DETAILED ACTION
This Action is in response to the Amendment for Application Number 18645516 received on 12/09/2025.
Claims 1-9, 11-21 are presented for examination.
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 .
Claim Objections
Claims 6, 13, and 20 are objected to because of the following informalities:
Claims 6, 13, and 20 recite the limitation, “wherein the one or more machine learning models include one or more an aggregate model, a neighbor-based predictor, and time-to-event predictor” which includes minor typographical errors. The limitation will be interpreted to recite, “wherein the one or more machine learning models include one or more of an aggregate model, a neighbor-based predictor, and a time-to-event predictor”.
Appropriate correction is required.
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, 3-6, 8, 11-13, 15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kwong et al. (US 20210103840) in view of Behera et al. (US 20240056366).
Regarding claim 1, Kwong disclosed an apparatus comprising a memory and a processor (Kwong, [0003], “system” including “memory” and “hardware processor”), wherein the processor is configured to:
receive a change request to be applied to a target network element in a network, wherein the change request comprises at least a method of procedure specifying one or more changes to be applied to the target network element (Kwong, Fig. 2B, [0072], “As shown in FIG. 2B, in the process 200B, when the prediction tool is deployed and used, data for a new change request is provided to the prediction tool”, and “The client device 201 forwards the change request data to the change request analysis device 202”; [0003], “The instructions instruct the at least one hardware processor to receive one or more parameters corresponding to a new change request associated with the IT infrastructure” and “Each change request corresponds to a request for a modification to one or more sections of the IT infrastructure.”; See [0022] reciting IT infrastructure to include network elements such as servers, routers, and switches);
obtain, in response to the received change request, one or more previous change requests that have been applied to the target network element and information related to the target network element (Kwong, In response to receiving the new change request recited in [0072], Kwong disclosed, at [0073] “The change request analysis device 202 prepares the data for training (250). In some implementations, preparing the data for training includes data processing (251), which is similar to data processing (221).” As disclosed in [0066], Kwong defines “data processing (221)” to include retrieving prior change request information from the repository; Therefore, preparing data 250, in Fig. 2B, includes the prediction tool obtaining one or more prior change requests in response to the receipt of a new change request; See also [0003]-[0005] and [0013] in for additional information on prior change requests; [0040] “the repository 120 stores database entries, with the entries including information about the prior change requests” which “includes a change request identifier 122 that identifies a particular prior change request that was made, and acts as key to access the corresponding information in the repository 120”);
generate a risk analysis report based on the method of procedure and the obtained one or more previous change requests and the obtained information using one or more machine learning models (Kwong, [0003], Kwong disclosed, “In response to providing the one or more parameters as input to the at least one prediction tool, a probability of success of the new change request is received as an output of the at least one prediction tool”; [0004], “based on providing the accessed information as input to the prediction tool, configuring the at least one prediction tool to generate a target output”; See [0008] in which Kwong disclosed an output of the prediction tool which includes an ordering of the parameters in accordance with estimated impact of each parameter in effecting the requested modification or one or more recommendations for modifying the new change request; [0012], Kwong disclosed the processor is configured to “analyze the one or more parameters corresponding to the new change request using the information corresponding to the one or more prior change requests stored in the repository“; [0014], “the at least one prediction tool includes a machine learning algorithm”),
by inputting at least the one or more previous change requests obtained in response to the received change request to the one or more machine learning models for risk analysis of the received change request (Kwong, Fig. 2B, 255, In response to receiving the new change request recited in [0072], Kwong disclosed, at [0073] “The change request analysis device 202 prepares the data for training (250). In some implementations, preparing the data for training includes data processing (251), which is similar to data processing (221).” As disclosed in [0066], Kwong defines “data processing (221)” to include retrieving prior change request information from the repository; Therefore, in response to the receipt of the new change request in Fig. 2B, Kwong disclosed preparing data 250 which includes the procedure defined by data processing (221) which includes obtaining and inputting information of prior change requests as part of the data processing and applying such to the prediction tool); and
determine whether to apply the change request to the target network element based on the generated risk analysis report using the one or more machine learning models (Kwong, [0011] Kwong disclosed comparing a probability of success of the new change request output and approving execution in response to satisfying criteria; See also [0074], “The prediction tool processes the prepared data using the selected algorithm and obtains an estimate of the success probability of the change request determined by the algorithm”);
wherein the risk analysis report includes data related to risks of applying the change request to the target network element on the network (Kwong, [0008], Kwong disclosed an output of the prediction tool which includes an ordering of the parameters in accordance with estimated impact of each parameter in effecting the requested modification or one or more recommendations for modifying the new change request; [0025], Kwong disclosed the prediction tool may “alert the change team to the risks associated with these change requests”; See also [0074]-[0075]).
