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 .
Status of Claims
This action is responsive to remarks filed 02/27/2026. Claims 2, 9, and 16 are amended. No claim has been cancelled, and there are no new claims.
Claims 1–20 are pending for examination.
Response to Arguments
In reference to 35 USC § 101
Applicant’s arguments, filed on 02/27/2026, with respect to the § 101 rejections have been fully considered but are not persuasive.
Applicant argues, beginning on Pg. 8 in the Remarks, that When properly considered as a whole, independent claim I is not directed to a mental process, but instead to a specific ML-based technical architecture.” See MPEP 2106.04(a)(2)(III)(C):
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea. The Court concluded that the algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." 409 U.S at 67, 175 USPQ at 675. See also Mortgage Grader, 811 F.3d at 1324, 117 USPQ2d at 1699 (concluding that concept of "anonymous loan shopping" recited in a computer system claim is an abstract idea because it could be "performed by humans without a computer").
In the instant application, the limitations are recited as being performed on a computer. However, Examiner contends Applicant’s method includes limitations that can be performed in the human mind even though they could be carried out using existing computers because a person, with the aid of pen and paper, could classify events into specific signals including a cause-effect classification and impact and recommend a solution. This is evinced by Applicant’s own Specification in at least paragraphs [0013] which states:
The site reliability engineers end up manually applying best practices related to golden signals using events arising from run-time IT operations data such as logs, metrics, and traces. As used herein, an event indicates that something has happened which should be brought to the notice of the SRE. It is associated with one or more applications, services, or other managed resources. Events can indicate anomalous behaviour or information … It requires (or will require in the future) human or automatic attention and actions toward remediation.
Applicant argues, beginning on Pg. 11 in the Remarks, that “Applicant respectfully submits that the claim integrates any such alleged abstract idea into a practical application.” Examiner respectfully disagrees. See MPEP 2106.04(d)(1) and MPEP 2106.05(a) which respectively state:
if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. The claim itself does not need to explicitly recite the improvement described in the specification (e.g., "thereby increasing the bandwidth of the channel").
An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. For example, in McRO, the court relied on the specification’s explanation of how the particular rules recited in the claim enabled the automation of specific animation tasks that previously could only be performed subjectively by humans, when determining that the claims were directed to improvements in computer animation instead of an abstract idea. McRO, 837 F.3d at 1313-14, 120 USPQ2d at 1100-01. In contrast, the court in Affinity Labs of Tex. v. DirecTV, LLC relied on the specification’s failure to provide details regarding the manner in which the invention accomplished the alleged improvement when holding the claimed methods of delivering broadcast content to cellphones ineligible. 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016).
In the instant application, examiner contends the purported improvements are not improvements to a computer but are instead improvements directed to the abstract ideas themselves (i.e., the determining classifications and generating recommendations) and therefore, do not integrate the abstract ideas into a practical application. For example, Examiner contends Applicant’s methods merely train existing machine learning models using existing techniques to perform a human job which merely involves purported improvements to the abstract ideas themselves. Without details related to how the computer functions/technology have been improved, the abstract ideas are not integrated into a practical application and the additional elements to do not amount to significantly more. See detailed analysis of the newly amended claims in § 101 below.
Thus, the § 101 rejections are maintained.
In reference to 35 USC § 102
Applicant’s arguments filed on 02/27/2026, with respect to the § 102 rejections have been fully considered but are not persuasive.
