DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant first office action is in response to communication filed on 02/06/2026.
Claims 1-20 are pending of which claims 1, 11 and 17 are the base independent claims.
Allowable Subject Matter
Claims 11-16 are allowed.
Claims 3-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Claim(s) 1-2 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gorretta et al(US 2018/0192157) and further in view of Osinki et al (US 2022/0361009).
Regarding claim 1, Gorretta’157 discloses a device(see fig.2, Admin Node 245 and Access node 250 as device), comprising:
a processing system including a processor(see fig.4, which shows CPUs 402, see para.0066, which discusses a device with one or more processors and non-transitory memory such as the administration node 245 or the access node 250, see para.0079); and
a memory that stores executable instructions(see fig.4, shows memory 410, see para.0066, which discusses a device with one or more processors and non-transitory memory such as the administration node 245 or the access node 250 as device, see para.0079) that, when executed by the processing system, facilitate performance of operations, the operations (see para.0066, which discusses the method 300 is performed by a processor executing code stored in a non-transitory computer-readable medium (e.g., a memory), see para.0079)comprising:
receiving outage data about service issues including a service outage(see fig.3, which shows detect service outage 3-1, see para.0067, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) detects a service outage based on data coming from various components of the SP network, feedback from the endpoints associated with the subscribers, feedback from the subscribers themselves, and/or the like) in a mobility network(see para.0047-0051, via wireless connection and mobile phone, thus mobility network due mobile phone communicate via wireless connection), the service outage(see fig.3, which shows detect service outage 3-1) affecting a plurality of affected users(see fig.3, which shows identify a set of subscribers affected by the service outage 3-2, see para.0072, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) identifies a set of subscribers affected by the service outage such as all subscriber accounts serviced by a specific access node experiencing a service outage) of the mobility network(see para.0047-0051, via wireless connection and mobile phone, thus mobility network due mobile phone communicate via wireless connection), the plurality of affected users including reported users(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires);
identifying, in the outage data, patterns(see para.0104-107, which discusses UEBA solutions look at patterns of human behavior, and then apply algorithms and statistical analysis to detect meaningful anomalies from those patterns. As one example, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) inspects endpoint/consumption date to measure the difference in time between a tuner lock and a reboot to determine that this user was likely affected by the outage. According to some implementations, this algorithm further validates the likelihood that an endpoint was affected by the outage by comparing the geographic data of the outage with that of location of the user. In some implementations, in addition to determining whether the endpoint was affected by the outage, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) also attempts to determine the subjective impact on a subscriber based on inspecting at least some of the following data source: Details of the user behavior from user consumption data including information focusing on key presses, and remote control or UI usage across time such as typical EPG usage. Service calls made by a user. Geographical location of a user, , see para.0090, 0098, see fig.3-3, thus identify patterns related to measure the difference in time…, comparing the geographic data of the outage with that of location of the user…,… ) about the reported users (see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires);
inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23) of the mobility network(see para.0047-0051, via wireless connection and mobile phone, thus mobility network due mobile phone communicate via wireless connection) based on the patterns(see para.0104-107, which discusses UEBA solutions look at patterns of human behavior, and then apply algorithms and statistical analysis to detect meaningful anomalies from those patterns. As one example, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) inspects endpoint/consumption date to measure the difference in time between a tuner lock and a reboot to determine that this user was likely affected by the outage. According to some implementations, this algorithm further validates the likelihood that an endpoint was affected by the outage by comparing the geographic data of the outage with that of location of the user. In some implementations, in addition to determining whether the endpoint was affected by the outage, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) also attempts to determine the subjective impact on a subscriber based on inspecting at least some of the following data source: Details of the user behavior from user consumption data including information focusing on key presses, and remote control or UI usage across time such as typical EPG usage. Service calls made by a user. Geographical location of a user, , see para.0090, 0098, see fig.3-3, thus identify patterns related to measure the difference in time…, comparing the geographic data of the outage with that of location of the user…,…) about the reported users(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires), the impacted users including users who experienced the service outage but did not report the service outage(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, than another subscriber who makes no such inquiries, thus users who experienced the service outage but did not report the service outage due to subscriber who makes no such inquiries during the service outage).
