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 .
Election/Restrictions
Applicant’s election without traverse of Group I (Claims 1-10) in the reply filed on 06/23/2026 is acknowledged.
Newly submitted claims 21-30, in the reply filed on 06/23/2026, directed to an invention that is independent or distinct from the invention originally claimed for the following reasons:
I. Claims 1-10 drawn to a device, for collecting information about network performance and capacity for a network, the network comprising a first plurality of network edge cloud nodes and a second plurality of service region devices, wherein service region devices of the second plurality of service region devices are configured to provide mobility network communication services to end users located in service regions associated with the service region devices, the end users accessing radio access networks (RAN) served by respective service region devices of the second plurality of service region devices, wherein the network edge cloud nodes are configured to process core network traffic associated with one or more respective radio access networks; determining a set of key performance indicators (KPis) for network performance for the service regions; and automatically allocating traffic workload of a selected respective service regions to one or more designated network edge cloud nodes to satisfy one or more KPis of the set of KPis for respective service regions, classified in CPC H04W 24/08.
II. Claims 21-30 are drawn to A non-transitory machine-readable medium, and a method for collecting information about network performance and network capacity and usage for a mobility network, the mobility network comprising a first plurality of network edge cloud nodes and a second plurality of service region devices, wherein service region devices of the second plurality of service region devices are configured to provide mobility network communication services to end users located in service regions associated with the service region devices, the end users accessing a radio access networks (RAN) served by respective service region devices of the second plurality of service region devices, and wherein the network edge cloud nodes are configured to process core network traffic associated with one or more respective radio access networks; determining a set of key performance indicators (KPis) for network performance for the service regions, wherein each KPI of the set of KPis is associated with a respective threshold; generating, using an artificial intelligence model trained using time-series data derived from the information about network performance and the information about network capacity and usage, projected values of traffic workload for a selected service region of the service regions and available capacity of respective network edge cloud nodes of the first plurality of network edge cloud nodes for a future time interval; automatically allocating traffic workload of the selected service region to one or more designated network edge cloud nodes of the first plurality of network edge cloud nodes based on the projected values, forming an allocated workload; and confirming that the allocated workload satisfies one or more KPis of the set of KPis according to the respective thresholds for the future time interval, classified in CPC H04W28/082.
The inventions of Groups I and II are distinct, each from the other because they are related as subcombinations disclosed as usable together in a single combination. The subcombinations are distinct since they do not overlap in scope and are not obvious variants, and at least one subcombination is separately usable. In the instant case, subcombination Group II has a separate utility such as the use of artificial intelligence, a model trained using time-series data derived from the information about network performance and the information about network capacity and usage, to predict future workload and available capacity. Based on those predictions, the system can reassign service regions before performance problems occur, and also update assignments when current KPI values are already out of range. See MPEP § 806.05(d).
Restriction for examination purposes as indicated is proper because the inventions in groups I and II are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
Each invention in groups I and II are distinct, and can be distinguished by the differences in the features in bold as illustrated above, and thus has attained recognition in the art as a separate subject for inventive effort, and also a separate field of search.
Since applicant has received an action for the originally presented invention and effectively elected without traverse the invention of Claims 1-10 in the reply filed on 06/23/2026, this invention has been constructively elected for prosecution on the merits. Accordingly, claims 21-30 withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-6, 8 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Raval et al. (US 20230077501).
Regarding claim 1, Raval discloses a device, comprising: a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations (FIG. 7 illustrates a representation of a machine 700 in the form of a computer system within which a set of instructions may be executed for causing the machine 700 to perform any one or more of the methodologies and techniques discussed herein; [0062]), the operations comprising:
collecting information about network performance and capacity for a network (service node may collect information regarding its current load, current network slices running on the service node, current load on each individual slice, KPIs of each slice, configuration of the slices, available resources, available bandwidth, predicted usage, etc. KPIs of a network slice may include capacity, qualification, SLA, network health monitoring, resource allocation, etc.; [0041]),
the network comprising a first plurality of network edge cloud nodes and a second plurality of service region devices (service nodes may be provided near the radio access network (RAN), and, therefore, breaking out the customer traffic in the radio network. This may result in achieving better performance experience to the user while at the same time distributing the network processing load amongst a plurality service nodes. Each service node’s location can be known as an edge location; [0035].