Kwong additionally disclosed, in response to determining to apply the change request to the target network element, automatically approve implementation of the requested change (Kwong, [0030], Kwong disclosed “if an AI algorithm that is used determines that the success probability of a change request is above a certain threshold value (for example, above 95% probability of success), then the AI algorithm automatically approves implementation of the requested change, without requiring review or approval from members of the change team”).
While Kwong disclosed the AI algorithm approving the implementation of the requested change, Kwong did not explicitly disclose actually applying the change to the network element.
In an analogous art, Behera disclosed applying the change request to the target network element (Behera, [0019], Behera disclosed systems and methods of automating approval and administration of network changes in a computerized network such as an Information Technology (IT) infrastructure and service network; [0136] Behera disclosed change implementation workflow after approved stage; [0139] Behera disclosed coordinating implementation of the change at block 706).
One of ordinary skill in the art would have been motivated to combine the teachings of Kwong and Behera as they both relate to the handling of network configuration change requests, and as such, they are within similar environments.
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate Behera’s change implementation techniques within the teachings of Kwong in order to further automate the process for managing change requests in the network, thereby making the teachings more desirable to be used by its customers.
Claim 8 recites a method with limitations that are substantially similar to the limitations of claim 1. Claim 15 recites a non-transitory computer-readable recording medium having recorded thereon instructions executable by an apparatus to cause the apparatus to perform a method having limitations that are substantially similar to the limitations of claim 1.
Kwong and Behera disclosed a method as well as a non-transitory computer-readable recording medium having recorded thereon instructions executable by an apparatus to cause the apparatus to perform the method (Kwong, [0015], Behera, [0026]).
Claims 8 and 15 are therefore rejected under the same rationale applied above.
Regarding claims 3 and 17, Kwong and Behera disclosed the apparatus according to claim 1, wherein the processor is further configured to:
in response to determining to not apply the change request to the target network element, transmit a deny notification to a user (Kwong, [0012] in which Kwong disclosed upon a high risk of failure, the processor generates an alert about the risk of failure, which amounts to a deny notification). See motivation to combine above.
Regarding claims 4, 11, 18, Kwong and Behera disclosed the apparatus according to claim 3, method of claim 8, and medium of claim 17, further comprising:
monitoring statuses of the target network element and other network elements in the network in response to applying the change request to the target network element (Kwong, [0040], Kwong disclosed maintaining a repository 120 that stores historical data about change requests, in which the entries in the repository 120 include observed results 130, which indicates, for a change that was implemented, whether the change was successful or failed, and, for implemented changes that failed, the observed results 130 provide information on what was the negative impact of the failure on the organization's IT infrastructure, such as which systems and/or services of the organization, if any, were negatively affected by the failure of the change; See Behera [0140], Behera disclosed monitoring the network status and change impacts); and
generating an impact analysis report based on the statuses using the one or more machine learning models, wherein the impact analysis report includes data related to impacts of applying the change request to the target network element on the target network element and other network elements in the network (Kwong, [0027], Kwong disclosed the prediction tool can forward the output of the ML algorithm to a visualization tool that displays the estimated or predicted probability of success on a graphical user interface (GUI) presented on a computer device, which is generated based on information of prior change requests and their impacts; See Behera, [0140], Behera disclosed generating test result data regarding the change request). See motivation to combine above.
Regarding claims 5, 12, and 19, Kwong and Behera disclosed the apparatus according to claim 4, method of claim 11, and medium of claim 18, further comprising:
determining whether a roll back should be performed on the target network element based on the generated impact analysis report using the one or more machine learning models (The combined teachings of Kwong and Behera disclosed this limitation; Behera, [0141], Behera disclosed determining whether to implement a fallback plan to bring the network back to the prior state prior to the network change being implemented. Behera also refers to this as a “rollback”; Kwong disclosed the generation of an impact analysis report using the one or more machine learning models used to approve the change request, Kwong, [0003], Kwong disclosed, “In response to providing the one or more parameters as input to the at least one prediction tool, a probability of success of the new change request is received as an output of the at least one prediction tool”; [0004], “based on providing the accessed information as input to the prediction tool, configuring the at least one prediction tool to generate a target output”; See [0008] in which Kwong disclosed an output of the prediction tool which includes an ordering of the parameters in accordance with estimated impact of each parameter in effecting the requested modification or one or more recommendations for modifying the new change request; [0012], Kwong disclosed the processor is configured to “analyze the one or more parameters corresponding to the new change request using the information corresponding to the one or more prior change requests stored in the repository“; [0014], “the at least one prediction tool includes a machine learning algorithm”);
in response to determining to perform the roll back on the target network element, perform the roll back on the target network element (Behera, [0141], Behera disclosed “If the network change was not successful at block 710 or if network monitoring at block 714 indicates that network testing was not successful and/or the change impact are not within an acceptable range, the change management computer software 127 implements a fallback plan so that the network is provided in the same state that the network was in prior to the network change being implemented”); and
in response to determining to not perform the roll back on the target network element, transmit a result notification to a user (Behera, [0140], “If the network change was a success… the change management computer software 127 issues notifications that the change execution was successful”; See [0139] indicating notifications are sent to user devices). See motivation to combine above.