As discussed above, although Gorretta’157 discloses inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23), Gorretta’157 does not explicitly show the use of “modifying the mobility network based on the impacted users” as required by present claimed invention. However, including “modifying the mobility network based on the impacted users” would have been obvious to one having ordinary skill in the art as evidenced by Osinki’009.
In particular, in the same field of endeavor, Osinki’009 teaches the use of modifying the mobility network(see para.0061, which discusses a network control center may modify cell coverage areas temporarily so that adjacent base stations or eNodeB devices will provide coverage to the affected area) based on the impacted users(see para.0061, which discusses cell site outage affects customers of a cellular network provider…. the information received by the decision engine 232 may include or be based on information about the number of customers affected by the power outage).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Osinki’009, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “modifying the mobility network based on the impacted users” as taught by Osinki’009, since Osinki’009 stated in para.0034+ that such a modification would improve customer experience and improve operational efficiency.
Regarding claim 2, Gorretta’157 discloses wherein the modifying the mobility network comprises: identifying a portion of the mobility network associated with severely impacted users or(due to or alternative language, only one of them is being considered) with a relatively high number of impacted users(see fig.23, which shows identifying a portion 2302 of the mobility network associated with severely impacted users, see para.0067, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) detects a service outage based on data coming from various components of the SP network, feedback from the endpoints associated with the subscribers, feedback from the subscribers themselves, and/or the like), forming an impacted portion of the network(see fig.23, which shows forming impacted region portion 2304, see para.0067, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) detects a service outage based on data coming from various components of the SP network, feedback from the endpoints associated with the subscribers, feedback from the subscribers themselves, and/or the like).
As discussed above, although Gorretta’157 discloses inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23), Gorretta’157 does not explicitly show the use of “prioritizing repairs in the impacted portion of the mobility network” as required by present claimed invention. However, including “prioritizing repairs in the impacted portion of the mobility network” would have been obvious to one having ordinary skill in the art as evidenced by Osinki’009.
In particular, in the same field of endeavor, Osinki’009 teaches the use of prioritizing repairs in the impacted portion of the mobility network(see para.063, which discusses the decision engine 232 will consider if the location of the reported power outage is a priority location. A priority location is a location of a cell site which affects a critical facility that requires reliable, uninterrupted communication services. An example of a critical facility is a hospital. If the decision engine 232 receives alarms for two cell sites and the first serves a high priority location such as a hospital and the second does not serve a high priority location, the decision engine will likely choose to dispatch a portable generator to the cell site that serves the high priority location).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Osinki’009, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “prioritizing repairs in the impacted portion of the mobility network” as taught by Osinki’009, since Osinki’009 stated in para.0034+ that such a modification would improve customer experience and improve operational efficiency.
Regarding claim 10, Gorretta’157 discloses wherein the operations further comprise: identifying an area associated with a current outage(see fig.23, which shows identifying an area/region 2304 associated with current service outage, see para.0067).
As discussed above, although Gorretta’157 discloses inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23), Gorretta’157 does not explicitly show the use of “identifying a nonimpacted user traveling toward the area associated with the current outage; and limiting handoffs of communication by the nonimpacted user and the mobility network to avoid the area associated with the current outage” as required by present claimed invention. However, including “identifying a nonimpacted user traveling toward the area associated with the current outage; and limiting handoffs of communication by the nonimpacted user and the mobility network to avoid the area associated with the current outage” would have been obvious to one having ordinary skill in the art as evidenced by Osinki’009.
