DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to the Application filed on 03/03/2023. Claims 1-18 are pending in the case. Claims 1, 7, 12, and 13 are independent claims.
Claim Rejections - 35 U.S.C. § 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 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-6 and 12 are directed towards the statutory category of a process. Claims 7-11 are directed towards the statutory category of a machine. Claims 13-18 are directed towards the statutory category of an article of manufacture.
With respect to claim 1:
2A Prong 1: This claim is directed to a judicial exception.
A method of creating an output dataset, the method comprising (mental process):…
creating a bucket dataset comprising the input data file selected based on a highest first selection value, wherein the first selection value is a quantification value, wherein the quantification value for each of the input data files is determined based on a probability of occurrence of each of the attributes in the corresponding input data file (mental process);
iteratively sampling the dataset until all input data files of the dataset are added into the bucket list, wherein the iterative sampling for each iteration comprises (mental process):
creating a subset dataset including subset data files, wherein subset data files are determined based on a summation data file, wherein the summation data file is determined based on summation of attribute values for each of the attributes for each of the input data file of the bucket dataset (mental process);
adding the summation data file to the summation dataset (mental process);
determining a second selection value for each of the subset data files of the subset dataset, wherein the second selection value is a quantification value of each the subset data files determined based on probability of occurrence of each of the attributes in each of the corresponding subset data file (mental process); and
adding to the bucket dataset the input data file of the updated dataset corresponding to the subset data file with highest second selection value, wherein the dataset is updated by decrementing the input data file added to the bucket dataset (mental process);
determining a third selection value for each of the summation data files of the summation dataset (mental process); and
determining the output dataset as the bucket dataset determined for the sampling iteration based on an output criterion, wherein the output criterion is based on the third selection value (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, by one or more processors of a computing device, a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, by one or more processors of a computing device, a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 2:
2A Prong 1: This claim is directed to a judicial exception.
the output criterion comprises determining the bucket dataset as the output dataset corresponding to the sampling iteration for which the summation data file has highest sampling iteration number and minimum standard deviation (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 3:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is the quantification value for each of the input data files determined based on a quantification value of each attribute determined based on a probability of occurrence and a probability of absence of each of the attributes in the corresponding input data file (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 4:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is a quantification value determined based on cross entropy value and reverse cross-entropy value for each of the input data files, wherein the quantification value is determined based on a desired distribution attribute value for each of the attributes (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 5:
2A Prong 1: This claim is directed to a judicial exception.
each input data file is associated with a pre-defined counter value, wherein the pre-defined counter value associated to the input data file is decremented when the corresponding input data file is added to the bucket dataset (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 6:
2A Prong 1: This claim is directed to a judicial exception.
the input data file with highest quantification value and highest counter value is selected to be added to the bucket dataset (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 7:
2A Prong 1: This claim is directed to a judicial exception.
A system of creating an output dataset comprising (mental process):…
create a bucket dataset comprising the input data file selected based on a highest first selection value, wherein the first selection value is a quantification value, wherein the quantification value for each of the input data files is determined based on a probability of occurrence of each of the attributes in the corresponding input data file (mental process);
iteratively sample the dataset until all input data files of the dataset are added into the bucket list, wherein the iterative sampling for each iteration comprises (mental process):
create a subset dataset including subset data files, wherein subset data files are determined based on a summation data file, wherein the summation data file is determined based on summation of attribute values for each of the attributes for each of the input data file of the bucket dataset (mental process);
add the summation data file to the summation dataset (mental process);
determine a second selection value for each of the subset data files of the subset dataset, wherein the second selection value is a quantification value of each the subset data files determined based on probability of occurrence of each of the attributes in each of the corresponding subset data file (mental process); and
add to the bucket dataset the input data file of the updated dataset corresponding to the subset data file with highest second selection value, wherein the dataset is updated by decrementing the input data file added to the bucket dataset (mental process);
determine a third selection value for each of the summation data files of the summation dataset (mental process); and
determine the output dataset as the bucket dataset determined for the sampling iteration based on an output criterion, wherein the output criterion is based on the third selection value (mental process).2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
one or more processors in a data processing device communicably connected to a memory, wherein the memory stores a plurality of processor- executable instructions which upon execution cause the one or more processors to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
receive a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
one or more processors in a data processing device communicably connected to a memory, wherein the memory stores a plurality of processor- executable instructions which upon execution cause the one or more processors to (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
receive a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 8:
2A Prong 1: This claim is directed to a judicial exception.
