Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
1. This initial office action is based on the application filed on 07/26/2024, which claims 1-20 have been presented for examination.
Status of Claim
2. Claims 1-20 are pending in the application and have been examined below, of which, claims 1, 10 and 16 are presented in independent form.
Priority
3. No priority document has been filed in this application.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 07/26/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Examiner Notes
5. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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.
6. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis specific to Claims 1, 10 and 16 are being presented below.
Claim 1:
Step 1 Analysis:
Claims 1-9 of the instant application is direct to apparatus/system.
Claims 10-15 of the instant application is direct to process/method.
Claims 16-20 of the instant application is direct to product/computer-readable medium.
Thus, they are statutory categories.
Step 2 Analysis:
Claim 1 recites:
(a) obtain one or more logs that relate to a build process for an application in a continuous integration/continuous deployment (CI/CD) platform, wherein the one or more logs are generated by the CI/CD platform, wherein the Cl/CD platform uses one or more computing systems in connection with the build process, and wherein the build process is user-initiated by a user;
(b) process the one or more logs to identify whether an entry in the one or more logs indicates an error event for the build process;
(c) input, responsive to identification of the entry indicating the error event, context information to a machine learning language model to obtain a resolution output, wherein the context information includes the one or more logs and event information derived from the entry;
(d) obtain, from an incident tracking system and responsive to identification of the entry indicating the error event, incident data that relates to the one or more computing systems, wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected; and
(e) generate a communication that is for the user, wherein the communication includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Step 2A -- Prong 1:
The claim 1 recites the limitations of:
(a) obtain one or more logs that relate to a build process for an application in a continuous integration/continuous deployment (CI/CD) platform, wherein the one or more logs are generated by the CI/CD platform, wherein the Cl/CD platform uses one or more computing systems in connection with the build process, and wherein the build process is user-initiated by a user;
(d) obtain, from an incident tracking system and responsive to identification of the entry indicating the error event, incident data that relates to the one or more computing systems, wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected; and
(e) generate a communication that is for the user, wherein the communication includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Limitations (a) and (d) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. Limitations (e) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “generating” can be performed in the human mind with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 1 recites the additional limitations of “A system”, “one or more memories”, “one or more processors” and “one or more computing systems”. The limitations of “A system”, “one or more memories”, “one or more processors” and “one or more computing systems” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Claim 1 further recites “a machine learning model” as a tool perform abstract idea. Additionally, limitations (b) and (d) are merely insignificant extra solution activity of processing data and outputting data. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 10 recites:
(a) obtaining, responsive to an execution of a job in a platform, one or more logs that relate to the execution of the job, wherein the one or more logs are generated by the platform, and wherein the platform uses one or more computing systems in connection with the execution of the job;
(b) processing the one or more logs to identify whether an entry in the one or more logs indicates an error event for the job;
(c) determining, responsive to identification of the entry indicating the error event, a resolution output relating to the error event;
(d) obtaining, from an incident tracking system, incident data that relates to the one or more computing systems, wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected;
(e) generating a communication that includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Step 2A -- Prong 1:
The claim 10 recites the limitations of:
(a) obtaining, responsive to an execution of a job in a platform, one or more logs that relate to the execution of the job, wherein the one or more logs are generated by the platform, and wherein the platform uses one or more computing systems in connection with the execution of the job;
(c) determining, responsive to identification of the entry indicating the error event, a resolution output relating to the error event;
(d) obtaining, from an incident tracking system, incident data that relates to the one or more computing systems, wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected;
(e) generating a communication that includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Limitations (a), (c) and (d) are limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” and “determining” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. Limitations (e) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “generating” can be performed in the human mind with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 10 recites the additional limitation of “one or more computing systems”. The limitation of “one or more computing systems” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Additionally, limitation (b) merely insignificant extra solution activity of processing data. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2 Analysis:
Claim 16 recites:
(a) obtain one or more logs that relate to an execution of a job in a platform, wherein the one or more logs are generated by the platform, wherein the platform uses one or more computing systems in connection with the execution of the job, and wherein the job is user-initiated by a user;
(b) process the one or more logs to identify whether an entry in the one or more logs indicates an error event for the job;
(c) input, responsive to identification of the entry indicating the error event, context information to a machine learning language model to obtain a resolution output, wherein the context information includes event information derived from the entry;
(d) generate a communication that includes the resolution output;
(e) transmit the communication for delivery to the user by at least one of a chat message, a text message, an email message, or an automated phone call.
Step 2A -- Prong 1:
The claim 16 recites the limitations of:
(a) obtain one or more logs that relate to an execution of a job in a platform, wherein the one or more logs are generated by the platform, wherein the platform uses one or more computing systems in connection with the execution of the job, and wherein the job is user-initiated by a user;
(d) generate a communication that includes the resolution output;
Limitation (a) is limitation that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” can be performed in the human mind through observation, evaluation, judgement, opinion with the aid of pen and paper. Limitations (d) limitations that, as drafted, are processes that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “generating” can be performed in the human mind with the aid of pen and paper. As such, these limitations fall within the “Mental Processes” grouping of abstract ideas.
