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 .
Response to Arguments
1. Applicant’s arguments filed on 07/23/2026 regarding claims 1-20 in the remarks are fully considered but moot in view of new ground(s) of rejection.
Response to Amendments
Claim Rejections - 35 USC § 103
1. 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.
2. Claim(s) 1-5, 10, 11, 13-16, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Feldman (US PG Pub. No. 2021/0158254) in view of Azarian (US PG Pub. No. 2026/0253396).
As per claim 1:
Feldman teaches a method (see abstract: teaches a method of providing services to address occupant pairs) comprising:
obtaining, by a processing system (see paragraph [0016], service notification platform 102, construed as said processing system may obtain a first set of address-occupant pairs from the plurality of source devices 104) including at least one processor (see Figure 3, paragraph [0090], device 300 may correspond to service notification platform and comprising processor 320), network inventory data of a communication network (see paragraph [0016] said first set of address-occupant pairs from the plurality of source devices may be associated with credit record entity (e.g., credit bureau)), wherein the network inventory data is associated with a plurality of geographic areas (see paragraph [0016], said credit record entity is associated with the physical address of the respective devices);
obtaining, by the processing system, development infrastructure information associated with the plurality of geographic areas (see paragraph [0050], the service notification platform 102 may obtain infrastructure information from the service provider device 108. Said infrastructure information may indicate one or more services that are available to be provided at physical addresses);
obtaining, by the processing system, user information associated with users in the plurality of geographic areas (paragraph [0016] also disclose the first set of address-occupant pairs include identifications of physical addresses and identifications of occupants);
training, by the processing system, a prediction model to predict precedence metrics of a new fiber optic data service of the communication network in geographic areas of the plurality of geographic areas that do not include a current fiber optic data service of the communication network, wherein the prediction model comprises at least one machine learning model (see paragraph [0066], the service notification platform 102 may determine the likelihood of relevance of a notification based on historical outcomes of providing notifications related to the service for similar occupants. In some implementations, the service notification platform 102 may use a model trained using one or more machine learning processes to determine a likelihood that a notification to a prospective user (e.g., the occupants that might gain access to the service from the additional infrastructure) would be relevant to the prospective user. The service notification platform 102 or another may train the model with inputs such as types of businesses operated by the occupants, geographical regions associated with the physical addresses, demographics of geographical regions associated with the physical addresses, population growth of the geographical regions associated with the physical addresses and/or the like. Said service refers to fiberoptic cables, metal-based cables and/or the like, please see paragraph [0008] for example);
generating, by the processing system, at least one precedence metric of at least one of the plurality of geographic areas that does not include the current fiber optic data service, wherein the generating comprises applying to the prediction model (see Figure 1D, paragraphs [0062], [0067], the service notification platform 102 may perform one or more actions including providing a notification that includes a service offering to a device associated with an occupant. For example, the service notification platform 102 may provide a notification that includes information related to an available service to a device associated with the one or more occupants for which the service is available to be provided via the infrastructure, please see paragraph [0067]. The notification may exclude occupants that are already indicated as users of the service, or may include different information related to the service depending on the user’s status such as service levels, usage levels):
current network inventory data of the communication network associated with the plurality of geographic areas, current development infrastructure information associated with the plurality of geographic areas, and current user information associated with users in the plurality of geographic areas, wherein an output of the prediction model comprises the at least one precedence metric (see paragraph [0068], the one or more actions may include causing a series of notifications to be generated and/or provided to one or more user devices in a particular geographical region. In some implementations, the notification may provide information on different services based on types of businesses operated by the occupants, types of infrastructure available at the physical addresses of the occupants and/or the like. Users that currently subscribe to the service may receive information related to upcoming changes while prospective users may receive information related to service availability and capabilities).
Feldman does not clearly teach and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold.
Azarian teaches and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold (see paragraph [0110], a pseudo-conversion method can include bounding boxes above a threshold score, key points with a minimum probability of 0.05).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the teachings of Azarian. The motivation for doing so would be to draw inference using machine learning model (please see paragraph [0006] of Azarian).
As per claim 2:
Feldman in view of Azarian teaches the method of claim 1, wherein the network inventory data comprises at least one of:
existing network inventory data (Feldman, see paragraph [0052], one or more physical systems from which an occupant may gain access to a service);
current network inventory deployment projects in the communication network (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record);
or scheduled network inventory deployment projects in the communication network (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record).
