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 statements (IDS) submitted on 11/18/2025, 12/30/2025, 02/02/2026, 02/20/2026 and 04/15/206 were filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendment
The Response filed on 02/06/2026 has been correspondingly accepted and considered in the office action. Claims 1-8, 11-28, 31-38 and 40 are pending . Claims 1, 21 and 40 are independent and amended. Dependent Claims 3, 11, 21, 23, and 31 are also amended. Claims 9, 10, 29, 30, 39 and 41 are cancelled.
The rejections to Claims 39 and 41 under 35 U.S.C. § 102(a)(2) are moot as Claims 39 and 41 are cancelled.
Provisional Rejections of Claims 1-8, 12-17, 21-28, 32-37 and 40 under the obviousness-type double patenting as being unpatentable over Claims of copending Application No. 18/300,871 have been withdrawn in view of Applicant’s amendments to the independent Claims 1, 21 and 40 (inclusion of subject matter from claim 9, which has not been rejected as being nonstatutory double patenting) .
Response to Arguments
With respect to Interpretation of Claim 1 as a means plus function limitation Under 35 U.S.C §112(f), Applicant appears to be presenting following position on Remarks, pp 8-10, filed on 02/06/2026:
“…Amended claim 1 does not invoke 35 U.S.C. 112(f) because the claim features recite sufficient structure to perform the claimed functions and does not use generic placeholder terms that act as substitutes for "means". The claim features "first processing block" and "second processing block" do not invoke 35 U.S.C. 112(f) because these terms recite sufficient structural components to perform the claimed functions.…”.
In response, Examiner respectfully notes that limitations. “a first processing block, configured to receive first data derived from the noise and to output first output data”, “a second processing block, configured to receive, as second data, the first output data”, and “a styling element, configured to apply the conditioning feature parameters to the first data or normalized first data”, as disclosed in claim 1, clearly invoke 35 U.S.C. 112(f) as claim limitations meet the following 3-prong analysis:
Claim limitations use “block” and “element” as a substitute for "means" that is a generic placeholder for performing the claimed function;
the generic placeholders, “block” and “element”, are modified by functional language, typically, by the transition word "for" (e.g., "means for"), "configured to" or "so that”;
the terms “block” and “element” are not modified by sufficient structure, material, or acts for performing the claimed function. The modifiers, “first/second processing” and “styling” preceding “block” and “element”, do not provide sufficiently definite meaning as the name for structure.
Applicant further presents “structural composition” for “first processing block” and “block” as a structural term, but they all fail to recite sufficient structure beyond functional composition. For the provided reasons, Examiner respectfully disagrees, and therefore, Interpretation of Claim 1 as a means plus function limitation Under 35 U.S.C §112(f) is sustained.
Interpretation of Claims 21 as a means plus function limitation Under 35 U.S.C §112(f) is withdrawn because claim limitations with respect to “first/second processing block” and “styling element” do not meet the 3-prong analysis.
Applicant’s arguments with respect to claims 1-8, 11-28, 31-38 and 40 have been considered but are moot because applicants amendment necessitated the new ground of rejection (specifically the amendment to include multiple feed-forward learnable layers).
Please note Koishida et al. (US Pub No. 2021/0134312). For at least the supra provided reasons, Applicant's arguments have been fully considered but they are not persuasive.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a first processing block, a second processing block, a styling element in Claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
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.
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 1-8, 11-12, 14-18, 20-28, 31-32, 34-38 and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Skordilis et al., (US Pub No. 2021/0074308, hereinafter, Skordilis) in view of Koishida et al., (US Pub No. 2021/0134312, hereinafter, Koishida).
