DETAILED ACTION
This office action is made final. Claims 1-20 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Applicant’s amendment date 01/08/2026, amended claims 1-3, 6, 9-11, 14, and 17-19.
Response to Amendment
The previously pending rejection to claims 1-20, under 35 USC 101 (Alice), will be maintained.
Response to Arguments
Applicant’s arguments received on date 01/08/2026 have been fully considered, but they are not persuasive. Moreover, any new grounds of rejection have been necessitated by Applicant's amendments to the claims. The art rejection has been updated to address these amendments.
Response to Arguments under 35 USC 101:
Applicant argues that "Applicant respectfully submits that the amended independent Claims 1, 9, and 17 and the claims that depend therefrom are not directed to an abstract idea.” Examiner respectively disagrees.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether the claim recites any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
Here, under the first prong of Step 2A, the claims (claim 1, and similarly claims 9, and 17) recite “receive, a post-incident indication associated with an incident subsequent to resolution of the incident; determine if the incident satisfies post-incident report generation criteria; in response to determining that the incident satisfies the post-incident report generation criteria: determine, relevant post-incident data associated with the incident, wherein the relevant post-incident data comprises corrective action data associated with the incident, and wherein determining the corrective action data comprises: extracting first corrective action data within one or more communication channels associated with the incident; and correlating the first corrective action data with data extracted from one or more issue data objects associated with the incident to identify at least a portion of the corrective action data; generate, based on the relevant post-incident data, a post-incident report for the incident, wherein generating the post-incident report comprises generating a first segment of the post-incident report based on the corrective action data; and triggering one or more post-incident improvement actions based on the post-incident report at least in part by transmitting, provide the post-incident report.”
A claim recites mental processes when the claim recites concepts performed in the human mind (including an observation, evaluation, judgment, opinion), wherein if the claim, under its broadest reasonable interpretation, covers the claim being practically performed in the mind but for the recitation of generic computer components, then the claim is in the mental process category. Id. at 52 n.14. Therefore, contrary to Applicant’s assertions, the claims are directed to mental processes.
Response to Arguments under 35 USC 102/103:
Applicant's arguments with respect to the claim rejections have been considered, but are moot in view of the new ground(s) of rejection set forth below in this Office action. The art rejection has been updated to address these amendments.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1-20 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claims 1, 9, and 17 recite an abstract idea. Claims 1, 9, and 17 include “receive, a post-incident indication associated with an incident subsequent to resolution of the incident; determine if the incident satisfies post-incident report generation criteria; in response to determining that the incident satisfies the post-incident report generation criteria: determine, relevant post-incident data associated with the incident, wherein the relevant post-incident data comprises corrective action data associated with the incident, and wherein determining the corrective action data comprises: extracting first corrective action data within one or more communication channels associated with the incident; and correlating the first corrective action data with data extracted from one or more issue data objects associated with the incident to identify at least a portion of the corrective action data; generate, based on the relevant post-incident data, a post-incident report for the incident, wherein generating the post-incident report comprises generating a first segment of the post-incident report based on the corrective action data; and triggering one or more post-incident improvement actions based on the post-incident report at least in part by transmitting, provide the post-incident report”.
The limitations above recite an abstract idea under Step 2A Prong One. More particularly, the elements above recite mental processes-concepts performed in the human mind (including an observation, evaluation, judgment, opinion) because the elements describe a process for generating post-incident reports. As a result, claims 1, 9, and 17 recite an abstract idea under Step 2A Prong One.
Claims 2-8, 10-16, and 18-20 further describe the process for generating post-incident reports. As a result, claims 2-8, 10-16, and 18-20 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claims 1, 9, and 17.
With respect to Step 2A Prong Two of the framework, claims 1, 9, and 17 do not include additional elements that integrate the abstract idea into a practical application. Claims 1, 9, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 9, and 17 include machine learning models, enterprise applications, a computing device, processor, memory, and non-transitory computer-readable storage medium. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. As a result, claims 1, 9, and 17 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2-7, 10-15, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 9, and 17. As a result, claims 2-7, 10-15, and 18-20 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above with respect to claims 1, 9, and 17.