Step 2A -- Prong 2:
The claim 16 recites the additional limitation of “A non-transitory computer-readable medium”; “one or more processors of a device” and “one or more computing system”. The limitations of “A non-transitory computer-readable medium”; “one or more processors of a device” and “one or more computing system” are recited at a high level of generality, i.e., merely instructions to implement the abstract idea on a generic computer or merely uses a computer as a tool to perform the abstract idea. Limitation “a machine learning model” recited as a tool that performs abstract idea. Additionally, limitations (b-c) merely insignificant extra solution activity of processing data and outputting data. Limitations (e) performs well-understood, routine and conventional activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: (Claims 1, 10 and 16)
As explained with respect to Step 2A Prong Two, the additional elements in the claim are recited at a high level of generality and amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The same analysis applies here in 2B, i.e., simply adding extra-solution activity or well-understood, routine and conventional activity or generic computer components does not integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B since the courts have identified functions such as gathering, displaying, updating, transmitting/receiving and storing/uploading data as well- understood, routine, conventional activity. See MPEP 2106.05(d) and See MPEP 2106.05(g) . Therefore, claims are ineligible.
Dependent claims
Additionally, claim 2 recites “wherein the context information further includes support information relating to one or more interactions between the user and a support entity” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 2 is ineligible.
Additionally, claim 3 recites “wherein the one or more processors, to obtain the one or more logs, are configured to obtain the one or more logs during the build process” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtain” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 3 is ineligible.
Additionally, claim 4 recites “transmit the communication for delivery to the user by at least one of: a chat message, a text message, an email message, or an automated phone call” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 4 is ineligible.
Additionally, claim 5 recites “to process the one or more logs, are configured to: scan a plurality of entries indicated in the one or more logs; and determine, for each of the plurality of entries, whether an error is indicated by that entry” is merely insignificant extra solution activity of processing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 5 is ineligible.
Additionally, claim 6 recites “cause storing of training data for the machine learning language model in accordance with the one or more logs” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 6 is ineligible.
Additionally, claim 7 recites “receive an indication that the communication does not resolve the error event for the user; and transmit, responsive to the indication and to a support system, an error report that includes at least one of: the one or more logs, the event information, or the entry” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 7 is ineligible.
Additionally, claim 8 recites “wherein the communication includes the incident output in accordance with the incident data indicating the incident” is merely insignificant extra solution activity of processing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 8 is ineligible.
Additionally, claim 9 recites “wherein the communication includes the resolution output without the incident output in accordance with the incident data not indicating any incident” is merely insignificant extra solution activity of processing data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 9 is ineligible.
Additionally, claim 11 recites “wherein determining the resolution output comprises: inputting context information to a machine learning language model to obtain the resolution output, wherein the context information includes event information derived from the entry” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “determining” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. These limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or provide an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 11 is ineligible.
Additionally, claim 12 recites “wherein the event information further includes the one or more logs” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 12 is ineligible.
Additionally, claim 13 recites “wherein the one or more computing systems include at least one of: one or more version control systems, or one or more cloud computing systems” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 13 is ineligible.
Additionally, claim 14 recites “obtaining historical data relating to one or more previous jobs executed in the platform; generating trend data in accordance with the historical data” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper and “generating” can be performed in the human mind with the aid of pen and paper. The limitation “transmitting the trend data” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 14 is ineligible.
Additionally, claim 15 recites “obtaining historical data relating to one or more previous jobs executed in the platform; determining, using the historical data, whether a survey is to be provided” as drafted, is a process that, under its broadest reasonable interpretations, covers performance of the limitation in the mind. As such, this limitation falls within the “Mental Processes” grouping of abstract idea. That is, nothing in the claim elements precludes the step from practically being performed in the mind or with a pen and paper, i.e. “obtaining” and “determining” can be performed in the human mind through observation, evaluation, judgment, opinion with the aid of pen and paper. The limitation “transmitting, based on a determination that the survey is to be provided, the survey” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 15 is ineligible.
Additionally, claim 17 recites “wherein the one or more instructions, that cause the device to transmit the communication, cause the device to transmit the communication proactively, in the absence of a request for support made by the user” which perform as well-understood, routine and conventional activity. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 17 is ineligible.
Additionally, claim 18 recites “wherein the event information further includes the one or more logs” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 18 is ineligible.
Additionally, claim 19 recites “wherein the platform is a continuous integration/continuous deployment (CI/CD) platform, and wherein the execution of the job is a build process for an application” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 19 is ineligible.