Regarding Claim 1,
Skordilis discloses Audio generator (Abstract, Fig.1,par [047], voice decoder 104), configured to generate an audio signal (Fig.1, reconstructed speech signal 105) from an input signal (Fig.3, par [074], "…The residual data generated by the neural network model 334...") and target data, the target data representing the audio signal (Fig.1, speech signal 101; Fig.5A/B, voice signal s(n)), comprising:
a first processing block (Figs.4-5, par [078-079, 101-105], neural network model 534 including frame rate network 442 and a sample rate network 452 in Fig. 4 and part of a neural network model 534 in the voice decoder 504 in Fig. 5A/5B), configured to receive first data derived from the input signal and to output first output data (Fig.5A, par [104], "…The neural network model 534 generates an LTP residual ê ( n ) by effectively decoding the input features into the LTP residual ê ( n )..."),
wherein the first output data comprises a plurality of channels (Fig.5A, par [064], "…the feature 541 includes linear prediction LP coefficients, line spectral pairs LSPs, line spectral frequencies LSFs, pitch lag with integer or fractional accuracy, pitch gain, pitch correlation"; i.e., channels with different feature data), and
a second processing block (Fig.5A, including LTP Engine 522 and Short-term LP Engine 520), configured to receive, as second data, the first output data (Fig.5A, para [082, 088], "…input e(n) to adder in LTP Engine 822 and then to short-term LP Engine 820, similar to "+" in Figs. 5A/5B),
wherein the first processing block comprises for each channel of the first output data (Fig.4, para [078-079], "…frame rate network 442...the sample rate network 452..."):
a conditioning set of learnable layers (Fig.4, par [078], a first convolutional 1x3 layer 444, the second convolutional layer 446) configured to process the target data to obtain conditioning features parameters (Fig.4, par [078, 104], 128-dimensional conditioning vector f processed by convolution layer 444 and convolution layer 446); and
a styling element (Fig.4, par [079], concatenation layer 454, GRU 456, 458), configured to apply the conditioning feature parameters to the first data or normalized first data (Fig.4 and 5A/5B, par [079, 104], applying 128-dimensional conditioning vector f, through at least 420 and p(n) returned from short-term LP engine 520 in fig. 5A/5B and also pitch estimation engine 566 and pitch gain estimation engine 564 from the features 541 in figs. 5A/5B); and
wherein the second processing block is configured to combine the plurality of channels of the second data to generate the audio signal (Fig 5A/5B,
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as reconstructed audio signal through adders in 521 and 523), Skordilis does not explicitly disclose a set of multiple feed-forward learnable layers, configured to process data derived from the first data using a gated activation function as a second activation function.
However, Koishida, in the analogous field of endeavor, discloses an audio generator (Abstract, Fig.1, par [015], speech enhancement system 100) where a conditioning set of multiple feed-forward learnable layers (Fig.1, ResBlock in encoder 116 and upsample/Conv2D of decoder 122; par [019], "…an autoencoder 116 with skip connections. The encoder 116 may use four residual blocks (two 2D convolution operations per block, i.e., multiple feed-forward convolutional layers)...Convolution may be performed over time-frequency dimensions and may be followed by batch normalization and a leaky rectified linear unit (LReLU) activation...") configured to process the target data (Fig.1, the output from Log-Mel 114 is processed through autoencoder the encoder 116 and decoder122).
Koishida further discloses wherein the first processing block further comprises: a further set of multiple feed-forward learnable layers, configured to process data derived from the first data using a second activation function, wherein the second activation function is a gated activation function (Koishida, Fig.1, SE Fusion 106; Fig.2, SE fusion block architecture, par [023], "…SE fusion block architecture 200 applies a second linear transform, g, such that G=σ(g(M)) [Symbol font/0xCE] Rz where σ is sigmoid activation and z varies with gating dimension (i.e., a gated linear unit (GLU) activation function)...").
Therefore, It would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the voice coding system of Skordilis with multiple feed-forward convolutional layers and a gated linear unit activation function of audio generating autoencoder taught by Koishida to improve quality of generated audio sound by improving the perceptual quality of a targeted acoustic signal and by improving performance in reducing the number of modal parameters (Koishida, para [010, 032, 040]).
Regarding Claim 2,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the conditioning set of learnable layers comprises one or at least two convolution layers (Koishida, Fig.1, ResBlock in encoder 116 and upsample/Conv2D of decoder 122; par [019], "…an autoencoder 116 with skip connections. The encoder 116 may use four residual blocks (two 2D convolution operations per bloc...").
Regarding Claim 3,
Skordilis in view of Koishida discloses Audio generator according to claim 2, wherein a first convolution layer is configured to convolute the target data or up-sampled target data to acquire first convoluted data using a first activation function (Koishida, Fig.1, par [019], "…an autoencoder 116 with skip connections. The encoder 116 may use four residual blocks (two 2D convolution operations per block, i.e., multiple feed-forward convolutional layers)...Convolution may be performed over time-frequency dimensions and may be followed by batch normalization and a leaky rectified linear unit (LReLU) activation...").
Regarding Claim 4,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the conditioning set of learnable layers and the styling element are part of a weight layer in a residual block of a neural network comprising one or more residual blocks (Koishida, Fig.1, ResBlock in encoder 116 and upsample/Conv2D of decoder 122; par [019], "...Convolution may be performed over time-frequency dimensions and may be followed by batch normalization and a leaky rectified linear unit (LReLU) activation..."; i.e., a weight layer consists of convolutional and upsample layers, batch normalization, and LReLU activations.)
