX.5; Ex-1003, 

¶186. 

11. [Claim 16] “The apparatus as claimed in claim 10, wherein 

the at least one frequency domain component feature of the 

feature vector comprises at least one of the following: a 

group of a plurality of energy levels of the audio signal, 

wherein each of the plurality energy levels corresponds to 

the energy of an overlapping band of the audio signal; a 

value representing a centroid of the frequency domain 

spectrum of the audio signal; and; a value representing the 

degree of flatness of the frequency domain spectrum.” 

As explained in Claim 7, the combination of Nilsson and Vos discloses that 

the at least one frequency domain component feature of the feature vector 

comprises at least one of the following: a group of a plurality of energy levels of 

the audio signal, wherein each of the plurality energy levels corresponds to the 

energy of an overlapping band of the audio signal; a value representing a centroid 

of the frequency domain spectrum of the audio signal; and; a value representing the 

degree of flatness of the frequency domain spectrum. See Section IX.6; Ex-1003, 

¶187. 

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X. GROUND 4: NILSSON, ISER, AND KONTIO RENDER OBVIOUS 

CLAIMS 6, 8, 15, AND 17 

1. POSITA Would Have Combined Nilsson with Iser and 

Kontio 

Nilsson, Iser, and Kontio all are directed to a method for bandwidth 

extension to generate bandwidth extended audio signal using a narrow-band audio 

signal in a frequency range of 0-4 kHz. Ex-1003, ¶¶188-206. Each reference 

includes similar module with similar functions, for instance, Nilsson, Iser, and 

Kontio disclose adjusting the high-band (e.g., 4-8 kHz) signal components. Thus, a 

POSITA would have sought to combine Nilsson with Iser and Kontio due to the 

compatible components. Ex-1003, ¶¶188-190. 

Second, both Nilsson and Kontio are directed to extracting feature vectors 

from the audio signal, determining spectral shape parameters from the feature 

vectors, and shaping the high-band signal using the spectral shape parameters. Ex1003, ¶189. 

In addition, Kontio discloses a neural network to provide the spectral shape 

parameters. Kontio also discloses extracting a gradient index. Considering the 

advantages provided by the neural network, POSITA would have adopted the 

neural network of Kontio in Nilsson’s system to produce the spectral shaper 

parameters. Also, considering the benefit provided by the gradient index, POSITA 

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would have extracted a gradient index in Nilsson’s system, as taught by Kontio, as 

discussed in detail below. Ex-1003, ¶190. 

2. [Claim 6] “The method as claimed in claim 1, wherein 

determining at least one spectral shape parameter from the 

feature vector comprises: using a neural network to 

determine the at least one spectral shape from the feature 

vector, wherein the feature vector extracted from the audio 

signal forms an input target vector to the neural network, 

and wherein the neural network is trained to provide a sub 

band spectral shape parameter for the input target vector.” 

As shown below in annotated FIG. 5, Nilsson discloses determining the at 

least one spectral shape parameter ݕො	from the feature vector zNB(r, c), wherein the 

feature vector extracted from the audio signal forms an input target vector to a 

high-band shape estimator 104b. 

Nilsson, FIG. 5 (annotated) 

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In the event that Patent Owner argues that Nilsson does not explicitly 

disclose that the high-band shape estimator 104 is a neural network, Kontio

explicitly discloses it. Ex-1003, ¶¶191-197. 

i. Kontio discloses using a neural network to 

determine the at least one spectral shape from 

the feature vector. 

As shown below in annotated FIG. 1, Kontio discloses using a neural 

network to determine spectral shape parameters. For instance, Kontio discloses that 

“[t]he neural network is the most crucial part of the algorithm, since it predicts 

the shape of the highband envelope, which, in turn, has a major impact on 

perceptual quality of the bandwidth-expanded sound.” Ex-1008, page 874, 2:26-30. 

Because the high-band envelope is across all the high-band bandwidth, therefore, 

contains at least one spectral shape parameters. 

