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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/29/2025 has been entered.
Status of Claims
Claims 1-18 are pending and examined on the merits.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. Priority of US application 16/236,797 filed 12/31/2018 is acknowledged.
Withdrawn Rejections/Objections
The objections to Specification in the Office action mailed 18 December 2024 are withdrawn in view of persuasive argument (penultimate para., page 5) filed 18 March 2025.
Claim Rejections - 35 USC § 101
This rejection is maintained from the previous Office Action. Modifications are necessitated by claim amendments.
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.
Step 1: Process, Machine, Manufacture or Composition
Claims 1-9 are directed to a machine, here a “processor”.
Claims 10-16 are directed to a machine or manufacturer, here a "system," with structural components like "a processor and a memory device”.
Claims 17-18 are directed to another machine or manufacturer, here a "non-transitory computer-readable medium".
Step 2A Prong One: Identification of an Abstract Idea
The claims recite:
One or more circuits to provide to one or more computer-implemented neural networks a plurality of ATAC-seq counts at each base pair position of a genome that exclude base identity, and to use the one or more computer-implemented neural networks to generate one or more inferences corresponding to the ATAC-seq information based, at least in part, on the plurality of ATAC-seq counts (Claims 1, 10 and 17).
----This step recites “to generate one or more inferences corresponding to the ATAC-seq information” in a general, simple way. This limitation, as drafted, is a process that, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind but for the recitation of generic computing steps. That is, other than reciting “one or more circuits to provide to one or more computer-implemented neural networks,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “one or more circuits to provide to one or more computer-implemented neural networks” language, the claim encompasses a user simply reading the ATAC-seq multiple alignment data and determining inferences in his/her mind. The mere nominal recitation of one or more generic processors/circuits/neural networks do not take the claim limitation out of the mental processes grouping. Thus, the claims recite a mental process.
Claim 7 recites : wherein the neural networks are trained with suboptimal ATAC-seq information using a loss function to compare neural network output to model ATAC-seq information, and adjusting the neural network until the loss function is minimized.
----training a neural network involves adjustment of many parameters according mathematical formulas (such as minimizing the loss function). Therefore this claim equates to an abstract idea of mathematical concepts.
Claim 8 recites: wherein the loss function is a mean square error loss function.
----this claim further limit the loss function used in training a neural network. Therefore this claim equates to an abstract idea of mathematical concepts.
Claim 16 recites: wherein the system further comprises: a training engine executable on the processor according to software instructions stored in the memory device, wherein the training engine is configured to: train the neural networks with suboptimal ATAC-seq information using a loss function to compare neural network output to model ATAC-seq information, and adjust the neural network until the loss function is minimized.
----training a neural network involves adjustment of many parameters according mathematical formulas (such as minimizing the loss function). Therefore this claim equates to an abstract idea of mathematical concepts.
Claim 18 recites: wherein the medium further comprises instructions for a training engine executable on the processor, wherein the training engine is configured to train the neural networks with suboptimal ATAC-seq information using a loss function to compare neural network output to model ATAC-seq information, and adjust the neural network until the loss function is minimized.
----training a neural network involves adjustment of many parameters according mathematical formulas (such as minimizing the loss function). Therefore this claim equates to an abstract idea of mathematical concepts.
Hence the claims individually and in combination, recites elements that classified as abstract ideas in the form of mental processes and mathematical concepts. An abstract idea performed in a generic computer environment does not change the fact the steps are directed to mathematical concepts. The claims must therefore be examined further to determine whether the claims integrate the above-identified abstract ideas into a practical application (MPEP 2106.04(d)).
Step 2A Prong Two: Consideration of Practical Application
The claims result in a process “to generate one or more inferences corresponding to the ATAC-seq information”, which reads on generating new information from existing data. The claims do not recite any additional elements that integrate the abstract idea/judicial exception into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
Claims 1, 10 and 17 recites: A processor comprising: One or more circuits to provide to one or more computer-implemented neural networks a plurality of ATAC-seq counts at each base pair position of a genome that exclude base identity, and to use the one or more computer-implemented neural networks to generate one or more inferences corresponding to the ATAC-seq information based, at least in part, on the plurality of ATAC-seq counts.
Claim 2 recites: wherein the neural network comprises at least three successive convolutional layers followed by a residual connection that sums the ATAC-seq counts at each base pair position input with the final convolutional layer output.
