Jump to content

Patent Application 17771458 - SYSTEM METHOD AND ASSOCIATED COMPUTER READABLE - Rejection

From WikiPatents

Patent Application 17771458 - SYSTEM METHOD AND ASSOCIATED COMPUTER READABLE

Title: SYSTEM, METHOD AND ASSOCIATED COMPUTER READABLE MEDIA FOR FACILITATING MACHINE LEARNING ENGINE SELECTION IN A NETWORK ENVIRONMENT

Application Information

  • Invention Title: SYSTEM, METHOD AND ASSOCIATED COMPUTER READABLE MEDIA FOR FACILITATING MACHINE LEARNING ENGINE SELECTION IN A NETWORK ENVIRONMENT
  • Application Number: 17771458
  • Submission Date: 2025-04-09T00:00:00.000Z
  • Effective Filing Date: 2022-04-23T00:00:00.000Z
  • Filing Date: 2022-04-23T00:00:00.000Z
  • National Class: 370
  • National Sub-Class: 200000
  • Examiner Employee Number: 88825
  • Art Unit: 2415
  • Tech Center: 2400

Rejection Summary

  • 102 Rejections: 0
  • 103 Rejections: 3

Cited Patents

The following patents were cited in the rejection:

Office Action Text


    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 .
The Examiner thanks the Applicant for the well-prepared amendment. The Examiner appreciates the Applicant’s effort to carefully analyze the Office action, and make appropriate arguments and amendments.
	Status of Claims
Claims 1-16 responded on November 14, 2024 are pending, claims 1, 10 and 11 are amended and claim 2 is canceled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 January 07 2025 has been entered. 
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 10 and 11 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.
In identifying step, KPI requirement does not link to the following step, and the identifying step is not disclosed in the original filed specification.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.