Applicant argues, beginning on Pg. 13 in the Remarks, that “Garapati does not disclose classifying run-time IT operations into golden signals, nor does it disclose using a machine learning model to generate a golden signal classification, as required by claim 1.” Examiner respectfully disagrees. Examiner notes that the BRI of golden signal is any data associated with the IT system (see instant application paragraphs [0002 and 0013]). In paragraph [0013], the Specification states that the methods determine “golden signal classifications using historical context from information technology (IT) support data,” and goes on to define the historical context as “events arising from run-time IT operations data such as logs, metrics, and traces … Events can indicate anomalous behaviour or information.” Emphasis added. Examiner contends that Garapati indeed teaches classifying run-time IT operations into golden signals in at least cited paragraphs [0050, 0072, 0105, 0266, 0278] because Garapati teaches a system configured to utilize captured information created by the tree generator that “correspond to the presence of an event” related to historic IT operation data obtained from historic database 2422. Garapati teaches classification techniques including golden-signal (e.g., events may relate to, or describe different types of optimal or sub-optimal network behavior) and cause-effect and impact (e.g., classifying root cause and effect) because Garapati teaches users “may wish to identify, classify, describe, or predict various network occurrences or other events. For example, such events may relate to, or describe different types of optimal or sub-optimal network behavior" which impacts the system in “such as a crash or a freeze, a memory that reaches capacity, or a resource that becomes inaccessible.” Furthermore, Examiner contends Garapati indeed teaches using a machine learning model to generate the classifications in at least cited paragraphs [0050, 0105] because Garapati teaches “machine learning algorithms may be trained using the actual situation, root cause, and/or resolution data.” See § 102 below for a detailed analysis.
Applicant argues, beginning on Pg. 14 in the Remarks, that “claim 1 requires determining a golden signal classification using a machine learning model applied to run-time IT operations data.” Specifically, Applicant argues that “golden signal classification of claim 1 cannot be equated to metrics and event identification of Garapati.” Examiner respectfully disagrees. Examiner notes the discussion of golden signal classification and event data above. Examiner contends that, under BRI, Garapati indeed teaches classification using a machine learning model applied to run-time IT operations (events arising from run-time IT operations data such as logs, metrics, and traces) because Garapati teaches using a machine learning model trained with historic run-time IT data to output classifications including classification and identification of anomalies/errors (e.g., events may relate to, or describe different types of optimal or sub-optimal network behavior), root cause, and effect analysis. See § 102 below for a detailed analysis.
Applicant argues, beginning on Pg. 14 in the Remarks, that “Garapati infers causal relationships between event nodes and ranks nodes using graph based techniques, which are different from determining cause-effect and impact using golden signals and run time IT operations.” Examiner respectfully disagrees. Examiner contends classifying events, root causes, and effects using machine learning models, under BRI, indeed anticipates classifying golden signals, causes, and effects. See § 102 below for a detailed analysis.
Applicant argues, beginning on Pg. 14 in the Remarks, that “The cited portions do not disclose a resolution recommendation on such classification outputs.” Examiner respectfully disagrees. Examiner notes the cited portions of Garapati including Fig. 1 and at least paragraph [0058] which states: “FIG. 1 is a block diagram of a system for directed incremental clustering of causally related events. In the example of FIG. 1, an IT landscape manager 102 may be configured to provide the types of causal chain determination, root cause analysis, performance prediction, and remediation actions referenced above, and described in detail, below.” Emphasis added.
Thus, examiner maintains the § 102 rejections.
In reference to 35 USC § 103
Applicant’s arguments filed on 02/27/2026, with respect to the § 103 rejections have been fully considered but are not persuasive.
With respect to the remaining arguments including the dependent claims, without specific arguments detailing how the cited art does not teach each limitation, examiner maintains the rejections.
Thus, examiner maintains the § 103 rejections.
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.
Regarding Claim 1:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 1 is directed to a method i.e., a process.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“determining, [by the processor set,] a golden signal classification, a cause-effect classification, and an impact using the run-time IT operations and the machine learning model”
“generating, [by the processor set,] a resolution recommendation based on the golden signal classification, the cause-effect classification, and the impact”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine/classify a golden signal, a cause-effect, an impact using operations and models, and generate a recommendation based on the determining.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
“by the processor set” — This limitation is reciting generic computer components at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
“creating, by a processor set, a training dataset using historic information technology (IT) operations data and historic event data of a computer system” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Processing data for machine learning models to create training sets invokes computers or other machinery as a tool to perform an existing process.
