DETAILED ACTION
This Action is responsive to Claims filed 10/08/2025.
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 .
Status of the Claims
Claims 1, 10-12, 19-21, and 28-30 have been amended. Claims 1-30 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/02/2026 was filed after the mailing date of the first action on the merits filed 07/09/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The replacement Drawings filed 10/08/2025 are acknowledged. These Drawings are acceptable.
Response to Amendment
The amendments to the Specification are acknowledged.
The amendments to Claims 11, 20, and 29 have overcome the Objections to minor informalities.
Response to Arguments
Applicant's arguments, see Pages 13-, filed 10/08/2025, regarding the 35 U.S.C. 101 Rejection of Claims 1-30 have been fully considered but they are not persuasive.
On Page 13-14, the Applicant argues the claimed limitations embody a specific improvement in light of the Specification. The Examiner respectfully disagrees with the Applicant. As presently drafted, the independent claims recite no limitations or implemented details connecting the claims to the alleged field or improvements. There is no reference to domain adaptation, differing devices, model size, or how implementation of the claimed steps of the independent claims pertain to these subjects.
As presently drafted, the “accessing…” step is recited highly generally, amounting to mere data-gathering. The first “generating…” step is not reliant on this “accessing…” other than on the data itself, and is practically performed within the human mind or with the aid of pen and paper. The second “generating…” is again practically performed within the human mind or with the aid of pen and paper, given the limitation merely recites performing operations on a tensor without specific implementation or limitations precluding the operations from being performed mentally. The “providing…” limitation is recited highly generally, amounting to data-transmittal. The newly-amended final “generating…” step is recited highly generally, with no specific details or implementation for the neural network, amounting to instructions to apply the aforementioned abstract idea mental process steps required to manipulate or transform the input tensor(s).
Given the limitations not interpretable as abstract idea mental processes are recited so highly generally, the Examiner contends the alleged improvement is a direct result of the data manipulation performed on the one, two, or three data tensors, which as presently drafted, are not recited in such a way as to preclude their manipulation by the human mind with or without the aid of pen and paper. Per MPEP 2106.05(a), the specific improvement cannot come from the abstract idea.
The Applicant argues, both on Pages 16 and 17, in reference to Ex Parte Chari and BASCOM, that the claims recite eligible subject matter because they recite specific improvements or techniques or non-generic methods. The Examiner respectfully disagrees with the Applicant based on the generality highlighted above. In regards to the Applicant’s reference to Cosmokey, the Examiner contends the claims in question of that case contain significantly more technical implementation or details, versus the instant Application’s generic data gathering and recitation of a neural network merely operating on data manipulated through a series of abstract idea mental process steps. See the updated 35 U.S.C. 101 Rejection below.
Applicant’s arguments, see Pages 18-20, regarding the Prior Art Rejection(s) of claim(s) 1-30 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-30 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1-11 recite a processor-implemented method, which falls under the statutory category of a process. Claims 12-20 recite a processing system comprising a memory comprising computer-executable instructions, which falls under the statutory category of a machine. Claims 21-29 recite a non-transitory computer-readable medium, which falls under the statutory category of a manufacture. Claim 30 recites a processing system comprising means for receiving a first tensor, which falls under the statutory category of a machine.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations of “generating a second tensor by applying a frequency-based instance normalization operation to the first tensor,”, “computing a respective frequency- specific mean of the first tensor;”, “generating a third tensor by: scaling the first tensor by a scale value;”, and “aggregating the scaled first tensor and the second tensor;” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Generating a tensor by applying a normalization operation is practically performed within the human mind or with the aid of pen and paper. Computing a mean is practically performed within the human mind or with the aid of pen and paper. Generating a third tensor by scaling another tensor is practically performed within the human mind or with the aid of pen and paper. Aggregating tensors is practically performed within the human mind or with the aid of pen and paper.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A processor-implemented method” and “tensor(s)…comprising…dimension(s)” are recognized as generic computer components recited at a high level of generality. Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements of “machine learning”, “a frequency-based instance normalization operation”, “a respective frequency- specific mean”, “a scale value”, and “a layer of a neural network” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “accessing a first tensor comprising a frequency dimension and a temporal dimension;”, and ”providing the third tensor as input to a layer of a neural network;” amount to mere pre- or post-extra solution activity or data gathering steps (See MPEP 2106.05(g)).
