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 .
DETAILED ACTION
Response to Amendment
Claims 1, 8 and 15 have been amended.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments with respect to the pending claims 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.
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.
CLAIMS 1-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over PANDEY et al (US 2026/0163824) in view of WANG et al (USPN 11,882,046).
a. Per claim 1, PANDEY et al teach method, comprising:
receiving, by a device, network data associated with networks, network devices, ports, bandwidth usage, and address assignments of a network architecture (paras 0003, 0042, 0045, 0053—receiving configuration parameters including network data, usage and capabilities);
predicting, by the device, network types for the network architecture based on analyzing locations, network capacities, utilizations, and total customers identified in the network data (paras 0003, 0015, 0028, 0030, 0036, 0042-45—predicting a set of configuration parameters, physical and virtual characteristics based on the network functions, data and architecture);
processing, by the device, the network data, with one or more machine learning models, to determine device types, detected ports, bandwidth, and available addresses for the network architecture (paras 0042-43 and 0045—using MLM processing to determine device parameters and capabilities);
generating, by the device, network device configurations and network configuration changes based on the network types, the device types, the detected ports, the bandwidth, and the available addresses (paras 0013, 0018—different configurable features dependent on device family, series and type); and
implementing, by the device, the network device configurations and the network configuration changes in the network architecture (paras 0014-16, 0060—applying optimal configurations to the relevant network functions).
PANDEY et al teach the limitations as applied above, yet fail to explicitly teach the limitations of “wherein processing the network data, with the one or more machine learning models, to determine the detected ports comprises: calculating, for each of the ports, a port score based on utilization of each port during or more time intervals; ranking the ports based on the port score; and identifying the detected ports based on the rank of the ports”. WANG et al teach a rank field that indicates the rank of the identified port with respect to an amount of data received during a measurement period, wherein ranking each port based on a packets received/transmitted value of the port at each event across all interfaces of a multi-port network device and determining the value for a port as the number of events where the port is ranked over measurement periods based on an aggregated duration window (col.5 lines 12-19, col.5 line 58-col.6 line 60, col.7 line 45-col.8 line 67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of PANDEY et al with WANG et al for provisioning port scores or values for each port to rank the ports based on measured use during a time period, which is well-known in a network determining a hierarchy of ports for transmission/reception.
Claim 8 contains limitations that are substantially equivalent to the limitations of claim 1 and are therefore rejected under the same basis.
b. Per claim 2, PANDEY et al with WANG et al teach the method of claim 1, PANDEY et al further teach the method further comprising: receiving feedback associated with implementing the network device configurations and the network configuration changes in the network architecture; and updating one or more of the network device configurations and one or more of the network configuration changes based on the feedback and to generate one or more updated network device configurations and one or more updated network configuration changes (paras 0049-52, 0054—receiving feedback with respect to the configuration parameters, the feedback may comprise one or more changes to the set of configuration parameters; WANG et al: col.9 line 22-55—periodically updating the machine learning model based on updated historical information, additional training and feedback).
Claim 16 contains limitations that are substantially equivalent to the limitations of claim 2 and are therefore rejected under the same basis.
c. Per claim 3, PANDEY et al with WANG et al teach the method of claim 2, PANDEY et al further teach the method further comprising: implementing the one or more updated network device configurations and the one or more updated network configuration changes in the network architecture (paras 0047, 0055-56—update configuration parameters, changes used to tune parts of the network pipeline, the configuration parameters of the network functions that can be changed and the specific settings or values for the configuration patterns).
Claim 17 contains limitations that are substantially equivalent to the limitations of claim 3 and are therefore rejected under the same basis.
d. Per claim 4, PANDEY et al with WANG et al teach the method of claim 1, PANDEY et al further teach the method further comprising: receiving historical network data identifying historical network configurations, historical port performance, historical bandwidth usage, historical address assignments, historical network performance, and historical demand patterns associated with the network architecture; preprocessing the historical network data; extracting features from the historical network data; dividing the historical network data into training data and test data; training a machine learning model, selected from the one or more machine learning models, based on the training data and the features and to generate a trained machine learning model; and validating the trained machine learning model based on the test data (paras 0030, 0042-43, 0045-46, 0056, page 8 claim 18—predicted based on records of historical operations of the communications network, machine learning model may also be trained to predict optimal arrangements, training to detect when events that may cause a temporary surge in network traffic; WANG et al: col.8 line 51-col.9 line 58, col.10 lines 35-64—machine learning model training based on historical information for ports, machine learning algorithm selected from many different potential machine learning algorithms).
Claim 18 contains limitations that are substantially equivalent to the limitations of claim 4 and are therefore rejected under the same basis.
e. Per claim 5, PANDEY et al with WANG et al teach the method of claim 4, PANDEY et al further teach the method further comprising: evaluating the trained machine learning model using metrics; and adjusting the machine learning model, based on evaluating the trained machine learning model, to generate a final model (paras 0030, 0042-43, 0045-46, 0056—training machine learning model; WANG et al: col.8 lines 51-67, col.9 lines 56-58, col.10 lines 10-67—model training based on historical information input, machine learning algorithm selected from many different potential machine learning algorithms, machine learning model training process and selection).
f. Per claim 6, PANDEY et al with WANG et al teach the method of claim 4, PANDEY et al further teach the method wherein preprocessing the historical network data comprises: cleaning and normalizing the historical network data (page 8 claim 18—predicted based on records of historical operations of the communications network; WANG et al: col.8 line 51-col.9 line 55, col.10 lines 35-64, col.14 lines 40-47—machine learning model training based on historical activity data and historical information, prediction modules, rankings from multiple previous measurement periods).