Claims 8 and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8 and 16 include machine learning models and generative artificial intelligence. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 8 and 16 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
With respect to Step 2B of the framework, claims 1, 9, and 17 do not include additional elements amounting to significantly more than the abstract idea. As noted above, claims 1, 9, and 17 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 1, 9, and 17 include machine learning models, enterprise applications, a computing device, processor, memory, and non-transitory computer-readable storage medium. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements are generic computing elements that are merely used as a tool to perform the recited abstract idea. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, independent claims 1, 9, and 17 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Claims 2-7, 10-15, and 18-20 do not include any additional elements beyond those recited with respect to claims 1, 9, and 17. As a result, claims 2-7, 10-15, and 18-20 do not include additional elements that amount to significantly more than the abstract idea under Step 2B for the same reasons as stated above with respect to claims 1, 9, and 17.
Claims 8 and 16 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements of claims 8 and 16 include machine learning models and generative artificial intelligence. The additional elements do not amount to significantly more than the abstract idea because the additional computing elements do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 8 and 16 do not include additional elements that amount to significantly more than the abstract idea under Step 2B.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 9-10, and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Goswami et al. (US Pub No. 2024/0037464) (hereinafter Goswami et al.) in view of Siracusano et al. (US Pub No. 2024/0411994) (hereinafter Siracusano al.).
Regarding claims 1, 9, and 17, Goswami discloses an apparatus for generating post-incident reports and at least one non-transitory computer-readable storage medium for generating post- incident reports, the at least one non-transitory computer-readable storage medium having computer coded instructions configured to (see Goswami, Fig. 2 and paras [0050] & [0078]) , when executed by at least one processor:
receive, a post-incident indication associated with an incident subsequent to resolution of the incident (see Goswami, para [0165], wherein (i.e., for some incidents), the received alerts may include at least some of cause data….Cause data may also be obtained from (or provide by) responders assigned to incidents. For example, as an incident is investigated or after it has been resolved; and para [0111], wherein with respect to a resolved alert, the data store 410 can include information regarding the resolving entity that resolved the alert (and/or, equivalently, the resolving entity of the event that triggered the alert), the duration that the alert was active until it was resolved);
determine if the incident satisfies post-incident report generation criteria (see Goswami, para [0133], wherein that the data store 410 includes sufficient data regarding frequent incidents can mean that the data store 410 of FIG. 4 includes a number of incidents that meet a frequency criterion. In an example, the frequency criterion can be that the data store includes at least a predefined number of resolved incidents that are similar to the incident);
in response to determining that the incident satisfies the post-incident report generation criteria: determine, using one or more machine learning models and based on one or more enterprise applications, relevant post-incident data associated with the incident, wherein the relevant post-incident data comprises corrective action data associated with the incident, and wherein determining the corrective action data (see Goswami, Fig. 2 and paras [0133] & [0195], wherein the technique 800 may, responsive to determining that the incident is of the rare type or the frequent type, generate, by the machine-learning model recommendation engine, the list of recommended responders by generating the list of recommended responders based on fuzzed incident titles, historical data of past responders for the alert, a skillset needed to respond to the incident and/or responder skillset data; and paras [0111]-[0112], wherein with respect to a resolved alert, the data store 410 can include information regarding the resolving entity that resolved the alert (and/or, equivalently, the resolving entity of the event that triggered the alert), the duration that the alert was active until it was resolved……The resolving entity can be a responder (e.g., a human). The resolving entity can be an integration ( e.g., automated system), which can indicate that the alert was auto resolved. That the alert is auto resolved can mean that the system 400 received, such as from the integration, an event indicating that a previous event, which triggered the alert, is resolved) comprises:
extracting first corrective action data within one or more communication channels associated with the incident (see Goswami, wherein paras [0165], [0064] & [0079], wherein with respect to incident causes, in some cases (i.e., for some incidents), the received alerts may include at least some of cause data. As such, incident causes can be extracted therefrom. Cause data may also obtained from (or provide by) responders assigned to incidents. For example, as an incident is investigated or after it has been resolved, responders associated with the incident may add cause data to the incident…..the audio interface 256 may be coupled to a speaker and microphone (not shown) to enable telecommunication with others or generate an audio acknowledgement for some action. A microphone in the audio interface 256 can also be used for input to or control of the client computer 200, e.g., using voice recognition, detecting touch based on sound….Exchanged communications may include, but are not limited to, queries, searches, messages, notification messages, events, alerts, performance metrics, log data, API calls, or the like); and