Claims 1, 3-16 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement.  The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA  the inventor(s), at the time the application was filed, had possession of the claimed invention.  
Regarding claim 1 recites "the user devices or network components experience a rate of error that is higher than a maximum error threshold" and claims 10-11 recite "one or more user devices or network components experiencing a rate of error that is higher than a maximum error KPI threshold". A review of the disclosure finds no support for the limitation. The term "a rate of error" or "error rate" equivalent terms does not appear in the disclosure, Therefore, this subject matter is construed to constitute the addition of new matter. 
Regarding. A review of the disclosure finds no support for the limitation. The cited prior art Tapia [0043] discloses these features but original filed disclosure does NOT disclose similar feature. Therefore, this subject matter is construed to constitute the addition of new matter. 
Claims 3-9 and 12-16 are similarly rejected based upon claim dependency to claims 1 and 10-11.
Claim Objections
Claims 1, 7, 10, 11, and 13-14 objected to because of the following informalities: Claim 1 recites "KPI requirement" in line 8, "the one or more KPI requirements" in line 14, "any KPI requirements" in line 17, and "the one or more KPI requirements" in line 20. It is not clear all KPI requirements cited in the claims refer to the same one. Claims 7, 10, 11, and 13-14 have similar issue. Appropriate correction is required.		
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, 3, 5, 10-14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tapia (US 2017/0353991 A1, hereinafter "Tapia") in view of Vijayan et al. (US 2020/0401936 A1, hereinafter "Vijayan").
Regarding claim 1, Tapia discloses a method of effectuating fault analysis in a network comprising a plurality of nodes, the method comprising: 
determining by a topology mapper that at least one of a topological configuration comprised of any of a physical, virtual, or hybrid configuration of the network has changed according to a threshold due to any of the plurality of nodes being added, deleted, or reconfigured; and one or more key performance indicator (KPI) requirements associated with the network have changed by a corresponding threshold (Tapia, [0016, 0021, 0043-44] The data collection (i.e. topology mapper) may further provide network topology data, network expansion/modification data (i.e. added or deleted or reconfigured), network coverage data…may include locations of network cells, network backhauls, core network components, and/or so forth; The root cause analysis module 119 comprises a KPI tracker 209 and an issue investigation module 210… The issue investigation module 210 analyzes KPIs as well as the various sources of data 110-114 obtained by the data adaptor platform 116 identify the quality of service issues within the network, wherein the quality of service issues negatively impact the performance of the user devices and/or network components so that the performance level falls below a predetermined threshold and/or the user devices and/or network components experience a rate of error that is higher than a maximum error threshold; the computing nodes 126 may be in the form of virtual machines, such as virtual engines (VE) and virtual private servers (VPS)); and
identifying by a key performance indicator (KPI) tracker and an issue investigation module KPI requirement comprising quality of service issues which impact the performance of the user devices or network components such quality of service issues being that the performance level falls below a predetermined threshold or the user devices or network components experience a rate of error that is higher than a maximum error threshold (Tapia, [0043-44] The root cause analysis module 119 comprises a KPI tracker 209 and an issue investigation module 210… The issue investigation module 210 analyzes KPIs as well as the various sources of data 110-114 obtained by the data adaptor platform 116 identify the quality of service issues within the network, wherein the quality of service issues negatively impact the performance of the user devices and/or network components so that the performance level falls below a predetermined threshold and/or the user devices and/or network components experience a rate of error that is higher than a maximum error threshold);
responsive to the determining that the at least one of a topological of the network and the one or more KPI requirements has changed by the corresponding threshold, selecting a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network after the topological or any KPI requirements of the network have changed (Tapia, [0042-43, 49] The live performance data may be analyzed to provide a predicted root cause and a network fix prioritization that are generated using the machine learning model…The KPI tracker 209 may measure the performance of network components of the wireless carrier network and/or performance of device components of user devices that use the wireless carrier network…The artificial intelligence module 122 comprise…one or more machine learning trained models 220A, 220N. The model training module 219 may train machine learning models 220A, 220N to analyze the performance data from the data sources 110-114 to determine root causes for the quality of service issues for subscribers and to prioritize network fix for each problem related to the root causes).
	Tapia discloses topology and the performance of the performance of network components but does not explicitly disclose at least one of topological configuration of the network and wherein identifying the ML engine comprises identifying a particular ML engine from a plurality of predetermined ML engines that respectively correspond to different types of network topological configurations and KPI requirements of the network; and selecting the identified ML engine.
