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
Applicant’s Application filed on 08/06/2025 has been reviewed.
Claims 1-20 have been examined.
Notice of Pre-AIA or AIA Status
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.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
(Step 1) The claim(s) 1-20 recite(s) a method, system and non-transitory computer-readable storage medium, and are directed toward statutory subject matter.
(Step 2A1-does the claim recite an abstract idea, law of nature, or natural phenomenon?)
The enumerated groupings of abstract ideas are defined as:
1) Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations (see MPEP § 2106.04(a)(2), subsection I);
2) Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II); and
3) Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III).
The limitation of system claim 8 (similarly in method claim 1 and medium claim 15) “a system for determining related information technology event data in a system, the system comprising: a memory having processor-readable instructions stored therein; and at least one processor configured to access the memory and execute the processor-readable instructions to perform operations including: receiving a data object indicating an occurrence of a current incident associated with a configurable item, the data object including a short description; applying a first machine learning model to the short description of the data object to determine a first cluster associated with the data object; receiving a plurality of data object indicating occurrences of current incidents that occurred within a set period of time of the current incident, the plurality of data objects including a plurality of short descriptions; applying the first machine learning model to the short descriptions of the plurality of data objects to determine associated clusters for each of the plurality of data objects; determining, based on association rules and the associated clusters, a set of similar data objects from the plurality of data objects; and outputting the set of similar data object”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind/manual process but for the recitation of generic computer components. That is, other than reciting “by a processor,” nothing in the claim element precludes the step from practically being performed in the mind/manually performance. For example, but for the “by a processor” language, “…receiving…applying…receiving…applying…determining…outputting…” in the context of this claim encompasses the user mental/manually perform the process.
The claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics, 774 F.3d 755, 763, 113 USPQ2d 1241, 1246 (Fed. Cir. 2014);
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011); and
Further, if a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. In this case, for claims 8, except for using generic elements such as processor, database, graphical user interface, all other element can be performed by human mind as a mental process and/or performed manually using pencil and paper (The use of a physical aid (e.g., pencil and paper or a slide rule) to help perform a mental step (e.g., a mathematical calculation) does not negate the mental nature of the limitation, but simply accounts for variations in memory capacity from one person to another. For instance, in CyberSource, the court determined that the step of "constructing a map of credit card numbers" was a limitation that was able to be performed "by writing down a list of credit card transactions made from a particular IP address." In making this determination, the court looked to the specification, which explained that the claimed map was nothing more than a listing of several (e.g., four) credit card transactions. The court concluded that this step was able to be performed mentally with a pen and paper, and therefore, it qualified as a mental process. 654 F.3d at 1372-73, 99 USPQ2d at 1695. See also Flook, 437 U.S. at 586, 198 USPQ at 196 (claimed "computations can be made by pencil and paper calculations"); University of Florida Research Foundation, Inc. v. General Electric Co., 916 F.3d 1363, 1367, 129 USPQ2d 1409, 1411-12 (Fed. Cir. 2019) (relying on specification’s description of the claimed analysis and manipulation of data as being performed mentally "‘using pen and paper methodologies, such as flowsheets and patient charts’"); Symantec, 838 F.3d at 1318, 120 USPQ2d at 1360 (although claimed as computer-implemented, steps of screening messages can be "performed by a human, mentally or with pen and paper").) (MPEP 2106.04(a)(2).)
Thus, if a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind and/or manually performed, but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
(Step 2A2-Practical Application?)This judicial exception is not integrated into a practical application.
The courts have also identified limitations that did not integrate a judicial exception into a practical application:
• Merely reciting the words “apply it” (or an equivalent) with the judicial exception, or merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f);
• Adding insignificant extra-solution activity to the judicial exception, as discussed in MPEP § 2106.05(g); and
• Generally linking the use of a judicial exception to a particular technological environment or field of use, as discussed in MPEP § 2106.05(h).
In particular, the claim only recites one additional element – using a processor to perform ““…receiving…applying…receiving…applying…determining…outputting…””. The processor in performing the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
(Step 2B- does the claim recite additional elements that amount to significantly more than the judicial exception?)
