DETAILED ACTION
This office action is in response to the request for continuation filed on January 15, 2026 in application 18/070,589.
Claims 1-5, 8-19 and 21 are presented for examination. Claims 1, 3, 11, 14 and 19 are amended. Claims 6-7 and 20 are cancelled.
IDS submitted on November 29, 2022 was acknowledged.
35 USC 112 for claims 3, 14, and 19 are withdrawn based on amendments.
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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-5, 8-19 and 21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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, 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, 4-5, 8-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakade et al. (US 2023/0409460) in further view of Seshadri et al. (2021/0182297) in further view of Hartman (US 2019/0205197) in further view of Deshpande et al. (US 2003/0218628).
In regard to claim 1, Kakade et al. teach a method, comprising:
saving (the data 210, may serve as a repository for storing data processed, received and generated by one or more modules, fig. 2, 29), by a processor set, data logs from a plurality of user devices (monitoring interconnected metrics, fig. 4, 404);
creating (the data processing module is configured to construct an interrelated KPI tree structure to model the workflow of the process includes plotting inter-relations among the KPIs in the form of a tree or network, para. 30, the root cause detection module is configured to determine one or more root causes for an issue affecting the process performance based on the autonomous monitoring of the plurality of interconnected metrics, para. 32), by the processor set, a knowledge corpus based on the data logs (determining root cause of an issue, fig. 4, 405), wherein the knowledge corpus includes data that defines error states detected in use of the user devices (monitoring anomalies, para. 9) and user actions that users performed on the user devices to resolve the error states (recommending one or more actions correlating with remediate the root causes, para. 9);
identifying, by the processor set, a root cause of one of the error states by performing deep learning analysis on the knowledge corpus (autonomous identification using artificial intelligence and machine learning technology, para. 8)
creating (action determination module is configured to determine one or more actions to remediate the one or more root causes, para. 34, the recommendation further includes the one or more actions correlated with one or more entities represented in the KPI tree structure on which the actions are to be performed, para. 35, the determination of said specific remedial actions may be based on analysis of historical actions, para. 43), by the processor set, a remediation library based on the knowledge corpus, the remediation library include plural entries (constructing a KPI tree structure modelling workflow of the process and interrelationships among the participating KPIs using data received from a client device, para. 10, fig. 3), wherein each respective entry in the remediation library includes data defining a respective error state from the knowledge corpus and respective user actions associated with the respective error state (recommending actions correlating with workflow levels to address the root cause, fig. 4, 408);
detecting, by the processor set, a real time error state in one of the user devices (enabling performance of the action with process workflow, fig. 4, 410);
identifying, by the processor set, one of the entries in the remediation library based on the real time error state in the one of the user devices (generation of action recommendation may include continually determine alternate action, fig. 4, 408); and
causing, by the processor set, the one of the user devices to display a message indicating the respective user actions included in the identified one of the entries in the remediation library (action determination module is configured to generate a recommendation including the one or more determined actions, para. 35).
Kakade et al. does not explicitly teach but Seshadri et al. teach wherein the data logs include system logs of operating systems of the user devices and application logs of applications of the user devices (event types including, e.g., transaction logs, application logs, system logs, event logs and the like, para. 30, fig. 5A-5C).
It would have been obvious to modify the method of Kakade et al. by adding Seshadri et al. real-time dashboard alerts and analytics. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in analyzing multiple types of event logs (para. 30).
Kakade et al. and Seshadri et al. does not explicitly teach but Hartman teaches developing, by the processor set, a software update (the error resolution is a software patch/update, para. 16) in response to the identifying the root cause of one of the error states by performing the deep learning analysis on the knowledge corpus (error log analyzer is a format agnostic tool that is capable of analyzing and distilling error logs and output the analyzed error logs or identified known solutions, para. 14-15).
It would have been obvious to modify the method of Kakade et al. and Seshadri et al. by adding Hartman analyzing and responding to errors. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in identifying a specific error log pattern and take corrective action (para. 16).
Kakade et al., Seshadri et al. and Hartman does not explicitly teach but Deshpande et al. teach receiving, by the processor set, user input approval in response to displaying the message indicating the respective user actions included in the identified one of the entries in the remediation library; and automatically, by the processor set, implementing the respective user actions included in the identified one of the entries in the remediation library in response to receiving the user input approval (every installation may be displayed to the user 2 and require approval from the user prior to installation, where the process iterates and the patch module consults the software list on the target device without direct user supervision, para. 21).
It would have been obvious to modify the method of Kakade et al., Seshadri et al. and Hartman by adding Deshpande et al. patch installation via a graphical user interface. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in allowing user approval before installing missing software patch (para. 20).