Additionally, claim 20 recites “wherein the one or more computing systems comprise multiple computing systems” is merely insignificant extra solution activity of defining data. Accordingly, these limitations do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea or providing an inventive concept and thus do not amount to significantly more than the abstract idea. As such, these claims fail both Step 2A prong 2 and Step 2B. Therefore, claim 20 is ineligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
7. Claim(s) 1-6, 8, 10-13 and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teja et al. (US Pub. No. 2024/0345904 A1 – herein after Teja) in view of Damjakob et al. (US Pub. No. 2024/0036962 A1 – herein after Damjakob).
Regarding claim 1.
Teja discloses
A system for proactive error resolution (a proactive error resolution approach is provided that provides consistency in a CI/CD setup, for example, after periodic updates and/or releases of software shared modules, by providing one or more error resolution scripts that automate a set of practices provided – See paragraph [0083]), the system comprising:
one or more memories (a memory – See paragraph [0102]); and
one or more processors (a processor – See paragraph [0102]), communicatively coupled to the one or more memories, configured to:
obtain one or more logs that relate to a build process for an application in a continuous integration/continuous deployment (CI/CD) platform (the job log errors 108 provide information characterizing one or more errors in at least one pipeline job of a software deployment pipeline – See paragraph [0040]. A log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]),
wherein the one or more logs are generated by the CI/CD platform (the continuous integration module 110, the version control module 112 and/or the continuous deployment module 114, or portions thereof, may be implemented using functionality provided, for example, by commercially available DevOps and/or CI/CD tools, such as the GitLab development platform, the GitHub development platform – See paragraph [0031]. An automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069] and Fig. 6),
wherein the Cl/CD platform uses one or more computing systems in connection with the build process (FIG. 1 shows a computer network (also referred to herein as an information processing system) 100 configured in accordance with an illustrative embodiment. The computer network 100 comprises a plurality of user devices 102-1, 102-2, . . . 102-M, collectively referred to herein as user devices 102. The user devices 102 may be employed, for example, by software developers and other DevOps professionals to perform, for example, software development and/or software deployment tasks – See paragraphs [0025]. Perform CI/CD tasks and to provide access to DevOps tools and/or repositories. The continuous integration module 110 provides functionality for automating the integration of software code changes from multiple software developers or other DevOps professionals into a single software project – See paragraphs [0031-0032]), and
wherein the build process is user-initiated by a user (a user employing a user device 305 utilizes the GUI 310 to interact with the software development system 300, such as one or more visual representations of a software deployment pipeline or components thereof (e.g., pipeline jobs). Generally, the GUI 310 provides access to a visual software deployment pipeline editor, a pipeline manager, a DevOps toolkit and a reusable CI/CD resource library – See paragraph [0056]);
process the one or more logs to identify whether an entry in the one or more logs indicates an error event for the build process (an automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]. if a given pipeline job employs a particular version of Angular software (e.g., version 8), and the DevOps team has updated the deployed version of Angular software to a new version (e.g., version 12), then upon building the given pipeline job, an “angular mismatch” error may be received – See paragraph [0083]);
input, responsive to identification of the entry indicating the error event, context information to a machine learning language model to obtain a resolution output (The natural language processing models are trained using the information in the error table 400 and may be retrained for updates to the error table 400. For example, for a given error description, the natural language processing models may be trained to identify the corresponding error keyword (and thus, the corresponding error resolution script to execute to resolve the given error). For example, the natural language processing models may identify the record of the error table 400 with the best match based on the description provided for the given error – See paragraph [0061] and [0069-0071]),
wherein the context information includes the one or more logs and event information derived from the entry (once the natural language processing models identify a given record of the error table 400, the corresponding error keyword may be used as a pointer or lookup value to obtain the correct error resolution script to resolve a given pipeline job error or set of pipeline job errors – See paragraphs [0059]);
Teja does not disclose
obtain, from an incident tracking system and responsive to identification of the entry indicating the error event, incident data that relates to the one or more computing systems,
wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected;
generate a communication that is for the user;
wherein the communication includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Damjakob discloses
obtain, from an incident tracking system and responsive to identification of the entry indicating the error event (AutoComplete 828 may be implemented using an auto-completion model (not shown) that tracks all the fields that a user may enter in tickets and issues and learns to fill in these fields – See paragraph [0121]), incident data that relates to the one or more computing systems (Incidents 210 may correspond to one or more events from the event clusters occurrence of which may be important for notification to the one or more entities operating in the distinct ecosystems – See paragraph [0054]),
wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected (The correlation of incidents from the distinct ecosystems allows the determination of the impact of the incidents to entities operating in relation to the distinct ecosystems – See paragraph [0100]); and
generate a communication that is for the user (Communications and/or notifications may occur at various points within an event correlation map. For example, a UI/API 826 may be used to communicate information to API users – See paragraph [0112]),
wherein the communication includes at least one of the resolution output or an incident output relating to the incident (The event correlation map may provide a coherent converged picture of a data stream pertaining to events captured from the various distinct ecosystems. The event correlation map may include events correlated to form incidents representing the event necessary for indication to a user-end entity and/or a developer-end entity operating in one or more distinct ecosystems – See paragraph [0035]), in accordance with whether the incident data indicates the incident (The incidents are automatically triaged to identify tickets and the severity of the tickets is ascertained from an object associated with the incidents. The tickets refer to one or more incidents that may require certain cosmetic modifications. Further, the tickets that may reference underlying defects with the product or service are converted into issues. The incidents, tickets, and issues, referred to collectively as items are provided onto the event correlation map. Each item of the event correlation map may be associated with a plurality of objects providing a description of the activities leading to the generation of the item – See paragraph [0035]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to correlate events to from incidents representing the event necessary for indication to a user-end entity as suggested by Damijakob (See paragraph [0035]).
