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 .
This Office action is in response to the amendment filed on 01/22/2026.
Claims 1-20 are presented for examination.
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 (i.e., changing from AIA to pre-AIA ) 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, 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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over DHAMIJA et al. (US 2024/0179088 A1), in view of Narahari et al. (US 2024/0163225 A1), BHAT et al. (US 2020/0204452 A1).
As to claims 1, 11 and 20, DHAMIJA discloses the invention as claimed, including a network management system (Fig. 3, 310-320; Fig. 5, 540) comprising:
a memory (Fig. 1, 130-140); and
one or more processors (Fig. 1, 120) in communication with the memory and configured to (Fig. 1):
instruct a first network device (Fig. 5, 510) to obtain one or more parameters related to a Wireless Area Network (WAN) link between the first network device (Fig. 5, 510) and a second network device (Fig. 5, 520) (Fig. 5, 530A-530N; ¶0015, “providing, to the AI/ML model, traffic matrix information related to slicing flow bandwidth and latency used in the transport network”; ¶0038, “The performance information of the aggregated transport network slice may be provided to an artificial intelligence/machine learning (AI/ML) model that may be configured to predict configuration changes to the aggregated transport network slice that may maintain and/or improve performance of the aggregated transport network slice”; ¶0054, “a wireless communications system, according with various embodiments of the present disclosure. The wireless communications system 200 (which may also be referred to as a wireless wide area network (WWAN)) may include one or more user equipment (UE) 210, one or more base stations 220, at least one transport network 230, and at least one core network 240”; ¶0100, “as shown in FIG. 5, at a given interface level between transport network devices R1 510 and R2 520 (e.g., ingress PE 332 and egress PE 336 of FIGS. 3 and 6), a given network device (or node) may identify slicing flows (e.g., 530A, 530B, . . . , 530N) in addition to performance parameters for each slicing flow, such as, but not limited to, bandwidth usage and latency values”);
apply, to the one or more parameters related to the WAN link, a machine learning model trained with parameters of links to predict bandwidths of the links, to predict a maximum bandwidth of the WAN link (Figs. 3 and 6, 320; ¶0038, “The performance information of the aggregated transport network slice may be provided to an artificial intelligence/machine learning (AI/ML) model that may be configured to predict configuration changes to the aggregated transport network slice that may maintain and/or improve performance of the aggregated transport network slice”; ¶0094, “an AI/ML classification model configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth)”; ¶0095, “The AI/ML classification model of the AI/ML predictor 320 may be configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth), based on prediction input information 620 related to a particular transport network slice provided to the AI/ML classification model”).
Although DHAMIJA discloses instruct a first network device (Fig. 5, 510) to obtain one or more parameters related to a Wireless Area Network (WAN) link between the first network device (Fig. 5, 510) and a second network device (Fig. 5, 520) (Fig. 5, 530A-530N; ¶0015; ¶0038; ¶0054; ¶0100), DHAMIJA does not specifically disclose instruct a first network device to perform one or more speed tests for a Wireless Area Network (WAN) link between the first network device and a second network device to obtain one or more parameters related the WAN link between the first network device and the second network device.
However, BHAT discloses instruct a first network device to perform one or more speed tests for a Wireless Area Network (WAN) link between the first network device and a second network device to obtain one or more parameters related the WAN link between the first network device and the second network device (Fig. 1C, 142, 146, 148, 150; Fig. 2; ¶0009, “service management platform 102 can act as an orchestration device that performs end-to-end diagnostic testing and remediation of issues for multiple different virtualized network services”; ¶0017, “a diagnostics and troubleshooting service 140, which can include a device health test component 142, a reset/upgrade component 144, a data path test component 146, an application session test component 148, a management connectivity component 150”; ¶0020; ¶0022; ¶0033, “apply a set of diagnostic tests to obtain the service information and/or to detect issues associated with SD-WAN deployment 106. For example, service management platform 102 can execute a set of diagnostic tests in accordance with a test-suite workflow specification, which can define a sequence of tests, a set of conditional flows for selection of tests”; ¶0041, “perform one or more tests regarding device cloud manageability, device controller health, device WAN transport (e.g., by performing primary path connectivity testing, secondary path connectivity testing, bandwidth status, speed testing, traceroute testing, real-time packet monitoring, etc)…perform one or more tests regarding a device health status (e.g., a processing utilization, memory utilization, network interface status, etc.), an end-user application session service level agreement status (e.g., a bandwidth test, a latency test, a jitter test, etc.)”; ¶0077). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of DHAMIJA to include instruct a first network device to perform one or more speed tests for a Wireless Area Network (WAN) link between the first network device and a second network device to obtain one or more parameters related the WAN link between the first network device and the second network device, as taught by BHAT because it would enable the system to identify the bottlenecks, high latency, or packet loss, thereby improving connection quality (BHAT; ¶0031; ¶0034; ¶0041).
DHAMIJA does not specifically disclose outputting an indication of the predicted maximum bandwidth of the WAN link.