Vijayan from the same field of endeavor discloses responsive to the determining that the at least one of a topological configuration of the network and one or more KPI requirements has changed by the corresponding threshold, selecting a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network after the topological configuration or any KPI requirements of the network have changed wherein identifying the ML engine comprises identifying a particular ML engine from a plurality of predetermined ML engines that respectively correspond to different types of network topological configurations and KPI requirements of the network; and selecting the identified ML engine (Vijayan, [0023] If network stability does not change a threshold amount, the ML engine can change which KPIs are processed for predictive actions or select different machine learning algorithms for determining symptoms and thresholds. This can, over time, change the models by which the KPIs and faults are linked to determine alerts and corrective actions. Finally, algorithms for temporal and spatial analysis can be changed such that new ML techniques are incorporated…The ML engine can continue to analyze network stability and tune the model symptoms, thresholds, and algorithms based on evidence of network stability advantages.).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have modified AI based network disclosed by Tapia and machine learning in network disclosed by Vijayan with a motivation to make this modification in order to test which one works more effectively over a period of time for improving network health (Vijayan, [0037]). 
Regarding claim 3, Tapia discloses wherein the selecting of an ML engine comprises dynamically predicting and training an ML engine corresponding to the topological configuration change or the KPI requirements detected in the network (Tapia, [0012-13] network components are identified from the performance data and then analyzed using one or more trained machine learning model to correlate detected symptoms to the potential root cause for each quality of service issues… each of the issues associated with a predicted root cause based on expected impact, duration, short or long-term effect, available resources).
Regarding claim 5, Tapia discloses categorizing the plurality of nodes of the network into edge nodes, intermediate nodes and core nodes based on at least one of topological configuration or performance capability of the nodes (Tapia, [0021] The network topology data may include locations of network cells, backhauls, core network components, and/or so forth. The network coverage data may include information on the signal coverage and communication bandwidth capabilities of the network cells, the performance specifications and operation statuses of backhaul, network cells, and core network components).
Regarding claim 10, Tapia discloses apparatus for effectuating fault analysis in a network comprising a plurality of nodes, the apparatus, comprising:
a data collector configured to receive from a topology mapper at least one of a topological of the network comprised of any of a physical, virtual, or hybrid configuration, said data collector further configured to determine that the topological configuration has changed according to a configuration threshold due to any of the plurality of nodes being added, deleted, or reconfigured (Tapia, [0021, 25] data collections are obtained via data adapters, a data mining algorithm of the data adaptor platform; The data collection may further provide network topology data, network expansion/modification data (i.e. added or deleted or reconfigured), network coverage data…may include locations of network cells, network backhauls, core network components, and/or so forth), said data collector further comprising a key performance indicator (KPI) tracker operable to identify one or more quality of service requirements which impact the performance of user devices or network components wherein such one or more quality of service requirements includes any of a performance level falling below a performance KPI threshold or one or more user devices or network components experiencing a rate of error that is higher than a maximum error KPI threshold (Tapia, [0043-44] The root cause analysis module 119 comprises a KPI tracker 209…The KPI tracker 209 may measure the performance of network components of the wireless carrier network and/or performance of device components of user devices that use the wireless carrier network…wherein the quality of service issues negatively impact the performance of the user devices and/or network components so that the performance level falls below a predetermined threshold and/or the user devices and/or network components experience a rate of error that is higher than a maximum error threshold. Upon determining that there is at least one quality of service issues); and
a topology and performance manager module, responsive to the determining that the at least one of a topological of the network and one or more KPI requirements has changed by the corresponding threshold, configured to select a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network after the topological or any KPI requirements of the network have changed (Tapia, [0042-43, 49] The live performance data may be analyzed to provide a predicted root cause and a network fix prioritization that are generated using the machine learning model…The KPI tracker 209 may measure the performance of network components of the wireless carrier network and/or performance of device components of user devices that use the wireless carrier network…The artificial intelligence module 122 comprise…one or more machine learning trained models 220A, 220N. The model training module 219 may train machine learning models 220A, 220N to analyze the performance data from the data sources 110-114 to determine root causes for the quality of service issues for subscribers and to prioritize network fix for each problem related to the root causes).
	Tapia discloses topology and the performance of the performance of network components but does not explicitly disclose at least one of topological configuration of the network and wherein such identification of the ML engine comprises identifying a particular ML engine from a plurality of predetermined ML engines that respectively correspond to different types of network topological configurations and KPI requirements of the network; and said topology and performance manager module operable to select the identified ML engine.