correlating the first corrective action data with data extracted from one or more issue data objects associated with the incident to identify at least a portion of the corrective action data (see Goswami, wherein paras [0165], [0112] & [0079], wherein …..The data store 410 can include data related to actions performed with respect to alerts. The data store 410 can include data indicating whether an action cleared (or contributed to clearing) a triggering event, or equivalently, the event. The data store 410 can also include associations (i.e., action-component associations) between actions and IT components and associations (i.e., alert-to-component associations) between alerts (i.e., alert types) and IT components…..with respect to incident causes, in some cases (i.e., for some incidents), the received alerts may include at least some of cause data. As such, incident causes can be extracted therefrom. Cause data may also obtained from (or provide by) responders assigned to incidents. For example, as an incident is investigated or after it has been resolved, responders associated with the incident may add cause data to the incident);
generate, based on the relevant post-incident data configured for an incident management platform, a post-incident report for the incident, wherein generating the post-incident report comprises generating a first segment of the post-incident report based on the corrective action data (see Goswami, para [0108], wherein events may be variously formatted messages that reflect the occurrence of events or incidents that have occurred in the computing systems or infrastructures of one or more managed organizations. Such events may include facts regarding system errors, warning, failure reports, customer service requests, status messages, or the like; and para [0165], wherein (i.e., for some incidents), the received alerts may include at least some of cause data….Cause data may also be obtained from (or provide by) responders assigned to incidents. For example, as an incident is investigated or after it has been resolved; and para [0110], wherein data related to events, alerts, incidents, notifications, other types of objects, or a combination thereof may be stored in the data store 410. The data store 410 can include data related to resolved and unresolved alerts. The data store 410 can include data identifying whether alerts are or not acknowledged); and
triggering one or more post-incident improvement actions based on the post incident report at least in part by transmitting the post-incident report to one or more of a client computing device or an enterprise application from the one or more enterprise applications (see Goswami, paras [0111]-[0112], wherein with respect to a resolved alert, the data store 410 can include information regarding the resolving entity that resolved the alert (and/or, equivalently, the resolving entity of the event that triggered the alert), the duration that the alert was active until it was resolved……The resolving entity can be a responder (e.g., a human). The resolving entity can be an integration ( e.g., automated system), which can indicate that the alert was auto resolved. That the alert is auto resolved can mean that the system 400 received, such as from the integration, an event indicating that a previous event, which triggered the alert, is resolved; and para [0108], wherein events may be variously formatted messages that reflect the occurrence of events or incidents that have occurred in the computing systems or infrastructures of one or more managed organizations. Such events may include facts regarding system errors, warning, failure reports, customer service requests, status messages, or the like….provide the events to the system 400. Events as described above may be comprised of, or transmitted to the system 400 via, SMS messages, HTTP requests/posts, API calls, log file entries, trouble tickets, emails, or the like. An event may include associated information, such as, source, a creation time stamp, a status indicator, more information, fewer information, other information, or a combination thereof, that may be tracked).
Goswami et al. fails to explicitly disclose using a generative artificial intelligence model configured for an incident management platform.
Analogous art Siracusano discloses generate, based on the relevant post-incident data and using a generative artificial intelligence model configured for an incident management platform (see Siracusano, para [0149], wherein industry reports, news articles, government intelligence reports, and incident response reports; and para [0147], wherein LLM querying procedure is introduced over the recent approaches used in the design of LLM-based generative AI Agents).
Goswami directed to a system for incident that requires a resolution responsive to an event detected in a managed information technology. Siracusano directed to extracting information from reports using large language models. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goswami, regarding the Smart Incident Responder Recommendation, to have included generate, based on the relevant post-incident data and using a generative artificial intelligence model configured for an incident management platform because both inventions teach resolving issues efficiency. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 2, 10, and 18, Goswami discloses the apparatus of claim 1, wherein the relevant post-incident data comprises one or more of fault data or timeline data for the incident (see Goswami, para [0021], wherein issue (such as system errors in web applications or web services applications); and paras [0038] & [0118], wherein responsible for handling an incident based on at least an on-call schedule and/or the content of the incident and/or active during the period of time they are designated by the schedule to be available).