The additional element recited in the limitation of “generating, by the neural network, a classification for the first tensor based on the third tensor.” is found to mere instructions to apply the abstract idea mental process steps of manipulating the aforementioned data tensor(s) (See MPEP 2106.05(f)).
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “A processor-implemented method” and “tensor(s)…comprising…dimension(s)” These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements of “machine learning”, “a frequency-based instance normalization operation”, “a respective frequency- specific mean”, “a scale value”, and “a layer of a neural network” are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
The additional elements recited in the limitation “accessing a first tensor comprising a frequency dimension and a temporal dimension;”, and ”providing the third tensor as input to a layer of a neural network;” are found to be well-understood, routine, or conventional pre- or post-solution activity (See WURC examples MPEP 2106.05(d)(II)(i)).
The additional element recited in the limitation of “generating, by the neural network, a classification for the first tensor based on the third tensor.” is found to mere instructions to apply the abstract idea mental process steps of manipulating the aforementioned data tensor(s) (See MPEP 2106.05(f)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 12, 21, and 30.
Claim 12 recites similar limitations to claim 1, save for “A processing system comprising: memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation comprising:” (generic computer components), therefore, both claims are similarly rejected.
Claim 21 recites similar limitations to claim 1, save for “A non-transitory computer-readable medium comprising computer- executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising:” (generic computer components), therefore, both claims are similarly rejected.
Claim 30 recites similar limitations to claim 1, save for “A processing system comprising:” (generic computer components) and “receiving a first tensor comprising a frequency dimension and a temporal dimension;” (data gathering or transmittal, WURC activity); therefore, both claims are similarly rejected.
Dependent Claims:
Claim 2 (claims 13 and 22) recites refinements to the abstract idea mental process steps of the independent claims, further reciting the “computing a respective frequency-specific variance of the first tensor.” abstract idea mental process step.
Claim 3 (claims 14 and 23) recites refinements to the abstract idea mental process steps of the independent claims, further reciting the “computing a respective frequency-specific difference between the first tensor and the respective frequency-specific mean of the first tensor; and dividing the respective frequency-specific difference by the respective frequency-specific variance of the first tensor.” abstract idea mental process steps.
Claim 4 (claims 15 and 24) recites refinements to the additional elements that are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 5 (claims 16 and 25) recites refinements to the additional elements that are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 6 recites refinements to the additional elements that are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 7 recites refinements to the additional elements that are recognized as non-generic computer components, but are recited at a high level of generality and are found to generally link the abstract idea to a particular technological environment or field of use (See MPEP 2106.05(h)).
Claim 8 (claims 17 and 26) recites abstract idea mental process step “applying the frequency-based instance normalization operation as a pre-processing operation prior to processing an input layer of the neural network,” and additional element “the third tensor is provided as input to the input layer of the neural network.” (data gathering and WURC activity).
Claim 9 (claims 18 and 27) recites abstract idea mental process steps “determining a size of the neural network; and refraining from applying the frequency-based instance normalization operation between layers of the neural network, based on determining that the size is below a defined threshold.”
Claim 10 (claims 19 and 28) recites abstract idea mental process step “applying the frequency-based instance normalization operation after each convolution stage, of a plurality of convolution stages, in the neural network.”
Claim 11 (claims 20 and 29) recites abstract idea mental process step “determining a size of the neural network, wherein the frequency-based instance normalization operation after each convolution stage based on determining that the size is above a defined threshold.”
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 CFR 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.
Claim(s) 1-30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kong et al. (PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition, 2020), hereinafter Kong; Li et al. (Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution, 2020), hereinafter Li; and Shao et al. (Is normalization indispensable for training deep neural networks?, 2020), hereinafter Shao.
In regards to claim 1: The present invention claims: “A processor-implemented method for machine learning, comprising:” Kong tests pretrained audio neural networks (PANNs) in an Experiments section (starting on Page 5), implying hardware implementation.
