Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . See 35 U.S.C. § 100 (note).
Art Rejections
Obviousness
The following is a quotation of 35 U.S.C. § 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1–9 and 14 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Sampo Vesa and Aki Härma, Automatic Estimation of Reverberation Time from Binaural Signals, in Proceedings of the IEEE Int’l Conf. on Acoustics, Speech and Signal Processing (09 May 2005) (“Vesa”); Acoustics Eng'g, Measuring Impulse Responses Using DIRAC (August 2007) (“AE”) and Kazuhiro Nakadai, Sound Source Tracking with Directivity Pattern Estimation Using a 64ch Microphone Array, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems 196 (2005) (“Nakadai”).
Claim 10 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai and US Patent Application Publication 2021/0110841 (published 15 April 2021) (“Weber”).
Claim 11 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai and Tomohiro Nakatani and Masato Miyoshi, in Blind Dereverberation of Single Channel Speech Signal Based on Harmonic Structure, Proceedings of the 2003 IEEE Int’l Conf. on Acoustics, Speech and Signal Processing (21 May 2003) (“Nakatani”).
Claim 12 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai; Thiago de M. Prego et al., A Blind Algorithm for Reverberation-Time Estimation Using Subband Decomposition of Speech Signals, 131 J. Acoustical Soc’y of Am. 2811 (April 2012) (“Prego”) and US Patent Application Publication 2019/0385587 (published 19 December 2019) (“Audfray”).
Claim 13 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai; Nakatani and Audfray.
Claim 14 is rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai and US Patent 11,598,962 (filed 24 December 2020) (“Das”).
Claims 15–20 are rejected under 35 U.S.C. § 103 as being unpatentable over the combination of Vesa; AE; Nakadai and US Patent Application Publication 2014/0169575 (published 19 June 2014) (“Gao”).
Claim 1 is drawn to “a method.” The following table illustrates the correspondence between the claimed method and the Vesa reference.
Claim 1
The Vesa Reference
“1. A method comprising:
The Vesa reference describes a corresponding method to blindly estimate reverberation time (RT) of a room based on a recording of an audio signal propagating in the room.
“receiving an audio signal;
Vesa describes recording binaural signals with a pair of microphones in a room. Vesa at Abs., § 1, ¶ 2.
“determining whether the audio signal meets [[a]] at least one requirement for an audio mapping of an environment,
Vesa performs a two-tier analysis on a received audio signal to detect potential candidates for reverberation time estimation. Id. at § 2. The first analysis is segmentation. Id. at § 2.1. The second analysis is testing of the segments. Id. at § 2.3.
“wherein the at least one requirement comprises
Vesa segments the audio signal by comparing the short-time signal energy to a background noise level. Vesa at § 2.1, ¶ 1. This is a type of minimum SNR constraint since the short-time signal energy must exceed the background noise level by a certain amount (e.g., 10 dB). Id. Vesa tests the segments by analyzing the short-time coherence to identify transients. Id. at § 2.3, ¶ 3. This is a type of impulsive signal constraint since it identifies transients.
Applicant’s Specification describes the use of a VAD process as an example mechanism for making the determinations as to whether a collocation constraint and an omnidirectional constraint are met. Specifically, the Spec. at ¶ 275 states:
“For example, the VAD identifies the user's speech, and in accordance with this identification, the signal is determined to be collocated with the receiving device and to meet the collocation constraint.”
And the Spec. at ¶ 291 states:
“For example, if a user's voice is identified as a signal source (e.g., using VAD or other methods described herein), it is determined that the signal has a known radiation pattern and/or orientation.”
These two passages equate a VAD process with the determination of whether a sound source is within a threshold distance or has a known omnidirectional radiation pattern and/or orientation.
The Vesa reference similarly performs a VAD operation by determining whether a short-time, frame energy level exceeds a background energy level. Vesa at § 2.1, ¶ 1. In other words, Vesa’s VAD process, by using energy thresholding, detects a voice that is close enough to be heard over background noise (e.g., within a threshold distance) and detects voices which, as admitted by Applicant’s Spec. at ¶ 291 have a known omnidirectional radiation pattern and orientation.
“wherein determining that the omnidirectional constraint is met comprises:
“determining a level of the audio signal received at each of a plurality of microphones at different locations in the environment; and
“determining that the omnidirectional constraint is met based on the relative differences between the determined levels being within a threshold value;
Vesa does not describe a corresponding technique for determining if a recorded audio signal is omnidirectional
“in accordance with a determination that the at least one requirement is met, performing said audio mapping, wherein performing the audio mapping comprises analyzing the audio signal to determine one or more audio properties of the environment; and
“in accordance with a determination that the at least one requirement is not met, forgoing performing said audio mapping.”
