DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/25/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claim 6, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim Objections
Claim1 is objected to because of the following informalities: Claim 1 lists step numbers from the specification in the claims. The examiner suggests that the step numbers be removed. Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claim recites “…Receiving, by a processor, an input signal comprising noise; Determining, by the processor, a level-dependent minimum gain based on a level- dependent maximum noise suppression function and a level of the input signal; and Suppressing, by the processor, the noise of the input signal, wherein the noise is suppressed based on the level-dependent minimum gain, wherein the level-dependent maximum noise suppression function provides lower level-dependent minimum gain for higher levels of the input signal and wherein the level of the input signal comprises an amplitude or a power of the input signal”.
The limitations of claim 1 of “receiving…”, “determining…”, and “suppressing..” as drafted covers mental activity. More specifically, for claim 1, a human, after already receiving data related to an audio signal with noise and it’s corresponding plot in time and frequency and determining how much the audio signal needs to be suppressed. The human knows how much additive noise they can tolerate and suppressing the audio signal if needed.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites the additional element of a “processor” which is recited generally in the specification. For example, in paragraph [0028] of the as filed specification, there is a description of using a general purpose operating system. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea int a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception of using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
With respect to claim 2, the claim relates to range of the audio signal. The claim relates to a mental activity of determining a gain to be applied based on the noise determined. No additional limitations are present.
With respect to claim 3, the claim relates to determining, using a mathematical algorithm, how much the audio signal should be reduced or silenced. The claim relates to a human solving a mathematical formula. No additional limitations are present.
With regards to claim 4, the claim relates to determining how the noise is being suppressed. The claim relates to a mental activity of determining how much noise is to be suppressed and how to suppress it. No additional limitations are present.
With regards to claim 5, the claim relates to determining how the noise is being suppressed . The claim relates to a mental activity of determining how much noise is to be suppressed and how to suppress it. No additional limitations are present.
With regards to claim 6, the claim relates to determining how the noise is being suppressed. The claim relates to a mental activity of determining how much noise is to be suppressed and how to suppress it. No additional limitations are present.
With regards to claim 7, the claim relates to determining the threshold of the audio signal. The claim relates to a mental activity of determining if the audio signal has reached the limit that has been set. No additional limitations are present.
With regards to claim 8, the claim relates to determining the frequency spectrum of the audio signal. The claim relates to a mental activity of creating bins for the spectrum that is known and determining gains on that spectrum per band or bin basis. No additional limitations are present.
With respect to claims 9, 10, and 18, the claims relate using a mathematical algorithm to determining how much to reduce/silence the audio signal. The claims relate to a human solving a mathematical formula. No additional limitations are present.
With respect to claim 11, the claim relates to determining, using a mathematical algorithm, the frequency spectrum of the audio signal. The claim relates to a human solving a mathematical formula. No additional limitations are present.
With respect to claim 12, the claim relates to determining, using a mathematical algorithm, how much the audio signal should be reduced or silenced. The claim relates to a human solving a mathematical formula. No additional limitations are present.
With respect to claim 13, the claim relates to training a neural network to perform the noise reduction/silencing. For example, paragraph [0028] of the as filed specification, there is a description of using a processor (computing system) to train a neural network for noise suppression. As discussed above with respect to the integration of the abstract idea int a practical application, the additional element of using a computer to train a neural network general computer is noted. Mere instructions to apply an exception of using a generic computer component cannot provide an inventive concept. No additional limitations are present.
With respect to claim 14, the claim relates to increasing noise suppression as the audio level increases. The claim relates to a mental activity of reducing/silencing the audio the louder the audio gets. No additional limitations are present.
With respect to claim 15, the claim relates to determining if the audio signal contains a silent portion, a portion that contains additive noise, a portion that is a loud burst of noise, and a portion that contains speech and determine the threshold of the audio signal. The claim relates to a mental activity of a human listener knowing how much additive noise they can handle. No additional limitations are present.
With respect to claims 16 and 17, the claims relate to determine how much to reduce/silence the audio signal based on how much additive noise is present at a particular point in time. The claim relates to a mental activity of a human listener knowing how much additive noise they can handle. No additional limitations are present.