Claim 3 recites: wherein each convolutional layer and the residual connection is followed by an ReLU layer.
Claim 4 recites : wherein the convolutional layers have a receptive field size of 100 to 10,000.
Claim 5 recites: wherein the convolutional layers use a number of filters selected from 1 to 100.
Claim 6 recites: wherein the neural network comprises 3 successive convolutional layers of receptive field size 100, 100, and 300, respectively, and using 15, 15, and 1 filters, respectively.
Claim 10 recites: A system comprising: a processor and a memory device,
Claim 11 recites: The system of claim 10, wherein the neural network comprises at least three successive convolutional layers followed by a residual connection that sums the ATAC-seq counts of each segment input with the final convolutional layer output.
Claim 12 recites: The system of claim 11, wherein each convolutional layer and the residual connection is followed by an ReLU layer.
Claim 13 recites: The system of claim 11, wherein the convolutional layers have a receptive field size of 100 to 10,000.
Claim 14 recites: The system of claim 11, wherein the convolutional layers use a number of filters selected from 1 to 100.
Claim 15 recites: The system of claim 11, wherein the neural network comprises 3 successive convolutional layers of receptive field size 100, 100, and 300, respectively, and using 15, 15, and 1 filters, respectively.
Claim 17 recites: A non-transitory computer-readable medium.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements can be summarized as: 1) data input/output (claims 1, 9, 17); 2) software product (“a non-transitory computer-readable medium”, claim 17); and 3) technological environment (“A system comprising: a processor and a memory device”, “one or more neural networks”, “convolutional layer” and “ReLU layer”; claims 1-6, 10-15 ). Group 1) are insignificant extra-solution activities because they are necessary for data analysis; group 2) and 3) are merely providing technical environment to execute the abstract idea.
The claims do not include additional elements that are sufficient to amount of significantly more than the judicial exception because it is routine and conventional to perform the acts of modeling nucleic acid data using neural networks. Other elements of the method include computing components which are recitation of generic computer structures that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry.
Additionally, the conventionality of the additional elements like “convolutional neural networks” and a generic computer system can further be evidenced in the following references:
Koh (“Denoising genome-wide histone ChIP-seq with convolutional neural network” Bioinformatics, 33, 2017, i225–i233). Cited on the 4/11/2023 IDS.
Hiranuma (“DeepATAC: A deep-learning method to predict regulatory factor binding activity from ATAC-seq signals”, bioRxiv preprint doi: https://doi.org/ 10.1101/172767; this version posted August 6, 2017). Cited on the 4/11/2023 IDS.
Thibodeau, A., Uyar, A., Khetan, S. et al. A neural network based model effectively predicts enhancers from clinical ATAC-seq samples. Sci Rep 8, 16048 (2018). Cited on the 4/11/2023 IDS.
Koh discloses a method (Coda) to denoise the ChIP-seq data with convolution neural network (CNN) (page i226, col 2, para 1-3, and Fig 2, page i226; page i227, col 1, para 1).
However, the datasets of Koh are ChIP-seq data, not ATAC-seq data. These two data, ChIP-seq data and ATAC-seq data, are both about chromatin profiling. The ways genomic fragments captured are different but the final output are both NGS sequence reads. ChIP-seq is for detecting regulatory regions (such as transcription factor binding sites) in the genome, while ATAC-seq is slightly more general, about any open part in the genome. Koh points out that the method can be used for the ATAC-seq data (“We believe that a similar approach can be used in other biological assays, e.g. ATAC-seq and DNase-seq, where it is near impossible to analytically characterize all types of technical noise or the overall data distribution but possible to generate noisy versions of high-quality samples through experimental or computational perturbation. This can significantly reduce cost while maintaining or even improving quality, especially in high-throughput settings or when dealing with limited amounts of input material (page i230, col 2, last para through pg. i231, col 1, 1st para).
Similar to Koh., Hiranuma discloses a method (DeepATAC) to predict the transcription factors binding activities from the ATAC-seq datasets, using the CNN technology. Hiranuma’s method deals with the ATAC-seq, not the ChIP-seq. but Hiranuma’s method takes the base identity into consideration by encoding the DNA sequence samples using the 1 hot-encoded 200 base pair windows (equivalent to a segment. Page 1, Section “Abstract” and page 4, para 1-2 under section “Method”).
