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 Claims
Claims 1, 3-8, and 11-20 are currently pending and have been examined.
Claims 2, 9, 10 have been canceled.
Claims 1, 3-8, and 11-13 have been amended.
Claims 14-20 have been added.
Claims 1, 3-8, and 11-20 have been rejected.
Priority and Formal Matters
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed for Application No. FI20215700, filed on 11/29/2023.
The instant application therefore claims the benefit of priority under 35 U.S.C 119(a)-(d). Accordingly, the effective filing date for the instant application is 15 June 2021 claiming benefit to FI20215700.
The preliminary amendments to the claims, received on 29 November 2023 have been received and are accepted.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8, and 11-20 are 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.
Step 1 – Statutory Categories of Invention:
Claims 1, 3-8, and 11-20 are drawn to a method or apparatus, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method for analyzing measurement results of a communication network.
This independent claim recites the following steps best characterized as a mental process under MPEP § 2106.04(a)(2)(III) citing the abstract idea grouping for mental processes in general:
obtaining measurement results, wherein the measurement results comprise multiple data entries, and wherein each data entry comprises multiple data values on a lowest hierarchy level and hierarchy information defining with which entities the entry is related to on different hierarchy levels
determining an aggregated anomaly score for each data entry by calculating anomaly score for the data values of the data entry and aggregating the anomaly scores to determine the aggregated anomaly score for the data entry
choosing data entries, wherein the aggregated anomaly score fulfils predefined criteria, for further analysis
performing hierarchical clustering on the chosen entries based on dissimilarity of the chosen entries to combine at least some of the chosen entries together; and
using the hierarchically clustered entries to identify one or more anomalous entities on hierarchy levels above the lowest hierarchy level
Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as applying a mental process to a generic computing environment - see MPEP § 2106.04(a)(2)(III)(c)(2).
Furthermore, the steps of calculating an anomaly score and performing hierarchical clustering, while at a certain level of generality may be performed mentally, can also be characterized as representing mathematical relationships - see MPEP § 2106.04(a)(2)(I)(A).
Dependent claim 3 recites, in part, wherein the anomaly detection is performed using robust principal component analysis, RPCA.
Dependent claim 4 recites, in part, wherein top n highest aggregated anomaly scores fulfil the predefined criteria.
Dependent claim 5 recites, in part, wherein aggregated anomaly scores that exceed a predefined threshold fulfil the predefined criteria.
Dependent claim 6 recites, in part, wherein the data values comprise observed data values aggregated over a predefined period of time.
Dependent claim 7 recites, in part, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities.
Dependent claim 8 recites, in part, wherein the data values represent network performance.
Dependent claim 11 recites, in part, further comprising using the hierarchically clustered entries for making decisions on controlling the communications network.
Dependent claim 12 recites, in part, perform the method of claim 1.
Dependent claim 13 recites, in part, perform the method of claim 1.
Dependent claim 16 recites, in part, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities.
Dependent claim 17 recites, in part, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities.
Dependent claim 18 recites, in part, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities.
Dependent claim 19 recites, in part, wherein the data values represent network performance.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1 or 12 accordingly, and hence are nonetheless directed towards fundamentally the same mathematical concept abstract idea grouping as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 12 recites an apparatus comprising: a processor, and a memory including computer program code. Claim 13 recites a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code. The specification provides that the computer and corresponding hardware is a general purpose computer (see the instant specification on p. 6 lines 14-19). The use of a computer and corresponding hardware and software, in this case to perform the method of claim 1, only recites the computer as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 14 recites an automation system. The specification does not provide additional detail regarding the structure or algorithm design for the automation system. The use of an automation system, in this case to performing the method, only recites the automation system as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claim 1 recites using the hierarchically clustered entries… for controlling the communications network. Claims 15 and 20 recite a use the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network. The specification provides no additional detail regarding algorithmic or hardware design for the decisions on controlling the network (see the instant specification on p. 6 lines 5-7 offering general exemplarity embodiments for the possible outcomes). Therefore, the limitations amount to a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself (MPEP § 2106.05(h) similar to example vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) wherein the additional elements do not amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use). Additionally, the limitations would amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, and does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(I) see Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327, 120 USPQ2d 1353, 1366 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348, 115 USPQ2d 1414, 1417 (Fed. Cir. 2015))
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer. Claim 14 recites an automation system.