However, Narahari discloses outputting an indication of the predicted maximum bandwidth of the WAN link (¶0022, “the AS 104 may perform the calculations using a machine learning model that takes as input the network performance data and outputs as predictions the estimated lower limit, the estimated upper limit, and (optionally) the confidences”; ¶0035, “a machine learning model may be trained to take as input a set of performance data for a communications network and to generate, as an output, a prediction as to the estimated lower limit and the estimated upper limit of the available bandwidth in the communications network”; ¶0037, “the machine learning model may be trained to output a confidence value that is associated with each prediction of an estimated lower or upper limit on the available bandwidth”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of DHAMIJA to include outputting an indication of the predicted maximum bandwidth of the WAN link, as taught by Narahari because it would help the user endpoint devices to fine-tune their bandwidth usage by having access to the upper limit on the available bandwidth, thereby optimizing the experience of the end users (Narahari; ¶0014; ¶0022; ¶0035; ¶0037).
As to claim 2, Narahari discloses wherein the one or more processors are further configured to: compare a bandwidth of the WAN link measured at a given point in time against the predicted maximum bandwidth of the WAN link; and in response to determining that the bandwidth of the WAN link measured at the given point in time is less than the predicted maximum bandwidth by a predetermined amount, determine an occurrence of a fault with the WAN link (¶0012; ¶0039, “the query may be one query in a series of periodic queries from the network attached entity. For instance, the network attached entity may query the processing system every x minutes for the available bandwidth…periodic updates such as every x minutes, updates every time the estimated lower limit or the estimated upper limit changes by a threshold amount, etc.).”; ¶0043, “one or more of steps 204-212 may be repeated periodically, according to a predefined schedule, on-demand (e.g., in response to a request from a network attached entity)”; ¶0049, “receive a response to the query including an estimated lower limit of the available bandwidth and an estimated upper limit of the available bandwidth. The estimated lower limit and the estimated upper limit effectively define a range in which the actual available bandwidth likely falls, where the range is bounded by the lower limit and the upper limit and includes all values therebetween”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of DHAMIJA to include wherein the one or more processors are further configured to: compare a bandwidth of the WAN link measured at a given point in time against the predicted maximum bandwidth of the WAN link; and in response to determining that the bandwidth of the WAN link measured at the given point in time is less than the predicted maximum bandwidth by a predetermined amount, determine an occurrence of a fault with the WAN link, as taught by Narahari because it would improve the accuracy of the estimated bandwidth, and enhance the reliability of the network performance predictions (Narahari; ¶0022; ¶0036; ¶0050).
As to claim 3, DHAMIJA discloses the network management system of claim 2, wherein the one or more processors are further configured to determine the bandwidth of the WAN link measured at a given point in time based at least in part on one or more of: an amount of data transmitted by the first network device for each communication session of a plurality of communication sessions for which the first network device forwards network traffic; an amount of data received by the first network device for each communication session of the plurality of communication sessions; a duration of each communication session of the plurality of communication sessions; a number of the plurality of communication sessions; a total bandwidth of an interface of the first network device; and one or more of a measurement of jitter, packet loss, or latency of network data associated with communication session of the plurality of communication sessions (¶0061, “these performance commitments may include, but are not limited to, a guaranteed minimum bandwidth (e.g., bandwidth between two end points in a particular direction), a guaranteed maximum latency (e.g., network latency when transmitting between two endpoints), a maximum permissible delay variation (PDV) (e.g., a maximum difference in a one-way delay between sequentially transmitted packets in a flow), a maximum permissible packet loss rate (e.g., a ratio of packets dropped to packets transmitted), and a minimum availability ratio (e.g., a ratio of uptime to the sum of uptime and downtime)”; ¶0094, “an AI/ML classification model configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth)”; ¶0095, “The AI/ML classification model of the AI/ML predictor 320 may be configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth), based on prediction input information 620 related to a particular transport network slice provided to the AI/ML classification model”).
As to claim 4, it is rejected for the same reasons set forth in claim 1 above, In addition,
DHAMIJA discloses an indication of a fault with the WAN link based at least in part on the determination of the occurrence of the fault with the WAN link (¶0104, “the AI/ML classification model may set a binary flag (e.g., yes/no, pass/fail) indicating whether the one or more network path constraints may be met based on a determination”).
As to claim 5, DHAMIJA discloses the network management system of claim 2, wherein the one or more processors are configured to perform a remedial action to cause the first network device to avoid forwarding network traffic via the WAN link based at least in part on the determination of the occurrence of the fault with the WAN link (¶0104, “determine whether one or more network path constraints (e.g., low latency, high bandwidth, high reliability) of the transport network path 334 may be met based on the prediction input information. For example, the AI/ML classification model may set a binary flag (e.g., yes/no, pass/fail) indicating whether the one or more network path constraints may be met based on a determination”).