Vijayan from the same field of endeavor discloses a topology and performance manager module, responsive to the determining that the at least one of a topological configuration of the network and one or more KPI requirements has changed by the corresponding threshold, configured to select a machine language (ML) engine optimally adapted to facilitate root cause determination of any faults detected in the network  after the topological configuration or any KPI requirements of the network have changed and wherein such identification of the ML engine comprises identifying a particular ML engine from a plurality of predetermined ML engines that respectively correspond to different types of network topological configurations and KPI requirements of the network; and said topology and performance manager module operable to select the identified ML engine (Vijayan, [0023] If network stability does not change a threshold amount, the ML engine can change which KPIs are processed for predictive actions or select different machine learning (i.e. select identified ML)algorithms for determining symptoms and thresholds. This can, over time, change the models by which the KPIs and faults are linked to determine alerts and corrective actions. Finally, algorithms for temporal and spatial analysis can be changed such that new ML techniques are incorporated …The ML engine can continue to analyze network stability and tune the model symptoms, thresholds, and algorithms based on evidence of network stability advantages).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have modified AI based network disclosed by Tapia and machine learning in network disclosed by Vijayan with a motivation to make this modification in order to test which one works more effectively over a period of time for improving network health (Vijayan, [0037]).
Regarding claim 11, Tapia discloses a system configured to effectuate fault analysis in a network comprising a plurality of nodes the system comprising:
a data collector configured to collect topological data and key performance indicator (KPI) data associated with the network wherein the network comprises any of a physical, virtual, or hybrid network (Tapia, [0043] The issue investigation module 210 analyzes KPIs as well as the various sources of data 110-114 obtained by the data adaptor platform 116 identify the quality of service issues within the network); 
a topology and performance manager (TPM) module coupled to the data collector, the TPM module configured to determine that the topological configuration has changed according to a configuration threshold due to any of the plurality of nodes being added, deleted, or reconfigured (Tapia, [0021, 44] The issue investigation module 210 analyzes KPIs as well as the various sources of data 110-114 obtained by the data adaptor platform 116 identify the quality of service issues within the network, wherein the quality of service issues negatively impact the performance of the user devices and/or network components so that the performance level falls below a predetermined threshold and/or the user devices and/or network components experience a rate of error that is higher than a maximum error threshold... The data collection may further provide network topology data, network expansion/modification data, network coverage data, and planned maintenance data. The network topology data may include locations of network cells, network backhauls, core network components, and/or so forth);
said TPM module further operable to identify one or more quality of service requirements which impact the performance of user devices or network components wherein such one or more quality of service requirements includes any of a performance level falling below a performance KPI threshold or one or more user devices or network components experiencing a rate of error that is higher than a maximum error KPI threshold (Tapia, [0043-44] The root cause analysis module 119 comprises a KPI tracker 209…The KPI tracker 209 may measure the performance of network components of the wireless carrier network and/or performance of device components of user devices that use the wireless carrier network…wherein the quality of service issues negatively impact the performance of the user devices and/or network components so that the performance level falls below a predetermined threshold and/or the user devices and/or network components experience a rate of error that is higher than a maximum error threshold. Upon determining that there is at least one quality of service issues); and
a fault analytics module operative to perform root cause determination of a fault based on a selected ML engine provided by the ML engine selector, the selected ML engine optimally adapted to facilitate root cause determination of any faults detected in the network after any KPI requirements of the network have changed (Tapia, [0032] the network fix application 118 may leverage one or more trained machine learning model via the artificial intelligence module 122 to analyze the user device performance data and the network performance data within the network to determine likely root causes for a quality of service issues for subscribers and to determine the most optimal order of providing network fix to address the root causes).
Tapia discloses network topology data but does not explicitly disclose typology configuration and a machine language (ML) engine selector coupled to the TPM module, the ML engine selector operative to identify an ML engine based on input from the TPM module. 