“accessing a first tensor comprising a frequency dimension and a temporal dimension;” Kong teaches “To build a Wavegram, we first apply a one-dimensional CNN to time-domain waveform.” (Page 4, left column, mapping a time-domain waveform to the BRI of a tensor with a frequency and temporal dimension).
While Kong teaches interpretably normalizing an input time-domain tensor into a Wavegram tensor, Kong fails to explicitly teach: “comprising, for each respective frequency bin in the frequency dimension, computing a respective frequency-specific mean of the first tensor;” However, Li, in a similar field of time-frequency CNN learning, teaches a batch normalization occurring after a convolution (Figure 3, for example) with a mean and standard deviation (Page 3, left column).
Li highlights the need to temporal information in different scales when audio processing for speech recognition (Introduction). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to normalize the input tensor data after a convolution in the formulation of the Wavegram of Kong, using typical normalization techniques as described in Li.
“generating a third tensor by: scaling the first tensor by a scale value;” Li teaches scaling and shifting factors during the normalization of the input tensor (Page 3, left column).
“and aggregating the scaled first tensor and the second tensor;” Both Kong and Li teach multiple tensors being concatenated or aggregated before a final output. Kong teaches a Wavegram and Logmel feature map being combined (Figure 1), and Li teaches multiple normalized tensors being combined into an output (Figure 3). It would have been obvious to one of ordinary skill in the art combining Kong and Li to aggregate normalized or scaled tensors based on the input tensor.
“providing the third tensor as input to a layer of a neural network” Kong teaches a final, aggregated tensor being output to a neural network (Figure 1). See above how this would reasonably be the “third tensor” of aggregated scaled or normalized tensors based on the input tensor.
“generating, by the neural network, a classification for the first tensor based on the third tensor.” See Kong Figure 1 how the a prediction (mapping to classification) is the final output once the third tensor is input into the CNN layers.
While Kong teaches “We denote the output size of the one-dimensional CNN layers as T x C, where T is the number of frames and C is the number of channels. We reshape this output to a tensor with a size of T x F x C/F by splitting C channels into C/F groups, where each group has F frequency bins. We call this tensor a Wavegram.” (Page 4, right column). This is done to the time-domain waveform (second tensor normalized from the first tensor). The combination of Kong and Li fails to explicitly teach instance normalization as recited in: “generating a second tensor by applying a frequency-based instance normalization operation to the first tensor,” However, Shao, in a similar field of network normalization, teaches “Since the introduction of BN, several variants have been proposed that apply the underlying principle to a wider range of tasks: Layer Normalization for recurrent neural networks [2], Instance Normalization (IN) [33] for stylization, Group Normalization (GN) [36] for small-batch training, etc. Normalization operations are, by now, default components of the state of the art in many tasks.” (Introduction) and “Instance Normalization (IN) [33] computes the statistics for each channel in each datum independently.” (Page 2).
Shao demonstrates that instance normalization would have been a known method before the Applicant’s filing. A person of ordinary skill in the art combining Kong and Li, with that combination’s frequency based normalization or splitting, would have been aware of instance based normalization and would have reasonably implemented it rather than batch normalization if the specific implementation would have benefitted its use, such as stylization.
In regards to claim 2: The present invention claims: “wherein applying the frequency-based instance normalization operation further comprises, for each respective frequency bin in the frequency dimension: computing a respective frequency-specific variance of the first tensor.” Li teaches a batch normalization occurring after a convolution (Figure 3, for example) with a mean and standard deviation (Page 3, left column). A person of ordinary skill in the art combining Kong, Li, and Shao would reasonably calculate similar metrics given knowledge of how instance normalization functions (Shao, Page 2).
In regards to claim 3: The present invention claims: “wherein applying the frequency-based instance normalization operation further comprises, for each respective frequency bin in the frequency dimension: computing a respective frequency-specific difference between the first tensor and the respective frequency-specific mean of the first tensor;” Li teaches, in equation 1, which occurs after normalization (Page 3, left column), a difference being calculated between the concatenation of the input M, the filters F, and μ, the mean. A person of ordinary skill in the art combining Kong, Li, and Shao would reasonably calculate similar metrics given knowledge of how instance normalization functions (Shao, Page 2).