In particular, in the same field of endeavor, Osinki’009 teaches the use of identifying a nonimpacted user traveling toward the area associated with the current outage(see fig.1, which shows identify vehicle 126 as nonimpacted user traveling, see para.0061, cell site outage affects customers) ; and limiting handoffs of communication by the nonimpacted user and the mobility network to avoid the area associated with the current outage (see para.0061, which discusses during outage of a single cell or sector, a network control center may modify cell coverage areas temporarily so that adjacent base stations or eNodeB devices will provide coverage to the affected area, see fig.1, see para.0027, thus limiting by modify cell coverage areas temporarily so that adjacent base stations or eNodeB devices will provide coverage to the affected area).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Osinki’009, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “identifying a nonimpacted user traveling toward the area associated with the current outage; and limiting handoffs of communication by the nonimpacted user and the mobility network to avoid the area associated with the current outage” as taught by Osinki’009, since Osinki’009 stated in para.0034+ that such a modification would improve customer experience and improve operational efficiency.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gorretta et al(US 2018/0192157), in view of Osinki et al (US 2022/0361009) and further in view of Van Wiemeersch (US 20130158870).
Regarding claim 8, Gorretta’157 discloses wherein the operations further comprise: identifying an area associated with a current outage(see fig.23, which shows identifying a area/region 2304 associated with current service outage, see para.0067).
As discussed above, although Gorretta’157 discloses inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23), Gorretta’157 does not explicitly show the use of “identifying a nonimpacted user traveling toward the area associated with the current outage” as required by present claimed invention. However, including “identifying a nonimpacted user traveling toward the area associated with the current outage” would have been obvious to one having ordinary skill in the art as evidenced by Osinki’009.
In particular, in the same field of endeavor, Osinki’009 teaches the use of identifying a nonimpacted user traveling toward the area associated with the current outage(see fig.1, which shows connected vehicle travel, see para.0027, which discuses communication with a particular mobile device 124 or vehicle 126 may be handed off from one base station or access point 122 to another as the mobile device 124 or vehicle 126, see para.0061, which discusses a network control center may modify cell coverage areas temporarily so that adjacent base stations or eNodeB devices will provide coverage to the affected area, thus identify vehicle travel to affected area).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Osinki’009, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “identifying a nonimpacted user traveling toward the area associated with the current outage” as taught by Osinki’009, since Osinki’009 stated in para.0034+ that such a modification would improve customer experience and improve operational efficiency.
As discussed above, although the combined system of Gorretta’157 and Osinki’009 discloses identifying an area associated with a current outage(Gorretta’157, see fig.23, which shows identifying an area/region 2304 associated with current service outage, see para.0067), the combined system of Gorretta’157 and Osinki’009 does not explicitly show the use of “providing a notification to the nonimpacted user to enable the nonimpacted user to avoid the area associated with the current outage” as required by present claimed invention. However, including “providing a notification to the nonimpacted user to enable the nonimpacted user to avoid the area associated with the current outage” would have been obvious to one having ordinary skill in the art as evidenced by Van Wiemeersch’870.
In particular, in the same field of endeavor, Van Wiemeersch’870 teaches the use of identifying a nonimpacted user traveling toward the area associated with the current outage(see fig.5, 501-505, see para.0052-0055, identify driver as nonimpacted user of a vehicle traveling toward affected/outage area(s), see fig.2, route from Point to Point including identify area(s) affected by outages, see para.0007); and providing a notification to the nonimpacted user to enable the nonimpacted user to avoid the area associated with the current outage(see fig.5, 505-509, see para.0055, which discusses Actions include, but are not limited to, warning a driver generally of outages, warning a driver of areas to avoid, repeatedly checking outages as a driver travels to ensure a no-power area is not being entered on a low charge, etc. Any suitable warnings may then be delivered to the driver as necessary 509, thus notification to the nonimpacted user…to avoid the area associated with the current outage due to warning a driver of areas to avoid).
In view of the above, having the combined system of Gorretta’157 and Osinki’009 and then given the well-established teaching of Van Wiemeersch’870, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the combined system of Gorretta’157 and Osinki’009 to include “providing a notification to the nonimpacted user to enable the nonimpacted user to avoid the area associated with the current outage” as taught by Van Wiemeersch’870, since Van Wiemeersch’870 stated in para.0004+ that such a modification would expand the range of options and opportunities available for provision of services at the vehicle.