Regarding claim 2, the system of claim 1,
Damjakob discloses
wherein the context information further includes support information relating to one or more interactions between the user and a support entity (detect and interact with entities inside an organization. At the setup stage, one or more entities deployed in a connected environment with respect to a product of the organization may be auto-detected – See paragraph [0037]. Collectively referred to as capabilities 502, which is the core unit a user-end entity interacts with – See paragraph [0081]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to interact with the unified analysis platform, for example to provide information and/or to operate the unified analysis platform as suggested by Damijakob (See paragraph [0084]).
Regarding claim 3, the system of claim 1,
Teja discloses
wherein the one or more processors, to obtain the one or more logs, are configured to obtain the one or more logs during the build process (the at least one code repository may include different snapshots or versions of the software code 107, at least some of which can correspond to different branches of the software code 107 used for different development environments (e.g., one or more testing environments, one or more staging environments, and/or one or more production environments). The job log errors 108 provide information characterizing one or more errors in at least one pipeline job of a software deployment pipeline – See paragraph [0040]. Provide automated pipeline error resolution functionality of the type described above for one or more processes running on different ones of the containers. For example, a container host device supporting multiple containers of one or more container sets can implement one or more instances of automated pipeline error resolution control logic and associated software deployment pipeline recommendation functionality – See paragraph [0099]).
Regarding claim 4, the system of claim 1,
Damjakob discloses
wherein the one or more processors are further configured to:
transmit the communication for delivery to the user by at least one of:
a chat message,
a text message,
an email message, or
an automated phone call (User: Customer engagement, including omni-channel communication (voice, video, email, chat), and events from social media, client browsers; and/or (d) CRM: Customer journey events including clickstream events from tools such as Salesforce, Pendo, Gainsight, Mixpanel, Adobe CDP, Clickstream and Segment – See paragraph [0098]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to provide user management that including channel communication as suggested by Damijakob (See paragraph [0098]).
Regarding claim 5, the system of claim 1,
Teja discloses
wherein the one or more processors, to process the one or more logs, are configured to:
scan a plurality of entries indicated in the one or more logs (identifying the at least one error resolution script comprises identifying at least one record in an error database – See paragraph [0005]. After analyzing the associated error resolution steps, and using input from DevOps subject matter experts, to classify the job log errors in the error table 400 into one of multiple designated error classes – See paragraph [0060]); and
determine, for each of the plurality of entries (wherein each record comprises an error description and a corresponding error resolution script pointer, and wherein the one or more natural language processing models process the information and the error descriptions of the error database to identify the at least one record – See paragraphs [0005-0006]), whether an error is indicated by that entry (an automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]).
Regarding claim 6, the system of claim 1,
Teja discloses
wherein the one or more processors are further configured to:
cause storing of training data for the machine learning language model in accordance with the one or more logs (for a given error description, the natural language processing models may be trained to identify the corresponding error keyword (and thus, the corresponding error resolution script to execute to resolve the given error). For example, the natural language processing models may identify the record of the error table 400 with the best match based on the description provided for the given error – See paragraph [0061]).
Regarding claim 8, the system of claim 1,
Damjakob discloses
wherein the communication includes the incident output in accordance with the incident data indicating the incident (identified based on the event correlation map. In an example, the transmission of an indication of the actionable item is performed to send out one or more messages to the one or more entities operating in distinct ecosystems – See paragraph [0107]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to provide user management that including channel communication as suggested by Damijakob (See paragraph [0098]).
Regarding claim 10.