Likewise, based on the segmentation and testing analyses, Vesa determines the reverberation time for suitable segments. Id. at §§ 2.3, ¶ 1, §§ 2.4, 2.5, FIG.1 (depicting an RT estimate of a segment).
Table 1
The table above shows that the Vesa reference describes a method that corresponds closely to the claimed method. The Vesa reference does not anticipate the claimed method, however, because the Vesa reference does not describe determining whether a signal is omnidirectional in the same manner as claimed. Vesa also does not positively identify omnidirectionality as a critical component to measuring a room impulse response.
The differences between the claimed invention and the prior art are such that the invention as a whole would have been obvious to one of ordinary skill in the art at the time this Application was effectively filed. Vesa describes a method for estimating a room’s impulse response, particularly its reverberation time, based on a one-shot recording of a signal recorded as a binaural signal by microphones located on a headset worn near a user’s ears. Vesa further describes qualifying binaural signals prior to RT estimation to ensure they are suitable candidates for RT estimation. Vesa does not describe the claimed concept of qualifying omnidirectional signals and does not describe the claimed technique of determining whether signals are omnidirectional.
The AE reference addresses various concepts concerning the measurement of a room’s impulse response. AE at § 1. AE specifically teaches the use of several types of excitation signals. Id. at § 2. In order to estimate an RT, AE refers to ISO 3382, which prescribes the use of an omnidirectional sound source. Id. at § 3.1. This teaching reasonably suggests the use of an omnidirectional sound source and thus suggests modifying Vesa’s method to qualify a binaural test signal as omnidirectional prior to estimating RT.
The Nakadai reference describes a technique for making an azimuthal determination of a signal’s directivity pattern (e.g., determining how the signal propagates along a plane). Nakadai at Abs., § I. In particular, Nakadai teaches providing a microphone array with N (e.g., 64) microphones, or channels. Id. at § II(A), FIG.1. A beamformer processes the output of the array to produce R orientation angles. Id. Nakadai then calculates the intensity of each of the R beamformer outputs and integrates the intensity values to produce a total intensity
I
S
. Id. at § II(B). The directivity pattern
D
P
is then estimated as a set of ratios between the intensity
I
of each of the R beamformer outputs and the integrated intensity
I
S
. Id. In other words, Nakadai calculates a directivity pattern based on the intensity, or level, of an audio signal at each microphone and by determining the relative difference between each microphone and the sum of all microphones. An example directivity pattern for a human voice is depicted in Nakadai at FIG.6(c).
Nakadai does not teach a technique for analyzing the resulting directivity pattern to determine if it is omnidirectional. However, the term “omnidirectional” requires, by definition, a sound that radiates with the same intensity in every direction. This suggests evaluating
D
P
to ensure that each output is substantially the same by applying a thresholding operation to the
D
P
values. See id. at II(A) (describing the use of thresholds to ensure values meet a criteria); Vesa at § 2.3 (describing the use of thresholds to qualify whether a sound is suitable for RT measurement).
Thus, when read in light of Vesa and AE, Nakadai’s teachings reasonably suggest modifying Vesa to determine whether a binaural signal is omnidirectional by providing a microphone array, a beamformer that produces several look directions and a processor to calculate the intensity and relative differences between each look direction and all other look directions. The predicted pattern will then be evaluated to determine if it corresponds to an omnidirectional source, by determining if the
D
P
value for each look-direction R is within a threshold distance of each other (i.e., do the
D
P
values indicate that intensity is evenly radiated). For the foregoing reasons, the combination of the Vesa, the AE and the Nakadai references makes obvious all limitations of the claim.
Claim 2 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises a minimum signal-to-noise (SNR) constraint, and wherein the determining whether the minimum SNR constraint is met comprises determining whether a signal level exceeds a threshold value.”
Vesa segments the audio signal by comparing the short-time signal energy to a background noise level. Vesa at § 2.1, ¶ 1. This is a type of minimum SNR constraint since the short-time signal energy must exceed the background noise level by a certain amount (e.g., 10 dB). Id. For the foregoing reasons, the combination of the Vesa, the AE and the Nakadai references makes obvious all limitations of the claim.
Claim 3 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises a signal duration constraint, and wherein the determining whether the signal duration constraint is met comprises determining whether a signal level exceeds a threshold value for at least a threshold duration of time.”
In determining whether an audio signal exceeds the background noise level, Vesa calculates the energy of the audio signal over a short time to produce a frame-level energy. Vesa at § 2.1, ¶ 1. Thus, the comparison between the audio signal energy and the background noise energy requires that the energy of the audio signal over a frame (i.e., a period of time) exceeds the energy of the background noise energy. Id. For the foregoing reasons, the combination of the Vesa, the AE and the Nakadai references makes obvious all limitations of the claim.
