DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement submitted on 3/11/2025 has been considered by the Examiner and made of record in the application file.
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 1-20 are rejected under 35 USC 101.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Specifically, claim 1 includes
a processing system…configured to:
Receive [[gather]] key performance indicator (KPI) data
determine [[calculate]], based on the KPI data, a composite score and
execute an automated action.
Claim 2 recites a composite score result.
Claim 3 recites determining whether the composite score satisfies a criterion and then providing [[reporting]] the composite score to a remote device.
Claim 4 recites making a further determination regarding the composite score based on a subset of the KPI data.
Claims 5 and 6 recite additional types of data that the KPI data includes.
Claim 7 recites determining the composite score when the KPI data additionally includes anomaly data.
Claim 8 recites the anomaly data from claim 7 is determined by using a SARIMA model.
Claim 9 recites the anomaly data from claim 7 is related to an anomaly within the KPI data.
Claims 10-20 recite similar steps as above (see claims 1-9), with the only difference being that claims 16-20 include additional limitations that only store and retrieve processor-executable instructions in memory that are executed by a processor to perform the aforementioned steps.
The judicial exception is not integrated into a practical application, because the limitations from the aforementioned claims do not amount to more than steps performed using KPI data that include receiving [[gathering]] KPI data, determining [[calculating]] a score related to the KPI data, executing an action related to the score and providing [[reporting]] the score to a remote device and additional limitations that only store and retrieve processor-executable instructions in memory that are executed by a processor to perform the aforementioned steps. Although the claim limitations also recite the processing system and the remote device, they do not include any of the processing system or the remote device applying a practical application, based on the aforementioned steps having been performed, such as using the KPI-related data score to improve network performance by a certain amount.
The additional structural components that implement the claimed method by performing the step of determining/calculating the composite score include the generically recited elements of “processing system”, “remote device”, “non-transitory computer-readable medium” and “processors”, which do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a device or computer, which are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
In addition, the steps of receiving/gathering the KPI data, executing an action related to the score and providing/reporting the score to the remote device merely include extra-solution activity. Extra-solution activity includes both pre-solution and post-solution activity. In this case, the receiving/gathering of the KPI data includes pre-solution activity, and the executing of the action (recited without specifying what the action actually entails) related to the determination/calculation of the score and the providing/reporting of the determined/calculated score to the remote device include post-solution activity, which do not add a meaningful limitation to the abstract idea, as recognized by the court decisions listed in MPEP § 2106.05(g).
Claim Rejections - 35 USC § 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9, 10-14, 16-18 and 20 are rejected under 35 U.S.C. 102(a2) as being anticipated by Dhanaraja (US PG Publication 2026/0164270).
Regarding claim 1, Dhanaraja teaches
a system to perform temporal operations health score tracking in telecommunication networks, the system comprising:
a processing system comprising one or more electronic processors, the processing system configured to:
receive key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network
(Fig. 9, step 908, retrieve KPIs
[0020] KPIs may be used: (1) to monitor and optimize the radio network performance);
determine, based on the KPI data, a composite score indicative of an overall performance of the telecommunications network
([0058] Report 802 includes composite score 402 for wireless subscriber 132 and further comprises indication 812, for multiple KPIs, of the KPI's relevance to composite score 402, and/or trend indication 804 of composite score 402 for the wireless subscriber (e.g., wireless subscriber 132 or 134) over time
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130); and
responsive to determining the composite score, execute an automated action with respect to the telecommunications network
([0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 2, Dhanaraja teaches the system of claim 1,
wherein the composite score is to indicate an overall network health of the telecommunications network
([0020] KPIs may be used: (1) to monitor and optimize the radio network performance
[0058] Composite score 402 for wireless subscriber 132 and further comprises indication 812, for multiple KPIs).
Regarding claim 3, Dhanaraja teaches the system of claim 1,
wherein the processing system is configured to:
determine whether the composite score satisfies a criterion
([0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance); and
responsive to the composite score satisfying the criterion, provide an automated notification of the composite score to a remote device
([0020] KPIs may be used: (2) to detect current unacceptable performance related issues in the cellular network, enabling the operator to take rapid actions to preserve the quality of the existing network services; and (3) to provide planners with the detailed information for configuring network parameters for optimum use
[0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 4, Dhanaraja teaches the system of claim 1,
wherein the processing system is configured to determine the composite score based on a subset of the KPI data, wherein the subset of the KPI data is associated with a subset of the plurality of KPIs that is indicative of customer experience with respect to the telecommunications network
([0045] Score factor 416 and score factor 418 are provided to a composite scoring ML model 420, which generates composite score 402. The generation of composite score 402 is such that, if either of score factor 416 and score factor 418 indicate poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance
[0058] Score factor 416 and score factor 418 are combined into composite score 402
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134
(When score factor 416 and score factor 418 are combined into composite score 402, the composite score indicating poor network performance is based on part of the KPIs (either of score factor 416 and score factor 418 and not all of the data), the poor network performance also indicating poor network experience for a subscriber)).
