DETAILED ACTION
Claims 1 – 2, 8 – 9, 11 and 15 – 16 have been amended.
Claims 1 – 20 have been examined and are pending.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/11/2026 has been entered.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 5 – 8, 12 – 15 and 19 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2025/0247310 to Izhaki et al. (hereinafter Izhaki) and in view of US Patent No. 12,120,001 to Chadaga (hereinafter Chadaga).
Claim 1, Izhaki discloses (¶1) a network slice feasibility assessment for a latency-based service level agreement (SLA), which further includes:
one or more computer readable storage media (Izhaki discloses fig. 9, (¶8) non-transitory computer readable storage medium)
one or more processors (Izhaki discloses fig. 9, (¶6) one or more processors) operatively coupled with the one or more computer readable storage media; and
program instructions stored on the one or more computer readable storage media that, when executed by the one or more processors (fig. 9, ¶8), direct the computing apparatus to at least:
receive, from an application, a request for a network slice, wherein the request includes a specified delay (Izhaki discloses (¶6) one or more processors may individually or collectively be operable to execute the code to cause the device to obtain a request associated with a network slice of the wireless network, the request indicating a latency threshold associated with an SLA of the network slice)
a slice duration that indicates a length of time of slice usage (Izhaki discloses (¶23) the SLA may indicate an uplink latency threshold, a downlink latency threshold, an end-to-end latency threshold (i.e. length of time of slice usage) where such traversing includes core transport and RAN latency components, an uplink throughput threshold, a downlink throughput threshold, or any combination of these or other SLA characteristics for the requested network slice) and
slice context that indicates where and when the requested network slice hosting network service will be received (Izhaki discloses (¶76) once a request is made, a RAN NSSMF determine, measure, identify, or ascertain whether there is enough capacity, coverage, and resources in an envisioned slice coverage area by performing a slice feasibility assessment for the specific network parameters of the envisioned slice coverage area. The network parameters may include load information, RF conditions, traffic patterns, the network infrastructure (such as the physical locations of cells and cell coverage areas)
Izhaki does not explicitly disclose determine a projected congestion based on the slice context and the slice duration and identify one or more candidate slices for the requested network slice, based on a likelihood the one or more candidate slices will support the specified delay at the projected congestion, and indicate the one or more candidate slices to the application. However, in an analogous art, Chadaga teaches:
determine a projected congestion based on the slice context and the slice duration (Chadaga teaches (Fig. 2) a machine learning model to determine congestion using past training/historical data (¶Col. 6 Lines 25-30) and based on the received network data for a given time and day (¶Col. 4 Line 65 – 67 and Col. 5, lines 35-47) e.g. three hours duration on a particular day at a sporting event in a certain location, and based on (¶Col. 7 Line 31 - 50) number of users, the average throughput, the latency, and the other network KPIs the trained machine learning model may predict (¶Col. 7 Lines 51-60) a value of congestion) and
identify one or more candidate slices for the requested network slice (Chadaga teaches Fig. 1D and ¶Col. 4, Lines 42-54, slice activation system activate a new network slice based on the congestion decision and may select a region, a duration, and a type for the new network slice based on the data) based on a likelihood the one or more candidate slices will support the specified delay at the projected congestion (Chadaga teaches (Fig. 2) a machine learning model to determine congestion using past training/historical data (¶Col. 5, lines 35-47 and Col. 6 Lines 25-30) and using network KPIs the trained machine learning model may predict (¶Col. 7, Lines 51-60) a value of congestion)
indicate the one or more candidate slices to the application (Chadaga teaches in (¶Col. 2, Lines 18-20 and 53-61 and Col. 4, lines 42-46) the network may provide multiple network slices to user devices 110 associated with the network)
It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the teachings of Izhaki and Chadaga, for the purpose of implementing a slice activation system that utilizes a deep learning model to detect network congestion and automatically deploy a network slice (¶Col. 1, Lines 60-62).
Claim 5, Izhaki and Chadaga disclose all the elements of Claim 1. Further, they disclose:
wherein the program instructions further direct the computing apparatus to generate a cost for each of the one or more candidate slices based on attributes of the one or more candidate slices (Chadaga discloses ¶Col. 4, Lines 42 – 54 the slice activation system manage network resources in a cost-effective way by causing network resources to be allocated through a new network slice only when the network congestion data dictates allocation of such network resources.)
The motivation to combine the references is similar to the reasons in Claim 1.
Claim 6, Izhaki and Chadaga disclose all the elements of Claim 5. Further, they disclose:
wherein to receive the request for the network slice, the program instructions direct the computing apparatus to receive the request via an application programming interface from a remote computing device (Chadaga discloses ¶Col. 5, Lines 35-42 (23), the slice activation system gather data associated with events (e.g., via one or more application programming interfaces (APIs)).
The motivation to combine the references is similar to the reasons in Claim 1.