Claim 4 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises a collocation constraint, and wherein the determining whether the collocation constraint is met comprises: determining whether a source of the signal is within a threshold distance of a location of the receipt of the signal.”
Claim 5 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises a collocation constraint, and wherein the determining whether the collocation constraint is met comprises applying a voice activated detection (VAD) process based on the signal.”
Claim 6 depends on claim 1, and further requires the following:
“wherein the determining whether the omnidirectional constraint is met further comprises determining whether a source of the signal comprises an omnidirectional source.”
Claim 7 depends on claim 1, and further requires the following:
“wherein the determining whether the omnidirectional constraint is met further comprises determining one or more of a radiation pattern for a source of the signal and an orientation for the source of the signal.”
Claim 8 depends on claim 1, and further requires the following:
“wherein the determining whether the omnidirectional constraint is met further comprises applying a VAD process based on the signal.”
Claims 4–8 are treated together. These claims define how to evaluate a collocation constraint (claims 4 and 5) and an omnidirectional constraint (claims 6–8). The claims differ in the scope of detail. Notably, claims 5 and 8 describe the use of a VAD process in evaluating the constraints.
Applicant’s Specification describes the use of a VAD process as an example mechanism for making the determinations as to whether a collocation constraint and an omnidirectional constraint are met. Specifically, the Spec. at ¶ 275 states:
“For example, the VAD identifies the user's speech, and in accordance with this identification, the signal is determined to be collocated with the receiving device and to meet the collocation constraint.”
And the Spec. at ¶ 291 states:
“For example, if a user's voice is identified as a signal source (e.g., using VAD or other methods described herein), it is determined that the signal has a known radiation pattern and/or orientation.”
These two passages equate a VAD process with the determination of whether a sound source is within a threshold distance or has a known radiation pattern and/or orientation. Claim 5 is accordingly construed as a narrower version of claim 4 and claim 8 is construed as a narrower version of claims 6 and 7.
The Vesa reference similarly performs a VAD operation by determining whether a short-time, frame energy level exceeds a background energy level. Vesa at § 2.1, ¶ 1. In other words, Vesa’s VAD process, by using energy thresholding, detects a voice that is close enough to be heard over background noise (e.g., within a threshold distance) and detects voices which, as admitted by Applicant’s Spec. at ¶ 291 have a known radiation pattern and orientation. For the foregoing reasons, the combination of the Vesa, the AE and the Nakadai references makes obvious all limitations of the claims.
Claim 9 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises an impulse constraint, and wherein the determining the impulse constraint is met comprises determining whether the signal comprises one or more of an instantaneous signal, an impulse signal, and a transient signal.”
Vesa tests the segments by analyzing the short-time coherence to identify transients. Vesa at § 2.3, ¶ 3. This is a type of impulsive signal constraint since it identifies transients. For the foregoing reasons, the combination of the Vesa, the AE and the Nakadai references makes obvious all limitations of the claim.
Claim 10 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises an impulse constraint, and wherein the determining whether the impulse constraint is met comprises applying a dual envelope follower based on the signal.”
Vesa tests the segments by analyzing the short-time coherence to identify transients. Vesa at § 2.3, ¶ 3. This is a type of impulsive signal constraint since it identifies transients. However, Vesa does not employ a dual envelope follower—namely, Vesa does not compare the output of two envelope followers with differing time scales, but analyzes short-time coherence. Id.
The Weber reference teaches an alternative audio transient detection scheme that uses the claimed dual envelope follower approach that compares the output of a fast envelope follower and a slow envelope follower. Weber at ¶ 138. If the difference between the two followers exceeds a threshold, a transient is detected. Id. This teaching would have reasonably suggested modifying Vesa’s method to use the alternative Weber transient detection scheme. One of ordinary skill would have then reasonably modified Vesa’s method to analyze audio segments in two envelope followers as claimed. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Weber references makes obvious all limitations of the claim.
Claim 11 depends on claim 1, and further requires the following:
“wherein the at least one requirement further comprises an impulse constraint, and wherein the method further comprises:
“determining the impulse constraint is not met; and
“in accordance with the determination that the impulse constraint is not met:
“converting the signal into a clean input stream; and
“comparing the clean input stream with the signal.”