Teja discloses
A method of proactive error resolution (a proactive error resolution approach is provided that provides consistency in a CI/CD setup, for example, after periodic updates and/or releases of software shared modules, by providing one or more error resolution scripts that automate a set of practices provided – See paragraph [0083]), comprising:
obtaining, responsive to an execution of a job in a platform, one or more logs that relate to the execution of the job (automatically initiating an execution of one or more processing steps associated with the identified at least one error resolution script to address the at least one of the one or more errors in the at least one pipeline job – See paragraph [0003]. The software development system 105 can have at least one associated database 106 configured to store data pertaining to, for example, software code 107 of at least one application and a repository of one or more job log errors 108. For example, at least a portion of the at least one associated database 106 may correspond to at least one code repository that stores the software code 107. In such an example, the at least one code repository may include different snapshots or versions of the software code 107, at least some of which can correspond to different branches of the software code 107 used for different development environments (e.g., one or more testing environments, one or more staging environments, and/or one or more production environments). The job log errors 108 provide information characterizing one or more errors in at least one pipeline job of a software deployment pipeline, as discussed further below in conjunction with, for example, FIG. 4 and paragraph [0040]),
wherein the one or more logs are generated by the platform (the continuous integration module 110, the version control module 112 and/or the continuous deployment module 114, or portions thereof, may be implemented using functionality provided, for example, by commercially available DevOps and/or CI/CD tools, such as the GitLab development platform, the GitHub development platform – See paragraph [0031]. An automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069] and Fig. 6), and
wherein the platform uses one or more computing systems in connection with the execution of the job (FIG. 1 shows a computer network (also referred to herein as an information processing system) 100 configured in accordance with an illustrative embodiment. The computer network 100 comprises a plurality of user devices 102-1, 102-2, . . . 102-M, collectively referred to herein as user devices 102. The user devices 102 may be employed, for example, by software developers and other DevOps professionals to perform, for example, software development and/or software deployment tasks – See paragraphs [0025]. Perform CI/CD tasks and to provide access to DevOps tools and/or repositories. The continuous integration module 110 provides functionality for automating the integration of software code changes from multiple software developers or other DevOps professionals into a single software project – See paragraphs [0031-0032]);
processing the one or more logs to identify whether an entry in the one or more logs indicates an error event for the job process (an automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]. if a given pipeline job employs a particular version of Angular software (e.g., version 8), and the DevOps team has updated the deployed version of Angular software to a new version (e.g., version 12), then upon building the given pipeline job, an “angular mismatch” error may be received – See paragraph [0083]);
determining, responsive to identification of the entry indicating the error event, a resolution output relating to the error event (the natural language processing models are trained using the information in the error table 400 and may be retrained for updates to the error table 400. For example, for a given error description, the natural language processing models may be trained to identify the corresponding error keyword (and thus, the corresponding error resolution script to execute to resolve the given error). For example, the natural language processing models may identify the record of the error table 400 with the best match based on the description provided for the given error – See paragraph [0061] and [0069-0071]),
Teja does not disclose
obtaining, from an incident tracking system, incident data that relates to the one or more computing systems,
wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected; and
generating a communication that includes at least one of the resolution output or an incident output relating to the incident, in accordance with whether the incident data indicates the incident.
Damjakob discloses
obtaining, from an incident tracking system, incident data that relates to the one or more computing systems (AutoComplete 828 may be implemented using an auto-completion model (not shown) that tracks all the fields that a user may enter in tickets and issues and learns to fill in these fields – See paragraph [0121]. Incidents 210 may correspond to one or more events from the event clusters occurrence of which may be important for notification to the one or more entities operating in the distinct ecosystems – See paragraph [0054]),
wherein the incident data indicates whether an incident impacting the one or more computing systems has been detected (The correlation of incidents from the distinct ecosystems allows the determination of the impact of the incidents to entities operating in relation to the distinct ecosystems – See paragraph [0100]); and
generating a communication that includes at least one of the resolution output or an incident output relating to the incident (the event correlation map may provide a coherent converged picture of a data stream pertaining to events captured from the various distinct ecosystems. The event correlation map may include events correlated to form incidents representing the event necessary for indication to a user-end entity and/or a developer-end entity operating in one or more distinct ecosystems – See paragraph [0035]), in accordance with whether the incident data indicates the incident (The incidents are automatically triaged to identify tickets and the severity of the tickets is ascertained from an object associated with the incidents. The tickets refer to one or more incidents that may require certain cosmetic modifications. Further, the tickets that may reference underlying defects with the product or service are converted into issues. The incidents, tickets, and issues, referred to collectively as items are provided onto the event correlation map. Each item of the event correlation map may be associated with a plurality of objects providing a description of the activities leading to the generation of the item – See paragraph [0035]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to correlate events to from incidents representing the event necessary for indication to a user-end entity as suggested by Damijakob (See paragraph [0035]).