Limitations that the courts have found not to be enough to qualify as “significantly more” when recited in a claim with a judicial exception include:
i. Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984 (see MPEP § 2106.05(f));
ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or
iv. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
“As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). In Flook, the Court reasoned that “[t]he notion that post-solution activity, no matter how conventional or obvious in itself, can transform an unpatentable principle into a patentable process exalts form over substance. A competent draftsman could attach some form of post-solution activity to almost any mathematical formula”. 437 U.S. at 590; 198 USPQ at 197; Id. (holding that step of adjusting an alarm limit variable to a figure computed according to a mathematical formula was “post-solution activity”). “
As to claims 2-7, 9-14, 16-20, the claims further recites additional limitations regarding the nature of evaluated data and additional processing steps. The additional limitations further detailing with data and data manipulations, and add insignificant extra-solution activity. Refining the abstract idea and/or add insignificant extra-solution activity does not make an abstract idea beyond the abstract idea itself. The claim recites mental process and/or manual process where steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016). Thus, the claim does not mount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 8-11 and 15-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication No. 20200057953 to Livny et al. (hereinafter “Livny”).
As to claim 1, Livny teaches a computer-implemented method for determining related information technology event data by applying temporal associations, the method comprising (computer implemented method in a system comprising processor and non-transitory computer readable storage medium, par. 0005-0007, 0098-0109):
receiving a data object indicating an occurrence of a current incident associated with a configurable item, the data object including a short description (Fig. 2, par. 0044-0050, data object such as incident data descriptions associated with resource, i.e. “[0044] Flowchart 200 begins with step 202. In step 202, an incident notification is received. For example, event logger 302 described with reference to FIG. 3 may receive an incident notification 316. Incident notification 316 may comprise any type of notification or report relating to a potential threat or security incident on any resource or service coupled to network 110 as described herein. Incident notification 316 may include information associated with the generated alert, such as a timestamp, location, subscription, storage, an application, or any other identifier of a resource associated with the alert. Event logger 302 may further be configured to receive environmental data or contextual data 318 related to incident notification 316, such as information related to process creation, telemetry, or a network associated with incident notification 316 as will be described in greater detail below. In some implementations, each incident notification 316 may be assigned a spot in a queue, such as a sequence number or other identifier, such that incident clustering system 108 may uniquely identify and/or sort a plurality of incident notifications based on an order in which the notifications are generated.” );
applying a first machine learning model to the short description of the data object to determine a first cluster associated with the data object (par. 0043-0052, Fig. 2, step 206, apply a machine-learning-based model to identify a similar incident based on feature set, i.e. “[0052] In step 206, a machine-learning-based model is applied to identify a similar incident notification based on the feature set. For example, with reference to FIG. 3, machine learning engine 308 may apply a model 310 to identify one or more similar incident notifications (e.g., notifications similar to incident notification 316) based on feature set 306. Machine learning engine 308 may apply model 310 in a variety of ways to identify a similar incident notification. In examples, model 310 may be configured to implement one or more algorithms to generate a score or other measure of similarity between a plurality of incident notifications based on feature set 306 and training data. In one example, model 310 may implement a score model, a distance model, and/or other similarity metric to determine a likelihood that two incident notifications are similar. Model 310 may implement one or more of such algorithms discussed herein to identify which features or feature sets are important in determining whether multiple incident notifications are similar to each other.”);
receiving a plurality of data object indicating occurrences of current incidents that occurred within a set period of time of the current incident, the plurality of data objects including a plurality of short descriptions (par. 0043-0052, period of time, i.e. “[0048] Additionally, the set of features may comprise one or more features based on aggregated or pre-processed data, such as a number or frequency of alerts occurring on the same resource in a particular period of time (e.g., in the last hour, day, week, etc.).”);