Claim 7, Izhaki and Chadaga disclose all the elements of Claim 5. Further, they disclose:
wherein the program instructions further direct the computing apparatus to return, to the remote computing device, output comprising the attributes of the one or more candidate slices and the cost of each of the one or more candidate slices (Izhaki teaches ¶25 device may provide accurate empirical resource allocation to facilitate more efficient spectrum usage for network slice configurations by using one or more techniques (such as ML model). These attributes support accurate cost analysis associated with offering different network slice implementations (such as different types of network slices, different network slice parameters, different SLAs, different MNO slicing strategies, or any combination thereof) to different users, customers, enterprises, or other entities. The device may also assess a network slice profitability for a network slice request, and by using load information, the device account for a quantity of users (such as simulated users or observed users) served by the network slice.)
The motivation to combine the references is similar to the reasons in Claim 1.
Claim 8, do not teach or further define over the limitations in Claim 1. Therefore, claim 8 is rejected for the same rationale of rejection as set forth in Claim 1.
Claim 12, do not teach or further define over the limitations in Claim 5. Therefore, claim 12 is rejected for the same rationale of rejection as set forth in Claim 5.
Claim 13, do not teach or further define over the limitations in Claim 6. Therefore, claim 13 is rejected for the same rationale of rejection as set forth in Claim 6.
Claim 14, Izhaki and Chadaga disclose all the elements of Claim 13. Further, they teach:
returning, to the remote computing device, output comprising the attributes of the one or more candidate slices and the cost of each of the one or more candidate slices via the application programming interface (Izhaki teaches ¶25 device may provide accurate empirical resource allocation to facilitate more efficient spectrum usage for network slice configurations by using one or more techniques (such as ML model). These attributes support accurate cost analysis associated with offering different network slice implementations (such as different types of network slices, different network slice parameters, different SLAs, different MNO slicing strategies, or any combination thereof) to different users, customers, enterprises, or other entities. The device may also assess a network slice profitability for a network slice request, and by using load information, the device account for a quantity of users (such as simulated users or observed users) served by the network slice. Izhaki teaches ¶67-¶70 interface with one or more slicing APIs (such as to create, modify, or deactivate) and for slice configuration).
The motivation to combine the references is similar to the reasons in Claim 1.
Claim 15, do not teach or further define over the limitations in Claim 1. Therefore, claim 15 is rejected for the same rationale of rejection as set forth in Claim 1.
Claim 19, do not teach or further define over the limitations in Claim 5. Therefore, claim 19 is rejected for the same rationale of rejection as set forth in Claim 5.
Claim 20, Izhaki and Chadaga discloses all the elements of Claim 19. Further, they teach:
wherein to receive the request for the network slice (Izhaki teaches ¶80 a device associated with an SMO may obtain or receive a request for a network slice) the program instructions direct the computing apparatus to receive the request via an application programming interface (Izhaki teaches ¶67-¶70 APIs to create, modify, or deactivate and for slice configuration) from a remote computing device, and wherein the program instructions further direct the computing apparatus to return, to the remote computing device (Izhaki teaches ¶80 output or transmit a recommendation associated with the requested network slice in accordance with ML/AI-assisted resource allocation) output comprising the attributes of the one or more candidate slices and the cost of each of the one or more candidate slices via the application programming interface (Izhaki teaches ¶25 attributes support accurate cost analysis associated with offering different network slice implementations (such as different types of network slices, different network slice parameters, different SLAs, different MNO slicing strategies, or any combination thereof) to different users, customers, enterprises, or other entities. The device may also assess a network slice profitability for a network slice request, and by using load information, the device account for a quantity of users (such as simulated users or observed users) served by the network slice.)
The motivation to combine the references is similar to the reasons in Claim 7.
Claims 2 – 3, 9 – 10 and 16 - 17 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2025/0247310 to Izhaki, in view of US Patent No. 12,120,001 to Chadaga and further in view of US Patent Application Publication No. 2022/0255798 to Yavuz et al. (hereinafter Yavuz).
Claim 2, Izhaki and Chadaga disclose all the elements of Claim 1. Further, they disclose:
submit the slice context of the requested network slice and the projected congestion to an empirical model (Chadaga teaches (Fig. 2) a machine learning model to determine congestion using past training/historical data (¶Col. 5, lines 35-47 and Col. 6 Lines 25-30) and using network KPIs the trained machine learning model may predict (¶Col. 7 Lines 51-60) a value of congestion).
wherein the slice context of the requested network slice comprises includes a day, a time, and a geographic location of the network service to be hosted by the requested network slice (Chadaga teaches ¶Col. 4 Line 55 to Col. 6, Line 6 the context of the data includes the details on the location (i.e. sporting event), the time (i.e. three hours) and the particular day);
determine, by the empirical model, round-trip time profiles for available slices (Chadaga discloses the deep learning and network-based intelligent network slice selection system apply the trained machine learning model to select and activate a new network slice (¶Col. 12, Line 58 to Col. 13, Line 3) for the user device based on the number of users, average throughput and latency i.e. round-trip time) based on the projected congestion (Chadaga teaches (Fig. 2) a machine learning model to determine congestion using past training/historical data (¶Col. 5, lines 35-47 and Col. 6 Lines 25-30) and using network KPIs the trained machine learning model may predict (¶Col. 7 Lines 51-60) a value of congestion).