Regarding claim 5, Dhanaraja teaches the system of claim 1,
wherein the KPI data is time series data
([0048] FIG. 8 illustrates further detail for report 802. Composite score 402 is shown, in report 802, in a trend indication 804 that plots composite score 402 on a timeline 806 along a time axis 820 versus a score value axis 822
[0049] Some examples of report 802 also include an indication 812 of various KPI's relevance to composite score 402).
Regarding claim 6, Dhanaraja teaches the system of claim 1,
wherein the plurality of KPIs includes at least one of:
an accessibility KPI, a retainability KPI, a mobility KPI, an integrity KPI, an availability KPI, or a utilization KPI
([0018] Typically, 4G KPIs may be categorized into five categories: (1) accessibility, (2) retainability, (3) integrity, (4) availability, and (5) mobility).
Regarding claim 7, Dhanaraja teaches the system of claim 1,
wherein the processing system is configured to:
determine, based on the KPI data, anomaly data related to the telecommunications network
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
(Determination of anomalous KPI data that is removed)); and
determine the composite score based on the KPI data and the anomaly data
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
[0058] Score factor 416 and score factor 418 are combined into composite score 402, for at least each wireless subscriber (e.g., wireless subscriber 132 or 134) not having an anomalous subscriber KPI set, in operation 926
(Determine composite score, based on the removed anomalous KPI data)).
Regarding claim 9, Dhanaraja teaches the system of claim 7,
wherein the anomaly data includes data related to a presence of an anomaly within the KPI data
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
(Identification (determination) of anomalous KPI data present)).
Regarding claim 10, Dhanaraja teaches
a method to perform temporal operations health scores tracking in telecommunication networks, the method comprising:
receiving, with a processing system comprising one or more electronic processors, key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network
(Fig. 9, step 908, retrieve KPIs
[0020] KPIs may be used: (1) to monitor and optimize the radio network performance);
determining, with the processing system, based on the KPI data, a composite score indicative of an overall performance of the telecommunications network
([0058] Report 802 includes composite score 402 for wireless subscriber 132 and further comprises indication 812, for multiple KPIs, of the KPI's relevance to composite score 402, and/or trend indication 804 of composite score 402 for the wireless subscriber (e.g., wireless subscriber 132 or 134) over time
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130); and
responsive to determining the composite score, executing, with the processing system, an automated action with respect to the telecommunications network
([0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 11, Dhanaraja teaches the method of claim 10,
wherein determining the composite score includes determining a composite score indicative of an overall health of the telecommunications network
([0020] KPIs may be used: (1) to monitor and optimize the radio network performance
[0058] Composite score 402 for wireless subscriber 132 and further comprises indication 812, for multiple KPIs).
Regarding claim 12, Dhanaraja teaches the method of claim 10,
further comprising:
determining, with the processing system, whether the composite score satisfies a criterion
([0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance); and
responsive to the composite score satisfying the criterion, providing, with the processing system an automated notification of the composite score to a remote device
([0020] KPIs may be used: (2) to detect current unacceptable performance related issues in the cellular network, enabling the operator to take rapid actions to preserve the quality of the existing network services; and (3) to provide planners with the detailed information for configuring network parameters for optimum use
[0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 13, Dhanaraja teaches the method of claim 10,
wherein determining, with the processing system, the composite score includes determining, with the processing system, the composite score based on a subset of the KPI data, wherein the subset of the KPI data is associated with a subset of the plurality of KPIs that is indicative of user experience with respect to the telecommunications network
([0045] Score factor 416 and score factor 418 are provided to a composite scoring ML model 420, which generates composite score 402. The generation of composite score 402 is such that, if either of score factor 416 and score factor 418 indicate poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance
[0058] Score factor 416 and score factor 418 are combined into composite score 402
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134
(When score factor 416 and score factor 418 are combined into composite score 402, the composite score indicating poor network performance is based on part of the KPIs (either of score factor 416 and score factor 418 and not all of the data), the poor network performance also indicating poor network experience for a subscriber)).