“training, by the processor set, a machine learning model using the training dataset” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Training machine learning models invokes computers or other machinery as a tool to perform an existing process.
“receiving, by the processor set, run-time IT operations data of the computer system” — This limitation is insignificant extra-solution activity and is merely data gathering. See MPEP 2106.05(g).
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
“receiving, by the processor set, run-time IT operations data of the computer system” — This limitation is directed to the activity of acquiring data which is not an inventive concept because it is insignificant extra-solution activity of mere data gathering. See OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3). This limitation is well-understood, routine, and conventional because it involves receiving information over a network. MPEP 2106.05(d)(II).
Regarding Claim 2:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 2 depends from claim 1 (see analysis of claim 1 above) which is directed to a method i.e., a process.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“determining a feature golden signal classification for each of the extracted features”
“determining a feature type golden signal classification based on the feature golden signal classification of groups of the extracted features”
“determining an event golden signal classification for the event based on the feature type golden signal classification of groups plural feature types”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine a feature golden signal classification, a feature type golden signal classification, and an event golden signal classification based on extracted features.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional limitation:
“wherein the creating the training dataset comprises: extracting features associated with an event from the historic event data” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Extracting features with machine learning models invokes computers or other machinery as a tool to perform an existing process.
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 3:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 3 depends from claim 2 (see analysis of claim 2 above) which is directed to a method i.e., a process.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“wherein the creating the training dataset comprises: determining an event cause-effect classification for the event based on the event golden signal classification and a knowledge graph”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine an event cause-effect classification for the event based on a classification and a knowledge graph.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional limitation:
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 4:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 4 depends from claim 3 (see analysis of claim 3 above) which is directed to a method i.e., a process.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“wherein the creating the training dataset comprises: determining an event impact for the event based on the event golden signal classification and a knowledge base”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine an event impact for the event based on a classification and a knowledge base.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional limitation:
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 5:
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 4 which included an abstract idea (see rejection for claim 4 above). This claim merely recites a further limitation on the training dataset limitation which is directed to mere instructions to apply the abstract idea. The additional limitation:
“linking the event golden signal classification, the event cause-effect classification, and the event impact with a subset of the historic information technology (IT) operations data associated with the event” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Associating data invokes computers or other machinery as a tool to perform an existing process.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d)I.), failing Step 2A Prong 2. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
Regarding claim 6:
The claim is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim is dependent on claim 1 which included an abstract idea (see rejection for claim 1 above). This claim merely recites additional limitations on the claim. The additional limitations:
“receiving feedback in response to the resolution recommendation” — This limitation is insignificant extra-solution activity and is merely data gathering. See MPEP 2106.05(g).
“retraining the machine learning model based on the feedback” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Training and retraining machine learning models invokes computers or other machinery as a tool to perform an existing process.
Thus, the judicial exception is not integrated into a practical application (see MPEP 2106.04(d)I.), failing Step 2A Prong 2.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception under step 2B.
“receiving feedback in response to the resolution recommendation” — This limitation is directed to the activity of acquiring data which is not an inventive concept because it is insignificant extra-solution activity of mere data gathering. See OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3). This limitation is well-understood, routine, and conventional because it involves receiving information over a network. MPEP 2106.05(d)(II).
Thus, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception (see MPEP 2106.05(I.), failing step 2B.