Claim 12 recites an apparatus comprising: a processor, and a memory including computer program code. Claim 13 recites a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code. Claim 1 recites using the hierarchically clustered entries… for controlling the communications network. Claims 15 and 20 recite using the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (MPEP § 2106.07(a)(III)(A) integrating the evidentiary requirements in making a § 101 rejection as established in Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 3-8, and 11-20 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3 is rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Claim 2 recites the limitation " the anomaly detection”. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-8, and 11-20 rejected under 35 U.S.C. 103 as being unpatentable over Rezaei et al., Automatic fault detection and diagnosis in cellular networks using operations support systems data, IEEE Network Operations and Management Symposium 468-473 (April 2016)[hereinafter Rezaei] in view of Mdini et al. (US Patent Application No. 2020/0136891)[hereinafter Mdini].
As per claim 1, Rezaei teaches on the following limitations of the claim:
method for analyzing measurement results of a communications network, the method comprising is taught in the § Abstract on p. 468 (teaching on a hierarchical clustering cellular network performance monitoring system)
obtaining measurement results, wherein the measurement results comprise multiple data entries, and is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators (treated as synonymous to a data value with a measurement result) including measured traffic (TCH values) and signaling (SDCCH values) data for a plurality of monitored network over a time period (treated as synonymous to a data entry) )
wherein each data entry comprises multiple data values on a lowest hierarchy level and hierarchy information defining with which entities the entry is related to on different hierarchy levels is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on KPIs measured TCH and SDCCH values (treated as synonymous to a data value) each having categorical hierarchy breakdowns for the measured values; specifically c. Signal Quality (RxQuality) with 8 levels from 0 (best - treated as synonymous to "a lowest hierarchy level") to 7 (worst))
determining an aggregated anomaly score for each data entry by calculating anomaly score for the data values of the data entry and aggregating the anomaly scores to determine the aggregated anomaly score for the data entry is taught in the § III. Fault Detection on p. 470 (teaching on determining a value for each KPI (treated as synonymous to calculating anomaly score) and determining an overall Complete Correct Call (CCC) rate (treated as synonymous to an aggregated anomaly score) by aggregating each KPI score)"
choosing data entries, wherein the aggregated anomaly score fulfils predefined criteria, for further analysis is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold))
performing hierarchical clustering on the chosen entries based on dissimilarity of the chosen entries to combine at least some of the chosen entries together; and is taught in the § IV. Root Cause Analysis on p. 471 col 1 (teaching on performing applying unsupervised learning algorithms including agglomerative hierarchical clustering to assign specific fault clusters associated with a root cause wherein hierarchical clustering requires linkage criterion determining a dissimilarity distance between sets of observations)
using the hierarchically clustered entries to identify one or more anomalous entities on hierarchy levels above the lowest hierarchy level for controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
Rezaei fails to teach the following limitation of claim 1. Mdini, however, does teach the following:
a computer implemented method is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 3, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
the method of claim 1, wherein the anomaly detection is performed using robust principal component analysis, RPCA is taught in the Summary in ¶ 0008 (teaching on utilizing Robust Principal Component Analysis (RPCA) to detect performance anomalies)
Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself- that is in the substitution of the RPCA preprocessing algorithm of Mdini for the abnormality detection algorithm of Rezaei. Thus, the simple substitution of one known element for another producing a predictable result of identifying abnormal groups via a known algorithm renders the claim obvious.