Regarding claim 11, the method of claim 10,
Teja discloses
wherein determining the resolution output comprises:
inputting context information to a machine learning language model to obtain the resolution output (an automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error). A parser is applied to the log record in step 604 to obtain parsed error information (e.g., transforming the obtained error information into a designated format, or otherwise cleaning or preprocessing the log record). The parsed error information is applied to the natural language processing model in step 606 that identifies best record in the job log errors table (e.g., a best matching record in the error table 400). For example, the natural language processing model may employ cosine similarity techniques that identify a closest match in the job log errors table to the log record for the failed pipeline job. The error keyword, error class and error resolution script pointer are retrieved from the identified record of the job log errors table in step 608 – See paragraphs [0069-0071]),
wherein the context information includes event information derived from the entry (once the natural language processing models identify a given record of the error table 400, the corresponding error keyword may be used as a pointer or lookup value to obtain the correct error resolution script to resolve a given pipeline job error or set of pipeline job errors – See paragraphs [0059]);
Regarding claim 12, the method of claim 11,
Teja discloses
wherein the event information further includes the one or more logs (The designated error class may be obtained, for example, after analyzing the associated error resolution steps, and using input from DevOps subject matter experts, to classify the job log errors in the error table 400 into one of multiple designated error classes – See paragraph [0060]).
Regarding claim 13, the method of claim 10,
Teja discloses
wherein the one or more computing systems include at least one of:
one or more version control systems (The software development system 105 comprises a continuous integration module 110, a version control module 112, a continuous deployment module 114, an error resolution engine 116, one or more natural language processing models 118 and an error resolution script repository 120 – See paragraphs [0030-0032]), or
one or more cloud computing systems (The cloud-based systems can include object stores such as Amazon S3, GCP Cloud Storage, and Microsoft Azure Blob Storage – See paragraph [0092]).
Regarding claim 16.
Teja discloses
A non-transitory computer-readable medium storing a set of instructions for proactive error resolution (a proactive error resolution approach is provided that provides consistency in a CI/CD setup, for example, after periodic updates and/or releases of software shared modules, by providing one or more error resolution scripts that automate a set of practices provided, for example, by a DevOps team – See paragraph [0083]), the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device (a processor of a processing device such as a computer– See paragraph [0086]), cause the device to:
obtain one or more logs that relate to an execution of a job in a platform (the job log errors 108 provide information characterizing one or more errors in at least one pipeline job of a software deployment pipeline – See paragraph [0040]. A log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]),
wherein the one or more logs are generated by the platform (the continuous integration module 110, the version control module 112 and/or the continuous deployment module 114, or portions thereof, may be implemented using functionality provided, for example, by commercially available DevOps and/or CI/CD tools, such as the GitLab development platform, the GitHub development platform – See paragraph [0031]. An automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069] and Fig. 6),
wherein the platform uses one or more computing systems in connection with the execution of the job (FIG. 1 shows a computer network (also referred to herein as an information processing system) 100 configured in accordance with an illustrative embodiment. The computer network 100 comprises a plurality of user devices 102-1, 102-2, . . . 102-M, collectively referred to herein as user devices 102. The user devices 102 may be employed, for example, by software developers and other DevOps professionals to perform, for example, software development and/or software deployment tasks – See paragraphs [0025]. Perform CI/CD tasks and to provide access to DevOps tools and/or repositories. The continuous integration module 110 provides functionality for automating the integration of software code changes from multiple software developers or other DevOps professionals into a single software project – See paragraphs [0031-0032]), and
wherein the job is user-initiated by a user (a user employing a user device 305 utilizes the GUI 310 to interact with the software development system 300, such as one or more visual representations of a software deployment pipeline or components thereof (e.g., pipeline jobs). Generally, the GUI 310 provides access to a visual software deployment pipeline editor, a pipeline manager, a DevOps toolkit and a reusable CI/CD resource library – See paragraph [0056]);
process the one or more logs to identify whether an entry in the one or more logs indicates an error event for the job (an automated pipeline error resolution process, in accordance with an illustrative embodiment. In the example of FIG. 6, a log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]. if a given pipeline job employs a particular version of Angular software (e.g., version 8), and the DevOps team has updated the deployed version of Angular software to a new version (e.g., version 12), then upon building the given pipeline job, an “angular mismatch” error may be received – See paragraph [0083]);
input, responsive to identification of the entry indicating the error event, context information to a machine learning language model to obtain a resolution output (The natural language processing models are trained using the information in the error table 400 and may be retrained for updates to the error table 400. For example, for a given error description, the natural language processing models may be trained to identify the corresponding error keyword (and thus, the corresponding error resolution script to execute to resolve the given error). For example, the natural language processing models may identify the record of the error table 400 with the best match based on the description provided for the given error – See paragraph [0061] and [0069-0071]),
wherein the context information includes event information derived from the entry (once the natural language processing models identify a given record of the error table 400, the corresponding error keyword may be used as a pointer or lookup value to obtain the correct error resolution script to resolve a given pipeline job error or set of pipeline job errors – See paragraphs [0059]); Teja does not disclose
generate a communication that includes the resolution output; and
transmit the communication for delivery to the user by at least one of a chat message, a text message, an email message, or an automated phone call.