With respect to claims 19 and 20, the claims relate to the mental activity of claim 1. The additional elements of a “memory” and “processor” is recited generally in the specification. For example, in paragraph [0028] of the as filed specification, there is a description of using a general purpose operating system. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea int a practical application, the additional element of using a computer as a general computer is noted. Mere instructions to apply an exception of using a generic computer component cannot provide an inventive concept. No additional limitations are present.
Claim 20 is drawn to a "software" per se “computer program” and as such is non-statutory subject matter. See MPEP § 2106.1V.B.1 .a. Data structures not claimed as embodied in computer readable media are descriptive material per se and are not statutory because they are not capable of causing functional change in the computer. See, e.g., Warmerdam, 33 F.3d at 1361, 31 USPQ2d at 1760 (claim to a data structure per se held nonstatutory). Such claimed data structures do not define any structural and functional interrelationships between the data structure and other claimed aspects of the invention, which permit the data structure's functionality to be realized. In contrast, a claimed computer readable medium encoded with a data structure defines structural and functional interrelationships between the data structure and the computer software and hardware components which permit the data structure's functionality to be realized, and is thus statutory. Similarly, computer programs claimed as computer listings per se, i.e., the descriptions or expressions of the programs are not physical "things." They are neither computer components nonstatutory processes, as they are not "acts" being performed. Such claimed computer programs do not define any structural and functional interrelationships between the computer program and other claimed elements of a computer, which permit the computer program's functionality to be realized.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, 14, and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Upadhyay et al. (Speech Enhancement using Spectral Subtraction-type Algorithms: A Comparison and Simulation Study”, Procedia Computer Science, vol. 54, 21 August 2015 (2015-08-21), pages 574-584, XP093115843, AMSTERDAM, NL, ISSN: 1877-0509.
Regarding Claim 1, Upadhyay et al. discloses a method for level-dependent maximum noise suppression in a voice processing device, the method comprising: Receiving, by a processor (The processor as disclosed can encompass any general purpose computer which requires a processing unit to be able to operate. Therefore, the examiner has reason to believe that a functional limitation (the processor) asserted to be critical for establishing novelty in the claimed subject-matter may, in fact, be an inherent characteristic of the prior art.) , an input signal comprising noise (Consider a noisy signal which consists of the clean speech degraded by statistically independent additive noise as y[n] = s[n] +d[n]) (Section 2, Equation 1); Determining, by the processor, a level-dependent minimum gain based on a level-dependent maximum noise suppression function and a level of the input signal (Thus, the principle of the spectral subtractive-type algorithms is to estimate the short-time spectral magnitude of the speech by subtracting estimated noise from the noisy speech spectrum or by multiplying the noisy spectrum with gain functions and to combine it with the phase of the noisy speech) (Section 3 A, Equation 10); and Suppressing, by the processor, the noise of the input signal, wherein the noise is suppressed based on the level-dependent minimum gain (where H(ω) is the gain function and known spectral subtraction filter (SSF). The H(ω) is a zero phase filter, with its magnitude response in the range of 0 ≤ H(ω) ≤ 1.) (Section 2, Equation 8), wherein the level-dependent maximum noise suppression function provides lower level-dependent minimum gain for higher levels of the input signal and wherein the level of the input signal comprises an amplitude or a power of the input signal (The over-subtraction factor controls the amount of noise power spectrum subtracted from the noisy speech power spectrum in each frame and spectral floor parameter prevent the resultant spectrum from going below a preset minimum level rather than setting to zero (spectral floor). The over-subtraction factor depends on a-posteriori segmental SNR (SSNR)) (Section 3 A, Equation 11)
Regarding Claim 2, Upadhyay et al. discloses the method wherein the level-dependent minimum gain also depends on estimated noise spectra of the input signal (The over-subtraction factor controls the amount of noise power spectrum subtracted from the noisy speech power spectrum in each frame and spectral floor parameter prevent the resultant spectrum from going below a preset minimum level rather than setting to zero (spectral floor)) (Section 3 A).