As per claim 4, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein top n highest aggregated anomaly scores fulfil the predefined criteria is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold) wherein CCC less than 85 are labeled as fault and over 85 (treated as synonymous to top n highest) are labeled not fault - Examiner notes utilization of the "n lowest" for fault v "n highest" CCC scores is merely a function of the unclaimed mathematical calculation for the aggregated anomaly score and wherein the instant specification in ¶ 0060-61 is consistent with the prior art teaching of further analyzing the "most anomalous entries")
As per claim 5, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein aggregated anomaly scores that exceed a predefined threshold fulfil the predefined criteria is taught in the § III. Fault Detection on p. 470 and Fig. 2 on p. 470 (teaching on categorizing the CCC rate for each network based on a CCC rate threshold (treated as fulfilling a predefined threshold) wherein CCC less than 85 are labeled as fault and over 85 (treated as synonymous to top n highest) are labeled not fault - Examiner notes utilization of the "n lowest" for fault v "n highest" CCC scores is merely a function of the unclaimed mathematical calculation for the aggregated anomaly score and wherein the instant specification in ¶ 0060-61 is consistent with the prior art teaching of further analyzing the "most anomalous entries")
As per claim 6, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the data values comprise observed data values aggregated over a predefined period of time is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators (treated as synonymous to a data value with a measurement result) including measured traffic (TCH values) and signaling (SDCCH values) data for a plurality of monitored network over a time period (treated as synonymous to a data entry))
As per claim 7, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 8, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the data values represent network performance is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on key performance indicators measured TCH and SDCCH values (treated as synonymous to network performance a data value) each having categorical hierarchy breakdowns)
As per claim 11, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, further comprising using the hierarchically clustered entries for making decisions on controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 12, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
an apparatus comprising: a processor, and a memory including computer program code; the memory and the computer program code configured to, with the processor, cause the apparatus to perform the method of claim 1 is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 13, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei fails to teach the following; Mdini, however, does disclose:
a non-transitory computer readable medium, having stored thereon a computer program comprising computer executable program code which, when executed by a processor, causes an apparatus to perform the method of claim 1 is taught in the Abstract, in the Background in ¶ 0016, and in the Detailed Description in ¶ 0139-141 (teaching on computer and corresponding hardware and software for executing a hierarchical clustering cellular network performance monitoring system)
One of ordinary skill in the art would have recognized that applying the known technique of implemented the method on a general purpose computer would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of a Mdini to the network performance evaluation and correction method of Rezaei would have yielded predictable results of utilizing software for efficient advantages. Further, the combination would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow computer automation of the process.
As per claim 14, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the method is performed by an automation system is taught in the § Abstract on p. 468 (teaching on a hierarchical clustering cellular network performance monitoring system being "automatic")
As per claim 15, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, further comprising using the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network is taught in the A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
As per claim 16, the combination of Rezaei and Mdini discloses all of the limitations of claim 1. Rezaei also discloses the following:
the method of claim 1, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 17, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the hierarchy levels relate to subscription types and/or network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 18, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the hierarchy levels relate to network devices and/or technology types and/or logical network entities is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on collecting key performance indicators associated with cell phone network coverage (treated as synonymous with logical network entities consistent with the instant specification in ¶ 0056) wherein the KPI categorical hierarchy breakdowns are related to cell networking parameters)
As per claim 19, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the data values represent network performance is taught in the § A. KPIs and Data Set on p. 469 col 2 - p. 470 col 1 (teaching on key performance indicators measured TCH and SDCCH values (treated as synonymous to network performance a data value) each having categorical hierarchy breakdowns)
As per claim 20, the combination of Rezaei and Mdini discloses all of the limitations of claim 12. Rezaei also discloses the following:
the apparatus of claim 12, wherein the memory and the computer program code are further configured to, with the processor, cause the apparatus to use the identified one or more anomalous entities on hierarchy levels above the lowest hierarchy level for making decisions on controlling the communications network is taught in the § A. Traffic Channel KPIs on p. 471-472 (teaching on different channel improvements to implement for each root cause cluster labeled as fault besides the normal records not labeled as "fault" (treated as synonymous to "a lowest hierarchy level") that are grouped and not included)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Macia-Fernandez, Hierarchical PCA-Based Multivariate Statistical Network Monitoring for Anomaly Detection, 2016 IEEE International Workshop on Information and Security (Jan 19, 2017) teaching on a PCA hierarchical approach for multivariant statistical network monitoring in the § IV. Hierarchical MSNM on p. 3
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN LYNN JACKSON whose telephone number is (571)272-5389. The examiner can normally be reached Monday-Friday 8:30AM-4:30PM ET.
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, Arleen M Vazquez can be reached at 571-272-2619. 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.
/JORDAN L JACKSON/Primary Examiner, Art Unit 2857
Prosecution Insights