Izhaki and Chadaga does not explicitly disclose identify the one or more candidate slices from the available slices based on comparing the specified delay to the round-trip time profiles. However, in an analogous art, Yavuz teaches:
identify the one or more candidate slices from the available slices based on comparing the specified delay to the round-trip time profiles (Yavuz teaches (Fig. 3A: 304) available microslice profiles candidates that are compared and selected (Fig. 3C: Microslice Profiles Selection) based on (¶206, ¶208) the KPIs (e.g. round-trip delay measurement) and several network profile parameters (Fig. 4) such as maximum latency for any packet flowing through this slice etc.)
It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the teachings of Izhaki and Chadaga with Yavuz, for the purpose of implementing methods and apparatus for slicing the network to allow network administrators and businesses and other enterprises to more closely tailor network access with communication needs, and provide a way to more efficiently use network resources (Yavuz, ¶2).
Claim 3, Izhaki, Chadaga and Yavuz disclose all the elements of claim 2. Further, they disclose:
wherein the empirical model comprises a dataset of round-trip time data according to congestion level and network slice; Izhaki discloses ¶95 the SMO 506 may save the data associated with the cell-by-cell resource estimation in a database, and (¶107) during operation of the device, the ML model may receive input data (per-cell data or per-slice data), such as network parameters associated with network snapshots, quantities of RRC users (such as UEs 115), quantities of scheduled users, quantities of desired users, PRB utilizations, CQI distributions, traffic patterns, one or more SLA throughput thresholds, one or more SLA latency thresholds, or any combination thereof.
The motivation to combine the references is similar to the reasons in Claim 1.
Claim 9, do not teach or further define over the limitations in Claim 2. Therefore, claim 9 is rejected for the same rationale of rejection as set forth in Claim 2.
Claim 10, do not teach or further define over the limitations in Claim 3. Therefore, claim 10 is rejected for the same rationale of rejection as set forth in Claim 3.
Claim 16, do not teach or further define over the limitations in Claim 2. Therefore, claim 16 is rejected for the same rationale of rejection as set forth in Claim 2.
Claim 17, do not teach or further define over the limitations in Claim 3. Therefore, claim 17 is rejected for the same rationale of rejection as set forth in Claim 3.
Claims 4, 11 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication No. 2025/0247310 to Izhaki, in view of US Patent No. 12,120,001 to Chadaga in view of US Patent Application Publication No. 2022/0255798 to Yavuz and further in view of US Patent Application Publication No. 2025/0119888 to Pathania et al. (hereinafter Pathania).
Claim 4, Izhaki in view of Chadaga in view of Yavuz disclose all the elements of Claim 3. Izhaki in view of Chadaga in view of Yavuz does not explicitly disclose wherein the dataset of the empirical model comprises test data from a wireless network simulation using simulated network slices and simulated data traffic of variable TCP/UDP data ratios. However, in an analogous art, Pathania teaches:
wherein the dataset of the empirical model comprises test data from a wireless network simulation using simulated network slices and simulated data traffic of variable TCP/UDP data ratios (Pathania teaches ¶68 network slice templates are stored in the repository (i.e. dataset). Further, Pathania teaches ¶100 wireless RAN controlled environment that simulates network conditions of a real or actual service provisioning environment. Pathania teaches ¶133, ¶148 the tested, simulated slice templates are recorded along with their QoS attributes based upon maximum throughput, latency, jitter, packet loss rates, text data, real-time applications, web browsing (i.e. TCP), voice/video quality (i.e. UDP), emergency call priority, access delay, handover success rate, service continuity, signal strength, signal-to-noise ratio, spectral efficiency etc.)
It would have been obvious as of the effective filing date to one of ordinary skill in the art to combine the teachings of Izhaki, Chadaga and Yavuz with Pathania, for the purpose of implementing algorithms and techniques to fine-tune the resource allocation among RAN slices to maximize network efficiency and meet service requirements for each network consumer (Pathania, ¶100).
Claim 11, do not teach or further define over the limitations in Claim 4. Therefore, claim 11 is rejected for the same rationale of rejection as set forth in Claim 4.
Claim 18, do not teach or further define over the limitations in Claim 4. Therefore, claim 18 is rejected for the same rationale of rejection as set forth in Claim 4.
Response to Arguments
Claim Rejections - 35 USC § 103
Applicant’s arguments and amendments, filed on 03/19/2026 with respect to the Claims 1 – 20 have been fully considered and they are persuasive. Hence, the 35 USC § 103 rejection is withdrawn. However, based on the claim amendments and the newly introduced limitations, the search is updated and a new reference (US Patent Application Publication No. 2025/0247310 to Izhaki) is being introduced for the 35 USC § 103 rejection.
Conclusion
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/H. A. K./
Examiner, Art Unit 2451
/Chris Parry/Supervisory Patent Examiner, Art Unit 2451
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