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 .
Status of Claims
This office action is a response to an application filed on 11/04/2024, wherein claims 1-20 are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 11/04/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 4, 13 and 17 of US Patent No. 12177092. For example, claim 1 of the instant application encompasses all of the limitations of claim of US Patent No. 12177092 as follows:
Instant Application: 18936761
Patent Number: 12177092
Claim 1: A system, comprising:
one or more memories configured to store computer instructions; and
one or more hardware processors configured to execute the computer instructions, which upon execution cause the one or more hardware processors to:
Claim 1: A system, comprising:
one or more hardware processors configured to:
during a first time:
acquire first device measurements and performance indicators for a first plurality of user devices;
acquire first network measurements and performance indicators for a plurality of network slices used to provide a plurality of network services;
generate one or more machine learning models based on first device indicators for a first plurality of user devices using a plurality of network services of a wireless network and first network indicators for a plurality of network slices used to provide the plurality of network services;
generate one or more machine learning models based on the first device measurements and performance indicators and the first network measurements and performance indicators for the plurality of network slices used to provide the plurality of network services;
during a second time:
acquire second device indicators for a second plurality of user devices;
acquire second device measurements and performance indicators for a second plurality of user devices;
acquire a plurality of subscriber profiles associated with the second plurality of user devices, each profile of the plurality of subscriber profiles includes a quality of service parameter for a network service of the plurality of network services;
acquire second network indicators for a first set of network slices used to provide a network service to the second plurality of user devices;
acquire second network measurements and performance indicators for a set of network slices used to provide the network service to the second plurality of user devices;
determine whether a quality-of-service parameter was not satisfied for the second plurality of user devices based on the second device indicators and the second network indicators; and
determine a number of the second plurality of user devices for which the quality of service parameter was not satisfied based on the second device measurements and performance indicators and the second network measurements and performance indicators;
detect that the number of the plurality of user devices for which the quality of service parameter was not satisfied is greater than a threshold number of user devices;
in response to determining that the quality-of-service parameter was not satisfied for the second plurality of user devices, employ the one or more machine learning models to configure a second set of network slices to provide the network service for the second plurality of user devices by outputting instructions to one or more core network functions to replace the first set of network slices with the second set of network slices to provide the network service.
in response to detection that the number of the plurality of user devices for which the quality of service parameter was not satisfied is greater than the threshold number of user devices, employ the one or more machine learning models to:
identify a second set of network slices to be used to provide the network service; and
configure the second set of network slices to provide the network service for the plurality of user devices by outputting instructions to one or more core network functions to replace the set of network slices with the second set of network slices to provide the network services.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 4, 13 and 17 of U.S. Patent No. 12177092. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 3, 4, 13 and 17 of U.S. Patent No. 12177092 are in essence a “species” of the generic invention of claims 1-20 of the instant application. It has been held that a generic invention is “anticipated” by a “species” within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993).
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Bogineni et al. (US 2020/0196155), hereinafter Bogineni, in view of Hedman et al. (WO 2022/152616 A2), hereinafter Hedman.
Regarding claim 1, White discloses a system, comprising (Bogineni, Fig. 1: NWDAF device):
one or more memories configured to store computer instructions (Bogineni, Fig. 3, [0071]-[0073]); and
one or more hardware processors configured to execute the computer instructions, which upon execution cause the one or more hardware processors to (Bogineni, Fig. 3, [0071]-[0073]):
generate one or more machine learning models based on first device indicators for a first plurality of user devices using a plurality of network services of a wireless network and first network indicators for a plurality of network slices used to provide the plurality of network services (Bogineni, [0022]: performs training operation on (i.e., generates) a machine learning model with historical data including historical RAN data (first device indicators) and historical analytics data (first network indicators); [0015]: RAN data (device indicators) includes scheduling and/or resource management for user devices; subscriber usage patterns, mobility patterns, traffic patterns, user device mix/characteristics; [0017]: analytics data (network indicators) include bit rates, packet drop rates, latencies, flow data, log data, time-series monitoring data; [0016]: network services include 5G gigabyte and 5G low latency; [0013], [0058]: network slices are used to provide the services);
acquire second device indicators for a second plurality of user devices (Bogineni, [0015]: receive RAN data (second device indicators); [“second” is for later/current user devices vs. “first” for historical/training user devices]);
acquire second network indicators for a first set of network slices used to provide a network service to the second plurality of user devices (Bogineni, [0017]: receive analytics data (second network indicators); [“second” is for later/current user devices vs. “first” for historical/training user devices]);
determine whether a quality-of-service parameter was not satisfied for the second plurality of user devices based on the second device indicators and the second network indicators (Bogineni, [0021]: processing RAN data (second device indicators) and analytics data (second network indicators) with the machine learning model to determine actions; [0030], [0039]: actions include verifying SLA derived resource requirements against current availability and providing predictable service experience per QoS flow (i.e., determining whether QoS/SLA requirements are satisfied or not)); and
in response to determining that the quality-of-service parameter was not satisfied for the second plurality of user devices, employ the one or more machine learning models to configure a second set of network slices to provide the network service for the second plurality of user devices by outputting instructions to one or more core network functions to provide the network service (Bogineni, [0007]-[0009], [0028], [0039], [0041]: when SLA/QoS requirements are not satisfied, actions are performed, including rebalancing a network slice (i.e., configuring a second set of network slices); [0008], [0021]: machine learning models are used to determine the actions to be performed; [0011]: triggering network function placement options (i.e., outputting instructions) through NSSF, PCF, etc. (core network functions); [0058]: NSSF selects customized network slices for providing network services to user devices.