As per claim 3:
Feldman in view of Azarian teaches the method of clam 1, wherein the development infrastructure information comprises at least one of:
information associated with existing buildings in the plurality of geographic areas (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record);
information associated with current building construction projects in the plurality of geographic areas (Feldman, see paragraph [0051], disclose infrastructure information may list physical addresses that have access to the infrastructure associated with the service provider. Said infrastructure includes fiberoptic cables, metal-based cables, plumbing pipes, and/or the like);
or information associated with scheduled building construction projects in the plurality of geographic areas (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record).
As per claim 4:
Feldman in view of Azarian teaches the method of claim 1, wherein the user information comprises at least one of:
demographic information of the plurality of geographic areas (Feldman, see paragraph [0066], demographics of geographical regions associated with the physical addresses);
information associated with current users of the communication network who do not have the current fiber optic data service (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record);
or information associated with users requesting the new fiber optic data service (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record).
As per clam 5:
Feldman in view of Azarian teaches the method of claim 1, wherein the current network inventory data of the communication network associated with the plurality of geographic areas comprises a most recent available network inventory data associated with the plurality of geographic areas, wherein the current development infrastructure information associated with the plurality of geographic areas comprises a most recent available development infrastructure information associated with the plurality of geographic areas (Feldman, see paragraph [0051], disclose infrastructure information lists physical addresses that have access to the infrastructure associated with the service provide. Additionally or alternatively, the infrastructure information may identify geographical regions that have access to the structure), and wherein the current user information associated with the users in the plurality of geographic areas comprises a most recent user information associated with the users in the at least one geographic areas (Feldman, see paragraph [0066], infrastructure information together with other information of the respective occupants like demographics, population growth, physical addresses are provided as inputs to the trained model).
As per claim 10:
Feldman in view of Azarian teaches the method of claim 1, wherein the at least one machine learning model comprises a forecasting model for forecasting a new user demand for the new fiber optic data service in the plurality of geographic areas (Feldman, see paragraphs [0044], [0053], using the model to predict service currently provided via the infrastructure (fiberoptic) to the occupants associated with the physical address).
As per claim 11:
Feldman in view of Azarian teaches the method of claim 1, wherein the at least one machine learning model comprises a forecasting model for forecasting a cost of deployment of the new fiber optic data service in the plurality of geographic areas (Feldman, see paragraph [0051], infrastructure information may identify geographical regions that have access to the infrastructure for which a cost to provide access would be relatively inexpensive).
As per claim 13:
Feldman in view of Azarian teaches the method of claim 1, wherein the network inventory data includes a central office proximity metric (Feldman, see paragraph [0037], service notification platform may maintain association of the physical address using one or more inputs such as address of occupant pairs, historical accuracy of one or more source devices, geographical regions associated with address, etc.).
As per claim 14:
Feldman in view of Azarian teaches the method of claim 1, wherein the development infrastructure information include a utility type information, wherein the utility type information comprises one of:
a buried utilities type (Feldman, see paragraph [0008], said service refers to fiberoptic cables, metal-based cables, plumbing pipes, electrical lines, on-premises or near-premises equipment and/or the like); or an overhead utilities type (Feldman, see paragraph [0008], said service refers to fiberoptic cables, metal-based cables, plumbing pipes, electrical lines, on-premises or near-premises equipment and/or the like).
As per claim 15:
Feldman in view of Azarian teaches the method of claim 1, wherein the prediction model is further trained to generate a recommended deployment type for the new fiber optic data service in the geographic areas of the plurality of geographic areas that do not include a current fiber optic data service of the communication network (Feldman, see paragraphs [0066], [0069], based on the trained model, notification(s) are sent to the occupants associated with the respective addresses. The notification indicates the confirmation of the presence of infrastructure at the address, feedback on services provided, desire for services and the like, please see paragraph [0069] of Feldman).
As per claim 16:
Feldman in view of Azarian teaches the method of claim 15, wherein the recommended deployment type comprises a selected one of a plurality of deployment types including two or more of:
a fiber to the premises deployment type (Feldman, see paragraph [0065], the one or more actions may include providing a recommendation to deploy additional infrastructure);
a fiber to the curb deployment type (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record); or
a fixed wireless broadband fiber hybrid deployment type (Note: Limitation(s) is/are recited in alternate form and thus not addressed by the prior art(s) of record).