The Vesa reference describes determining a reverberation time in segments of a speech signal that include a clear impulse. Vesa at § 2.3, ¶ 3. The Nakatani reference further teaches a reverberation estimation method that, in contrast with Vesa, does not rely on segmentation and testing of segmented sound to detect impulsive segments: Nakatani’s technique estimates a reverberation transfer function by dividing (i.e., comparing by forming a ratio between) a reverberant signal
Y
by an estimate of a direct signal
X
^
'
, or clean signal. Nakatani at § 1, ¶ 3, § 2.1, ¶¶ 1, 2 (“only reverberant signals and general attributes of speech are used”). This would have reasonably suggested, in cases where a speech signal does not include a suitable impulsive section (and where Vesa’s estimation technique would be unsuitable), determining a reverberation transfer function through a conversion of a reverberant signal into an estimate of a direct signal, or clean signal, and a comparison of the direct signal to the reverberant signal. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Nakatani references makes obvious all limitations of the claim.
Claim 12 depends on claim 1, and further requires the following:
“wherein the one or more audio properties of the environment comprise a decay time and a reverberation gain, and
“wherein determining the one or more audio properties comprises:
“
“filtering the audio signal, wherein filtering the signal comprises separating the signal into a plurality of sub-bands; and
“for a sub-band of the sub-bands:
“identifying a peak of the audio signal;
“identifying a decay of the audio signal;
“based on the peak, the decay, or both the peak and the decay:
“determining [[a]] the decay time; and
“determining [[a]] the reverberation gain.”
The Vesa reference describes determining a decay time, or reverberation time, based on least squares fit of a Schroeder integration in order to characterize a room’s impulse response. Vesa at §§ 2.4, 2.5. Vesa, however, does not describe estimating a reverberation gain nor the sub-band process claimed.
The Prego reference further teaches and suggests an alternative technique for measuring RT. In particular, the Prego reference describes a method for blindly estimating reverberation time (RT) and a direct-to-reverberant energy ratio through subband speech decomposition. Prego at Abs., § I. Prego describes receiving an audio signal
s
r
(
n
)
. Id. at § II(A), ¶ 1. Prego splits the audio signal into a plurality of subbands. Id. at § II(A), ¶ 1. Each subband is analyzed for free decay regions (FDRs). Id. at § II(B), ¶ 1. FDRs include a consistent energy drop in consecutive signal frames. Id. Evaluating FDRs will inherently identify peaks and decays. In particular, if a second frame includes a peak, there will be no FDR because the peak will have exceeded the energy of the previous frame. See id. However, each frame after a peak will exhibit a decrease in frame energy. See id. If the decrease lasts at least 500 ms, an FDR will be detected. Id. at § II(B), ¶ 2–3, FIGs.1(a), 1(b) (depicting FDR patterns with peaks followed by a steady decay over at least 500 ms). Prego evaluates the FDRs to estimate reverberation time RT, or decay time. Id. at §§ II(C), II(D) (describing subband RT estimation and statistical analysis of all RT estimates for a subband to derive a unitary RT for each subband). Read in light of Vesa, Prego suggests an alternative technique for modifying Vesa’s method for estimating RT.
Neither Vesa nor Prego describe, teach or suggest determining a reverberation gain. The Audfray reference describes a reverberator that includes an adjustable signal amplitude gain (e.g., RIP correction factor) in cascade with a reverberator. Audfray at ¶ 50–51, 61, FIGs.5A. Audfray teaches and suggests that this configuration allows a designer to tune decay time and reverberation loudness/gain independently. Id. This would have reasonably suggested modifying Prego’s method to further include a step of determining a reverberation gain in addition to reverberation time so that the proper amount of reverb may be maintained when a designer adjusts reverberation time. For example, one of ordinary skill would have derived an RIP correction factor from the reverb time estimated by Prego. See id. at ¶¶ 50, 67, FIG.7 (describing a RIP correction factor as a function of RMS power decay extrapolated to time zero). For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai, the Prego and the Audfray references makes obvious all limitations of the claim.
Claim 13 depends on claim 1, and further requires the following:
“wherein the one or more audio properties of the environment comprise a decay time and a reverberation gain, and wherein determining the one or more audio properties comprises
“
“generating a direct path signal;
“deconvolving the audio signal based on the direct path signal;
“based on said deconvolving:
“determining [[a]] the decay time; and
“determining [[a]] the reverberation gain.”
The Vesa reference describes determining a decay time, or reverberation time, based on least squares fit of a Schroeder integration in order to characterize a room’s impulse response. Vesa at §§ 2.4, 2.5. Vesa, however, does not describe estimating a reverberation gain nor the deconvolution process claimed.
The Nakatani reference further teaches and suggests an alternative technique for measuring an RIR. In particular, the Nakatani reference describes a method for estimating reverb through deconvolution of a sound source signal. Nakatani at Abs., § 2.1, ¶¶ 1–2 (describing the frequency-domain division of Y and
X
^
’
.) Nakatani describes receiving a reverberant sound source signal
Y
(
w
)
,
where
w
is a frequency index. Id. Nakatani performs a harmonic analysis to estimate a direct path signal
X
^
'
from the source signal. Id. Nakatani then divides observed signal
Y
w
by
X
^
'
to create a deconvolved version of
H
^
'
, which is an estimate of the reverberant transfer function. Id. Nakatani does not describe determining a decay time and reverberation gain from the resulting transfer function.
