DETAILED ACTION
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 .
Claims 1-15 have been examined and are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/02/2024 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 10 and 13 are objected to because of the following informalities:
Claim 1, line 7 – address intentional use term: “being.” Limitation should positively recite.
Claim 5, line 4 – typographical error: “siad.”
Claim 10, line 8 – address intentional use term: “being.” Limitation should positively recite.
Claim 13, line 11– address intentional use term: “being.” Limitation should positively recite.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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.
Claim(s) 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over Parthasarathy et al, hereinafter (“Parthasarathy”), US PG Publication 20220156121 A1 was submitted in 10/02/2024 IDS, in view of Brown et al, hereinafter (“Brown”), US PG Publication 20210141900 A1.
Regarding claims 1, 10 and 13, Parthasarathy teaches a method for generating a recommendation for correcting a configuration file of an item of infrastructure of a computing environment within which services run, said infrastructure having been deployed automatically from said configuration file, said method comprising:
obtaining and storing in memory a runtime report a service is being run; [Parthasarathy 20220156121 A1 ¶0006 "the plurality of modules include a recommendation generation module configured to generate one or more recommendations for resolving the determined one or more anomalies in the application based on the generated one or more application fingerprints and prestored information by using the trained infrastructure management based ML model" ¶0007 "The method also includes determining configuration information associated with the application based on the identified set of infrastructure components." ¶0025 “the anomaly determination module 218 obtains anomaly information, such as information associated with configuration rule” ¶0027 "The computing system 104 determines configuration information associated with the application based on the identified set of infrastructure components", ¶0033 "the configuration information includes a blueprint configuration, custom application configurations, infrastructure as code data", one or more services used by the plurality of users, one or more activities performed by the plurality of users during workload and the like. ¶0039 the anomaly determination module 218 obtains anomaly information, such as information associated with configuration rule ¶0046 "the configuration information includes a blueprint configuration, custom application configurations, infrastructure as code data"]
- presenting an anomaly correction request as input to a previously trained generative artificial intelligence model, said anomaly correction request comprising a question formulated in natural language and anomaly context information comprising at least the runtime report as output a configuration file correction recommendation that includes a recommendation for correcting the configuration file; [Parthasarathy 20220156121 A1 ¶0006 "The plurality of modules also include a configuration determination module configured to determine configuration information associated with the application based on the identified set of infrastructure component" ¶0027 "the computing system 104 determines one or more anomalies in the application based on the generated one or more application fingerprints", ¶0025 “The set of user devices 110 may be used by the other users to receive the one or more recommendations for resolving the determined one or more anomalies in the one or more applications.” ¶0037 "In determining the one or more anomalies in the application based on the generated one or more application fingerprints", ¶0042 "the computing system 104 generate the one or more application fingerprints corresponding to the application based on the identified plurality of patterns, such as the application uses service 1 and service 2. The computing system 104 determines the one or more anomalies in the application based on the generated one or more application fingerprints by using the trained infrastructure management based ML model." Examiner interprets that the application fingerprints by using a trained infrastructure management based Machine Learning (ML) model is analogous to the identification and inclusion of natural language of the identified patterns].
While Parthasarathy teaches validating the recommendation for correcting the configuration file [Parthasarathy 20220156121 A1 ¶0027 "The computing system 104 generates the one or more recommendations for resolving the determined one or more anomalies in the application based on the generated one or more application fingerprints and prestored information by using the trained infrastructure management based ML model", ¶0038 "generate the one or more recommendations for resolving the determined one or more anomalies in the application based on the generated one or more application fingerprints and the prestored information by using the trained infrastructure management based ML model", ¶0044 "The one or more one or more recommendations along with the effect of the one or more recommendations on the application may be provided to the trained infrastructure management based ML model 306 as reaction based learning data for improving accuracy of the trained infrastructure management based ML model 306… The computing system 104 also outputs the one or more recommendations to the one or more user devices 108 and the set of user devices 110 to resolve the one or more anomalies, at step 316. At step 318, it is determined if the one or more anomalies are resolved."]; however, Parthasarathy fails to explicitly teach but Brown teaches validating the recommendation for correcting the configuration file, comprising determining a similarity measurement with respect to said anomaly context information and comparing said similarity measurement with an acceptability threshold; [Brown et al 20210141900 A1 ¶0127 log messages are relatively cryptic, including generally only one or two natural-language words and/or phrases as well as various types of text strings that represent file names, path names, and, perhaps various alphanumeric parameters. ¶0128 FIG. 17 shows an example of a log write instruction 1702. The example log write instruction 1702 also includes text strings and natural-language words and phrases that identify the type of event that triggered the log write instruction. ¶¶0133 and 0135 Fig. 21 shows management system 2100 with an receives log messages represented by directional arrows 2112 and streams of metric data represented by directional arrows 2114; as well as an application discovery manager 2102 that performs a machine learning approach to discovering applications running a distribute computing system. Each metric processors 2108, 2109, 2110 generate forecast metric data and detects anomalous behavior at resources. ¶¶0338-0339 The event log manager 2104 in FIG. 21 performs log message analysis to determine the event type of each log message received and, for each time frame, maintains a record count, or relative frequency, of each event type associated with the application; determines the event type of each log message considering each log message as comprising tokens separated by non-printed characters. Parameters are tokens or message fields that are, likely to be highly variable over a set of messages of a particular type. By contrast, the phrase “Error” or “Failure” in a log message likely occur within each of many error and failure recognition log messages. Brown et al 20210141900 A1 ¶0339 A textualized log message represents an event type. Other textualized log messages with the same non-parametric text strings and natural language words and phrase as the textualized log messages are the same event type. ¶0340 The different event types are denoted by event_type.sub.i, where i is an event type index.]