Claims 3-4, 7-8, 11-12, 15-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Goswami et al. (US Pub No. 2024/0037464) (hereinafter Goswami et al.), in view of Siracusano et al. (US Pub No. 2024/0411994) (hereinafter Siracusano al.), and further in view of Deng et al. (US Pub No. 2024/0330672) (hereinafter Deng al.).
Regarding claims 3, 11, and 19, Goswami discloses the apparatus of claim 2, wherein generating the relevant post-incident data comprises:
extracting one or more incident features from incident data associated with the incident (see Goswami, paras [0163]-[0168], wherein content-based filtering (i.e., extracting), features of previously resolved incidents are used to provide the responder recommendation, such as the incident features);
extracting one or more alert features from alert data associated with the incident (see Goswami, paras [0163]-[0165], wherein filtering, features of the similar incidents and features of responders can be combined to obtain the list of recommended responders. More specifically, features of incidents and features of responders in a historical data set (i.e., a training set) can be used to train a collaborative filtering mode to, given an incident as an input, output a list of recommended responders…..With respect to incident causes, in some cases (i.e., for some incidents), the received alerts may include at least some of cause data. As such, incident causes can be extracted therefrom);
extracting one or more communication features from communication data associated with the incident and within the one or more communication channels (see Goswami, paras [0118]-[0119], wherein various performance characteristics of each message provider may be stored and/or associated with a corresponding provider performance profile……The selected message providers may transmit (e.g., communicate, etc.) the notification message to the responder…..The message providers may generate an acknowledgment message that may be provided to system 400 indicating a delivery status of the notification message (e.g., successful or failed delivery); paras [0165], [0064] & [0079], wherein…..the audio interface 256 may be coupled to a speaker and microphone (not shown) to enable telecommunication with others or generate an audio acknowledgement for some action. A microphone in the audio interface 256 can also be used for input to or control of the client computer 200, e.g., using voice recognition, detecting touch based on sound….Exchanged communications may include, but are not limited to, queries, searches, messages, notification messages, events, alerts, performance metrics, log data, API calls, or the like);
generating, using a sequence labeling model, the (recommendation) data based on the one or more communication features (see Goswami, paras [0158] & [0176], wherein generate, using one or more machine-learning models, a list of recommended responders based an incident type of an incident, a historical frequency of the incident (based on the incident type), the respective skillsets of responders available to address the incident, other data, or a combination thereof. The technique 700 may analyze processed data such as templatized or "tokenized" incident data parsed from the received incident…….Tokenized incidents and incident titles may be generated for use in collaborative filtering algorithms. Tokenization, when applied to a title, may remove new lines, table spaces, and the like, and replace multiple consecutive white spaces with a single whitespace. Another example of tokenization may be when the recommendation engine processes an incident title, identifies any substring that may indicate a time (e.g., a date, a timestamp, a date and time) and replaces the time with the token ( e.g., string) "datetime."; and paras [0118]-[0119], wherein various performance characteristics of each message provider may be stored and/or associated with a corresponding provider performance profile……The selected message providers may transmit (e.g., communicate, etc.) the notification message to the responder…..The message providers may generate an acknowledgment message that may be provided to system 400 indicating a delivery status of the notification message (e.g., successful or failed delivery); and
generating the fault data based on one or more of (i) the one or more incident features or (ii) the one or more alert features (see Goswami, para [0039], wherein incidents may be a failure or error that occurs in the operation of a managed network and/or computing environment. One or more events may be associated
with one or more incidents; and paras [0165]-[0166], wherein with respect to incident causes, in some cases (i.e., for some incidents), the received alerts may include at least some of cause data…..incident features can include resolution data, which can be data relating to how incidents were resolved).
Goswami et al. and Siracusano et al. combined fail to explicitly disclose generating, the corrective action data.