As to claim 6, it is rejected for the same reasons set forth in claim 1 above. In addition, DHAMIJA discloses the network management system of claim 1, wherein the one or more processors are further configured to schedule the first network device to obtain the one or more parameters related to the WAN link based at least in part on one or more of: a maintenance window of the first network device; a low usage time associated with the WAN link; a high usage time associated with the WAN link; or a bandwidth of the WAN link measured at a given point in time exceeding a predetermined threshold (¶0016, “configuration of the transport network slice is a maximum aggregated bandwidth of the transport network slice”; ¶0061, “a guaranteed minimum bandwidth (e.g., bandwidth between two end points in a particular direction), a guaranteed maximum latency (e.g., network latency when transmitting between two endpoints), a maximum permissible delay variation (PDV) (e.g., a maximum difference in a one-way delay between sequentially transmitted packets in a flow), a maximum permissible packet loss rate (e.g., a ratio of packets dropped to packets transmitted), and a minimum availability ratio (e.g., a ratio of uptime to the sum of uptime and downtime)”; ¶0093-¶0095; ¶0103, “configured to determine a bandwidth usage pattern for each TN Slice-ID 318 configured in the transport network 300, based at least on the existing network topology (e.g., transport network path 334) and the historical bandwidth usage information”; ¶0107; ¶0110).
As to claim 7, DHAMIJA discloses the network management system of claim 1, wherein the one or more processors are further configured to train the machine learning model using training data that include, for each of the links, a corresponding set of parameters and an associated maximum bandwidth (¶0102, “a supervised classification algorithm may use labels corresponding to each of the elements comprised by the prediction input information as class identifiers to train the AI/ML classification model. After completion of the training, the AI/ML classification model may be prepared to predict the aggregate bandwidth usage of transport network slices based at least on the prediction input information”; ¶0094, “an AI/ML classification model configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth)”; ¶0095, “The AI/ML classification model of the AI/ML predictor 320 may be configured to predict at least one required configuration of the transport network slice (e.g., maximum aggregate bandwidth), based on prediction input information 620 related to a particular transport network slice provided to the AI/ML classification model”).
As to claim 8, DHAMIJA discloses the network management system of claim 7, wherein the corresponding set of parameters for a link includes one or mor of: measurements of a bandwidth of the link associated with different usage rates associated with the link; measurements of a bandwidth of an interface of a corresponding network device associated with the different usage rates associated with the link; measurements of the bandwidth of the link associated with a maintenance window of the corresponding network device; or measurements of the bandwidth of the link associated with different times of day (¶0099, “the traffic matrix information 540 related to slicing flow bandwidth and latency used in the transport network 300. The traffic matrix information 540 may be used to determine the per flow bandwidth and latency at the network-to-network interfaces (NNI) of the devices in the transport network 600”; ¶0100, “performance parameters for each slicing flow, such as, but not limited to, bandwidth usage and latency values”; ¶0101, “the historical bandwidth usage information 624, and the traffic matrix information 540”).
As to claim 9, DHAMIJA does not specifically disclose wherein the WAN link comprises a software-defined Wide Area Network (SD-WAN) link. However, Narahari discloses wherein the WAN link comprises a software-defined Wide Area Network (SD-WAN) link (¶0030, “the communications network may be a software defined network. The software defined network may be a terrestrial network or a mobile network (e.g., a 5G network)”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of DHAMIJA to include wherein the WAN link comprises a software-defined Wide Area Network (SD-WAN) link, as taught by Narahari because it would enhance security and improve performance by using software to intelligently manage the network traffic (Narahari; ¶0030).
As to claim 10, DHAMIJA discloses the network management system of claim 1, wherein the network management system (Fig. 3, 310-320; Fig. 5, 540) is physically separate from the first network device (Fig. 3, 332; Fig. 4, 510; Fig. 6, 332) and the second network device (Fig. 3, 336; Fig. 4, 520; Fig. 6, 336), and wherein, to instruct the first network device to obtain the one or more parameters related to the WAN link, the one or more processors are configured to invoke an application, executed by the first network device, that is configured to cause the first network device to obtain the one or more parameters (Fig. 5, 530A-530N; ¶0015, “providing, to the AI/ML model, traffic matrix information related to slicing flow bandwidth and latency used in the transport network”; ¶0038, “The performance information of the aggregated transport network slice may be provided to an artificial intelligence/machine learning (AI/ML) model that may be configured to predict configuration changes to the aggregated transport network slice that may maintain and/or improve performance of the aggregated transport network slice”; ¶0100, “as shown in FIG. 5, at a given interface level between transport network devices R1 510 and R2 520 (e.g., ingress PE 332 and egress PE 336 of FIGS. 3 and 6), a given network device (or node) may identify slicing flows (e.g., 530A, 530B, . . . , 530N) in addition to performance parameters for each slicing flow, such as, but not limited to, bandwidth usage and latency values”).
As to claims 12-19, they are rejected for the same reasons set forth in claims 2-9 above, respectively.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JUNGWON CHANG/Primary Examiner, Art Unit 2454 April 4, 2026
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