Additionally, Hiranuma points out that the ChIP-seq can be used to get high-quality subset of ATAC-seq data (“The DeepATAC model was trained on the subset of DeepSEA data. Specifically, the DNA sequence samples were 1 hot-encoded 200 base pair windows (4 by 200 matrix), where we observed (using ChIP-seq peaks) at least one of the 163 and 91 regulatory factors for the K562 and GM12878 cell types respectively. (page 4, lines 1-4 under section “4 Methods”).
Hence, just to look at the data handed by CNN models, ChIP-seq looks exactly like ATAC-seq. therefore, the subject matter explored in instant claims had been explored by at least two groups.
Thibodeau taught a neural network based model to predict enhancers from clinical ATAC-seq samples. Although not using the convolutional neural network model, Thibodeau’s neural network has several layers (“multiple-layer perceptron neural network”).
Viewed as a whole, the additional claim elements do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Applicant’s Argument
In the Remarks filed 9 December 2025, Applicant argued (page 5, penultimate para) that the amendment “computer-implemented neural networks” excludes the steps from being performed in the mind.
Applicant’s argument refers to Step 2A/Prong one in the 101 analysis, relating to whether claims recites abstract ideas or not. Applicant’s argument is not persuasive. An abstract idea executed in computer is still an abstract idea. (MPEP §2106.04(a)(2).III.C).
In the Remarks, Applicant argued (page 5, last para through page 6, 1st para) that excluding the base-pair identity at each base-pair position in the neural network (NN) input data (Examiner: an input sequence alignment which does not require the base identities) provides an unexpected advantage. Applicant’s argument refers to Step 2A/Prong Two in the 101 analysis, relating to whether claims are integrated into a practical application or not.
Applicant’s argument is not persuasive. Inputting ATAC-seq counts into a NN and generating an inference corresponding to ATAC-seq “information, acts like the same as reducing or filtering data, which fits the definition for mental processes (of data analysis or data manipulation). What Applicant argued is drawn to better data modeling, or more specifically, better filtering of low-quality ATAC-seq data. The step ends at “information”, or better “information”, there is no significant additional element apply this feature. There is no significant additional element that captures or reflects this alleged improvement rooted in judicial exceptions. Hence there is no integration into a practical application at Step 2A/Prong Two.
Afterall, “denoising” is removed in the latest claim amendments. Applicant is arguing something not supported by claims.
In the Remarks, Applicant argued (page 6, 2nd para) that since the claimed method is not taught or suggested in art, Step 2B answer should be “Yes”.
Applicant’s argument is not persuasive. First, 35 USC 101 and 35 USC 102 or 103 are different statutes. They are examined separately under different criteria. Second at Step 2B the additional elements evaluated as to whether they are routine, conventional and well understood. Generic computer structures: neural network, processor, memory, individual or as a whole, are routine. The claims do not recite any non-routine additional elements, under Step 2B. The conventionality of recited additional elements are further evidenced by the reference of Koh (“Denoising genome-wide histone ChIP-seq with convolutional neural network” Bioinformatics, 33, 2017, i225–i233. Cited on the 4/11/2023 IDS), and Hiranuma (“DeepATAC: A deep-learning method to predict regulatory factor binding activity from ATAC-seq signals”, bioRxiv preprint doi: https://doi.org/10.1101/172767; this version posted August 6, 2017. Cited on the 4/11/2023 IDS)). Both Koh and Hiranuma have used a computer (including processors and memory) and neural networks to model epigenetics datasets that are output from the NGS facilities in the same format of aligned short reads.
Hence, the 101 rejection is maintained.
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 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.
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.
Claims 1-8 and 10-18 are rejected under 35 U.S.C. 103 as being unpatentable over Koh (“Denoising genome-wide histone ChIP-seq with convolutional neural network” Bioinformatics, 33, 2017, i225–i233. Cited on the 4/11/2023 IDS), in view of Hiranuma (“DeepATAC: A deep-learning method to predict regulatory factor binding activity from ATAC-seq signals”, bioRxiv preprint doi: https://doi.org/10.1101/172767; this version posted August 6, 2017. Cited on the 4/11/2023 IDS).