Damjakob discloses
generate a communication that includes the resolution output (the classification model 812 may also be referred to in some embodiments as an ML entity reference resolution model. The ML entity reference resolution model reads the incidents database 816 as well as the entity database 810 to identify which entities are referenced in each incident. Some examples may include: (a) which product/capability/feature is referenced in a user tracking incident at the user ecosystem; (b) which code component is referenced in a test failure incident; and/or (c) which ops service is referenced in an alert log incident – See paragraph [0117]. Communications and/or notifications may occur at various points within an event correlation map. For example, a UI/API 826 may be used to communicate information to API users – See paragraph [0112]. Logs and error messages associated with the hierarchical product parameters may be tracked, such that metrics on errors occurring for the hierarchical product parameters may be identified… The knowledgebase model autonomously creates knowledgebase articles based on common frequently occurring remedial steps that are undertaken by authorized entities in the one or more distinct ecosystems --See paragraphs [0122 and 0212]); and
transmit the communication for delivery to the user by at least one of a chat message, a text message, an email message, or an automated phone call (User: Customer engagement, including omni-channel communication (voice, video, email, chat), and events from social media, client browsers – See paragraph [0098]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to correlate events to from incidents representing the event necessary for indication to a user-end entity as suggested by Damijakob (See paragraph [0035]).
Regarding claim 17, the non-transitory computer-readable medium of claim 16,
Damjakob discloses
wherein the one or more instructions, that cause the device to transmit the communication, cause the device to transmit the communication proactively, in the absence of a request for support made by the user (As behavior of the one or more entities operating the developer-end ecosystem and the steps used by the entities follow a probabilistic behavior—or at least can be captured by probabilistic models, and because it may be inferred that information is missing—and even which piece of information is missing—as the known syntactic workflow rules suggest this, the knowledge graph enrichment model 1212 has the ability to enrich the graph at this stage – See paragraph [0172]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Damiakob's teaching into Teja's invention because incorporating Damiakob's teaching would enhance Teja to enable to capture the information that is missing as suggested by Damijakob (See paragraph [0172]).
Regarding claim 18, the non-transitory computer-readable medium of claim 16,
Teja discloses
wherein the event information further includes the one or more logs (The designated error class may be obtained, for example, after analyzing the associated error resolution steps, and using input from DevOps subject matter experts, to classify the job log errors in the error table 400 into one of multiple designated error classes – See paragraph [0060]).
Regarding claim 19, the non-transitory computer-readable medium of claim 16,
Teja discloses
wherein the platform is a continuous integration/continuous deployment (CI/CD) platform (the continuous integration module 110, the version control module 112 and/or the continuous deployment module 114, or portions thereof, may be implemented using functionality provided, for example, by commercially available DevOps and/or CI/CD tools, such as the GitLab development platform, the GitHub development platform, the Azure DevOps server and/or the Bitbucket CI/CD tool, or another Git-based DevOps and/or CI/CD tool – See paragraph [0031]), and
wherein the execution of the job is a build process for an application ((the job log errors 108 provide information characterizing one or more errors in at least one pipeline job of a software deployment pipeline – See paragraph [0040]. A log record is obtained in step 602 for a failed pipeline job (e.g., a pipeline job comprising an error) – See paragraph [0069]) – See paragraph [0031]).
Regarding claim 20, the non-transitory computer-readable medium of claim 16,
Teja discloses
wherein the one or more computing systems comprise multiple computing systems (systems (The cloud-based systems can include object stores such as Amazon S3, GCP Cloud Storage, and Microsoft Azure Blob Storage – See paragraph [0092]).
8. Claim(s) 7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teja and Damjakob as applied to claim 1 above, and further in view of Talwalkar et al. (US Pub. No. 2025/0173607 A1 – herein after Talwalkar).
Regarding claim 7, the system of claim 1,
Talwalkar discloses
wherein the one or more processors are further configured to:
receive an indication that the communication does not resolve the error event for the user (if a new incident is related to a recent/current event, then the incident agent may use information regarding the event to resolve the incident for the user, or at least may be enabled in alerting the user regarding the nature of the event and the likely time to resolution thereof. If the new incident is not related to a recent/current event, the incident agent may also benefit from such information, e.g., may be alerted to focus on potential user error to resolve the incident – See paragraph [0029]); and
transmit, responsive to the indication and to a support system , an error report that includes at least one of:
the one or more logs, the event information, or the entry (While extracting resolutions, custom templates and/or styles may be supported. Example algorithms may pick up relevant data from fields and/or sections like ‘steps followed’ or ‘Resolution’. When a resolution field is empty or not meaningful, a worklog may be used to get resolutions – See paragraph [0147]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Talwalkar's teaching into Teja's and Damiakob’s inventions because incorporating Talwalkar's teaching would enhance Teja and Damiakob to enable to support real-time updates on dashboards as suggested by Talwalkar (See paragraph [0100]).