Vijayan from the same field of endeavor discloses a machine language (ML) engine selector coupled to the TPM module, the ML engine selector operative to identify an ML engine based on input from the TPM module (Vijayan, [0023] If network stability does not change a threshold amount, the ML engine can change which KPIs are processed for predictive actions or select different machine learning (i.e. select identified ML) algorithms for determining symptoms and thresholds. This can, over time, change the models by which the KPIs and faults are linked to determine alerts and corrective actions. Finally, algorithms for temporal and spatial analysis can be changed such that new ML techniques are incorporated …The ML engine can continue to analyze network stability and tune the model symptoms, thresholds, and algorithms based on evidence of network stability advantages); and a fault analytics module operative to perform root cause determination of a fault based on a selected ML engine provided by the ML engine selector, the selected ML engine optimally adapted to facilitate root cause determination of any faults detected in the network after topological configuration or any KPI requirements of the network have changed (Vijayan, [0040] the ML engine to more accurately correlate the KPIs and fault information by linking the virtual and physical components, in an example. The topology represented by the graph database can continually and dynamically evolve based on a data collector framework and discovery process that creates the topology based on what is running in the Telco cloud. The discovery process can account for both physical and virtual components).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have modified AI based network disclosed by Tapia and machine learning in network disclosed by Vijayan with a motivation to make this modification in order to test which one works more effectively over a period of time for improving network health (Vijayan, [0037]).
Regarding claim 12, Tapia discloses wherein the ML engine selector is configured as part of a Network Operations Center (NOC), an Operations Support System (OSS), a Management and Orchestration (MANO) system or a Kubernetes orchestration system associated with the network (Tapia, [0021] The operation data source 111 may include a data collection that provides performance information about the wireless carrier network and the user devices that are using the wireless carrier network. In various embodiments, the performance information may include Radio Access Network (RAN) OSS counters, Call Detail Records (CDRs), VoLTE call traces, Session Initiation Protocol (SIP) trace data, Real-Time Transport Protocol (RTP) Control Protocol (RTCP) trace data, user device data traffic logs, user device system event logs, user device bug reports, and/or other device and network component performance information). 
Regarding claim 13, Tapia discloses wherein the ML engine selector is configured as a rule-based selector for selecting a particular ML engine from a plurality of predetermined ML engines that respectively correspond to different types of network topological configurations and KPI requirements of the network (Tapia, [0012, 14] perform a real or non-real time analysis to identify areas that comprise performance data that fall below a predetermined threshold. Issues negatively affecting the performance of user devices and network components are identified from the performance data and then analyzed using one or more trained machine learning model to correlate detected symptoms to the potential root cause for each quality of service issues).
Regarding claim 14, Tapia discloses wherein the ML engine selector is configured as a built-in ML-based module for dynamically predicting and training an ML engine corresponding to the topological configuration change or the KPI requirements detected in the network (Tapia, [0012-13] network components are identified from the performance data and then analyzed using one or more trained machine learning model to correlate detected symptoms to the potential root cause for each quality of service issues… each of the issues associated with a predicted root cause based on expected impact, duration, short or long-term effect, available resources).
Regarding claim 16, Tapia discloses wherein the topological configuration change detected in the network comprises at least one of a physical network topology change and a virtual network topology change or a combination thereof (Tapia, [0018] The data adaptor platform 116 may access the multiple data sources via a network. The network may be a local area network (LAN), a larger network such as a wide area network (WAN), or a collection of networks, such as the Internet…may use multiple connectors in the form of applications, APis, protocols, and services, to support connectivity with data sources and data stores).		
Claims 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tapia (US 2017/0353991 A1, hereinafter "Tapia") in view of Vijayan et al. (US 2020/0401936 A1, hereinafter "Vijayan") as applied to claim above, and further in view of Ma et al. (US 2020/0396135 A1, hereinafter "Ma").
Regarding claim 7, Tapia in view of Pietro does not explicitly disclose determining an adjacency matrix for the network based on the topological configuration change or the KPI requirements detected in the network; and providing the adjacency matrix to a selector configured to select the optimally adapted ML engine responsive to the topological configuration change or the KPI requirements detected in the network.
Ma from the same field of endeavor discloses determining an adjacency matrix for the network based on the topological configuration change or the KPI requirements detected in the network; and providing the adjacency matrix to a selector configured to select the optimally adapted ML engine responsive to the topological configuration change or the KPI requirements detected in the network (Ma, [0038] if A (m) is accurate when m is small, then the collection of the constructed extended adjacency matrices {A (1), A (2), . . . , A (m)} jointly determine the network topology with various granularity and accuracy tradeoffs, through which the network operator scan perform network planning tasks based on the optimization objectives and the topology granularity/accuracy preference levels. Thus, with this definition, one can construct the m-extended adjacency matrix for different m).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have to include the teachings of Ma’s system for prediction of behavior and topology into Tapia’s AI based network as modified by Vijayan with a motivation to make this modification in order to construct additional input data to improve the prediction accuracy (Ma, [0033]).