Kontio, FIG. 1 (annotated) 

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Therefore, Kontio discloses using a neural network to determine the at least 

one spectral shape from the feature vector. In addition, using a neural network to 

determine spectral shape from the feature vector is well-known in the art. See Ex1016 (in which Iser-2 also uses a neural network to determine spectral shape 

parameters). Ex-1003, ¶194. 

ii. Kontio discloses that the feature vector 

extracted from the audio signal forms an input 

target vector to the neural network. 

As shown above in annotated FIG. 1, Kontio discloses that the feature vector 

extracted from feature extraction module forms an input target vector to the neural 

network. 

iii. Kontio discloses that the neural network is 

trained to provide a sub band spectral shape 

parameter for the input target vector. 

Kontio discloses that “[t]he input to the neural network is computed solely 

from the narrowband speech signals which are obtained in the training phase by 

processing the corresponding wideband sounds.” Ex-1008, page 876, 2:13-15. 

Also, as shown above in annotated FIG. 1, the output of Kontio’s neural network is 

at least one spectral shape parameter. Therefore, Kontio discloses that the neural 

network is trained to provide a sub band spectral shape parameter for the input 

target vector. 

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Because Kontio shows good results of bandwidth extension by adopting the 

neural network, and also the advantages of using neural network to determine highband spectral shape parameters are well-known in the art (e.g., enhanced 

accuracy), POSITA would have motivated to implement the neural network of 

Kontio in high-band shaper estimator 104 of Nilsson. Ex-1003, ¶197. 

Therefore, the combination of Nilsson, Iser, and Kontio render obvious 

claim 6. Ex-1003, ¶198. 

3. [Claim 8] “The method as claimed in claim 1, wherein the at 

least one time domain component feature of the feature 

vector comprises at least one of the following: a gradient 

index based on the sum of the gradient at points in the 

audio signal which result in a change in direction of the 

waveform of the audio signal; a ratio of the energy of a 

frame of the audio signal to the energy of a previous frame 

of the audio signal; and; and a voice activity detector 

indicating whether a frame of the audio signal is classified 

as active or inactive.” 

Nilsson and Kontio disclose the elements of claim 8. Ex-1003, ¶¶199-204. 

i. Gradient Index 

In the event that the Patent Owner argues that Nilsson does not disclose this 

claim element directly, Kontio explicitly discloses it. 

For instance, Kontio discloses that “To differentiate between voiced and 

unvoiced frames, a gradient index feature is used.” Ex-1008, Page 875, 2:35-38. 

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ii. Energy ratio 

Nilsson discloses the energy ratio. For instance, Nilsson discloses that “[t]he 

energy-ratio estimator 104a, which is included in the wide-band envelope estimator 

104, receives the first component c0 in the vector of linear frequency cepstral 

coefficients c and produces, on basis thereof, plus on basis of the narrow-band 

shape x and the degree of voicing r an estimated energy-ratio ĝ between the highband and the narrow-band.” Ex-1005, 7:15-27. 

In addition, Kontio also discloses the energy ratio. For instance, Kontio

discloses that “frame energy ratio, takes into account temporal changes in the 

frame energy and is defined as the logarithmic ratio of the energies of two 

consecutive frames.” Ex-1008, page 875, 2:6-8. 

iii. Voice activity detector 

Nilsson discloses that the feature extraction unit 101 determines “[t]he 

degree of voicing r, which represents one such essential feature zNB(r, c), [ ] by 

localising a maximum of a normalised autocorrelation function within a lag range 

corresponding to 50-400 Hz.” Ex-1005, 6:25-32. 

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4. [Claim 15] “The apparatus as claimed in claim 10, wherein 

the at least one memory and the computer code configured 

to with the at least one processor to cause the apparatus to 

at least perform determining at least one spectral shape 

parameter from the feature vector is further configured to: 

use a neural network to determine the at least one spectral 

shape from the feature vector, wherein the feature vector 

extracted from the audio signal forms an input target vector 

to the neural network, and wherein the neural network is 

trained to provide a sub band spectral shape parameter for 

the input target vector.” 