In regard to claim 4, Kakade et al. teach the method of claim 1, wherein the identifying the entry in the remediation library comprises:
determining an entry in the knowledge corpus that is most similar to the real time error state in one of the user devices (identify behavioral and provide insights in near real time or real time, para. 32), wherein the determining comprises determining relevancy scores for entries in the knowledge corpus using a similarity scoring algorithm (relevant KPI trees that help measure health/performance of a process, para. 40, autonomous metric monitoring is performed to identify the most important problem, para. 41), wherein a respective one of the relevancy scores for a respective one of the entries in the knowledge corpus is a quantitative measure of similarity between (i) the respective one of the entries in the knowledge corpus and (ii) the real time error state in one of the user devices (connecting raw data from the client device and model output database, para. 40); and
identifying the entry in the remediation library that corresponds to the determined entry in the knowledge corpus (determine one or more actions to remediate, para. 34).
In regard to claim 5, Kakade et al. teach the method of claim 1, wherein the data logs include data that defines configurations, events, states, and operations of the user devices (the data may serve as a repository for storing data processed, received and generated by one or more of the modules … the data may also include a system database, para. 25-29), and wherein the creating the knowledge corpus comprises performing data mining of the data logs (the data processing module is configured to receive raw data and construct an interrelated KP tree fstructure to model the workflow, para. 30) to identify the error states and the user actions associated with the identified error states (root cause determination module is configured to identify the nodes of concerns, para. 33).
In regard to claim 8, Kakade et al. teach the method of claim 1, further comprising dynamically configuring the actions to adapt to a specific environment running on the one of the user devices (the recommended actions are evaluated using controlled experiments by monitoring the on-ground impact of the action as they are applied, para. 22).
In regard to claim 9, Kakade et al. teach the method of claim 1, further comprising receiving user input opting into the saving the data logs (expert user may determine the actions to be pushed for full scale implementation, para. 44).
In regard to claim 10, Kakade et al. teach the method of claim 9, wherein the user input includes consent for monitoring particular applications (expert user may determine the actions to be pushed for full scale implementation, para. 44).
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Claims 2-3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakade et al. (US 2023/0409460) in further view of Seshadri et al. (2021/0182297) in further view of Hartman (US 2019/0205197) in further view of Deshpande et al. (US 2003/0218628) in further view of Chakrabarti et al. (US 2024/0103908).
In regard to claim 2, Kakade et al. teach the method of claim 1, further comprising:
receiving, by the processor set, feedback from the one of the user devices, wherein the feedback comprises positive feedback or negative feedback about the actions (continually received feedback on implemented actions, para. 38-39);
revising, by the processor set, the remediation library based on the feedback (continually determined alternative action, para. 39).
Kakade et al., Seshadri et al., Hartman and Deshpande et al. does not explicitly teach but Chakrabarti et al. teach dynamically configuring, by the processor set, the respective user actions to a different operating system than an operating system of the one of the user devices (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
It would have been obvious to modify the method of Kakade et al., Seshadri et al., Hartman and Deshpande et al. by adding Chakrabarti et al. dynamic adaptive scheduling. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in steaming applications that can be modeled to outperform the optimization metrics (para. 36-37).
In regard to claim 3, Kakade et al. and Seshadri et al. does not explicitly teach but Hartman teaches the method of claim 1, wherein the creating the remediation library comprises:
creating frequency graphs of entries in the knowledge corpus (timeline graph indicates the frequency of occurrence of two exemplary error patterns, para. 28, fig. 4); and
creating the entries in the remediation library based on the frequency graphs (determining the appropriate corrective action, para. 28).
It would have been obvious to modify the method of Kakade et al. and Seshadri et al. by adding Hartman analyzing and responding to errors. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in identifying a specific error log pattern and take corrective action (para. 16).
Kakade et al., Seshadri et al., Hartman and Deshpande et al. does not explicitly teach but Chakrabarti et al. teach wherein the dynamically configuring the respective user actions to the different operating system than the operating system of the one of the user devices occurs by converting the respective actions of the first operating system to different actions of a second operating system by utilizing mappings between the first operating system and the second operating system (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
Refer to claim 2 for motivational statement.
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Claims 11, 15-16, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakade et al. (US 2023/0409460) in further view of Seshadri et al. (2021/0182297) in further view of Deshpande et al. (US 2003/0218628).