Regarding Claim 5,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the audio generator further comprises a normalizing element, which is configured to normalize the first data (Koishida, Fig.1, ResBlock in encoder 116 and upsample/Conv2D of decoder 122; par [019], "…Convolution may be performed over time-frequency dimensions and may be followed by batch normalization …").
Regarding Claim 6,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the audio signal is a voice audio signal (Skordilis, Fig.5A/B, voice signal s(n); Koishida, Title and Abstract, Speech Enhancement System; Fig.1, par [015], through the iSTFT by applying phase 130 and enhanced magnitude 126 in time-frequency domain as inputs to render enhanced waveform 128).
Regarding Claim 7,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the target data is up-sampled by a factor of at least 2 (Koishida, Table 1, See stride of 1/2 or 1/4 indicating 2x or 4x upsampling).
Regarding Claim 8,
Skordilis in view of Koishida discloses Audio generator according to claim 7, wherein the target data is up-sampled by non-linear interpolation (Koishida, Fig.1, paras [020-023], "…The decoder 122 may comprise eight upsampling blocks and a 2D convolution along time-frequency dimensions...The SE fusion block architecture 200 takes an audio feature Fjv from some layer j of the autoencoder and applies a second linear transform, g, such that G=σ(g(M)) [Symbol font/0xCE] Rz where σ is sigmoid activation and z varies with gating dimension (i.e., a gated linear unit (GLU) activation function) …"; i.e., The combination of an upsampling/Conv2D layer followed by a gated mechanism acts as a non-linear interpolation).
Regarding Claim 11,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the second activation function is a softmax-gated hyperbolic tangent, TanH, function (Skordilis, Fig.4, par [081], softmax activation of a softmax layer 462).
Regarding Claim 12,
Skordilis in view of Koishida discloses Audio generator according to claim 3, wherein the first activation function is a leaky rectified linear unit, leaky ReLu, function (Koishida, Fig.1, par [019], "…Convolution may be performed over time-frequency dimensions and may be followed by batch normalization and a leaky rectified linear unit (LReLU) activation…").
Regarding Claim 14,
Skordilis in view of Koishida discloses Audio generator according to claim 1, comprising eight first processing blocks and one second processing block (Koishida, Fig.1, First processing blocks: three ResBlock in encoder 116, four upsample/Conv2D of decoder 122, and Mask Prediction Block; Second processing block: through the iSTFT by applying phase 130 and enhanced magnitude 126 in time-frequency domain as inputs to render enhanced waveform 128).
Regarding Claim 15,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the first data comprises a lower dimensionality than the audio signal (Skordilis, par [044], "…Using the voice coding algorithm, the voice encoder 102 can generate a compressed signal (including a lower bit-rate stream of data) that represents the speech signal 101 using as few bits as possible, while attempting to maintain a certain quality level for the speech..."; par [048], "…The voice decoder 104 decodes the data of the compressed speech signal and constructs a reconstructed speech signal 105 that approximates the original speech signal 101..."; Koishida, Fig.1, A dimensionality of the layer output of each upsampling layer is larger than a dimensionality of the layer input of the upsampling layer; the compressed signal (i.e., first data) exists in a low-dimensional, sparse latent space, while the reconstructed signal (i.e., audio signal) exists in the original, high-dimensional sample space).
Regarding Claim 16,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the target data is a spectrogram (Skordilis, Fig.3C, par [043], "…at 342, outputting a mask via the decoder. Method 300 next comprises, at 344, generating an enhanced magnitude spectrogram by applying the mask to the audio spectrogram…at 348, method 300 comprises generating an enhanced waveform from the enhanced magnitude spectrogram...").
Regarding Claim 17,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the target data is a mel-spectrogram (Skordilis, Fig.1, par [015], "…The mixed magnitude spectrogram 110 is log-mel transformed 114, and input to an autoencoder (indicated as encoder 116, bottleneck 134, and decoder 122)…"; par [043], "…at 342, outputting a mask via the decoder. Method 300 next comprises, at 344, generating an enhanced magnitude spectrogram by applying the mask to the audio spectrogram…at 348, method 300 comprises generating an enhanced waveform from the enhanced magnitude spectrogram...").
Regarding Claim 18,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the target data is a bitstream (Skordilis, Fig.1, par [043], "…The speech signal 101 can include a digitized speech signal generated from an analog speech signal from a given source..."; paras [047-048], "…bitstream of compressed speech signal...The reconstructed speech signal 105 includes a digitized, discrete-time signal that can have the same bit-rate as that of the original speech signal 101…").