The Audfray reference describes a reverberator that includes an adjustable signal amplitude gain (e.g., RIP correction factor) in cascade with a reverberator. Audfray at ¶ 50–51, 61, FIGs.5A. Audfray teaches and suggests that this configuration allows a designer to tune decay time and reverberation loudness/gain independently. Id. This would have reasonably suggested modifying Nakatani’s method to further include a step of determining a decay time and reverberation gain from a reverberation transfer function (e.g., using known analysis techniques as admitted by Applicant in the Specification at ¶ 344) so that the proper amount of reverb may be maintained when a designer adjusts reverberation time. For example, one of ordinary skill would have derived an RIP correction factor from the reverb time estimated by Prego. See id. at ¶¶ 50, 67, FIG.7 (describing a RIP correction factor as a function of RMS power decay extrapolated to time zero). For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai, the Nakatani and the Audfray references makes obvious all limitations of the claim.
Claim 14 depends on claim 1, and further requires the following:
“wherein performing the audio mapping further comprises
“associating one or more portions of [[an]] the audio mapping of [[an]] the environment to a plurality of voxels located in the environment,
“wherein[[:]] each portion comprises an audio response property associated with a location of a respective voxel in the environment.”
The Vesa reference describes determining a decay time, or reverberation time, based on least squares fit of a Schroeder integration in order to characterize a room’s impulse response. Vesa at §§ 2.4, 2.5. Vesa, however, does not describe associating portions of an audio mapping to a plurality of voxels located in the environment as claimed.
The Das reference teaches and suggests a method that divides a room 300 into a set of locations for sources and a set of locations for a receiver. Das at Abs., col. 2 l. 48 to col. 3 l. 9. Das associates each discrete location (i.e., a plurality of voxels located in the environment) with a set of acoustic model parameters (i.e., one or more portions of an audio mapping of an environment), including reverberation parameters, like those describe by Vesa. Id. col. 2 l. 48 to col. 3 l. 9, col. 19 l. 39 to col. 20 l. 10, FIG.5B. Similarly, Das’s acoustic model parameters 545 include reverb settings, like time and level, (i.e., audio response properties) associated with a location of a source/receiver (i.e., respective voxel in the environment) in a room 300. Id. Read in light of Vesa, Das reasonably teaches and suggests associating Vesa’s RT values for each voxel, just as claimed, in order to provide a set of reverberation parameters for each location in an environment. One of ordinary skill would have reasonably predicted that doing so would facilitate the storage and retrieval of reverberation parameters for use in audio reproduction. See Das at col. 1 l. 14 to col. 2 l. 3. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Das references makes obvious all limitations of the claim.
Claim 15 is drawn to “a system.” The following table illustrates the correspondence between the claimed system and the Vesa reference.
Claim 15
The Vesa Reference
“15. A system comprising:
The Vesa reference describes a corresponding system and method to blindly estimate reverberation time (RT) of a room based on a recording of an audio signal propagating in the room.
“one or more processors configured to perform a method comprising:
Vesa does not describe the use of a processor.
“receiving an audio signal;
Vesa describes recording binaural signals with a pair of microphones in a room. Vesa at Abs., § 1, ¶ 2.
“determining whether the audio signal meets [[a]] at least one requirement for an audio mapping of an environment,
Vesa performs a two-tier analysis on a received audio signal to detect potential candidates for reverberation time estimation. Id. at § 2. The first analysis is segmentation. Id. at § 2.1. The second analysis is testing of the segments. Id. at § 2.3.
“wherein the at least one requirement comprises
Vesa segments the audio signal by comparing the short-time signal energy to a background noise level. Vesa at § 2.1, ¶ 1. This is a type of minimum SNR constraint since the short-time signal energy must exceed the background noise level by a certain amount (e.g., 10 dB). Id. Vesa tests the segments by analyzing the short-time coherence to identify transients. Id. at § 2.3, ¶ 3. This is a type of impulsive signal constraint since it identifies transients.
“wherein determining that the omnidirectional constraint is met comprises:
“determining a level of the audio signal received at each of a plurality of microphones at different locations in the environment; and
“determining that the omnidirectional constraint is met based on the relative differences between the determined levels being within a threshold value;
Vesa does not describe a corresponding technique for determining if a recorded audio signal is omnidirectional
“in accordance with a determination that the at least one requirement is met, performing said audio mapping, wherein performing the audio mapping comprises analyzing the audio signal to determine one or more audio properties of the environment; and
“in accordance with a determination that the at least one requirement is not met, forgoing performing said audio mapping.”