applying the first machine learning model to the short descriptions of the plurality of data objects to determine associated clusters for each of the plurality of data objects (Fig. 1-3, par. 0043-0052, cluster incident notifications, i.e. “[0043] Accordingly, in example embodiments, incident clustering system 108 may be configured to cluster incident notifications in a computing environment in various ways. For instance, FIG. 2 shows a flowchart 200 of a method for clustering incident notifications in a computing environment, according to an example embodiment. In implementations, the steps of flowchart 200 may be implemented by incident resolver UI 104 and/or incident clustering system 108. FIG. 2 is described with reference to FIG. 1 and FIG. 3. FIG. 3 shows a block diagram of incident clustering system 108. Incident clustering system 108 includes an event logger 302, a featurizer 304, a machine learning engine 308, a user interface (UI) engine 312, and an action resolver 314. As shown in FIG. 3, featurizer 304 may comprise a feature set 306. Machine learning engine 308 may be configured to generate, train, and/or apply a model 310 to identify one or more similar incident notifications. Other structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following discussion regarding flowchart 200, system 100 of FIG. 1, and incident clustering system 108 of FIG. 3.”);
determining, based on association rules and the associated clusters, a set of similar data objects from the plurality of data objects (par. 0051-0061, determining similar data objects, i.e. “[0052] In step 206, a machine-learning-based model is applied to identify a similar incident notification based on the feature set. For example, with reference to FIG. 3, machine learning engine 308 may apply a model 310 to identify one or more similar incident notifications (e.g., notifications similar to incident notification 316) based on feature set 306. Machine learning engine 308 may apply model 310 in a variety of ways to identify a similar incident notification. In examples, model 310 may be configured to implement one or more algorithms to generate a score or other measure of similarity between a plurality of incident notifications based on feature set 306 and training data. In one example, model 310 may implement a score model, a distance model, and/or other similarity metric to determine a likelihood that two incident notifications are similar. Model 310 may implement one or more of such algorithms discussed herein to identify which features or feature sets are important in determining whether multiple incident notifications are similar to each other.”);
assigning a set of associations between the data object and each of the set of similar data objects (Fig. 7, par. 0090-0097, associations of similar incidents, i.e. “[0092] Similar resolved incident notification 706 may comprise a similar incident notification identified by machine learning engine 308 as described above. In implementations, similar resolved incident notification 706 may comprise associated information 708 similar to associated information 702. Similar resolved incident notification 706 may also indicate a prior resolution action 710 that was previously executed to resolve the notification”); and
storing the set of associations (par. 0098, storing data and step/program including data association/clustering in a storage).
As to claim 2, Livny teaches the method of claim 1, wherein the data object includes metadata of a value indicating whether the current incident is a major incident, wherein if the value indicates that the current incident is a major incident the method will proceed with applying the first machine learning model (par. 0059, 0074, 0079, 0081, 0092, severity level, i.e. “[0059] In implementations, model 310 may also be continuously and automatically trained based on the actions of users of incident clustering system 108. For instance, as users of incident clustering system 108 respond to incident notifications (e.g., by marking as a false positive, elevating a severity level, taking another action to resolve the notification, etc.)” ).
As to claim 3, Livny teaches the method of claim 1, wherein determining the set of similar data objects further comprises: assigning, based on the association rules, confidence scores for each of the similar data objects (par. 0067-0069, measure of confidence, i.e. “For example, the measure of similarity may comprise a confidence measure, such as a value indicating a level of confidence that two incident notifications are similar. In some example embodiments, the measure of similarity may comprise a score or distance between two incident notifications, such as a value indicating how similar (or dissimilar) the two incident notifications are. For example, the measure of similarity may be higher where incident notifications are likely to be similar, whereas the measure of similarity may be lower where incident notifications are likely to be different.”).
As to claim 4, Livny teaches the method of claim 3, wherein outputting the set of similar data object further comprises: outputting, the set of similar data object in a ranking based on the confidence scores for each of the similar data objects (par. 0067-0069, ranking, i.e. “For instance, incident resolver UI 104 may be configured to display, via computing device 102, each similar incident notification identified by model 310 that is similar to incident notification 316, along with the measure of confidence associated with each similar incident notification. Incident resolver UI 104 may also be configured to display similar incident notifications in a ranked manner based on the measure of confidence (e.g., highest to lowest), such that similar incident notifications with a higher level of confidence are displayed first.”).
Regarding claims 8-11, are essentially the same as claims 1-4 except that it sets forth the claimed invention as system rather than method and rejected for the same reasons as applied hereinabove.
Regarding claims 15-18, are essentially the same as claims 1-4 except that it sets forth the claimed invention as non-transitory computer readable medium rather than method and rejected for the same reasons as applied hereinabove.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 5-7, 12-14, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Livny, and further in view of U.S. Patent Application Publication No. 20210224676 to Arzani et al. (hereinafter “Arzani”).