Regarding Claim 7:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 7 depends from claim 1 (see analysis of claim 1 above) which is directed to a method i.e., a process.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“determining an alert prioritization and a probable root cause based on the golden signal classification, the cause-effect classification, and the impact”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine an alert prioritization and a probable root cause based on a classification and impact.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional limitation:
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding Claim 8:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 8 is directed to a computer program product i.e., a machine.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“determine a golden signal classification, a cause-effect classification, and an impact using the run-time IT operations and the machine learning model”
“generate a resolution recommendation based on the golden signal classification, the cause-effect classification, and the impact”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine/classify a golden signal, a cause-effect, an impact using operations and models, and generate a recommendation based on the determining.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
“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” — This limitation is reciting generic computer components at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
“create a training dataset using historic information technology (IT) operations data and historic event data of a computer system” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Processing data for machine learning models to create training sets invokes computers or other machinery as a tool to perform an existing process.
“train a machine learning model using the training dataset” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Training machine learning models invokes computers or other machinery as a tool to perform an existing process.
“receive run-time IT operations data of the computer system” — This limitation is insignificant extra-solution activity and is merely data gathering. See MPEP 2106.05(g).
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
“receive run-time IT operations data of the computer system” — This limitation is directed to the activity of acquiring data which is not an inventive concept because it is insignificant extra-solution activity of mere data gathering. See OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3). This limitation is well-understood, routine, and conventional because it involves receiving information over a network. MPEP 2106.05(d)(II).
Regarding Claim 15:
Step 1 — Is the claim to a process, machine, manufacture, or composition of matter?
Yes, claim 15 is directed to a system i.e., a machine.
Step 2A — Prong 1 Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites an abstract idea.
“determine a golden signal classification, a cause-effect classification, and an impact using the run-time IT operations and the machine learning model”
“generate a resolution recommendation based on the golden signal classification, the cause-effect classification, and the impact”
These limitations, under their broadest reasonable interpretation, cover mental processes, concepts performed in the human mind (including an observation, evaluation, judgment, opinion). See MPEP 2106.04(a)(2). In particular, with the aid of pen and paper, a human can determine/classify a golden signal, a cause-effect, an impact using operations and models, and generate a recommendation based on the determining.
Step 2A — Prong 2 — Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, there are no additional elements that integrate the judicial exception into a practical application. The additional elements:
“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 program instructions executable to” — This limitation is reciting generic computer components at a high-level of generality (i.e., as a generic computer component performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer component. See MPEP 2106.05(f).
“create a training dataset using historic information technology (IT) operations data and historic event data of a computer system” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Processing data for machine learning models to create training sets invokes computers or other machinery as a tool to perform an existing process.
“train a machine learning model using the training dataset” — This limitation amounts to no more than mere instructions to apply the exception and is the equivalent to mere instruction to implement the abstract idea on a computer. See MPEP 2106.05(f). Training machine learning models invokes computers or other machinery as a tool to perform an existing process.
“receive run-time IT operations data of the computer system” — This limitation is insignificant extra-solution activity and is merely data gathering. See MPEP 2106.05(g).
Step 2B — Does the claim recite additional elements that amount to significantly more than the judicial exception?
No, there are no additional elements that amount to significantly more than the judicial exception.
“receive run-time IT operations data of the computer system” — This limitation is directed to the activity of acquiring data which is not an inventive concept because it is insignificant extra-solution activity of mere data gathering. See OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015); MPEP 2106.05(g)(3). This limitation is well-understood, routine, and conventional because it involves receiving information over a network. MPEP 2106.05(d)(II).
Regarding claims 9–14 and 16–20, although varying in scope, the limitations of claims 9–14 and 16–20 are substantially the same as the limitations of claims 2–7, respectively. Thus, claims 9–14 and 16–20 are rejected using the same reasoning and analysis as claims 2–7 above, respectively.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1–3, 6–10, 13–17, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Garapati et al., (US 20230102002 A1), hereinafter “Garapati.”