As explained in Claim 6, the combination of Nilsson and Kontio discloses 

using a neural network to determine the at least one spectral shape from the feature 

vector, wherein the feature vector extracted from the audio signal forms an input 

target vector to the neural network, and wherein the neural network is trained to 

provide a sub band spectral shape parameter for the input target vector. See Section 

X.2; Ex-1003 ¶205. 

5. [Claim 17] “The apparatus as claimed in claim 10, wherein 

the at least one time domain component feature of the 

feature vector comprises at least one of the following: a 

gradient index based on the sum of the gradient at points in 

the audio signal which result in a change in direction of the 

waveform of the audio signal; a ratio of the energy of a 

frame of the audio signal to the energy of a previous frame 

of the audio signal; and; a voice activity detector indicating 

whether a frame of the audio signal is classified as active or 

inactive.” 

As explained in Claim 8, the combination of Nilsson and Kontio discloses 

that the at least one time domain component feature of the feature vector comprises 

at least one of the following: a gradient index based on the sum of the gradient at 

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points in the audio signal which result in a change in direction of the waveform of 

the audio signal; a ratio of the energy of a frame of the audio signal to the energy 

of a previous frame of the audio signal; and; a voice activity detector indicating 

whether a frame of the audio signal is classified as active or inactive. See Section 

X.3; Ex-1003 ¶206. 

 

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XI. NON-INSTITUTION UNDER 35 U.S.C. §§ 314 OR 325 WOULD BE 

IMPROPER 

Non-institution under 35 U.S.C. §§ 314(a) or 325(d) would be improper. 

The existence of parallel district court proceedings should not prevent institution of 

this Petition. Cf. NHK Spring Co. v. Intri-Plex Techs., Inc., IPR2018-00752, Paper 

8, at 19-20 (PTAB Sept. 12, 2018); see also Litigation. No factor favors denial of 

institution because, at this time, there is no investment beyond the initial 

discoveries at the district court and this IPR petition. Moreover, the strong merits 

of this case favor institution. 

A. Non-Institution Under 35 U.S.C. §314(a) Is Improper 

First, non-institution under 35 U.S.C. §§ 314(a) would be improper. Under 

the factors articulated in Apple Inc. v. Fintiv, Inc., IPR2020-00019, Paper 11 

(PTAB Mar. 20, 2020 (precedential), non-institution in light of the litigations 

would be improper because Factors 2-4 and 6 of the Fintiv factors favor institution, 

and Factors 1 and 5 are neutral. 

Factor 1 (district court stay) is neutral. While ZTE moved to stay on 

December 30, 2020, see Litigation, Dkt. 46, there remains no indication that the 

district court will grant or deny the motion to stay. Int’l Bus. Machines Corp. v. 

Trusted Knight Corp., IPR2020-00323, Paper 15 at 9 (PTAB Jul. 10, 2020). As 

discussed below, the motion to stay is based on ZTE’s pending Motion to Dismiss 

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for Improper venue under § 1400, which unlike venue for convenience under 

§ 1404, is not discretionary. See Litigation, Dkt. 44. 

Factor 2 (proximity to district court trial) favors institution. The pending 

district court case is not scheduled for trial until June 20, 2022, and this date is 

subject to delays. Therefore, the Board will likely issue a final written decision 

before the pending district case. ZTE has moved to dismiss based on improper 

venue, and further moved to stay until venue is set. These motions are currently 

pending, making it “unclear that the court in the related district court litigation will 

adhere to any [future] scheduled jury trial date.” Sand Revolution II, LLC v. Cont’l 

Intermodal Group-Trucking LLC, IPR2019-01393, Paper 24, at 9 (PTAB June 16, 

2020) (informative). Should the district court grant either of the pending improper 

venue motion or the motion to stay, these cases will be assigned new, later trial 

dates likely in a new forum. Additionally, it is further noted that Judge Albright is 

unable to maintain trials based on their originally scheduled dates and is delaying 

the trials. As one example, the VLSI Tech. LLC v. Intel Corp., No. 1:19-cv-00254 

(W.D. Tex.) trial this month was delayed four months from its original date in 

October 2020. 