Analogous art Deng discloses generating, using a sequence labeling model, the corrective action data (see Deng, para [0040], wherein supervised sequence labeling tasks such as a long term short term memory conditional random field (LSTM-CRF) model….the input device of the mitigation guidance system receives labeled data 215 which includes closing notes 220 and sends the labeled data 215 which includes the closing notes 220 to the split labeled data module 230; para [0041], wherein corpus generation module 235 and the closing notes 220, the trained sequence tagger module 240…extracts a textual sequence which corresponds with the issue and the resolution of the issue (i.e., corrective action)).
Goswami directed to a system for incident that requires a resolution responsive to an event detected in a managed information technology. Deng directed to generation of mitigation information based on corrective action extraction. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goswami, regarding the Smart Incident Responder Recommendation, to have included generating, using a sequence labeling model, the corrective action data because both inventions teach resolving issues efficiency. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Regarding claims 4, 12, and 20, Goswami discloses the apparatus of claim 3, wherein the sequence labeling model, as set forth above with claim 3.
Goswami et al. fails to explicitly disclose BILSTM-CRF.
Analogous art Siracusano discloses BILSTM-CRF (see Siracusano, para [0269]).
One of ordinary skill in the art would have recognized that applying the known technique of Siracusano would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Regarding claims 7 and 15, Goswami discloses the apparatus of claim 6, wherein ranking, using a learning-to-rank model and based on user input, the (recommendation) action data (see Goswami, para [0160], wherein a list of recommended responders is obtained. The list of recommended responders can be obtained based at least in part on the type of the incident, as described herein. The list of recommended responders may be a ranked list of responders. The list may be ranked based on respective scores output by the machine-learning model used to generate the list of recommended responders. In the case of a novel incident, the responders may be ranked based on respective seniority scores of the recommended responders).
Goswami et al. fails to explicitly disclose generating the corrective action data further comprises ranking, the corrective action data.
Analogous art Deng discloses generating the corrective action data further comprises ranking, the corrective action data (see Deng, para [0040], wherein supervised sequence labeling tasks such as a long term short term memory conditional random field (LSTM-CRF) model….the input device of the mitigation guidance system receives labeled data 215 which includes closing notes 220 and sends the labeled data 215 which includes the closing notes 220 to the split labeled data module 230; para [0041], wherein corpus generation module 235 and the closing notes 220, the trained sequence tagger module 240…extracts a textual sequence which corresponds with the issue and the resolution of the issue (i.e., corrective action)).
One of ordinary skill in the art would have recognized that applying the known technique of Deng would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 3.
Regarding claims 8 and 16, Goswami discloses the 8. The apparatus of claim 1, wherein the one or more machine learning models, as set forth above with claim 1 .
Goswami et al. and Deng et al. fails to explicitly disclose generative artificial intelligence.
Analogous art Siracusano discloses generative artificial intelligence (see Siracusano, para [0147], wherein LLM querying procedure is introduced over the recent approaches used in the design of LLM-based generative AI).
One of ordinary skill in the art would have recognized that applying the known technique of Siracusano would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Goswami et al. (US Pub No. 2024/0037464) (hereinafter Goswami et al.), in view of Siracusano et al. (US Pub No. 2024/0411994) (hereinafter Siracusano al.), in view of Deng et al. (US Pub No. 2024/0330672) (hereinafter Deng al.), and further in view of L Wu et al. (Automatic performance diagnosis and recovery in cloud microservices) - 2022 - search.proquest.com (hereinafter Wu al.).
Regarding claims 5 and 13, Goswami discloses the apparatus of claim 3, wherein generating the fault data comprises:
identifying, based on the alert data, one or more services associated with the incident (see Goswami, paras [0113]-[0115], wherein can identify or can help in identifying causes of the incident or actions that might resolve the incident…..the resolution tracker 412 can receive data from the different services that process events, alerts, or incidents).
Goswami et al., Siracusano et al., and Deng et al. fails to explicitly disclose generating a causal graph of the one or more services; identifying, using a graph centrality model, initial fault location; and generating, using a link prediction model, a fault propagation path.
