CTNF 19/046,769 CTNF 80177 DETAILED ACTION Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. 12-151 AIA 26-51 12-51 Status of Claims This office action is a response to a preliminary amendment filed on 02/06/2025, wherein claims 1-20 are cancelled and claims 21-40 are newly added and presented for examination. Information Disclosure Statement The information disclosure statement (IDS) submitted on 02/06/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Double Patenting 08-33 AIA The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg , 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman , 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi , 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum , 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel , 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington , 418 F.2d 528, 163 USPQ 644 (CCPA 1969). A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b). The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA/25, or PTO/AIA/26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp. Claims 21, 22, 24-29, 31-33 and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 6-12, 16-18 and 20 of US Patent No. 12244473. For example, claim 21 of the instant application encompasses all of the limitations of claim 1 of US Patent No. 12244473 as follows: Instant Application: 19046769 Patented Application: 12244473 Claim 18: A method, comprising: Claim 1 : A method, comprising: collecting properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway; collecting properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway, wherein the at least one device comprises at least an endpoint device; monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths; monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths; monitoring the at least one device to determine a current load of the at least one device; generating, using machine learning and the current level of performance for each of the plurality of paths, a predictive analytics model for congestion on each of the plurality of paths; and analyzing, using machine learning, the current level of performance for each of the plurality of paths and the current load of the at least one device to produce a predictive analytics model for congestion on each of the plurality of paths; determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway and the plurality of paths, based on at least the current level of performance for each of the plurality of paths. determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway, the plurality of paths, and the at least one device based on at least the current level of performance for each of the plurality of paths and the current load of the at least one device; and performing the corrective action when the predictive analytics model indicates that the corrective action is needed. 08-34 AIA Claim s 21, 22, 24-29, 31-33 and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claim s 1, 2, 6-12, 16-18 and 20 of US Patent No. 12244473 . Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1, 2, 6-12, 16-18 and 20 of US Patent No. 12244473 are in essence a “species” of the generic invention of claims 21, 22, 24-29, 31-33 and 35 of the instant application. It has been held that a generic invention is “anticipated” by a “species” within the scope of the generic invention. See In re Goodman, 29 USPQ2d 2010 (Fed. Cir. 1993) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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 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. 07-20-aia AIA 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 of this title, 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. 07-21-aia AIA Claim s 21-23, 28-30 and 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Vasseur et al. (US 2015/0332145), hereinafter Vasseur, in view of Dinh et al. (US 2021/0136095), hereinafter Dinh. Dinh is cited by Applicant in the IDS filed on 02/06/2025 . Regarding claim 21 , Vasseur discloses a method, comprising: collecting properties associated with each of a plurality of paths between at least one device and gateway ( Vasseur, Fig. 1: CE router 110 (gateway); Fig. 2, [0026]- [0027]: CE router (gateway) includes a network analyzer module/NAM 246; [0048]: NAM 246 determines the performance of a plurality of paths connecting a remote office to a corporate network (at least one device); [0049]-[0053]: feature data (properties) collected by NAM 246 ); monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths ( Vasseur, [0048]: NAM 246 tracks all the network conditions visible to the router. NAM 246 determines the performance of each link/path available; [0049]-[0053]: feature data (properties) monitored by NAM 246 ); generating, using machine learning and the current level of performance for each of the plurality of paths, a predictive analytics model for congestion on each of the plurality of paths ( Vasseur, [0048]: NAM 246 is used to compute a model of the network performance using learning machine 404; [0055]: learning machine 404 uses a predictive model to predict future network performance metrics based on the