Regarding claim 1, Garapati teaches:
creating, by a processor set, a training dataset using historic information technology (IT) operations data and historic event data of a computer system (Garapati ¶0266: “Given a set of training data of event graphs and/or situations, such as those identified by the situation identifier 128 created from clustering of events, such as those generated by the cluster tree generator 144, a model implemented by the prediction manager 132 learns which changes in the event graph states, relationships, and topological relationships correspond to the presence of an event along with its relationship to other events as an edge”; see also Garapati ¶0105: “Moreover, a prediction manager 132 may be configured to utilize captured situation information, root cause information, and resolution information of multiple situations that occur over time, to thereby predict similar situations prior to such predicted situation actually occurring. For example, machine learning algorithms may be trained using the actual situation, root cause, and/or resolution data, so that the trained algorithms may then predict similar situation in the future. Additional features and functions of the prediction manager 132 are provided below, e.g., with respect to FIGS. 22-29”; see also Garapati ¶0278: “Training phase” and “event graph information is obtained from the historical data database 2422”—[wherein system configured to utilize the captured information created by the tree generator (i.e., by a processor set; e.g., CPU and instructions) to train machine learning algorithms]);
training, by the processor set, a machine learning model using the training dataset (Garapati ¶0105: “Moreover, a prediction manager 132 may be configured to utilize captured situation information, root cause information, and resolution information of multiple situations that occur over time, to thereby predict similar situations prior to such predicted situation actually occurring. For example, machine learning algorithms may be trained using the actual situation, root cause, and/or resolution data, so that the trained algorithms may then predict similar situation in the future. Additional features and functions of the prediction manager 132 are provided below, e.g., with respect to FIGS. 22-29”—[(emphasis added)]);
receiving, by the processor set, run-time IT operations data of the computer system (Garapati ¶0064–0066: “Thus, the systems 104, 108 should be understood to represent virtually any IT landscape 103 that may be monitored and managed using the IT landscape manager 102 … Accordingly, a plurality of metrics 118 may be obtained that provide data characterizing operations of the systems 104, 108, including, e.g., characterizations of a performance or other operations of the systems 104, 108, and of individual components 106, 110, thereof”—[(emphasis added)]);
determining, by the processor set, a golden signal classification, a cause-effect classification, and an impact using the run-time IT operations and the machine learning model (Garapati Figs. 1, 2, 14, 16, 20, 28, 29, 36, 37, 40, ¶0050: “Consequently, it is possible to identify root causes of events, analyze their effects, predict future events, and prevent undesired outcomes as a result of the events, even in complicated, dispersed, interconnected systems”; see also Garapati ¶0072: “As referenced above, the administrator or other user may wish to identify, classify, describe, or predict various network occurrences or other events. For example, such events may relate to, or describe different types of optimal or sub-optimal network behavior. For example, network characteristics such as processing speeds, available bandwidth, available memory, or transmission latencies may be evaluated. These and various other characteristics may be related to specific types of network events, such as a crash or a freeze, a memory that reaches capacity, or a resource that becomes inaccessible”—[wherein the BRI of golden signal is any data associated with the IT system (see instant application paragraphs [0002 and 0013])]); and
generating, by the processor set, a resolution recommendation based on the golden signal classification, the cause-effect classification, and the impact (Garapati Fig. 1, “Root Cause Inspector 130”, ¶0058: “FIG. 1 is a block diagram of a system for directed incremental clustering of causally related events. In the example of FIG. 1, an IT landscape manager 102 may be configured to provide the types of causal chain determination, root cause analysis, performance prediction, and remediation actions referenced above, and described in detail, below”—[(emphasis added) wherein]).
Regarding claim 2, Garapati teaches all the limitations of claim 1.