each service node 312, 314 may be coupled to its own respective RAN for serving a shared slice (e.g., slice #1); [0052]),
wherein service region devices of the second plurality of service region devices are configured to provide mobility network communication services to end users located in service regions associated with the service region devices, the end users accessing radio access networks (RAN) served by respective service region devices of the second plurality of service region devices (one or more electronic devices 108 are connected to the plurality of service nodes 204. The one or more electronic devices 108 may be used by one or more users associated with the organization to access the communication network 210 for accessing one or more services hosted on the internet 208. the one or more electronic devices 108 may access the computing system via a radio access network; [0021]),
wherein the network edge cloud nodes are configured to process core network traffic associated with one or more respective radio access networks (Service node 312 may include an edge manager 314, a NWDAF 316, and shared slice components 318. The shared slice components 318 may include any network function including 4G, 5G or Wi-Fi network functions, such as AMF, MME, SGW, PGW, HSS, PCRF, ePDG, TWAG, CU, SMF, UPF, N3IWF, NRF), NSSF, NEF, UDM, AUSF, PCF and the like; [0028]);
determining a set of key performance indicators (KPIs) for network performance for the service regions (service node (service nodes 302, 312, 322, 332) may send and/or receive configuration information of each edge location, information about one or more network slices at each edge location, one or more KPIs of each network slice, available resources at each edge location, current load, for example, available bandwidth at each edge location, etc. For example, KPIs of a network slice may include capacity, qualification, SLA, network health monitoring, resource allocation, etc; [0038]); and
automatically allocating traffic workload of a selected respective service regions to one or more designated network edge cloud nodes to satisfy one or more KPIs of the set of KPIs for respective service regions (enable each edge location to meet the network slice SLA KPIs when there is problem in the network. For example, the problem in the network may be due to increased congestion, a network device failure in the core network and/or RAN, etc. The network slice may then be moved to a nearby edge location via exchange through the cloud mesh link described above; [0057]).
Regarding claim 2, Raval discloses wherein the collecting information about network performance and capacity for the network comprises:
collecting information about current traffic workload of the respective service regions (current load on each individual slice, KPIs of each slice, configuration of the slices, available resources, available bandwidth, predicted usage, etc. KPIs of a network slice may include capacity, qualification, SLA, network health monitoring, resource allocation, etc. In some examples, a usage prediction may be calculated and included in the collected information; [0041]);
collecting information about current shared transport traffic capacity and usage (service nodes 302, 312 may exchange usage information, generate respective proposals, and come to an agreement regarding network slicing for time t1, as described herein (e.g., operations described in method 400, method 600, etc.). The agreement may include a provision that slice #1 will be shared by service nodes 302, 312 starting at time t1 for the next hour instead of being serviced by service node 302 alone; [0047]); and
collecting information about current network capacity and usage in the designated network edge cloud nodes (service node may collect information relating to its current load and conditions, as described herein. For example, the service node may collect information regarding its current load, current network slices running on the service node; [0041]).
Regarding claim 3, Raval discloses comparing the information about the current traffic workload in the respective service regions with the information about current network capacity and usage in a shared transport network and the designated network edge cloud nodes (edge manager 304 in service node 302 may then divert some traffic for slice #1 to service node 312 for further processing and effectuating load balancing. For example, service node 312 may prove network components such as SMF, UPF, NRF, NEF, NSSF, MME, SGW, PGW and the like for slice #1 while sharing the AMF/MME component in service node 302 for serving slice #1. The AMF/MME component in service node 302 may provide registration functionality for all slice #1 users; [0053]);
identifying violated KPis of the set of KPls, the violated KPis having values out of an accepted range for a particular KPI of the set of KPis; and automatically reassigning respective service region network workload to one or more new designated network edge cloud nodes to correct the violated KPis of the set of KPis (the SLA for a slice may require a minimum throughput. The UPF component is largely responsible for throughput. Therefore, if a slice with a minimum throughput requirement is being served by different service nodes, each service node may provide a dedicated UPC component to guarantee the minimum throughput requirement and other network components could be shared with other slices. each edge location to meet the network slice SLA KPIs when there is problem in the network. For example, the problem in the network may be due to increased congestion, a network device failure in the core network and/or RAN, etc. The network slice may then be moved to a nearby edge location via exchange through the cloud mesh link described above; [0054, 0057]).