As per claim 19:
Feldman teaches a non-transitory computer-readable medium storing instructions which, when executed by a processing system including at least one processor deployed in a communication network, cause the processing system to perform operations (see Figure 3, paragraph [0091], device 300 comprise of memory 330 for storing instructions and executable by processor 320), the operations comprising:
obtaining network inventory data of a communication network (see paragraph [0016], service notification platform 102, construed as said processing system may obtain a first set of address-occupant pairs from the plurality of source devices 104. Paragraph [0016] said first set of address-occupant pairs from the plurality of source devices may be associated with credit record entity (e.g., credit bureau)), wherein the network inventory data is associated with a plurality of geographic areas (see paragraph [0016], said credit record entity is associated with the physical address of the respective devices);
obtaining development infrastructure information associated with the plurality of geographic areas (see paragraph [0050], the service notification platform 102 may obtain infrastructure information from the service provider device 108. Said infrastructure information may indicate one or more services that are available to be provided at physical addresses);
obtaining user information associated with users in the plurality of geographic areas (paragraph [0016] also disclose the first set of address-occupant pairs include identifications of physical addresses and identifications of occupants);
training a prediction model to predict precedence metrics of a new fiber optic data service of the communication network in geographic areas of the plurality of geographic areas that do not include a current fiber optic data service of the communication network, wherein the prediction model comprises at least one machine learning model (see paragraph [0066], the service notification platform 102 may determine the likelihood of relevance of a notification based on historical outcomes of providing notifications related to the service for similar occupants. In some implementations, the service notification platform 102 may use a model trained using one or more machine learning processes to determine a likelihood that a notification to a prospective user (e.g., the occupants that might gain access to the service from the additional infrastructure) would be relevant to the prospective user. The service notification platform 102 or another may train the model with inputs such as types of businesses operated by the occupants, geographical regions associated with the physical addresses, demographics of geographical regions associated with the physical addresses, population growth of the geographical regions associated with the physical addresses and/or the like. Said service refers to fiberoptic cables, metal-based cables and/or the like, please see paragraph [0008] for example);
generating at least one precedence metric of at least one of the plurality of geographic areas that does not include the current fiber optic data service, wherein the generating comprises applying to the prediction model (see Figure 1D, paragraphs [0062], [0067], the service notification platform 102 may perform one or more actions including providing a notification that includes a service offering to a device associated with an occupant. For example, the service notification platform 102 may provide a notification that includes information related to an available service to a device associated with the one or more occupants for which the service is available to be provided via the infrastructure, please see paragraph [0067]. The notification may exclude occupants that are already indicated as users of the service, or may include different information related to the service depending on the user’s status such as service levels, usage levels):
current network inventory data of the communication network associated with the plurality of geographic areas, current development infrastructure information associated with the plurality of geographic areas, and current user information associated with users in the plurality of geographic areas, wherein an output of the prediction model comprises the at least one precedence metric (see paragraph [0068], the one or more actions may include causing a series of notifications to be generated and/or provided to one or more user devices in a particular geographical region. In some implementations, the notification may provide information on different services based on types of businesses operated by the occupants, types of infrastructure available at the physical addresses of the occupants and/or the like. Users that currently subscribe to the service may receive information related to upcoming changes while prospective users may receive information related to service availability and capabilities).
Feldman does not clearly teach and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold.
Azarian teaches and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold (see paragraph [0110], a pseudo-conversion method can include bounding boxes above a threshold score, key points with a minimum probability of 0.05).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the teachings of Azarian. The motivation for doing so would be to draw inference using machine learning model (please see paragraph [0006] of Azarian).