Regarding clam 9, as discussed above, although Gorretta’157 discloses inferring impacted users(see fig.3, shows inferring a set of subscribers affected by the service outage, see fig.14A-B, which shows inferring subscriber impacted, see fig.23), Gorretta’157 does not explicitly show the use of “the nonimpacted user is associated with a connected vehicle” as required by present claimed invention. However, including “the nonimpacted user is associated with a connected vehicle” would have been obvious to one having ordinary skill in the art as evidenced by Osinki’009.
In particular, in the same field of endeavor, Osinki’009 teaches the use of the nonimpacted user is associated with a connected vehicle (see fig.1, which shows connected vehicle travel, see para.0027, which discuses communication with a particular mobile device 124 or vehicle 126 may be handed off from one base station or access point 122 to another as the mobile device 124 or vehicle 126, see para.0061, which discusses a network control center may modify cell coverage areas temporarily so that adjacent base stations or eNodeB devices will provide coverage to the affected area, thus identify vehicle travel to affected area).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Osinki’009, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “the nonimpacted user is associated with a connected vehicle” as taught by Osinki’009, since Osinki’009 stated in para.0034+ that such a modification would improve customer experience and improve operational efficiency.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 17 and 19-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gorretta et al(US 2018/0192157).
Regarding claim 17, Gorretta’157 discloses a method, comprising:
receiving, by a processing system including a processor(see fig.4, which shows CPUs 402, see para.0066, which discusses a device with one or more processors and non-transitory memory such as the administration node 245 or the access node 250, see para.0079), outage data related to service issues including a service outage(see fig.3, which shows detect service outage 3-1, see para.0067, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) detects a service outage based on data coming from various components of the SP network, feedback from the endpoints associated with the subscribers, feedback from the subscribers themselves, and/or the like) in a mobility network(see para.0047-0051, via wireless connection and mobile phone, thus mobility network due mobile phone communicate via wireless connection), the service outage(see fig.3, which shows detect service outage 3-1) s(see fig.3, which shows identify a set of subscribers affected by the service outage 3-2, see para.0072, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) identifies a set of subscribers affected by the service outage such as all subscriber accounts serviced by a specific access node experiencing a service outage) of the mobility network(see para.0047-0051, via wireless connection and mobile phone, thus mobility network due mobile phone communicate via wireless connection), the plurality of affected users including reported users known to be affected by the service outage(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires);
identifying, by the processing system, in the outage data, critical key performance indicators (KPIs) associated with the service outage(see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system);
building, by the processing system, an inference model(see fig.5, see para.0090, which discusses at each stage of the process 500, a machine learning approach may be applied, see para.0093, 0098) based on the critical KPIs (see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system); and
inferring, by the processing system, silent users(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, than another subscriber who makes no such inquiries, thus users who experienced the service outage but did not report the service outage due to subscriber who makes no such inquiries during the service outage) based on output of the inference model(see fig.5, see para.0090, which discusses at each stage of the process 500, a machine learning approach may be applied, see para.0093, 0098), the silent users including affected users who are not reported users(see fig.5, see para.0090, which discusses at each stage of the process 500, a machine learning approach may be applied, see para.0093, 0098).
Regarding claim 19, Gorretta’157 discloses training, by the processing system, the inference model using information l(see fig.5, see para.0090, which discusses at each stage of the process 500, a machine learning approach may be applied, see para.0093, 0098) about the reported users(see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires) and information about unimpacted users who are not impacted by the service outage(see fig.14-14B, which shows information about subscriber/user not impacted by outage(s)).
Regarding claim 20, Gorretta’157 discloses crediting, by the processing system, subscriber accounts of the silent users to compensate for a lack of service during the service outage(see fig.3, 3-4-3-5, see para.0077, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) determines a per-subscriber redress measure (e.g., pro rata credit on next month's bill, VOD credit, gift card, and/or the like) commensurate with their corresponding impact scores determine in block 3-3).
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.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gorretta et al(US 2018/0192157) and further in view of Whatley (US 2023/0033680).