In regard to claim 11, Kakade et al. teach a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
save (the data 210, may serve as a repository for storing data processed, received and generated by one or more modules, fig. 2, 29) data logs from a plurality of user devices (monitoring interconnected metrics, fig. 4, 404), and wherein the data logs include data that defines configurations, events, states, and operations of the user devices (the data may serve as a repository for storing data processed, received and generated by one or more of the modules … the data may also include a system databases, para. 25-29);
create (the data processing module is configured to construct an interrelated KPI tree structure to model the workflow of the process includes plotting inter-relations among the KPIs in the form of a tree or network, para. 30, the root cause detection module is configured to determine one or more root causes for an issue affecting the process performance based on the autonomous monitoring of the plurality of interconnected metrics, para. 32) a knowledge corpus based on the data logs (determining root cause of an issue, fig. 4, 405);
identify a root cause of one of the error states by performing deep learning analysis on the knowledge corpus (autonomous identification using artificial intelligence and machine learning technology, para. 8);
create (action determination module is configured to determine one or more actions to remediate the one or more root causes, para. 34, the recommendation further includes the one or more actions correlated with one or more entities represented in the KPI tree structure on which the actions are to be performed, para. 35, the determination of said specific remedial actions may be based on analysis of historical actions, para. 43) a remediation library based on the knowledge corpus, wherein each entry in the remediation library includes data defining a respective error state from the knowledge corpus and user actions associated with the respective error state (recommending actions correlating with workflow levels to address the root cause, fig. 4, 408);
detect a real time error state in one of the user devices (enabling performance of the action with process workflow, fig. 4, 410);
identify an entry in the remediation library based on the real time error state in the one of the user devices (generation of action recommendation may include continually determine alternate action, fig. 4, 408); and
cause the one of the user devices to display a message indicating the actions included in the identified entry in the remediation library (action determination module is configured to generate a recommendation including the one or more determined actions, para. 35).
Kakade et al. does not explicitly teach but Seshadri et al. teach wherein the data logs include system logs of operating systems of the user devices and application logs of applications of the user devices (event types including, e.g., transaction logs, application logs, system logs, event logs and the like, para. 30, fig. 5A-5C).
It would have been obvious to modify the method of Kakade et al. by adding Seshadri et al. real-time dashboard alerts and analytics. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in analyzing multiple types of event logs (para. 30).
Kakade et al. and Seshadri et al. does not explicitly teach but Deshpande et al. teach receive user input approval in response to displaying the message indicating the respective user actions included in the identified one of the entries in the remediation library; and automatically implementing the actions included in the identified entry in the remediation library in response to receiving the user input approval (every installation may be displayed to the user 2 and require approval from the user prior to installation, where the process iterates and the patch module consults the software list on the target device without direct user supervision, para. 21).
It would have been obvious to modify the method of Kakade et al. and Seshadri et al. by adding Deshpande et al. patch installation via a graphical user interface. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in allowing user approval before installing missing software patch (para. 20).
In regard to claim 15, Kakade et al. teach the computer program product of claim 11, wherein the creating the knowledge corpus comprises identifying error states and user actions associated with the identified error states by data mining the data logs (root cause determination module is configured to identify the nodes of concerns, para. 33).
In regard to claim 16, Kakade et al. teach a system comprising:
a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media (the system may include at least one processing device, an input/output interface, a memory, modules and data, para. 25), the program instructions executable to:
save (the data 210, may serve as a repository for storing data processed, received and generated by one or more modules, fig. 2, 29) data logs from a plurality of user devices (monitoring interconnected metrics, fig. 4, 404);
create (the data processing module is configured to construct an interrelated KPI tree structure to model the workflow of the process includes plotting inter-relations among the KPIs in the form of a tree or network, para. 30, the root cause detection module is configured to determine one or more root causes for an issue affecting the process performance based on the autonomous monitoring of the plurality of interconnected metrics, para. 32) a knowledge corpus based on the data logs (determining root cause of an issue, fig. 4, 405);
identify a root cause of one of the error states by performing deep learning analysis on the knowledge corpus (autonomous identification using artificial intelligence and machine learning technology, para. 8);
create (action determination module is configured to determine one or more actions to remediate the one or more root causes, para. 34, the recommendation further includes the one or more actions correlated with one or more entities represented in the KPI tree structure on which the actions are to be performed, para. 35, the determination of said specific remedial actions may be based on analysis of historical actions, para. 43) a remediation library based on the knowledge corpus, wherein each entry in the remediation library includes data defining a respective error state from the knowledge corpus and user actions associated with the respective error state (recommending actions correlating with workflow levels to address the root cause, fig. 4, 408);
detect a real time error state in one of the user devices (enabling performance of the action with process workflow, fig. 4, 410);
identify an entry in the remediation library based on the real time error state in the one of the user devices (generation of action recommendation may include continually determine alternate action, fig. 4, 408); and
cause the one of the user devices to display a message indicating the actions included in the identified entry in the remediation library (action determination module is configured to generate a recommendation including the one or more determined actions, para. 35),
wherein the creating the knowledge corpus comprises identifying error states and user actions associated with the identified error states (constructing a KPI tree structure modelling workflow of the process and interrelationships among the participating KPIs using data received from a client device, para. 10, fig. 3) by data mining the data logs (the data processing module is configured to receive raw data and construct an interrelated KP tree structure to model the workflow, para. 30).