Regarding claim 4, Raval discloses collecting network workload projection information about future network workload in the respective service regions (NWDAFs 306, 316, 326, 336 may continuously analyze the parameters of each network slice and predict the usage or congestion that may occur at a future time. The prediction results as well as other data may be exchanged via a cloud mesh link with other service nodes; [0031]);
identifying potentially violated KPis of the set of KPls, the potentially violated KPis being at risk of having values out of an accepted range for a particular KPI of the set of KPis based on the network workload projection information; and automatically reassigning one or more selected respective service regions to one or more new designated network edge cloud nodes to correct the potentially violated KPis of the set of KPis (service node may collect information regarding its current load, current network slices running on the service node, current load on each individual slice, KPIs of each slice, configuration of the slices, available resources, available bandwidth, predicted usage, etc. KPIs of a network slice may include capacity, qualification, SLA, network health monitoring, resource allocation, etc. In some examples, a usage prediction may be calculated and included in the collected information. NWDAF 306 may generate a proposal based on the load information at the two service nodes. For example, the proposal may include a provision to start service of slice #1 on the second service node 312 for better load division based on current and future load information. At operation 624, the NWDAF 306 may forward the proposal involving service node 312 to edge manager 304; [0041, 0051]).
Regarding claim 5, Raval discloses processing the network performance and capacity information in an artificial intelligence module (NWDAFs 306, 316, 326, 336 may execute machine-learning algorithms, as described herein. The NWDAFs 306, 316, 326, 336 may continuously analyze the parameters of each network slice and predict the usage or congestion that may occur at a future time. The prediction results as well as other data may be exchanged via a cloud mesh link with other service nodes; [0031]),
the artificial intelligence module configured for automatically assessing and mapping communication traffic associated with respective service regions of the service regions to respective network edge cloud nodes of the first plurality of network edge cloud nodes to satisfy the one or more KPis of the set of KPis for the respective service regions (the machine-learning algorithm executed by NWADF used for predicting resources required for the one or more network slices may include linear regression algorithm, decision tree algorithm, time series analysis, K-means clustering, etc. In some embodiments, neural network-based machine-learning algorithm, such as artificial neural network (ANN) and/or recurrent neural network (RNN), etc., may also be used for predicting resources required for the one or more network slices; [0040]).
Regarding claim 6, Raval discloses wherein the automatically assessing and mapping communication traffic comprises: associating a network workload of a selected respective service region with the one or more designated network edge cloud nodes (each service node 312, 314 may be coupled to its own respective RAN for serving a shared slice (e.g., slice #1). Hence, the RAN for each service node may distribute the load for slice #1 to each service node accordingly. Each service node may then provide other network components for the slice based on the SLA requirements; [0052]); and
changing, by the artificial intelligence module, an association of the selected respective service region network workload from first designated network edge cloud nodes to second designated network edge cloud nodes in response to a failure to satisfy the one or more KPis of the set of KPis for the selected respective service region (resources for one or more network slices may be allocated based on usage analysis for one or more network slices using a machine-learning algorithm executed by a NWADF at each edge location. Based on the usage data of network resources allocated to one or more network slices, the NWADF may determine how the network resources for one or more network slices may be updated to meet the SLA for one or more network slices. Based on the analysis of the usage data, the NWADF may predict resources required for one or more network slices to meet the SLA for the one or more network slices; [0039].
enable each edge location to meet the network slice SLA KPIs when there is problem in the network. For example, the problem in the network may be due to increased congestion, a network device failure in the core network and/or RAN, etc. The network slice may then be moved to a nearby edge location via exchange through the cloud mesh link described above; [0057]).
Regarding claim 8, Raval discloses providing a user interface, the user interface adapted for monitoring and control of network workload and capacity and usage in the network; presenting, on the user interface, information about current network workload in the respective service regions; presenting, on the user interface, information about current network capacity and usage in a shared transport network and designated network edge cloud nodes; and receiving, from a user, control information to reassess and remap the selected respective service regions to the one or more designated network edge cloud nodes (FIG. 7 illustrates a representation of a machine 700 in the form of a computer system within which a set of instructions may be executed for causing the machine 700 to perform any one or more of the methodologies and techniques discussed herein. Specifically, FIG. 7 shows a diagrammatic representation of the machine 700 in the example form of a computer system, within which instructions 716 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the machine 700 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 716 may cause the machine 700 to execute any one or more operations of any one or more of the methods described herein. the instructions 716 transform a general, non-programmed machine into a particular machine 700 (e.g., service nodes, orchestrator nodes, edge managers, etc.) that is specially configured to carry out any one of the described and illustrated functions in the manner described herein. machine 700 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a smart phone, a mobile device, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 716, sequentially or otherwise, that specify actions to be taken by the machine 700. I/O components 750 include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 750 that are included in a particular machine 700 will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device; [0062-0066]).
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) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raval et al. (US 20230077501) in view of Singh (US 20260018170).