Analogous art Wu discloses generating a causal graph of the one or more services (see Wu, pages 25-29, wherein constructs a two-layered hierarchical causality graph, including service dependency graph and metric causality graph of each service, to show the dependency among metrics);
identifying, using a graph centrality model, initial fault location (see Wu, pages 29-30, wherein uses graph centrality algorithms to get the root causes; page 30, wherein identifies the faulty services and which resource, like CPU overhead, causes the service performance degradation. It is a proactive method that applies deep learning to massive data to identify root causes); and
generating, using a link prediction model, a fault propagation path (see Wu, pages 28 & 87, wherein causal inference, three types of causal properties are considered: (1) propagation across services; (2) propagation across resource and service-related metrics of linked service and container nodes; (3) propagation across resource metrics of linked container and server nodes; and page 28, wherein predict latent errors, faulty microservices, and fault types for traces captured at runtime….. Dependency inference learns the relationship between interconnected components for problem diagnosis, particularly when localizing the source of the problem that propagates across a distributed system. Specific insights sought by dependency inference include service dependencies, request or call paths, deployment information, and transaction tracking through a distributed system).
Goswami directed to a system for incident that requires a resolution responsive to an event detected in a managed information technology. Wu directed to monitoring metrics to enhance automatic performance diagnosis and recovery in cloud microservices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goswami, regarding the Smart Incident Responder Recommendation, to have included generating a causal graph of the one or more services; identifying, using a graph centrality model, initial fault location; and generating, using a link prediction model, a fault propagation path because both inventions teach resolving issues efficiency. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Goswami et al. (US Pub No. 2024/0037464) (hereinafter Goswami et al.), in view of Siracusano et al. (US Pub No. 2024/0411994) (hereinafter Siracusano al.), in view of Deng et al. (US Pub No. 2024/0330672) (hereinafter Deng al.), and further in view of in view of Anderson et al. (US Pub No. 9,824,093) (hereinafter Anderson al.).
Regarding claims 6 and 14, Goswami discloses the apparatus of claim 3, wherein generating the corrective action data comprises
data extracted from the communication data based on the one or more communication features (see Goswami, paras [0165]-[0166], wherein (i.e., for some incidents), the received alerts may include at least some of cause data. As such, incident causes can be extracted therefrom……incident features can include resolution data, which can be data relating to how incidents were resolved).
Goswami et al., Siracusano et al., and Deng et al. combined fail to explicitly disclose performing deduplication operation with respect to the first corrective action data and second corrective action data extracted from one or more other data sources, wherein the corrective action data comprises deduplicated corrective action data.
Analogous art Anderson discloses performing deduplication operation with respect to the first corrective action data and second corrective action data extracted from one or more other data sources, wherein the corrective action data comprises deduplicated corrective action data (see Anderson, column 8, lines 52-67, wherein the executor module 416 then resolves the issues in the queue 414 by performing one or more of a reconstruction or repair operation, a garbage collection operation, a deduplication operation, and/or a scrubbing operation, all of which are examples of maintenance or data protection operations……This could result in a situation where the newly uploaded data object is identified as garbage and deleted from the data nodes. As a result, object fragments or data objects may be associated with aging. Anew data object may be associated with an age window (e.g., 30 days) that prevents the repair component ( e.g., the executor module) from performing certain operations ( e.g., garbage collection); column 9, lines 11-26, wherein deduplication (remove object fragments that are redundant), repair (when less than the optimal number of object fragments are found and the data object can still be reconstructed), garbage collection (remove all object fragments associated with a data object because the data object is no longer referenced in any manifest), and scrub (some fragments exist, but not enough to reconstruct the data object). In a scrub action, an upload of the data object from the client is forced by removing the reference of the data object in the manifests. The scrub action may leave old data object fragments around but they will be garbage collected or deduplicated during the next scan after the new upload to the data center. Alternatively, the data object fragments will be deduplicated if the new uploaded data object has not changed and therefore has the same hash value).
Goswami directed to a system for incident that requires a resolution responsive to an event detected in a managed information technology. Anderson directed to performing a maintenance operation. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Goswami, regarding the Smart Incident Responder Recommendation, to have included performing deduplication operation with respect to the first corrective action data and second corrective action data extracted from one or more other data sources, wherein the corrective action data comprises deduplicated corrective action data because both inventions teach resolving issues efficiency. Further, the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Conclusion
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 extension fee 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 date of this final action.
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/HAFIZ A KASSIM/Primary Examiner, Art Unit 3623
03/11/2026