As per claim 20:
Feldman teaches an apparatus (see Figure 3, device 300) comprising:
a processing system including at least one processor (see Figure 3, processor 320);
a computer-readable storage medium storing instructions which, when executed by the processing system when deployed in a communication network, cause the processing system to perform operations (see Figure 3, paragraph [0091], device 300 comprise of memory 330 for storing instructions and executable by processor 320), the operations comprising:
obtaining network inventory data of a communication network (see paragraph [0016], service notification platform 102, construed as said processing system may obtain a first set of address-occupant pairs from the plurality of source devices 104. Paragraph [0016] said first set of address-occupant pairs from the plurality of source devices may be associated with credit record entity (e.g., credit bureau)), wherein the network inventory data is associated with a plurality of geographic areas (see paragraph [0016], said credit record entity is associated with the physical address of the respective devices);
obtaining development infrastructure information associated with the plurality of geographic areas (see paragraph [0050], the service notification platform 102 may obtain infrastructure information from the service provider device 108. Said infrastructure information may indicate one or more services that are available to be provided at physical addresses);
obtaining user information associated with users in the plurality of geographic areas (paragraph [0016] also disclose the first set of address-occupant pairs include identifications of physical addresses and identifications of occupants);
training a prediction model to predict precedence metrics of a new fiber optic data service of the communication network in geographic areas of the plurality of geographic areas that do not include a current fiber optic data service of the communication network, wherein the prediction model comprises at least one machine learning model (see paragraph [0066], the service notification platform 102 may determine the likelihood of relevance of a notification based on historical outcomes of providing notifications related to the service for similar occupants. In some implementations, the service notification platform 102 may use a model trained using one or more machine learning processes to determine a likelihood that a notification to a prospective user (e.g., the occupants that might gain access to the service from the additional infrastructure) would be relevant to the prospective user. The service notification platform 102 or another may train the model with inputs such as types of businesses operated by the occupants, geographical regions associated with the physical addresses, demographics of geographical regions associated with the physical addresses, population growth of the geographical regions associated with the physical addresses and/or the like. Said service refers to fiberoptic cables, metal-based cables and/or the like, please see paragraph [0008] for example);
generating at least one precedence metric of at least one of the plurality of geographic areas that does not include the current fiber optic data service, wherein the generating comprises applying to the prediction model (see Figure 1D, paragraphs [0062], [0067], the service notification platform 102 may perform one or more actions including providing a notification that includes a service offering to a device associated with an occupant. For example, the service notification platform 102 may provide a notification that includes information related to an available service to a device associated with the one or more occupants for which the service is available to be provided via the infrastructure, please see paragraph [0067]. The notification may exclude occupants that are already indicated as users of the service, or may include different information related to the service depending on the user’s status such as service levels, usage levels):
current network inventory data of the communication network associated with the plurality of geographic areas, current development infrastructure information associated with the plurality of geographic areas, and current user information associated with users in the plurality of geographic areas, wherein an output of the prediction model comprises the at least one precedence metric (see paragraph [0068], the one or more actions may include causing a series of notifications to be generated and/or provided to one or more user devices in a particular geographical region. In some implementations, the notification may provide information on different services based on types of businesses operated by the occupants, types of infrastructure available at the physical addresses of the occupants and/or the like. Users that currently subscribe to the service may receive information related to upcoming changes while prospective users may receive information related to service availability and capabilities).
Feldman does not clearly teach and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold.
Azarian teaches and performing, by the processing system, at least one network reconfiguration action in the communication network in response to the at least one precedence metric of the at least one of the plurality of geographic areas that does not include the current fiber optic data service satisfying a threshold (see paragraph [0110], a pseudo-conversion method can include bounding boxes above a threshold score, key points with a minimum probability of 0.05).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the teachings of Azarian. The motivation for doing so would be to draw inference using machine learning model (please see paragraph [0006] of Azarian).
3. Claims 6, 7 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Feldman in view of Azarian and further in view of Cole (US PG Pub. No. 2024/0202758).
As per claim 6:
Feldman in view of Azarian teaches the method of claim 1 with the exception of:
wherein the at least one precedence metric is based on an objective function with variables of: a forecast revenue for the new fiber optic service in a given geographic area and a forecast cost of deployment in the given geographic area.
Cole teaches wherein the at least one precedence metric is based on an objective function with variables of: a forecast revenue for the new fiber optic service in a given geographic area and a forecast cost of deployment in the given geographic area (see paragraph [0058], disclose the trained predictive model can be executed to predict the item price for each product identifier based in part on the geographic location. Paragraph [0018] disclose wired network 212 may include fiber optic networks).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the predictive model (as disclosed in Cole) into both Feldman and Azarian as a way of projecting the pricing report for a suitable time interval (please see paragraph [0040] of Cole).
As per claim 7:
Feldman in view of Azarian and further in view of Cole teaches the method of claim 6.
The combination of Feldman and Azarian do not teach wherein the objective function further includes at least one additional variable for a forecast number of users to be served in the given geographic area.
Cole teaches wherein the objective function further includes at least one additional variable for a forecast number of users to be served in the given geographic area (see paragraph [0033], the machine learning service can also include the demographic data such as number children, gender, etc.).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the predictive model (as disclosed in Cole) into both Feldman and Azarian as a way of projecting the pricing report for a suitable time interval (please see paragraph [0040] of Cole).