Regarding Claim 14, Upadhyay et al. discloses the method, wherein more noise suppression is expected for higher level of input signal (In this algorithm9, two additional parameters are introduced in the spectral subtraction method8: over-subtraction factor, and noise spectral floor to reduce the remnant noise) (Section 3 A Equation 1)) (It is being interpreted by the examiner that the louder the input signal is, more noise suppression is needed to bring it comfortable level.).
Regarding Claim 19, Upadhyay et al. discloses an apparatus for level-dependent maximum noise suppression in a voice processing device, the apparatus comprising a memory and a processor communicatively connected to the memory (The processor and memory as disclosed can encompass any general purpose computer which requires a processing unit and a memory storing instructions to be able to operate. Therefore, the examiner has reason to believe that the functional limitations (the processor and the memory) asserted to be critical for establishing novelty in the claimed subject-matter may, in fact, be an inherent characteristic of the prior art.) and configured to execute instructions to perform the method according to claim 1.
Claim 20 is rejected for the same reason as claim 1.
Claim Rejections - 35 USC § 103
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 4-6, and 8, 9, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al. in view of Dickins et al. (US 9,173,025).
Regarding Claim 4, Upadhyay et al. fails to teach the method, wherein the level-dependent maximum noise suppression function is a monotonically increasing function.
Dickins et al teaches the method, wherein the level-dependent maximum noise suppression function ( Embodiments of the present invention include determining an estimate of noise spectral content and using the estimate of noise spectral content to determine a noise suppression gain. In noise estimation, noise is usually assumed to be stationary, whereas voice is assumed to have a high flux. A spectrally monotonous voice signal might therefore be interpreted as noise, and should the suppression be based on such a noise estimate, there is a possibility that the voice will eventually be suppressed) (col. 47, line 60-col. 48, line 2) is a monotonically increasing function (In some particular embodiments, the band separation is monotonically increasing in a log-like manner) (col. 24, line 63-col. 25, line 3).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Dickins to have improved the robustness and quality of the audio processing system.
Regarding Claim 5, Upadhyay et al. fails to teach the method, where the level-dependent maximum noise suppression function is a piecewise linear function.
Dickins et al teaches the method, where the level-dependent maximum noise suppression function is a piecewise linear function (The approach of combing the gains or probability indicators into a single gain for each band, and then using direct linear and nonlinear filtering on the gains is a significant novel and effective technique presented) (col. 60, line 64-col. 61, line 2).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Dickins to have improved the robustness and quality of the audio processing system.
Regarding Claim 6, Upadhyay et al. fails to teach the method, where the level-dependent maximum noise suppression function is a non-linear function, such as a sigmoid shape.
Dickins et al teaches the method, where the level-dependent maximum noise suppression function is a non-linear function, such as a sigmoid shape (The desire is to use a smooth function. One suitable smooth function is a sigmoid function.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Dickins to have improved the robustness and quality of the audio processing system.
Regarding Claim 8, Upadhyay et al. fails to teach the method, further comprising splitting the input signal into a plurality of frequency bands or bins and wherein determining, by the processor, the level- dependent minimum gain comprises determining a level-dependent minimum gain per frequency band or bin based on a level-dependent maximum noise suppression function for the corresponding frequency band or bin and a level of the input signal in the corresponding frequency band or bin
Dickins et al. teaches the method, further comprising splitting the input signal into a plurality of frequency bands or bins (One such embodiment includes a system 100 comprising an input processor 103, 107, 109 to accept a plurality of sampled input signals and form a mixed-down banded instantaneous frequency domain amplitude metric 110 of the input signals 101 for a plurality B of frequency bands ) (col. 11, line 59-col. 12, line 5) and wherein determining, by the processor, the level- dependent minimum gain comprises determining a level-dependent minimum gain per frequency band or bin based on a level-dependent maximum noise suppression function (One aspect of the invention is the determination of a set of B suppression gains for the B bands) (col. 19, lines 33-35) for the corresponding frequency band or bin and a level of the input signal in the corresponding frequency band or bin (Thus, the post-processing applied is selected according to signal activity classification. The post-processing includes preventing the gains from falling below some pre-specified (frequency-band-dependent) minimum point, the manner of prevention dependent on the activity classification, how musical noise due to one or more isolated gain values can be effectively eliminated in a manner dependent on the activity classification, and how the gains may be smoothed, with the type and amount of smoothing dependent on the activity classification) (col. 19, lines 43-52).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Dickins to have improved the robustness and quality of the audio processing system.