when the similarity measurement is above the acceptability threshold, submitting the configuration file correction recommendation for user evaluation by a user. [Brown et al 20210141900 A1 ¶0315 the standard-score model anomaly detector in block 2711 of FIG. 27A uses a standard-score threshold for detecting anomalous metric values. ¶0316 the standard-score model anomaly detector in block 2711 of FIG. 27A may be implemented with using upper bound and lower bounds that are based on the standard-score threshold in Equation (39b); ub.sub.n=μ+sTh.sub.G (39c). When the metric value, z.sub.n, satisfies ul.sub.n>z.sub.n or z.sub.n>ub.sub.n, the metric value is abnormal, which triggers an alert identifying the resource or object as exhibiting anomalous behavior. The corresponding time stamp t.sub.n is identified as a point in time when anomalous behavior at the associated resource begin. ¶¶0349-0350 FIGS. 49A-49C, increased darkness corresponds to increased criticality of anomalous behavior associated with larger values of the selected performance model applied to resources used by the nodes. The forecast model has been selected by a user. In FIG. 49E, the log and action recommendation window 4922 is expanded and event log windows 4940-4943 of log messages of nodes N1, N2, N3, and N5 recorded in the time frame are displayed. The event log windows 4940-4943 displays the log messages recorded in the time frame described above with reference to FIG. 48. The log messages enable an administrator and/or application owner to troubleshoot the root cause of the anomalous behavior. The log messages may be searched for key words that reveal the anomalous behavior that started at about the time anomalous behavior was detected in metrics. ]
Parthasarathy teaches all the features of claim 9 not characterized in that it further comprising, when said triggering of the update of the configuration file occurs, recording request to obtain a recommendation and the configuration file correction recommendation as an example in a data table relating to a type of anomaly, in association with a score. Parthasarathy teaches method for determining one or more anomalies in the application based on the one or more application fingerprints and outputting the one or more anomalies and the one or more recommendations to one or more user devices. Brown teaches a methods/systems where anomalous behavior is detected in either the metrics and/or the log messages an alert identifying the anomalous behavior is generated. Because both Parthasarathy and Brown teach resolving, remediating or correcting identified anomalies, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to try the classes and function calls of the standard-score model as taught by Brown to enable remedial measures determined based on the root cause and automatically or manually executed to correct the problem [Brown, Abstract ¶¶0004, 0113, and 0158].
Regarding claim 2, the combination of Parthasarathy and Brown teach claim 1 as described above.
Parthasarathy teaches further comprising extracting attributes from the runtime report previously trained generative artificial intelligence module model during a first interaction with the previously trained generative artificial intelligence model, and obtaining previously trained generative artificial intelligence model Parthasarathy ¶0049 the identified plurality of patterns are abstracted by extracting core information from the configuration information in a consistent manner. ¶0050 one or more anomalies in the application are determined based on the generated one or more application fingerprints by using a trained infrastructure management based Machine Learning (ML) model.]
Regarding claim 3, the combination of Parthasarathy and Brown teach claim 2 as described above.
Parthasarathy teaches further comprising extracting keywords from said attributes of the runtime report additional context information by querying at least one data table of said keywords. [Parthasarathy ¶¶0049-0050 the identified plurality of patterns are abstracted by extracting core information from the configuration information in a consistent manner and creating a hash, digest or reduction of the extracted core information. The one or more anomalies include abnormal application resource utilization after the event, unexpected failures or outages and the like.]
Regarding claim 4, the combination of Parthasarathy and Brown teach claim 3 as described above.
Parthasarathy teacheswherein the additional context information belongs to a group comprising at least:
the configuration file that generated the compliance anomaly,
information on a security policy applicable to the computing environment,
-one or more examples of previously obtained correction requests/recommendations. [Parthasarathy ¶0051 the one or more feedbacks may include remedial steps taken by the plurality of users to resolve the one or more anomalies and effect of remedial steps]
Regarding claim 5, the combination of Parthasarathy and Brown teach claim 2 as described above.