Garapati teaches:
extracting features associated with an event from the historic event data (Garapati Fig. 30, ¶0301: “FIG. 30 illustrates an example flow diagram for overall operations of the remediation generator 134 of FIG. 1. In general, data from source alarms 3002 and target remedial actions 3004 are input into a feature extractor 3006. Features may be ones that describe the source alarms 3002, the target remedial actions 3004, or some relationship between the source alarms 3002 and the target remedial actions 3004. The features extracted by the feature extractor 3006 may be administrator or user-configurable and/or customizable. In the absence of administrator-configured or user-configured features and/or custom features, a set of default features may be extracted”);
determining a feature golden signal classification for each of the extracted features (Garapati ¶0194: “FIG. 15 is a block diagram of a more detailed example implementation of a system for event pair determination using multi-layered small world graphs. In the example of FIG. 15, the event pair selector 138 is configured to receive an event set, such as the event set 137 of FIG. 1, and to output a multi-layered small world graph 1514, which may also be represented as a nearest neighbor graph 1516. As referenced above, and described in detail, below, the multi-layered small world graph 1514 effectively samples or extracts the causal event pairs of FIG. 4 and their connected edges which optimize subsequent operations of the situation identifier 128 of FIG. 1”—[wherein the BRI of feature golden signal is any feature data associated with the IT system (see instant application paragraphs [0002 and 0013]) and wherein the system uses the extracted features to determine the causal event pairs (i.e., feature golden signal classification)]);
determining a feature type golden signal classification based on the feature golden signal classification of groups of the extracted features (Garapati ¶0127: “For example, the cluster tree generator 144 may be configured to generate the cluster tree 144a with each candidate event cluster 144b, 144c, 144d, 144e therein having a corresponding causal score. For example, two (or more) causal event pairs (that is, four or more total events) within the arborescence graph that have the same causal score may be grouped within a single candidate event cluster having that same causal score as its cluster score. Thus, individual candidate event clusters may be identified by their respective cluster scores and by their respective placements within the hierarchy of the cluster tree 144a. For example, two candidate event clusters (e.g., 144c and 144e) may have the same cluster score but may be differentiated by their respective placements within the cluster tree 144a”—[wherein the cluster tree (i.e., feature type golden signal classification) is generated (i.e., determined) by the generator based on the individual feature data and cluster scores (i.e., groups of the extracted features)]); and
determining an event golden signal classification for the event based on the feature type golden signal classification of groups of plural feature types (Garapati ¶0077: “In other examples, the event may be determined from one or more metric values using other techniques. For example, the neural network may be trained to recognize a metric value as being anomalous in specific contexts. In other examples, the event may be determined for a particular metric value when the metric value varies to a certain extent, or in a predefined way, from historical norms for that metric value”—[wherein the BRI of even golden signal is any event data associated with the IT system (see instant application paragraphs [0002 and 0013]), and wherein the system uses the trained neural network to determine the metric value (i.e., an event golden signal) based on the value, and the historical norms (i.e., type and features)]).
Regarding claim 3, Garapati teaches all the limitations of claim 2.
Garapati teaches:
wherein the creating the training dataset comprises: determining an event cause-effect classification for the event based on the event golden signal classification and a knowledge graph (Garapati ¶0126: “A cluster tree generator 144 may then be configured to convert the arborescence graph into a cluster tree 144a. As described below, e.g., with respect to FIGS. 6-8 and 14, the cluster tree 144a refers to a modification or enhancement of the arborescence graph in which potential or candidate event clusters 144b, 144c, 144d, 144e are identified and characterized for further evaluation”—[wherein generator creates (i.e., determines) the cluster tree (i.e., cause-effect classification) based on the identification and characterization of the event clusters and the arborescence graph (i.e., a knowledge graph)])).
Regarding claim 6, Garapati teaches all the limitations of claim 1.