the output criterion is based on determination of the bucket dataset as the output dataset corresponding to the sampling iteration for which the summation data file has highest sampling iteration number and minimum standard deviation (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 9:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is the quantification value for each of the input data files determined based on a quantification value of each attribute determined based on a probability of occurrence and a probability of absence of each of the attributes in the corresponding input data file (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 10:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is a quantification value determined based on cross quantification value and reverse cross-quantification value for each of the input data files, wherein the quantification value is based on a desired distribution attribute value for each of the attributes (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 11:
2A Prong 1: This claim is directed to a judicial exception.
each input data file is associated with a pre- defined counter value, wherein the pre-defined counter value associated to the input data file is decremented when the corresponding input data file is added to the bucket dataset (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 12:
2A Prong 1: This claim is directed to a judicial exception.
A method of creating an output dataset, the method comprising (mental process):…
wherein each input data file from the plurality of input data files comprises one or more pre-defined attributes (mental process);
iteratively sampling the dataset based on a pre-defined type of sampling (mental process); and
determining the output dataset based on the pre-defined type of sampling and an output criterion associated to the pre-defined type of sampling, wherein the output dataset comprises a threshold number of input data files and a threshold value of distribution of the input data files for each of the pre-defined attributes (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
receiving, by one or more processors of a computing device, a dataset from a plurality of data sources, wherein the dataset comprises a plurality of input data files (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
receiving, by one or more processors of a computing device, a dataset from a plurality of data sources, wherein the dataset comprises a plurality of input data files (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 13:
2A Prong 1: This claim is directed to a judicial exception.
creating a bucket dataset comprising the input data file selected based on a highest first selection value, wherein the first selection value is a quantification value, wherein the quantification value for each of the input data files is determined based on a probability of occurrence of each of the attributes in the corresponding input data file (mental process);
iteratively sampling the dataset until all input data files of the dataset are added into the bucket list, wherein the iterative sampling for each iteration comprises (mental process):
creating a subset dataset including subset data files, wherein subset data files are determined based on a summation data file, wherein the summation data file is determined based on summation of attribute values for each of the attributes for each of the input data file of the bucket dataset (mental process);
adding the summation data file to the summation dataset (mental process);
determining a second selection value for each of the subset data files of the subset dataset, wherein the second selection value is a quantification value of each the subset data files determined based on probability of occurrence of each of the attributes in each of the corresponding subset data file (mental process); and
adding to the bucket dataset the input data file of the updated dataset corresponding to the subset data file with highest second selection value, wherein the dataset is updated by decrementing the input data file added to the bucket dataset (mental process);
determining a third selection value for each of the summation data files of the summation dataset (mental process); and
determining the output dataset as the bucket dataset determined for the sampling iteration based on an output criterion, wherein the output criterion is based on the third selection value (mental process).2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A non-transitory computer-readable medium storing computer-executable instructions for creating an output dataset, the computer-executable instructions configured for (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
receiving a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g)).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A non-transitory computer-readable medium storing computer-executable instructions for creating an output dataset, the computer-executable instructions configured for (merely reciting the words "apply it" (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f));
receiving a dataset comprising a plurality of input data files, wherein each input data file comprises attribute values corresponding to a presence of a plurality of attributes (MPEP 2106.05(d) indicates that merely “storing and retrieving information in memory” and/or "receiving or transmitting data over a network" are well‐understood, routine, conventional functions when they are claimed in a merely generic manner (as it is in the present claim). Thereby, a conclusion that the claimed step is well-understood, routine, conventional activity is supported under Berkheimer).