Regarding claims 6, 13, and 20, Kwong and Behera disclosed the apparatus according to claim 1, method of claim 8, and medium of claim 15, wherein the one or more machine learning models include one or more an aggregate model, a neighbor-based predictor, and time-to-event predictor (Kwong, [0055], Kwong disclosed the prediction tool may be made up of one or more algorithms, which may be ML algorithms including “neural network (NN) algorithms or decision tree algorithms with feature importance and gradient boosting” and also “support vector machine (SVM) algorithms, linear or logistic regression algorithms, random forest algorithms, among other suitable types”; Applicant’s Specification at [0066] defines an aggregate model as “includ[ing] a combination of machine learning models, such as Random Forests of decision trees”).
Claims 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kwong et al. (US 20210103840) in view of Behera et al. (US 20240056366) and further in view of Thigpen et al. (US 20200348947).
Regarding claims 2, 9, and 16, Kwong and Behera disclosed the apparatus according to claim 1, method of claim 8, and medium of claim 15, but did not explicitly disclose wherein the risk analysis report includes one or more of: a list of network elements in the network that are expected to be impacted by the application of the change request to the target network element; one or more change requests to be applied to other network elements in the network that are expected to conflict with the change request to be applied to the target network element; one or more activities that are expected to conflict with the change request to be applied to the target network element; one or more issues that are expected to occur with the change request to be applied to the target network element; network elements survival analysis; and one or more suggestions on an implementation timing of the change request.
In an analogous art, Thigpen disclosed wherein the risk analysis report includes one or more of: a list of network elements in the network that are expected to be impacted by the application of the change request to the target network element; one or more change requests to be applied to other network elements in the network that are expected to conflict with the change request to be applied to the target network element; one or more activities that are expected to conflict with the change request to be applied to the target network element; one or more issues that are expected to occur with the change request to be applied to the target network element; network elements survival analysis; and one or more suggestions on an implementation timing of the change request (Thigpen, [0040], Thigpen disclosed an impact management module that helps manage change requests, “for example to reconfigure a server, the server may need to be rebooted. The change request may include a schedule for when the server is to be rebooted. However, that server may also host part of the email service.”; Thigpen disclosed impact management module 504 may obtain information from the item and lookup dependencies, to which, “The dependencies of the item may be checked as against the dependencies for the potentially impacted ES portfolio. Based on this check, a determination may be made, for example, that the server includes a portion of the email service. This information may then be displayed to the user via a user interface, such as an impact management user interface (UI)”; The reporting of this impact information to the user reasonably amounts to the claimed “one or more activities that are expected to conflict with the change request to be applied to the target network element” or the claimed “one or more issues that are expected to occur with the change request to be applied to the target network element”).
One of ordinary skill in the art would have been motivated to combine the teachings of Kwong and Behera with Thigpen as they both relate to reporting the impact of a change request to network elements (Kwong, [0027], Thigpen, [0040], Both involve reporting via GUI), and as such they are within similar environments.
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the impact management module/interface functionality of Thigpen within the teachings of Kwong and Behera in order to benefit users by providing them additional information regarding what impacts their change requests may have, thereby allowing them to make more informed decisions as to whether to proceed with such changes, thereby increasing desirability of use.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kwong et al. (US 20210103840) in view of Behera et al. (US 20240056366) and further in view of Deutsch et al. (US 20250139057).
Regarding claims 7 and 14, Kwong and Behera disclosed the apparatus according to claim 6, and method of claim 13, and further disclosed the prediction tool may be made up of one or more algorithms (aggregate model), which may be ML algorithms including “neural network (NN) algorithms or decision tree algorithms with feature importance and gradient boosting” and also “support vector machine (SVM) algorithms, linear or logistic regression algorithms, random forest algorithms, among other suitable types” (Kwong, [0055]).