Garapati teaches:
receiving feedback in response to the resolution recommendation; and retraining the machine learning model based on the feedback (Garapati Fig. 34, ¶0120: “Similarly, the knowledge graph 126 may include custom knowledge priors collected over time from administrators or users such as customers. For example, such knowledge may be obtained in the form of customer feedback, such as may occur after previously resolved situations. Knowledge needed to make accurate edge characterizations for causal event pairs may be obtained directly, through the use of generated questionnaires provided to administrators or users to collect needed information”; see also Garapati ¶0300: “The remediation generator 134 may provide a technical solution that leverages textual, temporal, and topological space as well as custom user attributes to correlate the problems and the remedial actions uniquely. The remediation generator 134 includes a remedial action recommendation (RAR) model that learns from various user interactions along with signals from monitoring automation systems to improve the recommendations in a continuous fashion. These learnings primarily come from two kinds of feedback: implicit feedback and explicit feedback, as discussed in more detail below”—[wherein the feedback is incorporated based on the user supplied feedback to help the model improve based on learning in a continuous fashion (i.e., retraining)].
Regarding claim 7, Garapati teaches all the limitations of claim 1.
Garapati teaches:
determining an alert prioritization and a probable root cause based on the golden signal classification, the cause-effect classification, and the impact (Garapati ¶0076: “For example, a metric may each be associated with a threshold value, and an event may be determined when the threshold value is exceeded (or not reached). For example, a memory being 80% full may cause a notification or alert to be generated, so that a response may be implemented to mitigate or avoid system failures”; see also Garapati Fig. 22, ¶0269: “The event graph 2300 may be generated using the techniques and components described above. In the event graph 2300 at time to (2302), node 2304 may represent the root cause and nodes 2305, 2306, 2307, 2308, 2309, and 2310 may represent computing devices. At time t.sub.1 (2312), a new node 2313 may represent another service that is being impacted. At time t.sub.2 (2314), a new edge 2315 may be added to the event graph 2300 when the techniques described previously determine that node 2306 is causally related to node 2313. At time t.sub.3 (2316), node 2306 is marked as being causally related to node 2313. At time t.sub.4 (2318), edge 2319 is updated to reflect a causal relationship tracing back to node 2304 as a root cause for the node 2313. The event graph 2300 provides data that graphically illustrates an understanding of how different situations and events are causally related to each other. The event graph 2300 represents a spatiotemporal graph that provides a spatiotemporal context for a situation as it changes (or propagates) over time intervals”—[wherein the root cause is determined based on the event information including the alert prioritization (e.g., Memory 80% full) and the classification of how the different situations and events are causally related to each other].
Regarding claim 8, Garapati teaches:
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: (Garapati ¶0007: “According to one general aspect, a computer program product may be tangibly embodied on a non-transitory computer-readable storage medium and may comprise instructions. The instructions, when executed by at least one computing device, may be configured to cause the at least one computing device to determine a plurality of events within a network.”).
Regarding the remaining limitations of claim 8, although varying in scope, the remaining limitations of claim 8 are substantially the same as the limitations of claim 1, respectively. Thus, the remaining limitations of claim 8 are rejected using the same reasoning and analysis as claim 1 above.
Regarding claim 15, Garapati teaches:
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 program instructions executable to: (Garapati ¶0008: “According to other general aspects, a computer-implemented method may perform the instructions of the computer program product. According to other general aspects, a system, such as a mainframe system or a distributed server system, may include at least one memory, including instructions, and at least one processor that is operably coupled to the at least one memory and that is arranged and configured to execute instructions that, when executed, cause the at least one processor to perform the instructions of the computer program product and/or the operations of the computer-implemented method”).
Regarding the remaining limitations of claim 15, although varying in scope, the remaining limitations of claim 15 are substantially the same as the limitations of claim 1, respectively. Thus, the remaining limitations of claim 15 are rejected using the same reasoning and analysis as claim 1 above.
Regarding claims 9–10, 13–14, 16–17, and 20 although varying in scope, the limitations of claims 9–10, 13, 16–17, and 20 are substantially the same as the limitations of claims 2–3 and 6–7, respectively. Thus, claims 9–10, 13, 16–17, and 20 are rejected using the same reasoning and analysis as claims 2–3 and 6–7 above, respectively.
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.
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.