feature data (current level of performance); [0050]-[0053]: performance metrics include delay, bandwidth, jitter and packet loss [These are all indicators of network congestion] ); and determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the gateway and the plurality of paths, based on at least the current level of performance for each of the plurality of paths ( Vasseur, Fig. 2, [0059]: predictive control manager/PCM 247 receives network performance model data generated by learning machine 404; [0059]: if an application SLA is predicted not to be met, PCM 247 takes corrective measures; [0063]: PCM 247 generates path selection parameters to ensure corresponding traffic is routed over different paths [i.e., CE router 110 (gateway) optimizes path selection] ). Vasseur does not explicitly disclose an Application Programing Interface (API) gateway. However, Dinh discloses collecting properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway ( Dinh, Fig. 1, Fig. 6, step 602: monitoring and logging transactions between clients (devices) and an API gateway ); determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway ( Dinh, Fig. 1, [0041]: AI/ML layer performs data analytics to make predictions regarding potential errors or failures; Fig. 6, steps 604-608 ). Vasseur uses machine learning based predictive routing and analysis to proactively maintain application SLA performance across multiple network paths. Dinh applies machine learning and anomaly detection to API gateway environments to proactively identify and remediate service degradation before failures occur. It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur and Dinh before him or her before the effective filing date of the claimed invention, to modify the predictive application aware routing framework as taught by Vasseur, to include API gateway monitoring and anomaly management techniques as taught by Dinh. The motivation for doing so would have been to improve the reliability and availability for API-based application traffic traversing multiple network paths. Regarding claim 28, Vasseur discloses gateway ( Vasseur, Fig. 1: CE router 110 ), comprising: one or more processors ( Vasseur, Fig. 2, [0026]-[0028] ); and one or more computer-readable non-transitory storage media coupled to the one or more processors that stores instructions operable when executed by the one or more processors to cause the gateway to perform operations comprising ( Vasseur, Fig. 2, [0026]-[0028] ): collecting properties associated with each of a plurality of paths between at least one device and the gateway ( Vasseur, Fig. 1: CE router 110 (gateway); Fig. 2, [0026]- [0027]: CE router (gateway) includes a network analyzer module/NAM 246; [0048]: NAM 246 determines the performance of a plurality of paths connecting a remote office to a corporate network (at least one device); [0049]-[0053]: feature data (properties) collected by NAM 246 ); monitoring the properties associated with each of the plurality of paths to determine a current level of performance for each of the plurality of paths ( Vasseur, [0048]: NAM 246 tracks all the network conditions visible to the router. NAM 246 determines the performance of each link/path available; [0049]-[0053]: feature data (properties) monitored by NAM 246 ); generating, using machine learning and the current level of performance for each of the plurality of paths, a predictive analytics model for congestion on each of the plurality of paths ( Vasseur, [0048]: NAM 246 is used to compute a model of the network performance using learning machine 404; [0055]: learning machine 404 uses a predictive model to predict future network performance metrics based on the feature data (current level of performance); [0050]-[0053]: performance metrics include delay, bandwidth, jitter and packet loss [These are all indicators of network congestion] ); and determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the gateway and the plurality of paths, based on at least the current level of performance for each of the plurality of paths ( Vasseur, Fig. 2, [0059]: predictive control manager/PCM 247 receives network performance model data generated by learning machine 404; [0059]: if an application SLA is predicted not to be met, PCM 247 takes corrective measures; [0063]: PCM 247 generates path selection parameters to ensure corresponding traffic is routed over different paths [i.e., CE router 110 (gateway) optimizes path selection] ). Vasseur does not explicitly disclose an Application Programing Interface (API) gateway. However, Dinh discloses an Application Programing Interface (API) gateway ( Dinh, Fig. 1: API management platform ), comprising: one or more processors ( Dinh, [0079] ); and one or more computer-readable non-transitory storage media coupled to the one or more processors that stores instructions operable when executed by the one or more processors to cause the API gateway to perform operations comprising ( Dinh, [0079] ): collecting properties associated with each of a plurality of paths between at least one device and an Application Programing Interface (API) gateway ( Dinh, Fig. 1, Fig. 6, step 602: monitoring and logging transactions between clients (devices) and an API gateway ); determining, using the predictive analytics model, if a corrective action is needed to maintain an optimal performance of the API gateway ( Dinh, Fig. 1, [0041]: AI/ML layer performs data analytics to make predictions regarding potential errors or failures; Fig. 6, steps 604-608 ). Vasseur uses machine learning based predictive routing and analysis to proactively maintain application SLA performance across multiple network paths. Dinh applies machine learning and anomaly detection to API gateway environments to proactively identify and remediate service degradation before failures occur. It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur and Dinh before him or her before the effective filing date of the claimed invention, to modify the predictive application aware routing framework as taught by Vasseur, to include API gateway monitoring and anomaly management techniques as taught by Dinh. The motivation for doing so would have been to improve the reliability and availability for API-based application traffic traversing multiple network paths. Regarding claim 35, the limitations have been addressed in the rejection of claim 21, and furthermore, Vasseur discloses one or more computer-readable non-transitory storage media embodying instructions that, when executed by a processor, cause the processor to perform operations ( Vasseur, [0028] ). Regarding claim 22, Vasseur discloses wherein monitoring the properties of each of the plurality of paths includes determining latency, jitter, available bandwidth, and packet loss of each of the plurality of paths ( Vasseur, [0049]-[0053] ). Regarding claim 23, Vasseur discloses further comprising performing the corrective action when the predictive analytics model indicates that the corrective action is needed ( Vasseur, [0060]: if an application SLA is predicted not to be met, PCM 247 takes corrective measures ). Regarding claims 29 and 36, the limitations have been addressed in the rejection of claim 22. Regarding claims 30 and 37, the limitations have been addressed in the rejection of claim 23 . 07-21-aia AIA Claim s 24-26, 31-33 and 38-40 are rejected under 35 U.S.C. 103 as being unpatentable over Vasseur in view of Dinh, further in view of Mui et al. (US 2019/0373531), hereinafter Mui . Regarding claim 24 , Vasseur as modified by Dinh discloses an API gateway ( Dinh, Fig. 1 ) but does not explicitly disclose wherein the corrective action comprises establishing an additional path between the at least one device and the API gateway. However, Mui discloses wherein the corrective action comprises establishing an additional path between the at least one device and the gateway ( Mui, [0017]: modifying a data path between a UE (device) and a core network node (gateway); [0064]-[0069]: GW node 148 (gateway) manages handover and path operations; [0060]: when a handover is required (i.e., corrective action), a new data path is created connecting to TBS 510 ). It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur, Dinh and Mui before him or her before the effective filing date of the claimed invention, to modify a predictive network optimization system in an API gateway environment as taught by Vasseur and Dinh, to include utilizing dynamic data path creation techniques as taught by Mui. The motivation for doing so would have been to enable the system to dynamically establish communication paths when predicted conditions indicate degraded API communication performance. Regarding claim 25, Vasseur as modified by Dinh discloses an API gateway ( Dinh, Fig. 1 ) but does not explicitly disclose wherein the corrective action comprises removing at least one path between the at least one device and the API gateway. However, Mui discloses wherein the corrective action comprises removing at least one path between the at least one device and the gateway ( Mui, [0017]: modifying a data path between a UE (device) and a core network node (gateway); [0064]-[0069]: GW node 148 (gateway) manages handover and path operations; [0060]: when a handover is required (i.e., corrective action), a ‘UE Context Release’ message is communicated to SB 500 leading to the removal of the previously established user data path ). It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur, Dinh and Mui before him or her before the effective filing date of the claimed invention, to modify a predictive network optimization system in an API gateway environment as taught by Vasseur and Dinh, to include utilizing dynamic path removal techniques as taught by Mui. The motivation for doing so would have been to enable the system to dynamically remove inefficient or unnecessary communication paths, thereby improving API communication performance and resource efficiency. Regarding claim 26, Vasseur as modified by Dinh discloses an API gateway ( Dinh, Fig. 1 ) but does not explicitly disclose wherein the corrective action comprises reconfiguring at least one path between the at least one device and the API gateway. However, Mui discloses wherein the corrective action comprises reconfiguring at least one