Bogineni does not explicitly disclose to replace the first set of network slices with the second set of network slices.
However, Hedman discloses to replace the first set of network slices with the second set of network slices to provide the network service (Hedman, [0105]: remapping a first network slice to a second network slice; [0107]: establishing a second PDU session on the remapped second network slice and releasing the first/original PDU session associated with the first network slice).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni and Hedman before him or her before the effective filing date of the claimed invention, to modify system that utilizes machine learning to determine when slice rebalancing should be performed to satisfy SLA/QoS requirements as taught by Bogineni, to include carrying such rebalancing by remapping service from first network slices to second network slices as taught by Hedman. The motivation for doing so would have been to improve service continuity and resource allocation by allowing the system to move traffic from a slice requiring rebalancing to another slice suitable for providing the network service.
Regarding claim 2, Bogineni discloses wherein the one or more hardware processors are configured to further execute the computer instructions to:
acquire the first device indicators for the first plurality of user devices (Bogineni, [0022]: training a machine learning model on, and therefore acquiring, historical RAN data (first device indicators)); and
acquire the first network indicators for the plurality of network slices used to provide the plurality of network services (Bogineni, [0022]: training a machine learning model on, and therefore acquiring, historical analytics data (first network indicators)).
Regarding claim 3, Bogineni discloses wherein the one or more hardware processors are configured to further execute the computer instructions to:
acquire device measurements and performance information as the first device indicators (Bogineni, [0022]: NWDAF device receives historical RAN data (first device indicators) to train a machine learning model; [0015]: RAN data (device indicators) include scheduling/resource management data, subscriber usage patterns, mobility patterns, traffic patterns, user device characteristics, etc.); and
acquire network measurements and performance information as the first network indicators (Bogineni, [0022]: NWDAF device receives historical analytics data (first network indicators) to train a machine learning model; [0017]: analytics data (network indicators) include bit rates, packet drop rates, latencies, flow data, time-series monitoring data).
Regarding claim 4, Bogineni discloses wherein the one or more hardware processors acquire the second device indicators for the second plurality of user devices by being configured to execute the computer instructions to: acquire device measurements and performance information from the second plurality of user devices as the second device indicators (Bogineni, [0015]: receiving RAN data (second device indicators); ); [“second” is for later/current user devices vs. “first” for historical/training user devices]).
Regarding claim 5, Bogineni discloses wherein the one or more hardware processors acquire the second network indicators for the first set of network slices by being configured to execute the computer instructions to: acquire network measurements and performance information for the first set of network slices as the second network indicators (Bogineni, [0017]: receiving analytics data (second network indicators); [“second” is for later/current user devices vs. “first” for historical/training user devices]; [0056]: monitoring network slices).
Regarding claim 6, Bogineni discloses wherein the first and second device indicators include user device location tracking data of the first and second pluralities of user devices (Bogineni, [0015]: RAN data (device indicators) include mobility patterns and geographic traffic patterns).
Regarding claim 7, Bogineni discloses wherein the first and second network indicators include link utilization data for the wireless network (Bogineni, [0017]: analytics data (network indicators) include bit rates through network resources, flow data and time-series monitoring data; [these indicate the amount and pattern of traffic being used over time]; [0016]: access technologies include LTE, 5G NR and NB IoT (i.e., wireless network)).