Regarding Claim 9, Upadhyay et al. fails to teach the method, further comprising: determining, by the processor, a SNR-dependent minimum gain based on a SNR of the input signal; wherein the processor suppress the noise by combining the SNR dependent minimum gain and the level-dependent minimum gain.
Dickins et al. teaches the method, further comprising: determining, by the processor, a SNR-dependent minimum gain based on a SNR of the input signal (; wherein the processor suppress the noise by combining the SNR dependent minimum gain and the level-dependent minimum gain (In one embodiment, the suppression probability indicator for in-beam signals, expressed as a beam gain 1012, called the spatial suppression gain, and denoted Gain.sub.b,S′ is determined by a spatial suppression gain calculator 1011 in element 129 (FIG. 10) and by a calculating suppression gain step 1103 in step 223 as Gain.sub.b,S′=BeamGain′.sub.b=BeamGain.sub.min+(1−BeamGain.sub.min)RPI′.sub.b.Math.PPI′.sub.b.Math.CPF′.sub.b. The spatial suppression gain 1012 is combined with other suppression gains in gain combiner 1015 and combining step 1109 to form an overall probability indicator expressed as a suppression gain. The overall probability indicator for simultaneous suppression of noise, echo, and out-of-beam signals, expressed as a gain Gain.sub.b,RAW′, is in one embodiment the product of the gains) (col. 57, lines 33-50).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Dickins to have improved the robustness and quality of the audio processing system.
Regarding Claim 16, Upadhyay et al. teaches the method, wherein a maximum noise suppression amount is adaptively applied depending on relative level of the input signal compared to an estimated level of stationary noise (An example of adaptation is multi-band spectral subtraction, which adapts the subtractive parameters in time and frequency based on the SSNR, leading to improved results, but remnant noise are not suppressed completely at low SNR’s10) (Section 3 D).
Regarding Claim 17, Upadhyay et al. teaches the method, wherein the estimated level of stationary noise is continuously calculated based on a minimum tracking approach within every certain time window (whereas the SSF uses the instantaneous spectra for noise signal and the running average (time-averaged spectra) of the noise) (Section 3 C).
Regarding Claim 18, Upadhyay et al. teaches the method, wherein a minimum gain is adaptively selected from the SNR-dependent minimum gain and the level-dependent minimum gain (The over-subtraction factor controls the amount of noise power spectrum subtracted from the noisy speech power spectrum in each frame and spectral floor parameter prevent the resultant spectrum from going below a preset minimum level rather than setting to zero (spectral floor). The over-subtraction factor depends on a-posteriori segmental SNR (SSNR)) (Section 3 A).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al. in view of Cartwright et al. (US 2023/0410829).
Regarding Claim 13, Upadhyay et al. fails to teach the method, wherein the processor is used in the target and/or loss function of training a neural network based noise suppressors.
Cartwright et al teaches the method, wherein the processor is used in the target and/or loss function of training a neural network based noise suppressors (In an embodiment, machine learning classifier 103 is a pre-trained neural network classifier that estimates and outputs probabilities 111 of a plurality of classes, including but not limited to a speech class, a stationary noise class, a nonstationary noise class and a reverberation class for each block/frame and each band) (page 3, paragraph [0035]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay with the teachings of Cartwright to use a neural network to provide low cost, high quality noise estimation and suppression for voice communication applications.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al. and Dickins et al. as applied to claim 4 above, and further in view of Lin et al. (US 2008/0063220).
Regarding Claim 7, Upadhyay et al. and Dickins et al fail to teach the method, wherein determining, by the processor, the level- dependent minimum gain comprises determining whether the level of the input signal is lower or equal than a minimum level Xmin and/or whether the level of the input signal is higher or equal than a maximum level and wherein the minimum level Xmin Level is lower than the maximum level Xmax Level and wherein a first predetermined value fmin is lower than a second predetermined value fmax. and if the level of the input signal is lower or equal than the minimum level Xmin l, the level-dependent minimum gain is calculated based on the first predetermined value fmin Level and, if the level of the input signal is higher or equal than the maximum level Xmax Level the level-dependent minimum gain is calculated based on the second predetermined value fmax, and if the level of the input signal is lower than the maximum level Xmax and the level of the input signal is higher than the minimum level Xmin Level , the level-dependent minimum gain is higher than the first predetermined value fmin and lower than the second predetermined value flevel.