Regarding claim 14, Dhanaraja teaches the method of claim 10,
wherein receiving, with the processing system, the KPI data includes receiving time series data, wherein the plurality of KPIs includes at least one of:
an accessibility KPI, a retainability KPI, a mobility KPI, an integrity KPI, an availability KPI, or a utilization KPI
([0018] Typically, 4G KPIs may be categorized into five categories: (1) accessibility, (2) retainability, (3) integrity, (4) availability, and (5) mobility
[0048] FIG. 8 illustrates further detail for report 802. Composite score 402 is shown, in report 802, in a trend indication 804 that plots composite score 402 on a timeline 806 along a time axis 820 versus a score value axis 822
[0049] Some examples of report 802 also include an indication 812 of various KPI's relevance to composite score 402).
Regarding claim 16, Dhanaraja teaches
a non-transitory computer-readable medium storing instructions that, when executed by one or more electronic processors of a processing system in a telecommunications network, cause the processing system to perform operations comprising
([0065] An example system comprises: a processor; and a computer-readable medium storing instructions that are operative upon execution by the processor to):
receiving key performance indicator (KPI) data relating to a plurality of KPIs describing a performance of a telecommunications network
(Fig. 9, step 908, retrieve KPIs
[0020] KPIs may be used: (1) to monitor and optimize the radio network performance);
determining, based on the KPI data, a composite score indicative of an overall performance of the telecommunications network
([0058] Report 802 includes composite score 402 for wireless subscriber 132 and further comprises indication 812, for multiple KPIs, of the KPI's relevance to composite score 402, and/or trend indication 804 of composite score 402 for the wireless subscriber (e.g., wireless subscriber 132 or 134) over time
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130); and
responsive to determining the composite score, executing an automated action with respect to the telecommunications network
([0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 17, Dhanaraja teaches the computer-readable medium of claim 16,
further comprising:
determining whether the composite score satisfies a criterion
([0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance); and
responsive to the composite score satisfying the criterion, providing an automated notification of the composite score to a remote device
([0020] KPIs may be used: (2) to detect current unacceptable performance related issues in the cellular network, enabling the operator to take rapid actions to preserve the quality of the existing network services; and (3) to provide planners with the detailed information for configuring network parameters for optimum use
[0045] Subscriber KPI set 302 is also provided to factor scoring 408, which generates a (second) score factor 418. Score factor 418 indicates poor network performance (i.e., are low when 0 is poor performance and 100 is ideal performance on a scale of 0 to 100), then composite score 402 also necessarily indicates poor network performance
[0059] Based on at least composite score 402 for wireless subscriber 132 indicating poorer network experience than indicated by composite score 402 for wireless subscriber 134, operation 932 performs maintenance or upgrade action 130).
Regarding claim 18, Dhanaraja teaches the computer-readable medium of claim 16,
further comprising:
determining, based on the KPI data, anomaly data related to the telecommunications network
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
(Determination of anomalous KPI data that is removed)); and determining the composite score based on the KPI data and the anomaly data
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
[0058] Score factor 416 and score factor 418 are combined into composite score 402, for at least each wireless subscriber (e.g., wireless subscriber 132 or 134) not having an anomalous subscriber KPI set, in operation 926
(Determine composite score, based on the removed anomalous KPI data)).
Regarding claim 20, Dhanaraja teaches the computer-readable medium of claim 18,
wherein determining the anomaly data includes determining data related to a presence of an anomaly within the KPI data
([0055] Operation 920 identifies and removes anomalous subscriber KPI sets from the score determination
(Identification (determination) of anomalous KPI data present)).
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.
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.
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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 8, 15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Dhanaraja, in view of Pal, et al (US PG Publication 2026/0120037), hereafter Pal.
Regarding claim 8, Dhanaraja teaches the system of claim 7.
Dhanaraja does not teach
wherein the processing system is configured to determine the anomaly data using a seasonal autoregressive integrated moving average (SARIMA) model.
In the same field of endeavor, Pal teaches
wherein the processing system is configured to determine the anomaly data using a seasonal autoregressive integrated moving average (SARIMA) model
([0057] In one or more embodiments, outlier detection module 126 (part of Forecasting Engine 104 – see Fig. 1A) employs one or more of the following methods for determining outliers. The Z-score measures how far a data point is from the mean in terms of standard deviations. Typically, a Z-score greater than 3 or less than −3 is considered an outlier. Boxplots visually display outliers as points that fall outside the “whiskers,” which represent 1.5 times an IQR. A moving average can smooth out short-term fluctuations and highlight sudden spikes or dips as outliers. Isolation Forest is an anomaly detection
[0062] Forecasting model module 132 (also part of Forecasting Engine 104 – see Fig. 1A) uses Seasonal Autoregressive Integrated Moving Average (SARIMA) as a modeling technique. SARIMA extends the Autoregressive Integrated Moving Average (ARIMA) model by adding components that handle seasonality in the data).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dhanaraja, which includes performing actions, based on KPI data, to include Pal’s teaching of performing actions, based on KPI data, for the benefit of applying aggregated datasets to advanced forecasting models for forecasting KPI (see [0061]).