Regarding claim 9, Bogineni discloses wherein the one or more hardware processors determines whether the quality-of-service parameter was not satisfied for the second plurality of user devices by being configured to execute the computer instructions to: detect whether the first set of network slices have failed to satisfy service level agreement requirements for the first set of network slices (Bogineni, [0007]: identifies the problem of not guaranteeing satisfying network slice SLA after network slices are created; [0009]: ensuring SLAs of network slices are satisfied; [0030]: translating SLA into network resource requirements and verifying them against current network resource availability).
Regarding claim 10, Bogineni discloses wherein the one or more hardware processors employs the one or more machine learning model to configure the second set of network slices to provide the network service for the second plurality of user devices by being configured to execute the computer instructions to: employ the one or more machine learning models to identify the second set of network slices to provide the network service (Bogineni, [0008], [0021]: processing the collected data with machine learning models to determine actions; [0028]: actions include rebalancing a network slice (i.e., configuring a second set of network slices); [0011]: triggering network function placement options through NSSF, PCF, etc.; [0013], [0058]: NSSF selects network slice instances (second set of network slices) customized for different services for the user devices).
Regarding claim 11, Bogineni discloses a method, comprising:
acquiring device indicators for a plurality of user devices using a wireless network (Bogineni, [0015]: receiving RAN data (device indicators) including scheduling/resource for user devices, subscriber usage patterns, mobility patterns and user device characteristics; [0016]: wireless access technologies provided by the network (i.e., wireless network));
acquiring network indicators for a first set of network slices used to provide a network service for the wireless network to the plurality of user devices (Bogineni, [0017]: receiving analytics data (network indicators) including bit rates, packet drop rates, latencies, flow data, log data and monitoring data; [0056]: analysis performed for network slices; [0056]: network slice instances are selected for user devices and customized for services (first set of network slices));
determining whether a quality-of-service parameter was not satisfied for the plurality of user devices based on the device indicators and the network indicators (Bogineni, [0021]: processing RAN data (device indicators) and analytics data (network indicators) with a machine learning model to determine actions; [0030], [0039]: actions include verifying SLA derived resource requirements against current availability, and providing predictable service experience per QoS flow (i.e., determining whether QoS/SLA requirements are satisfied or not)); and
in response to determining that the quality-of-service parameter was not satisfied for the plurality of user devices, applying at least one machine learning model to output instructions to one or more core network functions to replace the first set of network slices with a second set of network slices to provide the network service for the plurality of user devices (Bogineni, [0021]: processing RAN and analytics data with a machine learning model to determine actions; [0028], [0041]: actions include causing core devices to rebalance a network slice (i.e., to provide the network service); [0011]: triggering network function placement options (i.e., outputting instructions) through NSSF, PCF, etc. (core network functions)).
Bogineni does not explicitly disclose to replace the first set of network slices with a second set of network slices.
However, Hedman discloses to replace the first set of network slices with a second set of network slices to provide the network service (Hedman, [0105]: remapping a first network slice to a second network slice; [0107]: establishing a second PDU session on the remapped second network slice and releasing the first/original PDU session associated with the first network slice).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni and Hedman before him or her before the effective filing date of the claimed invention, to modify system that utilizes machine learning to determine when slice rebalancing should be performed to satisfy SLA/QoS requirements as taught by Bogineni, to include carrying such rebalancing by remapping service from first network slices to second network slices as taught by Hedman. The motivation for doing so would have been to improve service continuity and resource allocation by allowing the system to move traffic from a slice requiring rebalancing to another slice suitable for providing the network service.
Regarding claim 12, Bogineni discloses further comprising: training the at least one machine learning model based on historical device indicators for another plurality of user devices using a plurality of network services and historical network indicators for a plurality of network slices used to provide the plurality of network services (Bogineni, [0022]: training the machine learning model with historical RAN data (device indicators) and historical analytics data (network indicators); [0016]: network services include 5G gigabyte network and 5G low latency network; [0056]: analysis is performed for network slices; [0058]: network slices provide services to user devices).
Regarding claim13, Bogineni discloses wherein acquiring the device indicators for the plurality of user devices comprises: acquiring device measurements and performance information from the plurality of user devices as the device indicators (Bogineni, [0015]: receiving RAN data (device indicators) including scheduling/resource management data for user devices, subscriber usage patterns, mobility patterns, temporal/geographic traffic patterns, user device mix/characteristics).
Regarding claim 14, Bogineni discloses wherein acquiring the network indicators for the first set of network slices comprises: acquiring network measurements and performance information for the first set of network slices as the network indicators (Bogineni, [0017]: receiving analytics data (network indicators) including bit rates, packet drops, latencies, flow data, logs, monitoring data; [0056]: monitoring network slices).