Lin et al. teaches the method, wherein determining, by the processor, the level- dependent minimum gain comprises determining whether the level of the input signal is lower or equal than a minimum level Xmin and/or whether the level of the input signal is higher or equal than a maximum level and wherein the minimum level Xmin Level is lower than the maximum level Xmax Level and wherein a first predetermined value fmin is lower than a second predetermined value fmax. and if the level of the input signal is lower or equal than the minimum level Xmin l, the level-dependent minimum gain is calculated based on the first predetermined value fmin Level and, if the level of the input signal is higher or equal than the maximum level Xmax Level the level-dependent minimum gain is calculated based on the second predetermined value fmax, and if the level of the input signal is lower than the maximum level Xmax and the level of the input signal is higher than the minimum level Xmin Level , the level-dependent minimum gain is higher than the first predetermined value fmin and lower than the second predetermined value flevel (In addition, after determining the input signal level from the digital audio signal (Step 108) and before setting the output gain range (Step 110), the audio control method according to the present invention further comprises a step of determining whether the input signal level is lower than a threshold value (Step 109). When the input signal level is lower than the threshold (usually at a background noise volume) during the predetermined period of time, the maximum gain value and the minimum gain value of the output gain range of the amplifier are both set to 0.times., i.e. the gain range is set to the minimum volume output level) (page 2, paragraph [0022]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay and Dickins with the teachings of Lin to improve the quality of an audio signal by performing automatic gain adjustment based on the audio input.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Upadhyay et al., Dickins et al., and Lin et al. as applied to claim 7 above, and further in view of Sadasivam et al. (US 2014/0361905).
Regarding Claim 15, Upadhyay et al., Dickins et al., and Lin et al. fail to teach the method, wherein the input signal comprises a segment containing silence, a segment containing noise, a segment containing sudden bursts of loud noise, and a segment containing speech, and the minimum level Xmin Level and the maximum level Xmax X Level are determined by analysing the segment containing silence, the segment containing noise, the segment containing sudden bursts of loud noise, and the segment containing speech.
Sadasivam et al. teaches the method, wherein the input signal comprises a segment containing silence, a segment containing noise, a segment containing sudden bursts of loud noise, and a segment containing speech ( TTCM collects sensor data and determines features of device's audio environment, including microphone data with speech, quiet, loud noises, or other audio segments or clusters. TTCM can obtain each segment over a specified time period (e.g., approximately one minute or other specified duration). Each segment or cluster can correspond to a distinct audio environment) (page 5, paragraph [0038]), and the minimum level Xmin Level and the maximum level Xmax X Level are determined by analysing the segment containing silence, the segment containing noise, the segment containing sudden bursts of loud noise, and the segment containing speech (TTCM may monitor a data sensor stream for a change in status of one or more features. The feature may have an initial baseline or initialized value. In response to determining a predetermined threshold is met, TTCM may change the current status of a feature) (page 4, paragraph [0035]).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Upadhyay, Dickins, and Lin with the teachings of Sadasivam to improve the noise suppression of the audio signal by being able to monitor and detect when noise suppression is needed.
Allowable Subject Matter
Claims 3, 10, 11 and 12 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the 35 USC 101 rejections above are overcome.
Cited Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Deng et al .(US 2023/0032785) discloses intelligent noise suppression for audio signals within a communication platform.
Zheng et al. (US 2023/0386492) discloses suppressing noise for audio signal.
Furuta (US 2022/0208206) discloses noise suppression.
Lu et al. (US 2021/0074266) discloses deep neural network based audio processing.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SATWANT K SINGH whose telephone number is (571)272-7468. The examiner can normally be reached Monday thru Friday 9:00 AM to 6:00 PM EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Paras D Shah can be reached at (571}270-1650. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SATWANT K SINGH/Primary Examiner, Art Unit 2653
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