Likewise, based on the segmentation and testing analyses, Vesa determines the reverberation time for suitable segments. Id. at §§ 2.3, ¶ 1, §§ 2.4, 2.5, FIG.1 (depicting an RT estimate of a segment).
Table 2
The table above shows that the Vesa reference describes a system and method that corresponds closely to the claimed invention. The Vesa reference does not anticipate the claimed invention, however, because the Vesa reference does not describe determining whether a signal is omnidirectional in the same manner as claimed. Vesa also does not positively identify omnidirectionality as a critical component to measuring a room impulse response.
The differences between the claimed invention and the prior art are such that the invention as a whole would have been obvious to one of ordinary skill in the art at the time this Application was effectively filed. Vesa describes a method for estimating a room’s impulse response, particularly its reverberation time, based on a one-shot recording of a signal recorded as a binaural signal by microphones located on a headset worn near a user’s ears. Vesa further describes qualifying binaural signals prior to RT estimation to ensure they are suitable candidates for RT estimation. Vesa does not describe the claimed concept of qualifying omnidirectional signals and does not describe the claimed technique of determining whether signals are omnidirectional.
The AE reference addresses various concepts concerning the measurement of a room’s impulse response. AE at § 1. AE specifically teaches the use of several types of excitation signals. Id. at § 2. In order to estimate an RT, AE refers to ISO 3382, which prescribes the use of an omnidirectional sound source. Id. at § 3.1. This teaching reasonably suggests the use of an omnidirectional sound source and thus suggests modifying Vesa’s method to qualify a binaural test signal as omnidirectional prior to estimating RT.
The Nakadai reference describes a technique for making an azimuthal determination of a signal’s directivity pattern (e.g., determining how the signal propagates along a plane). Nakadai at Abs., § I. In particular, Nakadai teaches providing a microphone array with N (e.g., 64) microphones, or channels. Id. at § II(A), FIG.1. A beamformer processes the output of the array to produce R orientation angles. Id. Nakadai then calculates the intensity of each of the R beamformer outputs and integrates the intensity values to produce a total intensity
I
S
. Id. at § II(B). The directivity pattern
D
P
is then estimated as a set of ratios between the intensity
I
of each of the R beamformer outputs and the integrated intensity
I
S
. Id. In other words, Nakadai calculates a directivity pattern based on the intensity, or level, of an audio signal at each microphone and by determining the relative difference between each microphone and the sum of all microphones. An example directivity pattern for a human voice is depicted in Nakadai at FIG.6(c).
Nakadai does not teach a technique for analyzing the resulting directivity pattern to determine if it is omnidirectional. However, the term “omnidirectional” requires, by definition, a sound that radiates with the same intensity in every direction. This suggests evaluating
D
P
to ensure that each output is substantially the same by applying a thresholding operation to the
D
P
values. See id. at II(A) (describing the use of thresholds to ensure values meet a criteria); Vesa at § 2.3 (describing the use of thresholds to qualify whether a sound is suitable for RT measurement).
Thus, when read in light of Vesa and AE, Nakadai’s teachings reasonably suggest modifying Vesa to determine whether a binaural signal is omnidirectional by providing a microphone array, a beamformer that produces several look directions and a processor to calculate the intensity and relative differences between each look direction and all other look directions. The predicted pattern will then be evaluated to determine if it corresponds to an omnidirectional source, by determining if the
D
P
value for each look-direction R is within a threshold distance of each other (i.e., do the
D
P
values indicate that intensity is evenly radiated).
Further, as shown in the table above, Vesa does not describe the means for carrying out the described algorithm. The Gao reference teaches and suggests implementing a reverberation decay algorithm with a programmed digital signal processor that executes software. Gao at ¶¶ 38, 50, FIG.5. One of ordinary skill would have recognized that the term software plainly refers to instructions stored in a memory that is read by the processor that executes the software. Accordingly, Gao would have reasonably suggested implementing Vesa’s reverberation estimating algorithm with a similar device that includes a digital signal processor and a software-containing memory. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Gao references makes obvious all limitations of the claim.
Claim 16 is drawn to “a non-transitory computer-readable medium.” The following table illustrates the correspondence between the claimed medium and the Vesa reference.
Claim 16
The Vesa Reference
“16. A non-transitory computer-readable medium storing one or more instructions, which, when executed by one or more processors of an electronic device, cause the device to perform a method comprising:
The Vesa reference describes a corresponding system and method to blindly estimate reverberation time (RT) of a room based on a recording of an audio signal propagating in the room.