Regarding claim 15, Dhanaraja teaches the method of claim 10.
Dhanaraja does not teach
further comprising:
determining, with the processing system, using a seasonal autoregressive integrated moving average (SARIMA) model, anomaly data based on the KPI data, the anomaly data related to a presence of anomalies within the KPI data, wherein the composite score is determined based on the anomaly data and the KPI data.
In the same field of endeavor, Pal teaches
further comprising:
determining, with the processing system, using a seasonal autoregressive integrated moving average (SARIMA) model, anomaly data based on the KPI data, the anomaly data related to a presence of anomalies within the KPI data, wherein the composite score is determined based on the anomaly data and the KPI data
([0057] In one or more embodiments, outlier detection module 126 (part of Forecasting Engine 104 – see Fig. 1A) employs one or more of the following methods for determining outliers. The Z-score measures how far a data point is from the mean in terms of standard deviations. Typically, a Z-score greater than 3 or less than −3 is considered an outlier. Boxplots visually display outliers as points that fall outside the “whiskers,” which represent 1.5 times an IQR. A moving average can smooth out short-term fluctuations and highlight sudden spikes or dips as outliers. Isolation Forest is an anomaly detection
[0062] Forecasting model module 132 (also part of Forecasting Engine 104 – see Fig. 1A) uses Seasonal Autoregressive Integrated Moving Average (SARIMA) as a modeling technique. SARIMA extends the Autoregressive Integrated Moving Average (ARIMA) model by adding components that handle seasonality in the data).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dhanaraja, which includes performing actions, based on KPI data, to include Pal’s teaching of performing actions, based on KPI data, for the benefit of applying aggregated datasets to advanced forecasting models for forecasting KPI (see [0061]).
Regarding claim 19, Dhanaraja teaches the computer-readable medium of claim 18.
Dhanaraja does not teach
wherein determining the anomaly data includes determining the anomaly data using a seasonal autoregressive integrated moving average (SARIMA) model.
In the same field of endeavor, Pal teaches
wherein determining the anomaly data includes determining the anomaly data using a seasonal autoregressive integrated moving average (SARIMA) model
([0057] In one or more embodiments, outlier detection module 126 (part of Forecasting Engine 104 – see Fig. 1A) employs one or more of the following methods for determining outliers. The Z-score measures how far a data point is from the mean in terms of standard deviations. Typically, a Z-score greater than 3 or less than −3 is considered an outlier. Boxplots visually display outliers as points that fall outside the “whiskers,” which represent 1.5 times an IQR. A moving average can smooth out short-term fluctuations and highlight sudden spikes or dips as outliers. Isolation Forest is an anomaly detection
[0062] Forecasting model module 132 (also part of Forecasting Engine 104 – see Fig. 1A) uses Seasonal Autoregressive Integrated Moving Average (SARIMA) as a modeling technique. SARIMA extends the Autoregressive Integrated Moving Average (ARIMA) model by adding components that handle seasonality in the data).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the invention of Dhanaraja, which includes performing actions, based on KPI data, to include Pal’s teaching of performing actions, based on KPI data, for the benefit of applying aggregated datasets to advanced forecasting models for forecasting KPI (see [0061]).
Conclusion
Citation of Pertinent Prior Art Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gopal, et al (US PG Publication 2023/0259992), hereafter Gopal, teaches measuring and determining real-time composite scores, based on KPIs.
Meier-Hellstern, et al (US PG Publication 2021/0014698), hereafter Meier-Hellstern, teaches calculating where capacity is needed for individual communication technology based on one or more KPIs, example KPIs including network throughput, accessibility, retainability and a competitive quality score.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Frank Donado whose telephone number is (571) 270-5361. The examiner can normally be reached Mondays through Fridays between 8 am and 4 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s Supervisor Patent Examiner (SPE) Charles Appiah can be reached at 571-272-7904. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FRANK E DONADO/Examiner, Art Unit 2641
/CHARLES N APPIAH/Supervisory Patent Examiner, Art Unit 2641
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