While Kwong disclosed an aggregate model, Kwong and Behera did not explicitly disclose wherein the one or more machine learning models are integrated together via feature fusion.
In an analogous art, Deutsch disclosed wherein the one or more machine learning models are integrated together via feature fusion (Deutsch, [0072], Deutsch disclosed, “the one or more machine learning models may be trained to combine the extracted modality-specific features using fusion techniques. Doing so may allow the one or more machine learning models to capture cross-modal relationships and understand how the different modalities contribute to the first file or learning model data”).
One of ordinary skill in the art would have been motivated to combine the teachings of Kwong and Behera with Deutsch since Kwong suggested the utilization of multiple machine learning models, and Deutsch explicitly provides a manner for integrating multiple models using fusion techniques (Deutsch, [0072]).
Therefore it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the feature fusion technique of Deutsch within the teachings of Kwong and Behera in order to achieve the utilization of multiple machine learning models as suggested by Kwong, thereby allowing the multiple machine learning models of Kwong to capture cross-modal relationships and understand how the different modalities contribute to the results (Deutsch, [0071]) thereby providing more efficient impact analysis results.
Response to Amendment
Applicant’s arguments filed on 12/09/2025 have been carefully considered but they are not deemed fully persuasive.
In view of the amendments made to independent claims 1, 8, and 15, the previously applied 35 USC 101 rejection has been withdrawn.
With respect to the Kwong reference, Applicant asserts, “the cited prior art does not disclose or suggest receiving a change request (CR), obtaining a previous CR “in response to the received change request,” and inputting this this previous CR obtained that is obtained in response to the received CR to an ML model(s) for risk analysis of the received CR” [Response, 13]. Applicant additionally asserts “the prior CR data is only used in training and by the risk mitigation tool 118, which does not involve the AI/ML model or any input thereto” [Response, 14].
Examiner respectfully disagrees.
As shown in the above rejection, Kwong disclosed, in response to receiving a “new change request”, recited in [0072], Kwong disclosed, at [0073] “The change request analysis device 202 prepares the data for training (250). In some implementations, preparing the data for training includes data processing (251), which is similar to data processing (221).” As disclosed in [0066], Kwong defines “data processing (221)” to include retrieving prior change request information from the repository. Therefore, in response to the received new change request in [0072], the teachings of Kwong include preparing data 250, in Fig. 2B, to include obtaining one or more prior change requests. Kwong, at Fig. 2B, 255, also disclosed the prepared data being applied/input to the prediction tool/ML model.
Therefore, while the prior CR data is used for training, as Applicant notes, such training additionally may occur in response to the received new change request.
The rejection is therefore respectfully maintained.
It is the Examiner’s position that Applicant has not yet submitted claims drawn to limitations, which define the operation and apparatus of Applicant’s disclosed invention in manner, which distinguishes over the prior art.
Failure for Applicant to significantly narrow definition/scope of the claims and supply arguments commensurate in scope with the claims implies the Applicant intends broad interpretation be given to the claims. The Examiner has interpreted the claims with scope parallel to the Applicant in the response and reiterates the need for the Applicant to more clearly and distinctly define the claimed invention.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bhatnagar et al. (WO 2025203057) disclosed at [000117] “When a new configuration change request is received, the verification module (214)” may “retrieve the corresponding parameters from previously stored configuration change requests” and at [000118] “comparison process carried out by the verification module (214) may involve a multi-dimensional analysis of the potential impacts of the proposed change” and at [000122] “To perform its comparisons effectively, the verification module (214) may utilize advanced data analytics and machine learning techniques”.
Johnston et al. (US 20180248750) disclosed receiving a change request that must be implemented on some or all of the network elements in the network sites (Johnston, [0017]) and upon receiving the change request, the network controller 110 gathers technical and business factors that may impact the implementation of the change in the network elements, listed in this paragraph (Johnston, [0018]).
Clemm et al. (US 20150350015) disclosed monitoring the statuses of the target network element and other network elements in the network in response to applying a change request to a target network element (Clemm, [0009], Clemm disclosed receiving a change request for a configuration change, creating a change impact monitor, and monitoring performance indicators, and generating a change impact notification describing snapshots taken at different times).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY B DENNISON whose telephone number is (571)272-3910. The examiner can normally be reached M-F 8:30-5:50.
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, Hadi Armouche can be reached at 571-270-3618. 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.
/JERRY B DENNISON/Primary Examiner, Art Unit 2409