Vesa does not describe the use of a processor and a non-transitory computer-readable medium storing one or more instructions that are executed by the processors.
“receiving an audio signal;
Vesa describes recording binaural signals with a pair of microphones in a room. Vesa at Abs., § 1, ¶ 2.
“determining whether the audio signal meets [[a]] at least one requirement for an audio mapping of an environment,
Vesa performs a two-tier analysis on a received audio signal to detect potential candidates for reverberation time estimation. Id. at § 2. The first analysis is segmentation. Id. at § 2.1. The second analysis is testing of the segments. Id. at § 2.3.
“wherein the at least one requirement comprises
Vesa segments the audio signal by comparing the short-time signal energy to a background noise level. Vesa at § 2.1, ¶ 1. This is a type of minimum SNR constraint since the short-time signal energy must exceed the background noise level by a certain amount (e.g., 10 dB). Id. Vesa tests the segments by analyzing the short-time coherence to identify transients. Id. at § 2.3, ¶ 3. This is a type of impulsive signal constraint since it identifies transients.
“wherein determining that the omnidirectional constraint is met comprises:
“determining a level of the audio signal received at each of a plurality of microphones at different locations in the environment; and
“determining that the omnidirectional constraint is met based on the relative differences between the determined levels being within a threshold value;
Vesa does not describe a corresponding technique for determining if a recorded audio signal is omnidirectional
“in accordance with a determination that the at least one requirement is met, performing said audio mapping, wherein performing the audio mapping comprises analyzing the audio signal to determine one or more audio properties of the environment; and
“in accordance with a determination that the at least one requirement is not met, forgoing performing said audio mapping.”
Likewise, based on the segmentation and testing analyses, Vesa determines the reverberation time for suitable segments. Id. at §§ 2.3, ¶ 1, §§ 2.4, 2.5, FIG.1 (depicting an RT estimate of a segment).
Table 3
The table above shows that the Vesa reference describes a system and method that corresponds closely to the claimed invention. The Vesa reference does not anticipate the claimed invention, however, because the Vesa reference does not describe determining whether a signal is omnidirectional in the same manner as claimed. Vesa also does not positively identify omnidirectionality as a critical component to measuring a room impulse response.
The differences between the claimed invention and the prior art are such that the invention as a whole would have been obvious to one of ordinary skill in the art at the time this Application was effectively filed. Vesa describes a method for estimating a room’s impulse response, particularly its reverberation time, based on a one-shot recording of a signal recorded as a binaural signal by microphones located on a headset worn near a user’s ears. Vesa further describes qualifying binaural signals prior to RT estimation to ensure they are suitable candidates for RT estimation. Vesa does not describe the claimed concept of qualifying omnidirectional signals and does not describe the claimed technique of determining whether signals are omnidirectional.
The AE reference addresses various concepts concerning the measurement of a room’s impulse response. AE at § 1. AE specifically teaches the use of several types of excitation signals. Id. at § 2. In order to estimate an RT, AE refers to ISO 3382, which prescribes the use of an omnidirectional sound source. Id. at § 3.1. This teaching reasonably suggests the use of an omnidirectional sound source and thus suggests modifying Vesa’s method to qualify a binaural test signal as omnidirectional prior to estimating RT.
The Nakadai reference describes a technique for making an azimuthal determination of a signal’s directivity pattern (e.g., determining how the signal propagates along a plane). Nakadai at Abs., § I. In particular, Nakadai teaches providing a microphone array with N (e.g., 64) microphones, or channels. Id. at § II(A), FIG.1. A beamformer processes the output of the array to produce R orientation angles. Id. Nakadai then calculates the intensity of each of the R beamformer outputs and integrates the intensity values to produce a total intensity
I
S
. Id. at § II(B). The directivity pattern
D
P
is then estimated as a set of ratios between the intensity
I
of each of the R beamformer outputs and the integrated intensity
I
S
. Id. In other words, Nakadai calculates a directivity pattern based on the intensity, or level, of an audio signal at each microphone and by determining the relative difference between each microphone and the sum of all microphones. An example directivity pattern for a human voice is depicted in Nakadai at FIG.6(c).
Nakadai does not teach a technique for analyzing the resulting directivity pattern to determine if it is omnidirectional. However, the term “omnidirectional” requires, by definition, a sound that radiates with the same intensity in every direction. This suggests evaluating
D
P
to ensure that each output is substantially the same by applying a thresholding operation to the
D
P
values. See id. at II(A) (describing the use of thresholds to ensure values meet a criteria); Vesa at § 2.3 (describing the use of thresholds to qualify whether a sound is suitable for RT measurement).