Factor 3 (investment in district court case) favors institution. The parties’ 

and the court’s investment in this case has been minimal. Fintiv, Paper 11, at 11. 

Although preliminary infringement and invalidity contentions have been served, 

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claim construction has only just begun, and the claim construction hearing is not 

scheduled to occur for another two months in May 2021. In addition, the parties 

have not conducted any substantive fact discovery, as fact discovery does not open 

until after the claim construction hearing. Finally, the district court has not 

addressed the substance of the ’060 Patent—Patent Owner did not move for a 

preliminary injunction, and Defendants did not move to dismiss Patent Owner’s 

action based on the substance of the ’060 Patent, such as a motion to dismiss based 

on § 101. Where, as here, “the district court has not issued orders related to the 

patent at issue in the petition, this fact weighs against exercising discretion to deny 

institution.” Fintiv, Paper 11, at 10. 

Factor 4 (overlapping issues) favors institution. WSOU is a prolific filer of 

patent infringement lawsuits. Based on WSOU’s litigation activity, it is likely that 

WSOU will bring more suits against other parties based on the ‘060 patent. 

Resolving the invalidity questions raised herein would mitigate any concern of 

duplicative efforts in the future. Additionally, ZTE will stipulate that, if this IPR is 

instituted, it will not pursue the specific grounds identified in this Petition 

(Sections VII-X) before the district court. This stipulation mitigates any concern of 

duplicative efforts. Sand Revolution, Paper 24, at 11-12. 

Factor 5 (whether petitioner is also the defendant in district court) is neutral. 

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Factor 6 (other circumstances) favors institution. As explained above, the 

challenged claims are unpatentable over Hwang, Kim, Miklos, and TS25.301—

none of which were considered during prosecution. A determination of its validity 

by the Board here would still save resources in the associated district court, and 

any additional cases WSOU may bring. There is a significant public interest 

against “leaving bad patents enforceable,” Thryv, Inc v. Click-To-Call Techs., LP, 

140 S. Ct. 1367, 1374 (2020). 

B. Non-Institution Under 35 U.S.C. § 325 Is Improper 

Second, non-institution under § 325 would also be improper based on a 

weighing of the factors set forth in Becton, Dickinson & Co. v. B. Braun 

Melsungen AG, IPR2017-01586, Paper 8 (PTAB Dec. 15, 2017). The asserted 

combinations are materially different and not cumulative of the prior art involved 

during the examination of the challenged claims. During prosecution, the following 

references were applied by the examiner, Kornagel (Ex-1017), Iser-3 (Ex-1018). 

Both references are directed to bandwidth extension of audio signal. 

The prior art references presented in this Petition were never listed by the 

Patent Owner nor cited by the examiner. They were never discussed or applied by 

the examiner to reject any claims. The references, for example, Nilsson, Iser, and 

Vos, are materially different from and not cumulative of the references cited in the 

prosecution at least because they disclose elements that were missing from 

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Kornagel and Iser-3. There is thus little to no overlap between the current and 

prior arguments. Becton, Paper 8, 23. But Nilsson, Iser, Vos, Kontio and 

ETSI201.108 teach the limitations of ’060 patent, making denial under § 325(d) 

improper. Sections VII-X. 

XII. MANDATORY NOTICES UNDER 37 C.F.R. § 42.8 

A. Real Party-in-Interest 

The real parties-in-interest are ZTE Corporation, ZTE (USA), Inc., and ZTE 

(TX), Inc. 

B. Related Matters 

Patent Owner has asserted the ’060 patent in litigation against Petitioners in 

the Litigation, filed on June 3, 2020. See also WSOU Investments, LLC v. ZTE 

Corporation et al., 6:20-cv-00238 (WSOU initially asserted the ’060 patent against 

Petitioners on March 27, 2020 and dismissed the case on June 3, 2020). 

C. Lead and Back-Up Counsel and Service Information 

Petitioners provide the following counsel and service information. Pursuant 

to 37 C.F.R. § 42.10(b), Powers of Attorney accompany this Petition. Petitioners 

consent to e-mail service at the e-mail addresses identified in the table below, as 

well as at ZTE-WSOU-IPRs@finnegan.com. 