Kakade et al. does not explicitly teach but Seshadri et al. teach wherein the data logs include system logs of operating systems of the user devices and application logs of applications of the user devices (event types including, e.g., transaction logs, application logs, system logs, event logs and the like, para. 30, fig. 5A-5C).
Refer to claim 11 for motivational statement
Kakade et al. and Seshadri et al. does not explicitly teach but Deshpande et al. teach receive user input approval in response to displaying the message indicating the respective user actions included in the identified one of the entries in the remediation library; and automatically implementing the actions included in the identified entry in the remediation library in response to receiving the user input approval (every installation may be displayed to the user 2 and require approval from the user prior to installation, where the process iterates and the patch module consults the software list on the target device without direct user supervision, para. 21).
Refer to claim 11 for motivational statement
In regard to claim 21, Kakade et al. teach the system of claim 16, wherein
the data logs include data that defines configurations, events, states, and operations of the user devices (the data may serve as a repository for storing data processed, received and generated by one or more of the modules … the data may also include a system database, para. 25-29); and
the knowledge corpus includes data that defines error states detected in use of the user devices and user actions (the data processing module is configured to receive raw data and construct an interrelated KP tree structure to model the workflow, para. 30) that users performed on the user devices to resolve the error states (root cause determination module is configured to identify the nodes of concerns, para. 33, determining success of an action are identified, para. 43-44).
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Claims 12, 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakade et al. (US 2023/0409460) in further view of Seshadri et al. (2021/0182297) in further view of Deshpande et al. (US 2003/0218628) in further view of Hartman (US 2019/0205197).
In regard to claim 12, Kakade et al. teach the computer program product of claim 11, wherein the program instructions are executable to:
receive feedback from the one of the user devices, wherein the feedback comprises positive feedback or negative feedback about the actions (continually received feedback on implemented actions, para. 38-39);
revise the remediation library based on the feedback (continually determined alternative action, para. 39).
Kakade et al., Seshadri et al. and Deshpande et al. does not explicitly teach but Hartman teaches develop a software update (the error resolution is a software patch/update, para. 16) in response to the identifying the root cause of one of the error states by performing the deep learning analysis on the knowledge corpus (error log analyzer is a format agnostic tool that is capable of analyzing and distilling error logs and output the analyzed error logs or identified known solutions, para. 14-15).
It would have been obvious to modify the method of Kakade et al., Seshadri et al. and Deshpande et al. by adding Hartman analyzing and responding to errors. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in identifying a specific error log pattern and take corrective action (para. 16).
In regard to claim 17, Kakade et al. teach the system of claim 16, wherein the program instructions are executable to:
receive feedback from the one of the user devices, wherein the feedback comprises positive feedback or negative feedback about the actions (continually received feedback on implemented actions, para. 38-39); and
revise the remediation library based on the feedback (continually determined alternative action, para. 39).
Kakade et al., Seshadri et al. and Deshpande et al. does not explicitly teach but Hartman teaches develop a software update (the error resolution is a software patch/update, para. 16) in response to the identifying the root cause of one of the error states by performing the deep learning analysis on the knowledge corpus (error log analyzer is a format agnostic tool that is capable of analyzing and distilling error logs and output the analyzed error logs or identified known solutions, para. 14-15).
Refer to claim 12 for motivational statement.
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Claims 13-14, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kakade et al. (US 2023/0409460) in further view of Seshadri et al. (2021/0182297) in further view of Deshpande et al. (US 2003/0218628) in further view of Hartman (US 2019/0205197) in further view of Chakrabarti et al. (US 2024/0103908).
In regard to claim 13, Kakade et al., Seshadri et al. and Deshpande et al. does not explicitly teach but Hartman teaches the computer program product of claim 11, wherein the creating the remediation library comprises:
creating frequency graphs of entries in the knowledge corpus (timeline graph indicates the frequency of occurrence of two exemplary error patterns, para. 28, fig. 4); and
creating the entries in the remediation library based on the frequency graphs (determining the appropriate corrective action, para. 28).