Claim 1 is directed to a method of inferring information out of the ATAC-seq dataset. With respect to claim 1, Koh discloses a method (Coda) to denoise the ChIP-seq data with convolution neural network (CNN). The specific base identify in ChIP-seq data is excluded from downstream analysis, the peak signals are the information that matters. (page i226, col 2, paras 1-3 and Fig 2; page i227, col 1, para 1). Koh teaches using the CNN to denoise each bins of the ChIP-seq datasets.
However, the datasets of Koh are ChIP-seq data, not ATAC-seq data. These two data, ChIP-seq data and ATAC-seq data, are both similarly with respect to chromatin profiling. The ways genomic fragments captured are different but the final output are both NGS sequence reads. ChIP-seq is for detecting regulatory regions (such as transcription factor binding sites) in the genome, while ATAC-seq is slightly more general, about any open part in the genome. Koh points out that the method can be used for the ATAC-seq data (“We believe that a similar approach can be used in other biological assays, e.g. ATAC-seq and DNase-seq, where it is near impossible to analytically characterize all types of technical noise or the overall data distribution but possible to generate noisy versions of high-quality samples through experimental or computational perturbation. This can significantly reduce cost while maintaining or even improving quality, especially in high-throughput settings or when dealing with limited amounts of input material (page i230, col 2, last para through page i231, col 1, 1st para).
Similar to Koh., Hiranuma teaches a method (DeepATAC) to predict the transcription factors binding activities from the ATAC-seq datasets, using the CNN technology. Hiranuma’s method deals with the ATAC-seq, not the ChIP-seq. Hiranuma’s method takes the base identity into consideration by encoding the DNA sequence samples using the 1 hot-encoded 200 base pair windows (equivalent to a segment. Page 1, Section “Abstract” and page 4, paras 1-2 in “Method”).
Additionally, Hiranuma points out that the ChIP-seq can be used to get high-quality subset of ATAC-seq data (“The DeepATAC model was trained on the subset of DeepSEA data. Specifically, the DNA sequence samples were 1 hot-encoded 200 base pair windows (4 by 200 matrix), where we observed (using ChIP-seq peaks) at least one of the 163 and 91 regulatory factors for the K562 and GM12878 cell types respectively (page 4, line 1-4 under section “4 Methods”).
Regarding claim 2, Koh discloses a CNN configuration with two convolutional layers (Fig 2, page i226). Koh do have an output layer that omits the signal (reads on the sequence counts of each segment input in the claim limitation) for each bin in a window (page i227, col 1, para 2). Hiranuma teaches a CNN with three convolutional layers (page 4, Fig 3).
Regarding claim 3, Koh discloses “It does this by feeding the noisy data through a first convolutional layer, a rectified linear unit (ReLU) layer, a second convolutional layer, and then a final ReLU or sigmoid layer (for regression or classification, respectively)” (Page i226, col 2, 3rd para in “2 Materials and methods/2.1 Model”, lines 6--9), which suggests the claim limitation “wherein each convolutional layer and the residual connection is followed by an ReLU layer”, as Koh’s convolutional layers store the intensity counts for each bin.
Regarding claim 4, Hiranuma’s teaches a CNN with three convolutional layers that each has a receptive field size of 160 kernels, 240 kernels, and 480 kernels respectively (page 4, Fig 3). Hiranuma anticipate the claimed range in receptive field size (MPEP § 2131.03 I. “A SPECIFIC EXAMPLE IN THE PRIOR ART WHICH IS WITHIN A CLAIMED RANGE ANTICIPATES THE RANGE”).
Regarding claim 5, Koh discloses “For the first convolutional layer, we use 6 convolutional filters, each 51 bins in length (25bp/bin); for the second convolutional layer, we use a single filter of length 1001” (Page i226, col 2, 3rd para in “2 Materials and methods/2.1 Model”), which suggests on the claim limitation “wherein the convolutional layers use a number of filters selected from 1 to 100”, as 6 and 1 are both anticipate the range of 1 to 100 (MPEP § 2131.03 I. “A SPECIFIC EXAMPLE IN THE PRIOR ART WHICH IS WITHIN A CLAIMED RANGE ANTICIPATES THE RANGE”) in the number of filters.