LEAD COUNSEL (ZTE) BACK-UP COUNSEL (ZTE) 

Lionel Lavenue (Reg. No. 46,859) 

lionel.lavenue@finnegan.com 

Finnegan, Henderson, Farabow, 

Garrett & Dunner, LLP 

Cory Bell (Reg. No. 75,096) 

cory.bell@finnegan.com 

Finnegan, Henderson, Farabow, Garrett & 

Dunner, LLP 

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1875 Explorer Street, Suite 800 

Reston, VA 20190-6023 

Tel: (571) 203-2750 

Fax: (551) 203-2777 

Two Seaport Lane Boston, MA 02210-2001 

Tel: (617) 646-1641 

Fax: (617) 646-1666 

Bradford C. Schulz (Reg. No. 75,006) 

Bradford.schulz@finnegan.com 

Finnegan, Henderson, Farabow, 

Garrett, & Dunner LLP 

1875 Explorer Street, Suite 800 

Reston, VA 20190-6023 

Tel: 571-203-2739 

Fax: 202-408-440 

Mingji Jin (Reg. No. 69,674) 

mingji.jin@finnegan.com 

Finnegan, Henderson, Farabow, Garrett & 

Dunner, LLP 

901 New York Avenue NW, Washington, 

DC 20001-4413 

Tel: (202) 408-6020 

Fax: (202) 408-4400 

R. Maxwell Mauldin (Reg. No. 78,626) 

r.maxwell.mauldin@finnegan.com 

Finnegan, Henderson, Farabow, Garrett & 

Dunner, LLP 

271 17th Street, NW, Suite 1400 

Atlanta, GA 30363-6209 

Tel: (404) 653-6400 

Fax: (404) 653-6444 

XIII. GROUNDS FOR STANDING 

Petitioner certifies the ’060 patent is available for IPR and Petitioner is not 

barred or estopped from requesting IPR challenging the patent claims on the 

grounds identified in this Petition. 

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XIV. CONCLUSION 

Petitioner has established a reasonable likelihood of prevailing with respect 

to each of the challenged claims 1-18 of the ’060 patent. Petitioner therefore 

requests the Board institute inter partes review and cancel each of these claims as 

unpatentable. 

The Office may charge any required fees for this proceeding to Deposit 

Account No. 06-0916. 

Date: March 29, 2021 Respectfully Submitted, 

/Lionel M. Lavenue/ 

Lionel M. Lavenue, Lead Counsel 

Reg. No. 46,859

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CERTIFICATION UNDER 37 C.F.R. § 42.24(d) 

Pursuant to 37 C.F.R. § 42.24(a)(1)(i), the undersigned hereby certifies that 

the foregoing PETITION FOR INTER PARTES REVIEW contains 12,448 words, 

excluding the parts of this Petition that are exempted under 37 C.F.R. § 42.24(a), 

as measured by the word-processing system used to prepare this paper. 

/Lionel M. Lavenue/ 

Lionel M. Lavenue, Lead Counsel 

Reg. No. 46,859

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CERTIFICATE OF SERVICE 

The undersigned certifies that the foregoing Petition for Inter Partes

Review, the associated Power of Attorney, and Exhibits 1001 through 1018 are 

being served on March 29, 2021, by Priority Mail Express or by means at least as 

fast as Priority Mail Express at the following address of record for the subject 

patent.

Guy Gosnell 

Nokia Corporation and Alston & Bird LLP 

c/o Alston & Bird LLP 

One South at The Plaza, 101 South Tryon Street 

Suite 4000 

Charlotte NC 28280-4000 

A courtesy copy has been served on litigation counsel at: 

Ryan S. Loveless 

Etheridge Law Group, PLLC 

2600 E. Southlake Blvd., Suite 120 / 324 

Southlake, TX 76092 

/Daniel E. Doku/ 

Daniel E. Doku 

Litigation Clerk 

FINNEGAN, HENDERSON, FARABOW, 

GARRETT & DUNNER, LLP 

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