“and dividing the respective frequency-specific difference by the respective frequency-specific variance of the first tensor.” Li teaches, in equation 1, which occurs after normalization (Page 3, left column) a division occurring with the standard deviation as the divisor.
In regards to claim 4: The present invention claims: “wherein the layer of the neural network is an input layer at a start of the neural network.” Both Kong and Li refer to input layers of the neural network (Kong, Page 4, Section III.A) and (Li, Page 1, Section 2.1).
In regards to claim 5: The present invention claims: “wherein the layer of the neural network is an intermediate layer within the neural network.” Both Kong and Li reference intermediate layers of the network, especially Li, Figure 1, showing the normalization occurring between layers.
In regards to claim 6: The present invention claims: “wherein the scale value is a configurable hyperparameter of the neural network.” Li teaches “We evaluate the performance of TENets trained with or without MTConv, respectively. Note that, whether utilizing MTConv or not, the training hyperparameters maintain constant.” (Page 4, Section 3.2.2), these training hyperparameters are in reference to Equation 1.
In regards to claim 7: The present invention claims: “wherein the scale value is a trainable parameter of the neural network.” Li teaches trainable parameters for scaling and shifting (Page 3, left column).
In regards to claim 8: The present invention claims: “further comprising applying the frequency-based instance normalization operation as a pre-processing operation prior to processing an input layer of the neural network, wherein the third tensor is provided as input to the input layer of the neural network.” Kong teaches the construction of a Wavegram occurring before its input into convolutional layers (Figure 1).
In regards to claim 9: The present invention claims: “further comprising: determining a size of the neural network; and refraining from applying the frequency-based instance normalization operation between layers of the neural network, based on determining that the size is below a defined threshold.” Shao teaches “With this careful initialization, residual networks can be trained with large learning rate and limited performance degradation. This paper focuses on the second question. By analyzing what would happen when normalization layers are removed from the networks, we show how to train deep neural networks without normalization layers and without performance degradation.” (Page 2) in referring to networks without normalization.
If, as per applicant’s specification, the determination to include the normalization includes preprocessing the data, then one of ordinary skill in the art at the time of the Applicant’s filing combining Kong and Li would have been aware of cases where networks can benefit from initialization (preprocessing and input of the preprocessed data), rather than normalization between convolutions, as per Shao (Abstract).
In regards to claim 10: The present invention claims: “further comprising applying the frequency-based instance normalization operation after each convolution stage, of a plurality of convolution stages, in the neural network.” Li shows the normalization occurring between convolution stages (Figure 1).
In regards to claim 11: The present invention claims: “further comprising determining a size of the neural network, wherein the frequency-based instance normalization operation after each convolution stage is based on determining that the size is above a defined threshold.” Kong teaches “Each convolutional block consists of 2 convolutional layers with a kernel size of 3 x 3. Batch normalization [36] is applied between each convolutional layer, and the ReLU nonlinearity [37] is used to speed up and stabilize the training.” (Page 2, right column). See above how predeterminations of a given model, and its need for normalization or not, would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing.
In regards to claims 12-20: Claims 12-20 recite similar limitations to claims 1-5 and 8-11, save for “A processing system, comprising: memory comprising computer-executable instructions; and one or more processors configured to execute the computer-executable instructions and cause the processing system to perform an operation comprising:” therefore; both sets of claims are similarly rejected.
In regards to claim 21-29: Claims 21-29 recite similar limitations to claims 1-5 and 8-11, save for “A non-transitory computer-readable medium comprising computer- executable instructions that, when executed by one or more processors of a processing system, cause the processing system to perform an operation comprising:” therefore; both sets of claims are similarly rejected.
In regards to claim 30: Claim 30 recites similar limitations to claim 1, save for “A processing system comprising: means for,” therefore; both claims are similarly rejected.
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 GRIFFIN T BEAN whose telephone number is (703)756-1473. The examiner can normally be reached M - F 7:30 - 4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121