Regarding claim 6, Koh discloses a CNN of two convolutional layers that has 6 filter and 1 filter respectively (“For the first convolutional layer, we use 6 convolutional filters, each 51 bins in length (25bp/bin); for the second convolutional layer, we use a single filter of length 1001” (page i226, col 2, 3rd para in “2 Materials and methods/2.1 Model”, lines 6-11), which suggests the claim limitation “using 15, 15, and 1 filters, respectively”. Koh is silent in the receptive field size. Hiranuma’s teaches a CNN with three convolutional layers that each has a receptive field size of 160 kernels, 240 kernels, and 480 kernels respectively. (page 4, Fig 3). Hiranuma anticipate the claimed range in receptive field size (MPEP § 2131.03 II).
Regarding claim 7, Koh further discloses a method (Coda) to denoise the ChIP-seq data with convolution neural network (CNN). More specifically to use the CNN to map between “suboptimal” and “high quality” ChIP-seq data (“we introduce a convolutional denoising algorithm, called Coda, that uses convolutional neural networks (CNNs) to learn a generalizable mapping between ‘suboptimal’ and high quality ChIP-seq data (page i226, col 1, Fig. 1). Coda substantially attenuates three primary sources of noise—due to low sequencing depth, low cell input and low ChIP enrichment—enhancing signal in low-quality samples across individuals, cell types and species”. Page i225, col 2, para 2, lines 1-8). Further, Koh use a loss function to evaluate training progress (“We used the Keras package for training and AdaGrad as the optimizer, stopping training if validation loss did not improve for three consecutive epochs”. Page i227, col 2, 1st para lines 2-4).
Regarding claim 8, Koh teaches the MSE function (“For the regression tasks (predicting signal), we evaluated performance by computing the Pearson correlation and mean-squared error (MSE) between the predicted and measured high-quality fold enrichment signal profiles after an inverse hyperbolic sine transformation, which reduced the dominance of outliers. We compared this with the baseline performance obtained by directly comparing the noisy and high-quality signal profiles of the target mark (after the same inverse hyperbolic sine transformation)”. Page i227, col 1, para 2 in section “2.2 Training and evaluation”)
Claims 10-16 are the computer “system” version for the method version of claims 1-8. Since Koh also suggest a computer system (page i227, col 1, 2nd para), the art applied to claims 1-9 also teach claims 10-16.
Claims 17-18 are the software disk version for the method version of claims 1-8. Since Koh also suggest a computer system (page i227, col 1, 2nd para) which suggests a storage media, the art applied to claims 1-8 also teach claims 17-18.
An invention would have been obvious to one of ordinary skill in the art if some motivation would have led that person to modify prior art teachings to arrive at the claimed invention. Prior to the time of invention, said person would have been motivated to substitute the ChIP-seq datasets, handed by Koh’s CNN (for denoising the ChIP-seq data), with Hiranuma’s ATAC-seq datasets, to achieve a modified pipeline using CNN for denoising the ATAC-seq datasets. Because ATAC-seq is the most recent method and is rapidly gaining popularity due to its cost-efficiency and simplicity (Hiranuma: page 1, last para).
The said person would have been motivated to modify Koh’s CNN to preserve Koh’s features of data partitioning and base-pair identity-free data encoding, and to introduce Hiranuma’s features of elegant three-layered CNN configuration plus the receptive field size setups, Because such a modification and recombination will have the best parts from both Koh and Hiranuma.
We can reasonably expect success for the modification and recombination, as Koh’s ChIP-seq and Hiranuma’s ATAC-seq are both genome-wide chromatin accessibility assays. The raw ChIP-seq and the ATAC-seq data are both NGS sequence reads. They can both handed by the MACS2 program for peak calling, they look the same and processed by the two CNN frameworks of Koh and Hiranuma, Koh points out that his CNN can be used for Hiranuma’s ATAC-seq data (page i230, col 2, last para through page i231, col 1, 1st para) and Hiranuma points out that his CNN can hand Koh’s ChIP-seq to get high-quality subset of ATAC-seq data (page 4, line 1-4 under section “4 Methods”).
Conclusion
No claims are allowed.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUOZHEN LIU whose telephone number is (571)272-0224. The examiner can normally be reached Monday-Friday 8-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Larry D Riggs can be reached at (571) 270-3062. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GL/
Patent Examiner
Art Unit 1686
/Anna Skibinsky/
Primary Examiner, AU 1635