Regarding claim 8, Tapia discloses further comprising configuring the network to support a virtual LAN (VLAN) service, a virtual extensible LAN (VXLAN) service, a Multiprotocol Label Switching (MPLS) service, a virtual private routed network (VPRN) service, a virtual private LAN service (VPLS), a Virtual Router Redundancy Protocol (VRRP) redundancy service, or a tunneling service (Tapia, [0016] the computing nodes 126 may be in the form of virtual machines, such as virtual engines (VE) and virtual private servers (VPS)… the computing nodes 126 may provide data and processing redundancy) or (Pietro, [0016] routers 110, 120 may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like).
Regarding claim 9, Tapia discloses wherein the topological configuration change detected in the network comprises at least one of a physical network topology change and a virtual network topology change (Tapia, [0018] The data adaptor platform 116 may access the multiple data sources via a network. The network may be a local area network (LAN), a larger network such as a wide area network (WAN), or a collection of networks, such as the Internet…may use multiple connectors in the form of applications, APis, protocols, and services, to support connectivity with data sources and data stores).	
Claims 4, 6 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tapia (US 2017/0353991 A1, hereinafter "Tapia") in view of Vijayan et al. (US 2020/0401936 A1, hereinafter "Vijayan") as applied to claim above, and further in view of Di Pietro et al. (US 2021/0279632 A1, hereinafter "Pietro").
Regarding claim 4, Tapia does not explicitly disclose collecting at least one of topological configuration data and KPI data from the network using Simple Network Management Protocol (SNMP), Network Configuration (NetConf) protocol, Transaction Language 1 (TL1) protocol or Open Flow protocol.
Pietro from the same field of endeavor discloses collecting at least one of topological configuration data and KPI data from the network using Simple Network Management Protocol (SNMP), Network Configuration (NetConf) protocol, Transaction Language 1 (TL1) protocol or Open Flow protocol (Pietro, [0046] data feeds may comprise, but are not limited to, management information bases (MIBS) with Simple Network Management Protocol (SNMP)v2, JavaScript Object Notation (JSON) Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reporting in order to collect rich datasets related to network control planes).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have to include the teachings of Pietro’s system for machine learning and topology into Tapia’s AI based network as modified by Vijayan with a motivation to make this modification in order to identify one of the plurality of different networks as exhibiting an abnormal behavior (Pietro, [0012]).
Regarding claim 6, Tapia discloses provide network topology data but does not explicitly disclose comprising star topologies, extended star topologies, tree topologies, bus topologies, mesh topologies, ring topologies, extended ring topologies, ring-star hybrid topologies or extended ring-star hybrid topologies.
Pietro from the same field of endeavor discloses further comprising classifying network topological configurations into a plurality of classes comprising star topologies, extended star topologies, tree topologies, bus topologies, mesh topologies, ring topologies, extended ring topologies, ring-star hybrid topologies or extended ring-star hybrid topologies (Pietro, [0026-27] the techniques herein may be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers…network 100 may include one or more mesh networks, such as an Internet of Things network).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have to include the teachings of Pietro’s system for machine learning and topology into Tapia’s AI based network as modified by Vijayan with a motivation to make this modification in order to identify one of the plurality of different networks as exhibiting an abnormal behavior (Pietro, [0012]).
Regarding claim 15, Tapia does not explicitly disclose wherein the data collector is operative to collect at least one of topological configuration data and KPI data from the network using Simple Network Management Protocol (SNMP), Network Configuration (NetConf) protocol, Transaction Language 1 (TL1) protocol or Open Flow protocol.
Pietro from the same field of endeavor discloses wherein the data collector is operative to collect at least one of topological configuration data and KPI data from the network using Simple Network Management Protocol (SNMP), Network Configuration (NetConf) protocol, Transaction Language 1 (TL1) protocol or Open Flow protocol (Pietro, [0046] data feeds may comprise, but are not limited to, management information bases (MIBS) with Simple Network Management Protocol (SNMP)v2, JavaScript Object Notation (JSON) Files (e.g., WSA wireless, etc.), NetFlow/IPFIX records, logs reporting in order to collect rich datasets related to network control planes).
It would have been obvious for one with ordinary skill in the art before the effective filing date of the claimed invention to have to include the teachings of Pietro’s system for machine learning and topology into Tapia’s AI based network as modified by Vijayan with a motivation to make this modification in order to identify one of the plurality of different networks as exhibiting an abnormal behavior (Pietro, [0012]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUNA WEISSBERGER whose telephone number is (571)272-3315. The examiner can normally be reached Monday-Friday 8:00am-5:30pm.
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, Jeffrey Rutkowski can be reached on (571)270-1215. 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.





/LUNA WEISSBERGER/           Examiner, Art Unit 2415                                                                                                                                                                                             


    
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
        
            
    


(Ad) Transform your business with AI in minutes, not months

Custom AI strategy tailored to your specific industry needs
Step-by-step implementation with measurable ROI
5-minute setup that requires zero technical skills
Get your AI playbook

Trusted by 1,000+ companies worldwide

Cookies help us deliver our services. By using our services, you agree to our use of cookies.