With respect to claim 14:
2A Prong 1: This claim is directed to a judicial exception.
the output criterion comprises determining the bucket dataset as the output dataset corresponding to the sampling iteration for which the summation data file has highest sampling iteration number and minimum standard deviation (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 15:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is the quantification value for each of the input data files determined based on a quantification value of each attribute determined based on a probability of occurrence and a probability of absence of each of the attributes in the corresponding input data file (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 16:
2A Prong 1: This claim is directed to a judicial exception.
the first selection value is a quantification value determined based on cross entropy value and reverse cross-entropy value for each of the input data files, wherein the quantification value is determined based on a desired distribution attribute value for each of the attributes (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 17:
2A Prong 1: This claim is directed to a judicial exception.
each input data file is associated with a pre-defined counter value, wherein the pre-defined counter value associated to the input data file is decremented when the corresponding input data file is added to the bucket dataset (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
With respect to claim 18:
2A Prong 1: This claim is directed to a judicial exception.
the input data file with highest quantification value and highest counter value is selected to be added to the bucket dataset (mental process).
2A Prong 2: This judicial exception is not integrated into a practical application.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
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 are advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claim Rejections - 35 U.S.C. § 102
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 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.
Claim 12 is rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Bondar et al. (U.S. Pat. App. Pub. No. 2018/0191584, hereinafter Bondar).
As to independent claim 12, Bondar discloses:
A method of creating an output dataset, the method comprising:
receiving, by one or more processors of a computing device, a dataset from a plurality of data sources, wherein the dataset comprises a plurality of input data files ([0026] – “The network monitor repository 108 receives and stores traffic records collected by the monitoring devices 106 and/or collection devices.”; The “network monitor repository 108” (a computing device) receives a dataset (“traffic records”) from monitoring devices 106 and/or collection devices (plural data sources); Each “traffic record” corresponds to an input data file);
wherein each input data file from the plurality of input data files comprises one or more pre-defined attributes ([0027] – ““each traffic record identifies an IP address of source and/or target destination devices 104 that are communicating with one another in a particular conversation included in the corresponding traffic flow. Additional fields associated with each traffic record can include one or more of incoming traffic size, outgoing traffic size, destination port, source port, and protocol (e.g., TCP, UDP, etc.).”; Each “traffic record” (input data file) contains pre-defined attributes such as IP address, traffic size, ports, and protocol);
iteratively sampling the dataset based on a pre-defined type of sampling ([0007] – ““The method includes processing multiple iterations associated with respective traffic records of the large data set that satisfy particular criteria.” And [0033] – “The sampling module 120 can further decide whether to sample traffic records associated with the discovered traffic flows, and sample the traffic records based on application of a probabilistic function. The probabilistic algorithm compensates for potential skewing of the large data set. In embodiments, the probabilistic algorithm includes applying an exponentially decreasing probability of sampling the received traffic records.”; The method iteratively processes records (“multiple iterations”) and applies a pre-defined sampling type (probabilistic/exponentially decreasing probability) to sample the dataset); and
determining the output dataset based on the pre-defined type of sampling and an output criterion associated to the pre-defined type of sampling ([0056] – “sampling is performed, based on application of a probability function, only if it is determined that a total flow counter associated with the received traffic record is more than a predetermined sampling threshold SAMPLING_THRESHOLD, otherwise the received traffic record is stored in the appropriate bin(s) as sampled data without performing sampling.” And [0057] – “the received traffic record is stored in the sample storage disk and saved_flow_count is incremented. In particular, the sampled data is stored in the large bin that has an associated time interval that includes the time stamp associated with the traffic record.”; The output dataset (sampled data stored in bins) is determined by the sampling algorithm (probability function) and an output criterion (sampling threshold and time interval/bin assignment)), wherein the output dataset comprises a threshold number of input data files and a threshold value of distribution of the input data files for each of the pre-defined attributes ([0056] – ““sampling is performed, based on application of a probability function, only if it is determined that a total flow counter associated with the received traffic record is more than a predetermined sampling threshold SAMPLING_THRESHOLD…”; [0052] – ““flow_count, which is a counter that represents the number of received traffic records associated with an address ip1…”; [0080] – ““when stored sampled data that is associated with at least one address having an associated sample flow counter (saved_flow_count) that is less than a predetermined minimum storage threshold is removed from the being stored in a disk…”; The output dataset is controlled by a sampling threshold (SAMPLE_THRESHOLD) for the total number of files.; The system maintains per-attribute counters (e.g., per address) and applies a minimum storage threshold for each attribute, ensuring a threshold value of distribution in the output dataset).