path between the at least one device and the API gateway ( Mui, [0017]: modifying a data path between a UE (device) and a core network node (gateway); [0064]-[0072]: GW node 148 (gateway) handling and modifying the path; [0072]: ‘Handover Required’ (i.e., corrective action needed); the GW node is able to modify the existing data path and reassign it to the TBS ). It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur, Dinh and Mui before him or her before the effective filing date of the claimed invention, to modify a predictive network optimization system in an API gateway environment as taught by Vasseur and Dinh, to include utilizing dynamic path reconfiguration techniques as taught by Mui. The motivation for doing so would have been to enable the system to dynamically reconfigure communication paths to maintain efficient and reliable API communications under changing network conditions. Regarding claims 31 and 38, the limitations have been addressed in the rejection of claim 24. Regarding claims 32 and 39, the limitations have been addressed in the rejection of claim 25. Regarding claims 33 and 40, the limitations have been addressed in the rejection of claim 26 . 07-21-aia AIA Claim s 27 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Vasseur in view of Dinh and Mui, further in view of Shen et al. (US 2020/0413283), hereinafter Shen . Regarding claim 27 , Vasseur, Dinh and Mui do not explicitly disclose wherein reconfiguring the at least one path comprises: moving the API gateway to a new location; or instructing a network controller associated with a Software Defined-Wide Area Network (SD-WAN) to increase network resources of at least one of the plurality of paths. However, Shen discloses wherein reconfiguring the at least one path comprises: moving the API gateway to a new location; or instructing a network controller associated with a Software Defined-Wide Area Network (SD-WAN) to increase network resources of at least one of the plurality of paths ( Shen, [0077]-[0078]: SD-WAN controller receives a congestion indication (instruction) indicating that bandwidth utilization on a WAN interface of a branch edge router exceeds a threshold, and it reduces congestion at the branch edge router (i.e., on the path) by modifying a bandwidth shaper ratio from 100% to 40% (i.e., increasing bandwidth/resources) ). It would have been obvious to one of ordinary skill in the art, having the teachings of Vasseur, Dinh, Mui and Shen before him or her before the effective filing date of the claimed invention, to modify a predictive network optimization system in an API gateway environment as taught by Vasseur, Dinh and Mui, to include utilizing SD-WAN adaptive congestion control and resource allocation techniques as taught by Shen. The motivation for doing so would have been to enable the system to dynamically increase network resources and reconfigure SD-WAN communication paths when degraded conditions affect API communications. Regarding claim 34, the limitations have been addressed in the rejection of claim 27. Related Art 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure : Xiong et al. (US 2022/0303216) discloses monitoring API gateway cluster and service cluster load parameters to determine whether the API gateway cluster is congested, and performing corrective actions such as scaling out the API gateway cluster to maintain performance ([0007]-[0012], Fig. 3B and 4). Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to LESA M KENNEDY whose telephone number is (571)431-0704. The examiner can normally be reached Monday-Wednesday 9:30 am - 5:30 pm ET. 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, Umar Cheema can be reached on (571) 270-3037. 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. The examiner also requests, in response to this Office Action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application. /LESA M KENNEDY/Primary Examiner, Art Unit 2458 Application/Control Number: 19/046,769 Page 2 Art Unit: 2458 Application/Control Number: 19/046,769 Page 3 Art Unit: 2458 Application/Control Number: 19/046,769 Page 4 Art Unit: 2458 Application/Control Number: 19/046,769 Page 5 Art Unit: 2458 Application/Control Number: 19/046,769 Page 6 Art Unit: 2458 Application/Control Number: 19/046,769 Page 7 Art Unit: 2458 Application/Control Number: 19/046,769 Page 8 Art Unit: 2458 Application/Control Number: 19/046,769 Page 9 Art Unit: 2458 Application/Control Number: 19/046,769 Page 10 Art Unit: 2458 Application/Control Number: 19/046,769 Page 11 Art Unit: 2458 Application/Control Number: 19/046,769 Page 12 Art Unit: 2458 Application/Control Number: 19/046,769 Page 13 Art Unit: 2458 Application/Control Number: 19/046,769 Page 14 Art Unit: 2458 Application/Control Number: 19/046,769 Page 15 Art Unit: 2458 Application/Control Number: 19/046,769 Page 16 Art Unit: 2458 Application/Control Number: 19/046,769 Page 17 Art Unit: 2458 Application/Control Number: 19/046,769 Page 18 Art Unit: 2458 Application/Control Number: 19/046,769 Page 19 Art Unit: 2458 Application/Control Number: 19/046,769 Page 20 Art Unit: 2458
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