As per claim 12:
Feldman in view of Azarian teaches the method of claim 1 with the exception of:
wherein the at least one machine learning model comprises a forecasting model for forecasting an expected revenue of the new fiber optic data service in the plurality of geographic areas.
Cole teaches wherein the at least one machine learning model comprises a forecasting model for forecasting an expected revenue of the new fiber optic data service in the plurality of geographic areas (see paragraph [0058], disclose the trained predictive model can be executed to predict the item price for each product identifier based in part on the geographic location. Paragraph [0018] disclose wired network 212 may include fiber optic networks).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to implement the predictive model (as disclosed in Cole) into both Feldman and Azarian as a way of projecting the pricing report for a suitable time interval (please see paragraph [0040] of Cole).
4. Claims 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Feldman in view of Azarian and further in view of Cole (US PG Pub. No. 2023/0289680), hereinafter referred to as Cole’680.
As per claim 8:
Feldman in view of Azarian teaches the method of claim 1 with the exception of:
wherein the at least one machine learning model comprises a forecasting model for forecasting completion of new network inventory deployment projects in the communication network.
Cole’680 teaches wherein the at least one machine learning model comprises a forecasting model for forecasting completion of new network inventory deployment projects in the communication network (see paragraph [0016], disclose aggregating sample data regarding a plurality of factors associated with geographic region, prioritizing the infrastructure project and predicting what is needed to maintain and/or improve infrastructure).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the predictive model (as disclosed in Cole’680) into both Feldman and Azarian as a way of providing improved estimates based on factors such as migratory patterns, infrastructure usage and the like (please see paragraph [0052] of Cole’680).
As per claim 9:
Feldman in view of Azarian teaches the method of claim 1 with the exception of:
wherein the at least one machine learning model comprises a forecasting model for forecasting new building construction projects in the plurality of geographic areas.
Cole’680 teaches wherein the at least one machine learning model comprises a forecasting model for forecasting new building construction projects in the plurality of geographic areas (see paragraph [0016], disclose aggregating sample data regarding a plurality of factors associated with geographic region, prioritizing the infrastructure project and predicting what is needed to maintain and/or improve infrastructure).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the predictive model (as disclosed in Cole’680) into both Feldman and Azarian as a way of providing improved estimates based on factors such as migratory patterns, infrastructure usage and the like (please see paragraph [0052] of Cole’680).
5. Claims 17 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Feldman in view of Azarian and further in view of Craft (US PG Pub. No. 2022/0102946).
As per claim 17:
Feldman in view of Azarian teaches the method of claim 16 with the exception of:
wherein the recommended deployment type is selected based upon at least one of: respective projected costs associated with plurality of deployment types for a given geographic area or respective projected revenues associated with the plurality of deployment types for the given geographic area.
Craft teaches wherein the recommended deployment type is selected based upon at least one of: respective projected costs associated with plurality of deployment types for a given geographic area or respective projected revenues associated with the plurality of deployment types for the given geographic area (see paragraph [0024], disclose deploying a power plus fiber optic cable according to incremental cost (cost/foot) in urban areas).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the deployment of fiber optic cables in specific geographical areas (as disclosed in Craft) into both Feldman and Azarian as a way of providing both electric power and backhaul connectivity to the base station (please see paragraph [0024] of Craft).
As per claim 18:
Feldman in view of Azarian teaches the method of claim 15 with the exception of:
wherein the network inventory data comprises information associated with existing fiber optic deployments of a third party in the plurality of geographic areas, wherein the recommended deployment type comprises a third party fiber access deployment type.
Craft teaches wherein the network inventory data comprises information associated with existing fiber optic deployments of a third party in the plurality of geographic areas, wherein the recommended deployment type comprises a third party fiber access deployment type (see paragraph [0024], disclose deploying a power plus fiber optic cable according to incremental cost (cost/foot) in urban areas – one of the most common locations).
Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the application to incorporate the deployment of fiber optic cables in specific geographical areas (as disclosed in Craft) into both Feldman and Azarian as a way of providing both electric power and backhaul connectivity to the base station (please see paragraph [0024] of Craft).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PRINCE AKWASI MENSAH whose telephone number is (571)270-7183. The examiner can normally be reached Mon-Fri 8:00am-4:00pm.
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PRINCE AKWASI. MENSAH
Examiner
Art Unit 2474
/PRINCE A MENSAH/Examiner, Art Unit 2474
/Michael Thier/Supervisory Patent Examiner, Art Unit 2474
Prosecution Insights