Allowable Subject Matter
Claims 1-11 and 13-18 are allowable over the prior art, and would be allowed if the claims were to overcome the remaining 101 rejections.
The following is a statement of reasons for the indication of allowable subject matter: the prior art fails to teach or render obvious the following limitations: the quantification value for each of the input data files is determined based on a probability of occurrence of each of the attributes in the corresponding input data file; creating a subset dataset including subset data files, wherein subset data files are determined based on a summation data file, wherein the summation data file is determined based on summation of attribute values for each of the attributes for each of the input data file of the bucket dataset; adding the summation data file to the summation dataset; determining a second selection value for each of the subset data files of the subset dataset, wherein the second selection value is a quantification value of each the subset data files determined based on probability of occurrence of each of the attributes in each of the corresponding subset data file; and adding to the bucket dataset the input data file of the updated dataset corresponding to the subset data file with highest second selection value, wherein the dataset is updated by decrementing the input data file added to the bucket dataset; determining a third selection value for each of the summation data files of the summation dataset; and determining the output dataset as the bucket dataset determined for the sampling iteration based on an output criterion, wherein the output criterion is based on the third selection value; determining the output dataset based on the pre-defined type of sampling and an output criterion associated to the pre-defined type of sampling, wherein the output dataset comprises a threshold number of input data files and a threshold value of distribution of the input data files for each of the pre-defined attributes.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Muffat et al. (U.S. Pat. App. Pub. No. 2020/0250241) teaches subset selection and optimization for balanced sampled dataset generation. Methods and systems for data management of documents in one or more data repositories in a computer network or cloud infrastructure are provided. The method includes sampling the documents in the one or more data repositories and formulating representative subsets of the sampled documents. The method further includes generating sampled data sets of the sampled documents and balancing the sampled data sets for further processing of the sampled documents. The formulation of the representative subsets is performed for identification of some of the representative subsets for initial processing. Selz et al. (U.S. Pat. App. Pub. No. 2023/0342672) teaches classification and/or prediction on unbalanced datasets. A computer-implemented method for classification and/or prediction of data samples on unbalanced datasets, the method comprising: receiving unbalanced training dataset; generating at least one first model and at least one second model using unbalanced training dataset, the generation of the at least one first model comprises generating first training dataset comprising first data subset and second data subset of equal amounts of data; the generation of the at least one second model comprises generating second training dataset comprising third data subset and fourth data subset of equal amounts of data, machine learning algorithm(s) are employed for learning from the first and second training datasets; generating composite model using the first model(s) and the second model(s); employing composite model for classification and/or prediction on unbalanced test dataset for generating output, the output includes at least one classified data and/or a prediction for a data sample. Samwell et al. (U.S. Pat. App. Pub. No. 2025/0086155) teaches a system for clustering data into corresponding files comprises one or more processors and a memory. The one or more processors is/are configured to: 1) determine to cluster a set of data into a set of files; 2) determine a set of split points in a corresponding set of dimensions of the set of data to determine the set of files, wherein each file of the set of files has an approximate target size; and 3) store one or more items of the set of data into a corresponding file of the set of files based at least in part on the set of split points. The memory is coupled to the one or more processors and configured to provide the processor with instructions.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Casey R. Garner whose telephone number is 571-272-2467. The examiner can normally be reached Monday to Friday, 8am to 5pm, Eastern Time.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexey Shmatov can be reached on 571-270-3428. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Casey R. Garner/Primary Examiner, Art Unit 2123