Regarding claim 9, Raval discloses providing a visually meaningful response at the user interface for the user, the visually meaningful response illustrating one of current operating conditions in the network and potential operating conditions in the network (an edge manager within the service node may transmit the information via a mesh network connecting the service nodes. The transmitted information may include resource availability in terms of compute and storage and aggregated throughput, node congestion status, traffic pattern, subscriber connection pattern, cloud/infrastructure status, policies, ML based recommendation for current utilization and ability to share resources with other nodes ; [0042].
I/O components 750 include components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The specific I/O components 750 that are included in a particular machine 700 will depend on the type of machine. For example, portable machines such as mobile phones will likely include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I/O components 750 may include many other components that are not shown in FIG. 7. The I/O components 750 are grouped according to functionality merely for simplifying the following discussion and the grouping is in no way limiting. In various example embodiments, the I/O components 750 may include output components 752 and input components 754. The output components 752 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), other signal generators, and so forth; [0066]).
Raval does not expressly disclose receiving, by an artificial intelligence module from the user interface, spoken direction or textual direction from the user.
In an analogous art, Singh discloses receiving, by an artificial intelligence module from the user interface, spoken direction or textual direction from the user (Upon receiving the last active state and/or predicted states of the one or more discovered devices of environment 100, module 226 can use any suitable computer-implemented technique, chg., a machine learning model (e.g., an artificial neural network and/or any other suitable machine learning model) to predict probable commands, instructions and/or comments. In some embodiments, server 126 may determine and provide audio samples, e.g., associated with the predicted probable voice command, that can be stored locally in a home environment 100, and used for keyword spotting. Module 226 may be configured to predict user comments, chg., the voice command at 128, based at least in part on past observations, and/or based at least in part on received device state information (e.g., active device status and metadata) from environment 100; [0049]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to add the features taught by Singh into the system of Raval in order to minimize the use of computing resources by parsing voice input in real time using updated locally stored knowledge (Singh; [0050]).
Regarding claim 10, the combination of Raval and Singh, particularly Raval discloses receiving, by the artificial intelligence module, reassessment and remapping commands or textual reassessment and reallocation commands (resources for one or more network slices may be allocated based on usage analysis for one or more network slices using a machine-learning algorithm executed by a NWADF at each edge location. Based on the usage data of network resources allocated to one or more network slices, the NWADF may determine how the network resources for one or more network slices may be updated to meet the SLA for one or more network slices. For example, the usage data of network resources may be collected based on continuous or periodic monitoring of the network resource usage. The periodic monitoring may be configurable and/or user provided. Based on the analysis of the usage data, the NWADF may predict resources required for one or more network slices to meet the SLA for the one or more network slices; [0039]); and
automatically, by the artificial intelligence module, reassessing and reallocating the selected respective service regions to the one or more designated network edge cloud nodes (resources for one or more network slices may be allocated based on usage analysis for one or more network slices using a machine-learning algorithm executed by a NWADF at each edge location. Based on the usage data of network resources allocated to one or more network slices, the NWADF may determine how the network resources for one or more network slices may be updated to meet the SLA for one or more network slices. Based on the analysis of the usage data, the NWADF may predict resources required for one or more network slices to meet the SLA for the one or more network slices; [0039].
enable each edge location to meet the network slice SLA KPIs when there is problem in the network. For example, the problem in the network may be due to increased congestion, a network device failure in the core network and/or RAN, etc. The network slice may then be moved to a nearby edge location via exchange through the cloud mesh link described above; [0057]).
Singh discloses receiving, by the artificial intelligence module from the user interface, spoken commands or textual commands from the user (Upon receiving the last active state and/or predicted states of the one or more discovered devices of environment 100, module 226 can use any suitable computer-implemented technique, chg., a machine learning model (e.g., an artificial neural network and/or any other suitable machine learning model) to predict probable commands, instructions and/or comments. In some embodiments, server 126 may determine and provide audio samples, e.g., associated with the predicted probable voice command, that can be stored locally in a home environment 100, and used for keyword spotting. Module 226 may be configured to predict user comments, chg., the voice command at 128, based at least in part on past observations, and/or based at least in part on received device state information (e.g., active device status and metadata) from environment 100; [0049]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to add the features taught by Singh into the system of Raval in order to minimize the use of computing resources by parsing voice input in real time using updated locally stored knowledge (Singh; [0050]).
Allowable Subject Matter
Claim 7 is 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Young et al. (US 20240163724), “SYSTEMS AND METHODS FOR SUPPORTING NETWORK SLICE SERVICES USING TRANSPORT DEVICES.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OUSSAMA ROUDANI whose telephone number is (571)272-4727. The examiner can normally be reached 8:30 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, UN C CHO can be reached at (571) 272 7919. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/OUSSAMA ROUDANI/Primary Examiner, Art Unit 2413
Prosecution Insights