Regarding claim 15, Bogineni discloses wherein the device indicators include user device location tracking data of the plurality of user devices (Bogineni, [0015]: RAN data (device indicators) include mobility patterns and geographic traffic patterns).
Regarding claim 16, Bogineni discloses wherein the network indicators include link utilization data for the wireless network (Bogineni, [0017]: analytics data (network indicators) include bit rates through network resources, flow data and time-series monitoring; [0016]: wireless access technologies).
Regarding claim 18, Bogineni discloses wherein determining whether the quality-of-service parameter was not satisfied for the plurality of user devices comprises: detecting whether the first set of network slices have failed to satisfy service level agreement requirements for the first set of network slices (Bogineni, [0007]: identifies the problem of not guaranteeing satisfying network slice SLA after network slices are created; [0009]: ensuring SLAs of network slices are satisfied; [0030]: translating SLA into network resource requirements and verifying them against current network resource availability).
Regarding claim 19, Bogineni discloses wherein applying the at least one machine learning model to output instructions to the one or more core network functions to provide the network service for the plurality of user devices comprises (Bogineni, [0021]: NWDAF device processes RAN and analytics data with the machine learning model to determine actions; [0028]: actions include causing core devices to rebalance a network slice; [0011]: NWDAF device triggers network function placement options (i.e., outputs instructions) through NSSF and PCF (core network functions)):
applying the at least one machine learning model to identify the second set of network slices to provide the network service (Bogineni, [0021], [0028]: NWDAF processes the collected data with a machine learning model to determine actions, including rebalancing a network slice; [0011]: NWDAF triggers network function placement options through NSSF and PCF; [0013], [0058]: NSSF selects network slice instances (second set) to provide different services to user devices).
Bogineni does not explicitly disclose to replace the first set of network slices with the second set of network slices.
However, Hedman discloses to replace the first set of network slices with the second set of network slices to provide the network service (Hedman, [0105]: remapping a first network slice to a second network slice; [0107]: establishing a second PDU session on the remapped second network slice and releasing the first/original PDU session associated with the first network slice).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni and Hedman before him or her before the effective filing date of the claimed invention, to modify system that utilizes machine learning to determine when slice rebalancing should be performed to satisfy SLA/QoS requirements as taught by Bogineni, to include carrying such rebalancing by remapping service from first network slices to second network slices as taught by Hedman. The motivation for doing so would have been to improve service continuity and resource allocation by allowing the system to move traffic from a slice requiring rebalancing to another slice suitable for providing the network service.
Regarding claim 20, Bogineni discloses a non-transitory computer-readable medium storing computer instructions that, when executed by at least one processor, cause the at least one processor to perform actions (Bogineni, [0071]-[0073]), the actions comprising:
acquiring device indicators for a plurality of user devices using a network service of a wireless network (Bogineni, [0015]: receiving RAN data (device indicators) including scheduling/resource management data for user devices, subscriber usage patterns, mobility patterns, geographic traffic patterns, user device characteristics [0016]: services provided by the network include 5G gigabyte network and 5G low latency network (wireless network));
acquiring network indicators for a first set of network slices used to provide the network service for the wireless network to the plurality of user devices (Bogineni, [0017]: receiving analytics data (network indicators) including bit rates, packet drop rates, latencies, flow data, log data and time-series monitoring data; [0056]: analysis performed for network slices; [0058]: network slice instances are selected for user devices and customized for services (first set of network slices)); and
in response to determining that a network parameter is not being satisfied for the plurality of user devices (Bogineni, [0021]: processing RAN data (device indicators) and analytics data (network indicators) with a machine learning model to determine actions; [0030]: actions include verifying SLA derived resource requirements against current availability (i.e., determining when a network parameter is not satisfied)), employing a machine learning model to configure a second set of network slices to provide the network service for the plurality of user devices (Bogineni, [0021]: processing RAN and analytics data with a machine learning model to determine actions; [0028]: actions include rebalancing a network slice (i.e., configuring a second set of network slices)); [0058]: slices provide customized services to user devices).
Bogineni does not explicitly disclose to replace the first set of network slices.
However, Hedman discloses to replace the first set of network slices to provide the network service (Hedman, [0105]: remapping a first network slice to a second network slice; [0107]: establishing a second PDU session on the remapped second network slice and releasing the first/original PDU session associated with the first network slice).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni and Hedman before him or her before the effective filing date of the claimed invention, to modify system that utilizes machine learning to determine when slice rebalancing should be performed to satisfy SLA requirements as taught by Bogineni, to include carrying such rebalancing by remapping service from first network slices to second network slices as taught by Hedman. The motivation for doing so would have been to improve service continuity and resource allocation by allowing the system to move traffic from a slice requiring rebalancing to another slice suitable for providing the network service.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Bogineni in view of Hedman, further in view of Aksu et al. (US 2022/0053476), hereinafter Aksu.