Claims 4–5, 11–12, and 18–19 are rejected under 35 U.S.C. 103 as being unpatentable over Garapati as applied above in the §102 rejections, and further in view of Kumar et al., (US20230132465), hereinafter “Kumar”.
Regarding claim 4, Garapati teaches all the limitations of claim 3.
Garapati teaches:
wherein the creating the training dataset comprises: determining an event impact for the event based on the event golden signal classification [and a knowledge base] (Garapati Fig. 1, ¶0332: “The root cause inspector 130 may provide a technical solution to the technical problems encountered by a failed component that enables the root cause to be determined despite other system failures, events, and alarms, which may mask the root cause. In the described techniques, the root cause inspector 130 constructs a narrative for the administrator or end user, such that the root cause and its impact is determined and explained. The root cause inspector 130 constructs a causal graph that includes multiple causal priors including, for example, historical causal priors, topological causal priors, real-time causal priors, and custom causal priors. The root cause inspector 130 performs probabilistic root cause identification by ranking the graph vertices in their order of impact and importance, reducing the causal chains having multiple causal paths, and retaining the longest impacted path, which identifies the root cause”—[wherein Garapati teaches determining and event impact based on the signal classification and the knowledge graph].
Garapati does not appear to explicitly teach:
[wherein the creating the training dataset comprises: determining an event impact for the event based on the event golden signal classification] and a knowledge base.
However, Kumar teaches:
[wherein the creating the training dataset comprises: determining an event impact for the event based on the event golden signal classification] and a knowledge base (Kumar ¶0025: “This document describes systems and techniques for automated skill discovery, skill level computation, and intelligent matching using generated hierarchical skill paths. The systems and techniques use machine learning (ML) and/or artificial intelligence (AI) techniques to identify a hierarchy of skills from a historical database of artifacts. The automatically generated hierarchy of skills may be laid onto a knowledge graph. In this manner, a taxonomy of skills is autogenerated using ML and/or AI techniques from a database of artifacts”—[(emphasis added)]).
The system of Garapati, the teachings of Kumar, and the instant application are analogous art because they pertain to utilizing machine learning to identify and categorize data and events.
It would be obvious to a person of ordinary skill in the art before the effective filing date of the invention to modify the system of Garapati with the teachings of Kumar to provide for a historical knowledge base for engineering data. One would be motivated to do so to automatically generate a hierarchy of skills onto a knowledge graph. (Kumar ¶0025).
Regarding claim 5, Garapati in view of Kumar teaches all the limitations of claim 4.
Kumar teaches:
wherein the creating the training dataset comprises: linking the event golden signal classification, the event cause-effect classification, and the event impact with a subset of the historic information technology (IT) operations data associated with the event. (Kumar ¶0050: “Referring back to FIG. 1, a taxonomy construction algorithm 110 may be run that takes terms from each of the above static and dynamic skills, and generates embeddings from them in a space that can be latent, and clusters them together to find similar skills that need to be grouped or related to each other. In the example of FIG. 4, Oracle-support-assistance 427 will get linked to Oracle-Dev skill 421 and Oracle-R&D skill 422. The taxonomy construction algorithm 110 can regroup and relate these skills. For each skill identified, the taxonomy construction algorithm 110 identifies the set of tickets and associated agents who resolved the tickets for that skill cluster”—[wherein the system uses the event, and the cause-effect tree (e.g., taxonomy construction) to generate embeddings for similar skills to be grouped (i.e., linked)]).
The same motivation that was utilized for combining Garapati with Kumar, as set forth in claim 4, is equally applicable to claim 5.
Regarding claims 11–12 and 18–19, although varying in scope, the limitations of claims 11–12 and 18–19 are substantially the same as the limitations of claims 4–5, respectively. Thus, claims 11–12 and 18–19 are rejected using the same reasoning and analysis as claims 4–5 above, respectively.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.B.S./Examiner, Art Unit 2126
/DAVID YI/Supervisory Patent Examiner, Art Unit 2126
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