g. Per claim 7, PANDEY et al with WANG et al teach the method of claim 4, PANDEY et al further teach the method wherein extracting the features from the historical network data comprises: performing feature engineering to extract the features from the historical network data (page 8 claim 18, paras 0042, 0051—predicted based on records of historical operations of the communications network, long-term trend usage, parse the user feedback for language expressing an intent to make further changes to the set of configuration parameters; WANG et al: col.8 line 51-col.9 line 55, col.10 lines 35-64, col.14 lines 40-47—machine learning model training based on historical activity data and historical information, prediction modules, rankings from multiple previous measurement periods).
h. Per claim 9, PANDEY et al with WANG et al teach the device of claim 8, PANDEY et al further teach the method wherein the one or more machine learning models include one or more of a decision tree model, a random forest model, a k-nearest neighbors model, a logistic regression model, a time series analysis model, an autoregressive integrated moving average model, a long short-term memory model, a graph theory model, a constraint satisfaction problem solver model, a heuristic model, a rule-based system, or a support vector machine model (para 0030—machine learning model including vector database; WANG et al: col.9 line 56-col.10 line 14—logistic regression model, support vector machines).
i. Per claim 10, PANDEY et al with WANG et al teach the device of claim 8, PANDEY et al further teach the method wherein the one or more processors, to process the network data, with the one or more machine learning models, to determine the device types, are configured to: analyze features of the network devices, wherein the features include connected cell sites, traffic patterns, network topology, and redundancy requirements associated with the network devices; and determine the device types based on analyzing the features of the network devices (paras 0015-16, 0022, 0042—provision for multi-domain topologies adaptable network traffic surge; WANG et al: Abstract, col.3 lines 42-55, col.8 lines 39-60, col.12 lines 39-60—data indicating characterization of network traffic on a plurality of ports of the network device and link type, different sets of traffic information from each of a plurality of network switches or other multi-port network devices, characterize traffic passed by other types of devices, network topology).
Claim 15 contains limitations that are substantially equivalent to the limitations of claims 2-4 and 9-10 are therefore rejected under the same basis.
Claim 19 contains limitations that are substantially equivalent to the limitations of claim 10 and are therefore rejected under the same basis.
j. Per claim 11, PANDEY et al with WANG et al teach the device of claim 8, PANDEY et al further teach the method wherein the one or more processors, to process the network data, with the one or more machine learning models, to determine the detected ports, are configured to: analyze features of the ports, wherein the features include inventory data, historical trends, current port availability, and performance metrics associated with the ports; and determine the detected ports based on analyzing the features of the ports (paras 0025, 0031, 0042-43, 0048, 0058, 0060—long-term trend usage, detect network surge and demand, interface and port analysis and detection; WANG et al: col.8 lines 34-67—historical information and port performance measurement).
Claim 20 contains limitations that are substantially equivalent to the limitations of claim 11 and are therefore rejected under the same basis.
k. Per claim 12, PANDEY et al with WANG et al teach the device of claim 8, PANDEY et al further teach the method wherein the one or more processors, to process the network data, with the one or more machine learning models, to determine the bandwidth, are configured to: analyze features of the bandwidth usage, wherein the features include historical usage data, demand planning inputs, peak usage patterns, and expected growth associated with the bandwidth usage; and determine the bandwidth based on analyzing the features of the bandwidth usage (paras 0042-43—long-term trend usage, detect network surge, spike and demand; WANG et al: col.9 lines 1-9—features of the historical activity data).
l. Per claim 13, PANDEY et al with WANG et al teach the device of claim 8, PANDEY et al further teach the method wherein the one or more processors, to implement the network device configurations and the network configuration changes in the network architecture, are configured to: create circuits for the network devices in the network architecture (paras 0016, 0035—adapt to changing demands on the network, such as sudden, temporary surges in network traffic due to holidays, special events, natural disasters, emergencies, additional network functions can be deployed in the network on-the-fly, with the optimal configurations already applied, altering and substituting network elements; WANG et al: col.1 line 54-col.2 line 19, col.12 line 61-66—configuring the port to optimize for the type of network traffic carried by the port, classifying traffic over ports).
III. CLAIM 14 is rejected under 35 U.S.C. 103 as being unpatentable over PANDEY et al (US 2026/0163824) in view of WANG et al (USPN 11,882,046) and WOODWORTH et al (US 2023/0216738).
Per claim 14, PANDEY et al and WANG et al teach the device of claim 8, as applied above, yet fail to explicitly teach the device “wherein the one or more processors, to implement the network device configurations and the network configuration changes in the network architecture, are configured to: assign subnets and addresses to the network devices in the network architecture”. However, WOODWORTH et al teach the assignment of subnets and addresses to devices in a network (paras 0041, 0053, 0075).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed the invention to combine the teachings of PANDEY et al and WANG et al with WOODWORTH et al for the assignment of subnets and addresses to computing devices on a network, which is well-known in a network for the administering IP addresses to network devices.
Conclusion
IV. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure: US 2014/0164609, US 2026/0238286.
V. 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.
VI. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KRISTIE D SHINGLES whose telephone number is (571)272-3888. The examiner can normally be reached on Monday-Thursday 10am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamal Divecha can be reached on 571-272-5863. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KRISTIE D SHINGLES/
Primary Examiner, Art Unit 2453
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