As to claim 5, Livny teaches the method of claim 1, further comprising: receiving, a plurality of problem data objects indicating occurrences of problems that occurred within the set period of time of the current incident, the plurality of problem data objects including a plurality of problem short descriptions; applying a resolved incident notification 706 may comprise associated information 708 similar to associated information 702. Similar resolved incident notification 706 may also indicate a prior resolution action 710 that was previously executed to resolve the notification”).
Livny does not explicitly teach applying a second machine learning model; determining, based on a second set of association rules and the associated clusters, a set of similar problem data objects from the plurality of problem data objects; assigning a second set of associations between the data object and the set of similar problem data objects as claimed.
Arzani teaches applying a second machine learning model; determining, based on a second set of association rules and the associated clusters, a set of similar problem data objects from the plurality of problem data objects; assigning a second set of associations between the data object and the set of similar problem data objects (Fig. 2, 8A, 8B, par. 0100-0106, deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, from plurality of machine learning models, and for assigning a set of associations, i.e. “[0101] Flow continues when the monitoring module provides relevant monitoring data to a computation module (e.g., computation module 116). Monitoring data may be processed to form feature sets or may comprise raw data depending on the requirements and preferences of the computation module. Within the computation module, one or more machine learning models may then evaluate the provided monitoring data. The computation module or team-specific scouts may identify a single machine leaning model most likely to generate an accurate incident-classification prediction. Deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, as previously discussed. [0102] Flow continues when the computation module provides a machine learning model result to the team-specific scout. In some examples, the machine learning model result is an incident classification-prediction. In other examples, the result does not directly contain an incident-classification prediction and only contains data related to the machine learning model's evaluation of the provided monitoring data. In such an example, the team-specific scout may be equipped to parse this result data in order to generate an incident-classification prediction.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Livny with the teaching of Arzani because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Arzani would allow Livny to “…utilization of team-specific scouts (e.g., predictors) to make decisions that may be used to make automated incident-routing decisions. In an example, a team is responsible for a certain part of a cloud environment, and its members possess expertise relating to that part of the cloud environment. This team may utilize this expertise to create or to assist in creating an incident-routing scout. In some examples, the scout receives incident descriptions and, based on the scout's specifications, accesses monitoring data that may be related to an incident. Then, using a machine learning model, the scout may evaluate the accessed data to generate a prediction about whether or not the team with which the scout is associated will be able to resolve the described incident…” (Arzani, par. 0001-0003)
As to claim 6, Livny teaches the method of claim 1, further comprising: receiving, a plurality of change data objects indicating occurrences of changes that occurred within the set period of time of the current incident, the plurality of change data objects including a plurality of change short descriptions; applying a incident on any resource or service coupled to network 110 as described herein. Incident notification 316 may include information associated with the generated alert, such as a timestamp, location, subscription, storage, an application, or any other identifier of a resource associated with the alert. Event logger 302 may further be configured to receive environmental data or contextual data 318 related to incident notification 316, such as information related to process creation, telemetry, or a network associated with incident notification 316 as will be described in greater detail below. In some implementations, each incident notification 316 may be assigned a spot in a queue, such as a sequence number or other identifier, such that incident clustering system 108 may uniquely identify and/or sort a plurality of incident notifications based on an order in which the notifications are generated.” );
Livny does not explicitly teach applying a third machine learning model; determining, based on a third set of association rules and the associated clusters, a set of similar change data objects from the plurality of change data objects; assigning a third set of associations between the data object and the set of similar change data objects as claimed.