Regarding claim 18, Gorretta’157 discloses wherein the identifying the critical KPIs comprises: receiving, by the processing system, KPI data for impacted users who experienced the service outage (see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system, see fig.3, which shows detect service outage 3-1, see para.0067, which discusses the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) detects a service outage based on data coming from various components of the SP network, feedback from the endpoints associated with the subscribers, feedback from the subscribers themselves, and/or the like ); characterizing, by the processing system, the KPI data for the impacted users (see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system) using the reported users (see para.0075, which discusses a subscriber who makes telephonic, email, or other inquires during a service outage is more impacted, and, thus, may have a higher impact score, see para.106, which discusses Service calls made by a user, see para.0117, thus reporting due to telephonic, email, or other inquires) as ground truth, forming KPI patterns(see para.0104-107, which discusses UEBA solutions look at patterns of human behavior, and then apply algorithms and statistical analysis to detect meaningful anomalies from those patterns. As one example, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) inspects endpoint/consumption date to measure the difference in time between a tuner lock and a reboot to determine that this user was likely affected by the outage. According to some implementations, this algorithm further validates the likelihood that an endpoint was affected by the outage by comparing the geographic data of the outage with that of location of the user. In some implementations, in addition to determining whether the endpoint was affected by the outage, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) also attempts to determine the subjective impact on a subscriber based on inspecting at least some of the following data source: Details of the user behavior from user consumption data including information focusing on key presses, and remote control or UI usage across time such as typical EPG usage. Service calls made by a user. Geographical location of a user, , see para.0090, 0098, see fig.3-3, thus identify patterns related to measure the difference in time…, comparing the geographic data of the outage with that of location of the user…,…); and identifying, by the processing system, the critical KPIs (see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system) based on the KPI patterns(see para.0104-107, which discusses UEBA solutions look at patterns of human behavior, and then apply algorithms and statistical analysis to detect meaningful anomalies from those patterns. As one example, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) inspects endpoint/consumption date to measure the difference in time between a tuner lock and a reboot to determine that this user was likely affected by the outage. According to some implementations, this algorithm further validates the likelihood that an endpoint was affected by the outage by comparing the geographic data of the outage with that of location of the user. In some implementations, in addition to determining whether the endpoint was affected by the outage, the device or a component thereof (e.g., the administration node 245 or the access node 250 in FIG. 2) also attempts to determine the subjective impact on a subscriber based on inspecting at least some of the following data source: Details of the user behavior from user consumption data including information focusing on key presses, and remote control or UI usage across time such as typical EPG usage. Service calls made by a user. Geographical location of a user, , see para.0090, 0098, see fig.3-3, thus identify patterns related to measure the difference in time…, comparing the geographic data of the outage with that of location of the user…,…).
As discussed above, although Gorretta’157 discloses dentifying, by the processing system, the critical KPIs (see para.0090, which discusses the outage itself may be identified by watching the relevant key performance indicators (KPIs) of the system), Gorretta’157 does not explicitly show the use of “as ground truth” as required by present claimed invention. However, including “as ground truth” would have been obvious to one having ordinary skill in the art as evidenced by Whatley’680.
In particular, in the same field of endeavor, Whatley’680 teaches the use of as ground truth(see para.0220, which discusses The solid, variable line of FIG. 9A indicates the ground truth results for the service KPI, see para.0243, which discusses sing at least a portion of the set of training data records to train a machine learning (ML) model of network performance to predict expected performance characteristics given the plurality of operational features in the training data records as input and the one or more observed performance characteristics as ground truths, see para.0040, which discusses outages in telecommunications networks).
In view of the above, having the system of Gorretta’157and then given the well-established teaching of Whatley’680, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Gorretta’157 to include “as ground truth” as taught by Whatley’680, since Whatley’680 stated in para.0010+ that such a modification would provide new approaches and techniques that can accurately and reliably account for the effects on network performance of interactions between the many operational elements as they contribute to network performance, while at the same time providing an analysis which relates particular operational elements to network degradation in a non-linear manner. Further, these new systems are needed to improve the automated discovery of topological relations, discovering and controlling for the context of the interactions between the operational elements.
Conclusion
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/VINNCELAS LOUIS/Primary Examiner, Art Unit 2474
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