It would have been obvious to modify the method of Kakade et al., Seshadri et al. and Deshpande et al. by adding Hartman analyzing and responding to errors. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in identifying a specific error log pattern and take corrective action (para. 16).
Kakade et al., Seshadri et al., Deshpande et al. and Hartman does not explicitly teach but Chakrabarti et al. teach dynamically configuring the respective user actions to a different operating system than an operating system of the one of the user devices (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
It would have been obvious to modify the method of Kakade et al., Seshadri et al., Deshpande et al. and Hartman by adding Chakrabarti et al. dynamic adaptive scheduling. A person of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to make the modification because it would aid in steaming applications that can be modeled to outperform the optimization metrics (para. 36-37).
In regard to claim 14, Kakade et al. teach the computer program product of claim 13, wherein the identifying the entry in the remediation library comprises:
determining an entry in the knowledge corpus that is most similar to the real time error state in one of the user devices (identify behavioral and provide insights in near real time or real time, para. 32); and
identifying the entry in the remediation library that corresponds to the determined entry in the knowledge corpus (determine one or more actions to remediate, para. 34).
Kakade et al., Seshadri et al., Deshpande et al. and Hartman does not explicitly teach but Chakrabarti et al. teach wherein the dynamically configuring the respective user actions to the different operating system than the operating system of the one of the user devices occurs by converting the respective actions of the first operating system to different actions of a second operating system by utilizing mappings between the first operating system and the second operating system (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
Refer to claim 13 for motivational statement.
In regard to claim 18, Kakade et al., Seshadri et al., Deshpande et al. does not explicitly teach but Hartman teaches the system of claim 16, wherein the creating the remediation library comprises:
creating frequency graphs of entries in the knowledge corpus (timeline graph indicates the frequency of occurrence of two exemplary error patterns, para. 28, fig. 4); and
creating the entries in the remediation library based on the frequency graphs (determining the appropriate corrective action, para. 28).
Refer to claim 13 for motivational statement.
Kakade et al., Seshadri et al., Deshpande et al. and Hartman does not explicitly teach but Chakrabarti et al. teach dynamically configuring the respective user actions to a different operating system than an operating system of the one of the user devices (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
Refer to claim 13 for motivational statement.
In regard to claim 19, Kakade et al. teach the system of claim 18, wherein the identifying the entry in the remediation library comprises:
determining an entry in the knowledge corpus that is most similar to the real time error state in one of the user devices (identify behavioral and provide insights in near real time or real time, para. 32); and
identifying the entry in the remediation library that corresponds to the determined entry in the knowledge corpus (determine one or more actions to remediate, para. 34).
Kakade et al., Seshadri et al., Deshpande et al. and Hartman does not explicitly teach but Chakrabarti et al. teach wherein the dynamically configuring the respective user actions to the different operating system than the operating system of the one of the user devices occurs by converting the respective actions of the first operating system to different actions of a second operating system by utilizing mappings between the first operating system and the second operating system (dynamic adaptive scheduling (DAS) computing system is configured to dynamically switch between use of the first OS scheduler and second OS scheduler for the given scheduling task, para. 8-9).
Refer to claim 13 for motivational statement.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO 892.
Gao et al. (US 2020/0065083) (IBM) determine patch applicability and user feedback
Wookey (US 2008/0201705) (Sun Microsystem) user permission request for each installation step
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Wiginton et al. (US 2021/0303287) dynamically configurable, load OS in the system
Sengupta et al. (US 10,671,444) dynamically balance the load on the computing resources
Kang et al. (US 10,146,583) dynamic mapper configured a plurality of tasks
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Koneru et al. (US 9,459,994) application testing system
Ketonen et al. (US 2017/0339630) error logs for measurement and management
Lundsgaard (US 10,118,628) data logs for testing autonomous vehicle
Wangler et al. (US 2020/0322826) system logs and application logs (para. 51)
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Sambamurthy et al. (US 2012/0185736) dynamic model to related performance problems
Moser et al. (US 2022/0358023) graph-like model of observation data
Aoyama et al. (US 2020/0233736) symptom database
Sankar et al. (US 8,631,280) diagnosing misbehavior of software components
Yan et al. (US 8,533,536) health correlation with model
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Mahindru et al. (US 11,874,730) anomaly resolution
Kumar et al. (US 2020/0133823) graph of error message
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/Loan L.T. Truong/Primary Examiner, Art Unit 2114 HYPERLINK "mailto:Loan.truong@uspto.gov" Loan.truong@uspto.gov