Parthasarathy teachesfurther comprising obtaining examples of requests to obtain a correction recommendation by querying a reinforcement learning module reinforcement learning module being configured to select at least one example of request from said examples of requests to obtain a correction recommendation from a data table data table comprising examples of correction recommendation requests that have resulted in correction recommendations Parthasarathy ¶0044 At step 318, it is determined if the one or more anomalies are resolved. When the one or more anomalies are not resolved, the computing system 104 continues to monitor the workload associated with the user at step 320. Furthermore, when the one or more anomalies are resolved, the computing system 104 operation ends at step 322; the application may be provided to the trained infrastructure management based ML model 306 as reaction based learning data for improving accuracy of the trained infrastructure management based ML model 306.]
Regarding claim 6, the combination of Parthasarathy and Brown teach claim 1 as described above.
Parthasarathy teachesfurther comprising prior reading ( of a document or governance plan, comprising relative information belonging to a group comprising:
-information on a format of the runtime report,
information on at least one attribute of the compliance anomaly to be extracted from the runtime report,
information on the similarity measurement and the acceptability threshold to be used to validate the recommendation for correcting the configuration file. [See Parthasarathy ¶0049 the identified plurality of patterns are abstracted by extracting core information from the configuration information in a consistent manner and creating a hash, digest or reduction of the extracted core information.]
Regarding claim 7, the combination of Parthasarathy and Brown teach claim 1 as described above.
Parthasarathy teacheswherein said submitting the configuration file correction recommendation comprises an update request, or pull request, to update the configuration file configuration file correction recommendation. [See Parthasarathy ¶0044 the computing system 104 detects the set of user devices 110 associated with the other users connected to the network 106 and running the one or more applications similar to the application and broadcasts the generated one or more recommendations to each of the detected set of user devices 110; the one or more recommendations on the application may be provided to the trained infrastructure management based ML model 306]
Regarding claim 8, the combination of Parthasarathy and Brown teach claim 1 as described above.
However, Parthasarathy fails to explicitly teach but Brown teachesfurther comprising obtaining an evaluation outcome of the configuration file correction recommendation by said user, and, depending on the evaluation outcome that is obtained, triggering or not triggering an updateconfiguration file correction recommendation. [Brown et al 20210141900 A1 ¶0298 when a new metric value satisfies lower bound or forecast metric value; the new metric value is abnormal, which triggers an alert identifying the resource or object associated with the stream of metric data as exhibiting anomalous behavior.]
Parthasarathy teaches all the features of claim 9 not characterized in that it further comprising, when said triggering of the update of the configuration file occurs, recording request to obtain a recommendation and the configuration file correction recommendation as an example in a data table relating to a type of anomaly, in association with a score. Parthasarathy teaches method for determining one or more anomalies in the application based on the one or more application fingerprints and outputting the one or more anomalies and the one or more recommendations to one or more user devices. Brown teaches a methods/systems where anomalous behavior is detected in either the metrics and/or the log messages an alert identifying the anomalous behavior is generated. Because both Parthasarathy and Brown teach resolving, remediating or correcting identified anomalies, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to try the classes and function calls of the standard-score model as taught by Brown to enable remedial measures determined based on the root cause and automatically or manually executed to correct the problem [Brown, Abstract ¶¶0004, 0113, and 0158].
Regarding claim 9, the combination of Parthasarathy and Brown teach claim 8 as described above.
However, Parthasarathy fails to explicitly teach but Brown teaches characterized in that it further comprising, when said triggering of the update of the configuration file occurs, recording a request to obtain a recommendation and the configuration file correction recommendation as an example in a data table 20210141900 A1 ¶0151-0152 different types of anomalies: contextual, collective, or point]
Parthasarathy teaches all the features of claim 9 not characterized in that it further comprising, when said triggering of the update of the configuration file occurs, recording request to obtain a recommendation and the configuration file correction recommendation as an example in a data table relating to a type of anomaly, in association with a score. Parthasarathy teaches method for determining one or more anomalies in the application based on the one or more application fingerprints and outputting the one or more anomalies and the one or more recommendations to one or more user devices. Brown teaches a methods/systems where anomalous behavior is detected in either the metrics and/or the log messages an alert identifying the anomalous behavior is generated. Because both Parthasarathy and Brown teach resolving, remediating or correcting identified anomalies, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention was made to try the classes and function calls of the standard-score model as taught by Brown to enable remedial measures determined based on the root cause and automatically or manually executed to correct the problem [Brown, Abstract ¶¶0004, 0113, and 0158].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Muddu et al 11258807 B2 teaches Anomaly detection based on communication between entities over a network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SAKINAH WHITE-TAYLOR whose telephone number is (571)270-0682. The examiner can normally be reached Monday-Friday, 10:45a-6:45p.
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SAKINAH WHITE-TAYLOR
Primary Examiner
Art Unit 2407
/Sakinah White-Taylor/Primary Examiner, Art Unit 2407