Regarding Claim 20,
Skordilis in view of Koishida discloses Audio generator according to claim 1, wherein the target data is a compressed representation of audio data (Skordilis , Fig.1, paras [044, 047], "…the voice encoder 102 can generate a compressed signal (including a lower bit-rate stream of data) that represents the speech signal 101...The compressed speech signal can then be sent to and processed by a voice decoder 104...").
Claim 21 is a method claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale.
Claim 22 is a method claim with limitations similar to the limitations of Claim 2 and is rejected under similar rationale.
Claim 23 is a method claim with limitations similar to the limitations of Claim 3 and is rejected under similar rationale
Claim 24 is a method claim with limitations similar to the limitations of Claim 4 and is rejected under similar rationale
Claim 25 is a method claim with limitations similar to the limitations of Claim 5 and is rejected under similar rationale.
Claim 26 is a method claim with limitations similar to the limitations of Claim 6 and is rejected under similar rationale
Claim 27 is a method claim with limitations similar to the limitations of Claim 7 and is rejected under similar rationale.
Claim 28 is a method claim with limitations similar to the limitations of Claim 8 and is rejected under similar rationale.
Claim 31 is a method claim with limitations similar to the limitations of Claim 11 and is rejected under similar rationale.
Claim 32 is a method claim with limitations similar to the limitations of Claim 12 and is rejected under similar rationale.
Claim 34 is a method claim with limitations similar to the limitations of Claim 14 and is rejected under similar rationale.
Claim 35 is a method claim with limitations similar to the limitations of Claim 15 and is rejected under similar rationale.
Claim 36 is a method claim with limitations similar to the limitations of Claim 16 and is rejected under similar rationale.
Claim 37 is a method claim with limitations similar to the limitations of Claim 17 and is rejected under similar rationale.
Claim 38 is a method claim with limitations similar to the limitations of Claim 20 and is rejected under similar rationale.
Claim 40 is a non-transitory digital storage medium claim with limitations similar to the limitations of Claim 1 and is rejected under similar rationale. Additionally,
Skordilis discloses: a non-transitory digital storage medium having a computer program stored thereon to perform the method (Skordilis, par [147], "…in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable or machine readable storage medium may be non-transitory...")
…
Rationale for combination is similar to that provide for Claim 1.
Claims 13, 19 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Skordilis in view of Koishida further in view of Binkowski et al. (US Pub. No. 2021/0089909, hereinafter, Binkowski).
Regarding Claim 13,
Skordilis in view of Koishida discloses Audio generator according to claim 1, does not explicitly discloses the limitation, "wherein convolution operations run with maximum dilation factor of 2." However, Binkowski, in the analogous field of endeavor, discloses the audio generator (Fig.1, the generative neural network 110) configured to generate an audio signal from noise (Figs.1 and 2, par [025], noise input 104/204) and target data (Figs1 and 2, para [021-024], "…the generative neural network 110 to receive a conditioning text input 102 and to process the conditioning text input 102 to generate an audio output 112...") and further discloses wherein convolution operations run with maximum dilation factor of 2 (Binkowski, Fig.2, 212d, 216c, 232c, 236c, dilation 2, 4, 8; (par [063], "…a dilation value of (1,2,4, or 8)…").
Therefore, Therefore, It would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the voice/speech enhancement system with feed-forward conditioning convolutional layers of Skordilis in view of Koishida with a feedforward adversarial neural network (GAN) of Binkowski to process the conditioning input to generate audio data while maintaining a high degree of quality and greatly reduce the time and resources to generate the output audio data (Binkowski, Abstract, para [002-008, 20]).
Regarding Claim 19,
Skordilis in view of Koishida discloses Audio generator according to claim 1.
Binkowski discloses wherein the target data is a degraded audio signal (Binkowski, Fig. 1, par [025], "…the generative neural network 110 can also receive as input a noise input 104. For example, the noise input 104 can be randomly sampled from a predetermined distribution, e.g., a normal distribution…").
Rationale for combination is similar to that provided for Claim 13.
Claim 33 is a method claim with limitations similar to the limitations of Claim 13 and is rejected under similar rationale. Rationale for combination is similar to that provided for Claim 13.
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 CFR 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 CFR 1.17(a)) pursuant to 37 CFR 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JANGWOEN LEE whose telephone number is (703)756-5597. The examiner can normally be reached Monday-Friday 8:00 am - 5:00 pm ET.
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/JANGWOEN LEE/Examiner, Art Unit 2656
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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