Regarding claim 9, the system of claim 1,
Talwalkar discloses
wherein the communication includes the resolution output without the incident output in accordance with the incident data not indicating any incident (The worklog may include attempted resolutions performed by the incident agent 111, messages (e.g., emails or chat messages) between the user 105 and the incident agent 111, or written or auto-transcribed text of audio communications between the user 105 and the incident agent 111 – See paragraph [0040]).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Talwalkar's teaching into Teja's and Damiakob’s inventions because incorporating Talwalkar's teaching would enhance Teja and Damiakob to enable o include a resolution of the incident that caused the individual incident ticket to be generated as suggested by Talwalkar (See paragraph [0041]).
9. Claim(s) 14 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Teja and Damjakob as applied to claim 10 above, and further in view of Qian Cheng (AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges, 2023 – herein after Cheng).
Regarding claim 14, the method of claim 10, further comprising:
Teja discloses
obtaining historical data relating to one or more previous jobs executed in the platform (SCM techniques are employed to track a history of changes to a software code base and to resolve conflicts when merging updates from multiple software developers – See paragraphs [0030-0032]);
Teja and Damiakob do not disclose
generating trend data in accordance with the historical data; and
transmitting the trend data.
Cheng discloses
generating trend data in accordance with the historical data (future trends… Currently most of the decision making and action execution are rule-based or statistical learning based. With more powerful AI techniques, the remediation engine can then consume rich information and make more complex decisions – See page 21); and
transmitting the trend data (They propose a robust anomaly detector by using time series decomposition, and thus can easily handle time series with different characteristics, such as different seasonal length, different types of trends, etc.– See page 21).
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cheng's teaching into Teja's and Damiakob’s inventions because incorporating Cheng's teaching would enhance Teja and Damiakob to enable to handle time series with different characteristics, such as different seasonal length, different types of trends, as suggested by Cheng (See page 7).
Regarding claim 15, the method of claim 10, further comprising:
Teja discloses
obtaining historical data relating to one or more previous jobs executed in the platform (SCM techniques are employed to track a history of changes to a software code base and to resolve conflicts when merging updates from multiple software developers – See paragraphs [0030-0032]);
Teja does not disclose
determining, using the historical data, whether a survey is to be provided; and
transmitting, based on a determination that the survey is to be provided, the survey.
Chen discloses
determining, using the historical data, whether a survey is to be provided (In order to enable the community to adopt AIOps capabilities faster, in this paper, we present a comprehensive survey on the various AIOps problems and tasks and the solutions developed by the community to address them – See page 3, left column); and
transmitting, based on a determination that the survey is to be provided, the survey (In our survey we cover metric failure prediction (Section V-A) and log failure prediction (Section V-B) – See page 3, right column)
It would have been obvious to one ordinary skill in the art before the effective filing date of claimed invention to use Cheng's teaching into Teja's and Damiakob’s inventions because incorporating Cheng's teaching would enhance Teja and Damiakob to enable to provide a comprehensive survey on the various AIOps problems and tasks and the solutions developed by the community to address them as suggested by Cheng (See page 3).
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Som et al. (US Pub. No. 2025/0217222 A1) discloses resolve a selected error type that is associated with a group of skipped data records by: identifying an error pattern from an error message and matching it to a database of observed error patterns. In response to a match, retrieving a corrective action that is assigned to the observed error pattern and executing the corrective action to resolve the error on the group of skipped data records. The skipped data records are re-submitted and reprocessed where previous error should be resolved – See Abstract and specification for more details.
Balaka et al. (US Pub. No. 2025/0238299 A1) discloses detect a database system incident affecting database system availability or performance and to generate a database incident report characterizing the database system incident. The generative language model interface may be configured to determine a textual description of the database system incident and identify one or more records of the plurality of records by completing an incident evaluation prompt via a generative language model. An incident response engine may be configured to determine an instruction to resolve the database incident based on the textual description and the one or more records, wherein the database system is configured to execute the instruction to update one or more configuration parameters – See Abstract and specification for more details.
N et al. (US Pub. No. 2025/0370904 A1) discloses to improve application performance, error causal analysis may be performed that identifies error intercorrelations that impact application availability and other performance by identifying error effects on each other. Causal statements may be intelligently generated to then identify error intercorrelations. Once generated, these statements may be tested and verified to allow debugging teams and others to fix errors that reduce application performance and availability – See Abstract and specification for more details.
Engel et al. (US Patent No. 12,487,874 B1) discloses generating a plurality of sub-prompts to be provided to a machine-learning model for generating a report of the incident event in accordance with a predetermined criteria; inputting the prompt into a machine-learning model that has been trained to generate a report of the incident event based on the prompt; outputting, by the machine-learning model, the report of the incident event, wherein the report comprises an analysis of the incident event; transmitting the report to one or more computing devices associated with the computer system – See Abstract and specification for more details.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONGBAO NGUYEN whose telephone number is (571)270-7180. The examiner can normally be reached Monday-Friday 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hyung S. Sough can be reached at 571-272-6799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MONGBAO NGUYEN/ Examiner, Art Unit 2192
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