Thus, when read in light of Vesa and AE, Nakadai’s teachings reasonably suggest modifying Vesa to determine whether a binaural signal is omnidirectional by providing a microphone array, a beamformer that produces several look directions and a processor to calculate the intensity and relative differences between each look direction and all other look directions. The predicted pattern will then be evaluated to determine if it corresponds to an omnidirectional source, by determining if the
D
P
value for each look-direction R is within a threshold distance of each other (i.e., do the
D
P
values indicate that intensity is evenly radiated).
Further, as shown in the table above, Vesa does not describe the means for carrying out the described algorithm. The Gao reference teaches and suggests implementing a reverberation decay algorithm with a programmed digital signal processor that executes software. Gao at ¶¶ 38, 50, FIG.5. One of ordinary skill would have recognized that the term software plainly refers to instructions stored in a memory that is read by the processor that executes the software. Accordingly, Gao would have reasonably suggested implementing Vesa’s reverberation estimating algorithm with a similar device that includes a digital signal processor and a software-containing memory. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Gao references makes obvious all limitations of the claim.
Claim 17 depends on claim 15, and further requires the following:
“wherein the at least one requirement further comprises a minimum signal-to-noise (SNR) constraint, and wherein the determining whether the minimum SNR constraint is met comprises determining whether a signal level exceeds a threshold value.”
Vesa segments the audio signal by comparing the short-time signal energy to a background noise level. Vesa at § 2.1, ¶ 1. This is a type of minimum SNR constraint since the short-time signal energy must exceed the background noise level by a certain amount (e.g., 10 dB). Id. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Gao references makes obvious all limitations of the claim.
Claim 18 depends on claim 15, and further requires the following:
“wherein the at least one requirement further comprises a signal duration constraint, and wherein the determining whether the signal duration constraint is met comprises determining whether a signal level exceeds a threshold value for at least a threshold duration of time.”
In determining whether an audio signal exceeds the background noise level, Vesa calculates the energy of the audio signal over a short time to produce a frame-level energy. Vesa at § 2.1, ¶ 1. Thus, the comparison between the audio signal energy and the background noise energy requires that the energy of the audio signal over a frame (i.e., a period of time) exceeds the energy of the background noise energy. Id. For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Gao references makes obvious all limitations of the claim.
Claim 19 depends on claim 15, and further requires the following:
“wherein the at least one requirement further comprises a collocation constraint, and wherein the determining whether the collocation constraint is met comprises: determining whether a source of the signal is within a threshold distance of a location of the receipt of the signal.”
Claim 20 depends on claim 15, and further requires the following:
“wherein the at least one requirement further comprises a collocation constraint, and wherein the determining whether the collocation constraint is met comprises applying a voice activated detection (VAD) process based on the signal.”
Claims 19 and 20 are treated together. These claims define how to evaluate a collocation constraint. The claims differ in the scope of detail. Notably, claim 20 describes the use of a VAD process in evaluating the constraint.
Applicant’s Specification describes the use of a VAD process as an example mechanism for making the determinations as to whether a collocation constraint and an omnidirectional constraint are met. Specifically, the Spec. at ¶ 275 states:
“For example, the VAD identifies the user's speech, and in accordance with this identification, the signal is determined to be collocated with the receiving device and to meet the collocation constraint.”
This passage equates a VAD process with the determination of whether a sound source is within a threshold distance. Claim 20 is accordingly construed as a narrower version of claim 19.
The Vesa reference similarly performs a VAD operation by determining whether a short-time, frame energy level exceeds a background energy level. Vesa at § 2.1, ¶ 1. In other words, Vesa’s VAD process, by using energy thresholding, detects a voice that is close enough to be heard over background noise (e.g., within a threshold distance). For the foregoing reasons, the combination of the Vesa, the AE, the Nakadai and the Gao references makes obvious all limitations of the claim.
Summary
Claims 1–20 are rejected under at least one of 35 U.S.C. §§ 102 and 103 as being unpatentable over the cited prior art. In the event the determination of the status of the application as subject to AIA 35 U.S.C. §§ 102 and 103 (or as subject to pre-AIA 35 U.S.C. §§ 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Response to Applicant’s Arguments
Applicant’s Reply (20 April 2026) has substantively amended all the claims. This Office action has been updated accordingly.
Applicant’s Reply at includes comments pertaining to the rejections presented in the Non-Final Rejection (08 January 2026). Those comments have been considered, but are moot in light of the new grounds of rejection presented herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 C.F.R. § 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 C.F.R. § 1.17(a)) pursuant to 37 C.F.R. § 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Walter F Briney III/
/CAROLYN R EDWARDS/Supervisory Patent Examiner, Art Unit 2692
/Walter F Briney III/Primary ExaminerArt Unit 2692
6/12/2026
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