Arzani teaches applying a second machine learning model; determining, based on a third set of association rules and the associated clusters, a set of similar change data objects from the plurality of change data objects; assigning a third set of associations between the data object and the set of similar change data objects (Fig. 2, 8A, 8B, par. 0100-0106, deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, from plurality of machine learning models, and for assigning a set of associations, i.e. “[0101] Flow continues when the monitoring module provides relevant monitoring data to a computation module (e.g., computation module 116). Monitoring data may be processed to form feature sets or may comprise raw data depending on the requirements and preferences of the computation module. Within the computation module, one or more machine learning models may then evaluate the provided monitoring data. The computation module or team-specific scouts may identify a single machine leaning model most likely to generate an accurate incident-classification prediction. Deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, as previously discussed. [0102] Flow continues when the computation module provides a machine learning model result to the team-specific scout. In some examples, the machine learning model result is an incident classification-prediction. In other examples, the result does not directly contain an incident-classification prediction and only contains data related to the machine learning model's evaluation of the provided monitoring data. In such an example, the team-specific scout may be equipped to parse this result data in order to generate an incident-classification prediction.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Livny with the teaching of Arzani because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Arzani would allow Livny to “…utilization of team-specific scouts (e.g., predictors) to make decisions that may be used to make automated incident-routing decisions. In an example, a team is responsible for a certain part of a cloud environment, and its members possess expertise relating to that part of the cloud environment. This team may utilize this expertise to create or to assist in creating an incident-routing scout. In some examples, the scout receives incident descriptions and, based on the scout's specifications, accesses monitoring data that may be related to an incident. Then, using a machine learning model, the scout may evaluate the accessed data to generate a prediction about whether or not the team with which the scout is associated will be able to resolve the described incident…” (Arzani, par. 0001-0003)
As to claim 7, Livny teaches the method of claim 1, further comprising: receiving, a plurality of alert data objects indicating occurrences of alerts that occurred within the set period of time of the current incident, the plurality of alert data objects including a plurality of alert short descriptions; applying a
Livny does not explicitly teach applying a fourth machine learning model; determining, based on a fourth set of association rules and the associated clusters, a set of similar alert data objects from the plurality of alert data objects; assigning a fourth set of associations between the data object and the set of similar alert data objects; as claimed.
Arzani teaches applying a second machine learning model; determining, based on a fourth set of association rules and the associated clusters, a set of similar alert data objects from the plurality of alert data objects; assigning a fourth set of associations between the data object and the set of similar alert data objects (Fig. 2, 8A, 8B, par. 0100-0106, deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, from plurality of machine learning models, and for assigning a set of associations, i.e. “[0101] Flow continues when the monitoring module provides relevant monitoring data to a computation module (e.g., computation module 116). Monitoring data may be processed to form feature sets or may comprise raw data depending on the requirements and preferences of the computation module. Within the computation module, one or more machine learning models may then evaluate the provided monitoring data. The computation module or team-specific scouts may identify a single machine leaning model most likely to generate an accurate incident-classification prediction. Deciding which machine learning model is most likely to generate an accurate incident-classification prediction may also entail utilizing a meta-learning model, as previously discussed. [0102] Flow continues when the computation module provides a machine learning model result to the team-specific scout. In some examples, the machine learning model result is an incident classification-prediction. In other examples, the result does not directly contain an incident-classification prediction and only contains data related to the machine learning model's evaluation of the provided monitoring data. In such an example, the team-specific scout may be equipped to parse this result data in order to generate an incident-classification prediction.”)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Livny with the teaching of Arzani because they are in the same field of endeavor. One of ordinary skill in the art at the time of the invention would have been motivated to do so because the teaching of Arzani would allow Livny to “…utilization of team-specific scouts (e.g., predictors) to make decisions that may be used to make automated incident-routing decisions. In an example, a team is responsible for a certain part of a cloud environment, and its members possess expertise relating to that part of the cloud environment. This team may utilize this expertise to create or to assist in creating an incident-routing scout. In some examples, the scout receives incident descriptions and, based on the scout's specifications, accesses monitoring data that may be related to an incident. Then, using a machine learning model, the scout may evaluate the accessed data to generate a prediction about whether or not the team with which the scout is associated will be able to resolve the described incident…” (Arzani, par. 0001-0003)
Regarding claims 12-14, is essentially the same as claims 5-7 except that it sets forth the claimed invention as system rather than method and rejected for the same reasons as applied hereinabove.
Regarding claims 19-20, is essentially the same as claims 5-6 except that it sets forth the claimed invention as non-transitory computer readable medium rather than method and rejected for the same reasons as applied hereinabove.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANHTAI V TRAN whose telephone number is (571)270-5129. The examiner can normally be reached on Monday through Thursday from 8:00 AM to 4:00 PM.
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/ANHTAI V TRAN/Primary Examiner, Art Unit 2168
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