Regarding claim 8, Bogineni discloses wherein the one or more hardware processors determines whether the quality-of-service parameter was not satisfied for the second plurality of user devices by being configured to execute the computer instructions to (Bogineni, [0021]: processing RAN data and analytics data with a machine learning model to determine actions; [0030], [0039]: actions include verifying SLA derived resource requirements against current availability, and providing predictable service experience per QoS flow (i.e., determining whether QoS/SLA requirements are satisfied or not)).
Bogineni and Hedman do not explicitly disclose determine whether a number of the second plurality of user devices for which the quality of service parameter was not satisfied is greater than a threshold number of user devices.
However, Aksu discloses determine whether a number of the second plurality of user devices for which the quality of service parameter was not satisfied is greater than a threshold number of user devices (Aksu, [0035]-[0036]: maintains a session table including session identifiers and QoS related information; maintains a slice runt-time table indicating whether QoS thresholds are satisfied; [These tables enable identifying/counting sessions for which QoS is not satisfied]; [0027]: selecting an NSI based on a quantity of users associated with the NSI satisfying a threshold quantity of users; [0042]: instantiating a new NSI based on a quantity of application sessions satisfying a threshold quantity; [quantity of users/application sessions satisfying a threshold quantity = number of user devices greater than a threshold number]).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni, Hedman and Aksu before him or her before the effective filing date of the claimed invention, to modify machine learning based slice management system utilizing a slice remapping technique as taught by Bogineni and Hedman, to include utilizing a threshold-based evaluation technique as taught by Aksu. The motivation for doing so would have been to implement an objective trigger for initiating slice reconfiguration operations, thereby improving resource utilization and reducing unnecessary network slice reconfiguration operations.
Regarding claim 17, Bogineni discloses wherein determining whether the quality-of-service parameter was not satisfied for the plurality of user devices comprises (Bogineni, [0021]: processing RAN data and analytics data with a machine learning model to determine actions; [0030], [0039]: actions include verifying SLA derived resource requirements against current availability, and providing predictable service experience per QoS flow (i.e., determining whether QoS/SLA requirements are satisfied or not)).
Bogineni and Hedman do not explicitly disclose determining whether a number of the plurality of user devices for which the quality of service parameter was not satisfied is greater than a threshold number of user devices.
However, Aksu discloses determining whether a number of the plurality of user devices for which the quality of service parameter was not satisfied is greater than a threshold number of user devices (Aksu, [0035]-[0036]: maintains a session table including session identifiers and QoS related information; maintains a slice runt-time table indicating whether QoS thresholds are satisfied; [These tables enable identifying/counting sessions for which QoS is not satisfied]; [0027]: selecting an NSI based on a quantity of users associated with the NSI satisfying a threshold quantity of users; [0042]: instantiating a new NSI based on a quantity of application sessions satisfying a threshold quantity; [quantity of users/application sessions satisfying a threshold quantity = number of user devices greater than a threshold number]).
It would have been obvious to one of ordinary skill in the art, having the teachings of Bogineni, Hedman and Aksu before him or her before the effective filing date of the claimed invention, to modify machine learning based slice management system utilizing a slice remapping technique as taught by Bogineni and Hedman, to include utilizing a threshold-based evaluation technique as taught by Aksu. The motivation for doing so would have been to implement an objective trigger for initiating slice reconfiguration operations, thereby improving resource utilization and reducing unnecessary network slice reconfiguration operations.
Related Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
Shan (US 2009/0094340) discloses using an NWDAF to provide slice-specific network data analytics to core network functions such as the PCF and NSSF, including network slice level information such as load and delay information (see [0021]-[0024], [0027]). Shan further discloses that the NWDAF information may be used for 5G QoS configuration and adjustment, including NWDA-assisted QoS provisioning, NWDA-assisted policy determination, and NWDA-assisted QoS adjustment, and that PCF may determine whether existing PDU sessions are impacted and initiate a network initiated PDU session modification procedure (see [0023], [0036], [0053]-[0056]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LESA M KENNEDY whose telephone number is (571)431-0704. The examiner can normally be reached Monday-Wednesday 9:30 am - 5:30 pm ET.
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The examiner also requests, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
/LESA M KENNEDY/Primary Examiner, Art Unit 2458
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