The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 1-21 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-21 are directed to an abstract idea.
Regarding claim 1 and analogous claim 14:
Per step 1 of the Subject Matter Eligibility Test for Products and Processes, “is the claim to a process, machine, manufacture, or composition of matter?”, claim 1 is directed to an article of manufacture and claim 14 is directed to a process.
Step 1: yes.
Per step 2A prong 1, “Does the claim recite an abstract idea, law of nature, or natural phenomenon?”,
and process […] , the situation event graph and the corresponding scenario to determine a causal impact of the situation
is directed to an abstract idea (i.e., a mental process, e.g. evaluation.)
Step 2A prong 1: yes
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?", the following elements of claim 1 are directed to additional elements:
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (well-understood, routine, and conventional generic computer, see MPEP 2106.05(f))
input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation; (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g))
by the neural network (merely using a computer as a tool to perform an abstract idea. The specification of the instant application describes in paragraph [0077] and [0078] existing methods for evaluating causal impacts which “may be burdensome, impractical, or even impossible for manual manipulation”. However, MPEP 2106.5(f) states that “’claiming the improved speed or efficiency inherent with applying the abstract idea on a computer’ does not integrate a judicial exception into a practical application or provide an inventive concept”)
Step 2A prong 2: no
Per step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception, the claims do not include additional elements that are sufficient to amount to more than the judicial exception. Specifically, the claimed inventions simply append well-understood, routine, and conventional activities previously known to the industry (see analysis under prong 2), both when viewed independently and as an ordered combination, 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).
Step 2B: no.
Regarding claim 2 and analogous claim 15:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?", claim 2 is directed to an abstract idea (i.e. mental process, e.g. evaluation; see analysis for claim 1)
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?", the following elements of claim 2 are directed to additional elements:
wherein the situation event graph and corresponding scenario include topology data and knowledge graph data (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g)).
Step 2A prong 2: no.
Claim 2 does not add significantly more (step 2B).
Regarding claim 3 and analogous claim 16:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?" claim 3 is directed to an abstract idea (i.e. mental process, e.g. evaluation; see analysis for claim 2)
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?", the following elements of claim 3 are directed to additional elements:
wherein the causal impact of the situation includes a node representing an event, and a time indicator representing timing related to the event (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g)).
Step 2A prong 2: no.
Claim 3 does not add significantly more (step 2B).
Regarding claim 4 and analogous claim 17:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?" claim 4 is directed to an abstract idea (i.e. mental process, e.g. evaluation; see analysis for claim 2)
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?", the following elements of claim 4 are directed to additional elements:
wherein the instructions are further configured to cause the at least one computing device to: generate and output a visualization to a user interface, the visualization indicating the event and a predicted incident priority (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g)).
Step 2A prong 2: no
Claim 4 does not add significantly more (step 2B).
Regarding claim 5 and analogous claim 18:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
wherein the instructions are further configured to cause the at least one computing device to: order the event in relation to other events using the predicted incident priority.
Claim 5 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
Claim 5 does not add any additional elements (step 2A prong 2) or significantly more (step 2B).
Regarding claim 6 and analogous claim 19:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
and output and reorder the visualization to include the new event and a new predicted incident priority.
claim 6 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?"
input a new situation event graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation; (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g))
process, by the neural network model, the new situation event graph and the corresponding new scenario to determine a new causal impact of the new situation, the new causal impact including a new node representing a new event; (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g))
Step 2A prong 2: no
Claim 6 does not add significantly more (step 2B).
Regarding claim 7 and analogous claim 20:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?",
wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate.
Claim 7 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes
Claim 7 does not add any additional elements (step 2A prong 2) or significantly more (step 2B).
Regarding claim 8 and analogous claim 21:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
Claim 8 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?"
wherein the neural network model comprises a graph neural network (GNN) model (Using a graph neural network to process graph data is well-understood, routine, and generic activity, see MPEP 2106.05(d). Khemani et al. (Khemani B, Patil S, Kotecha K, Tanwar S. A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions. Journal of Big Data. 2024 Jan 16;11(1):18) states that “Neural graph networks are being used by practically all researchers in fields such as NLP, computer vision, and healthcare” (pg.3) and that “in 2010, GNNs were widely used in many applications” (pg. 5). Although root cause analysis is not explicitly listed here, the technology is well-understood, routine, and generic in the context of processing graph data using neural networks).
Step 2A prong 2: yes.
Claim 8 does not add significantly more (step 2B).
Regarding claim 9:
Per step 1 of the Subject Matter Eligibility Test for Products and Processes, “is the claim to a process, machine, manufacture, or composition of matter?”, claim 9 is directed to an article of matter.
Step 1: yes.
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
compare the predicted causal impact to an actual causal impact for the situation event graph to determine a loss;
Claim 9 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?"
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: (well-understood, routine, and conventional generic computer, see MPEP 2106.05(f))
input a situation event graph, topology data associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model; (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g)).
generate a predicted causal impact for the situation event graph using the neural network model; (insignificant extra-solution activity of mere data output, see MPEP 2106.05(g))
and input the loss as feedback to the neural network model to improve the neural network model. (insignificant extra-solution activity of mere data gathering, see MPEP 2106.05(g)).
Step 2A prong 2: no.
Per step 2B, does the claim recite additional elements that amount to significantly more than the judicial exception, the claims do not include additional elements that are sufficient to amount to more than the judicial exception. Specifically, the claimed inventions simply append well-understood, routine, and conventional activities previously known to the industry (see analysis under prong 2), both when viewed independently and as an ordered combination, 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).
Step 2B: no.
Regarding claim 10:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
Claim 10 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
wherein the instructions are further configured to cause the at least one computing device to: determine the actual causal impact for the situation event graph using chronological order of events and topology data changes related to the chronological order of events.
Claim 10 is directed to an abstract idea (I.e. mental process, e.g. evaluation).
Step 2A prong 1: yes.
Claim 10 does not add any additional elements (step 2A prong 2) or significantly more (step 2B).
Regarding claim 11:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
wherein the predicted causal impact includes a predicted event and a predicted incident priority
Claim 11 is directed to an abstract idea (i.e. mental process, e.g. evaluation)
Step 2A prong 1: yes
Claim 11 does not add any additional elements (step 2A prong 2) or significantly more (step 2B).
Regarding claim 12:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
wherein the actual causal impact includes an actual event and an actual predicted incident priority.
Claim 12 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes
Claim 12 does not add any additional elements (step 2A prong 2) or significantly more (step 2B).
Regarding claim 13:
Per step 2A prong 1, "Does the claim recite an abstract idea, law of nature, or natural phenomenon?"
Claim 13 is directed to an abstract idea (i.e. mental process, e.g. evaluation).
Step 2A prong 1: yes
Per step 2A prong 2, "Does the claim recite additional elements that integrate the judicial exemption into a practical application?"
wherein the neural network model includes a graph neural network model.
Step 2A prong 2: no
Claim 13 does not add significantly more (step 2B).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claims 1, 3, 7, 8, 14, 16, 20, and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gan et al. (Gan Y, Liu G, Zhang X, Zhou Q, Wu J, Jiang J. Sleuth: A trace-based root cause analysis system for large-scale microservices with graph neural networks. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4 2023 Mar 25 (pp. 324-337), hereafter referred to as Gan).
Regarding claim 1 and analogous claim 14:
Gan teaches
A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium (On Gan page 7, ”The GNN model of Sleuth is implemented with PyTorch Geometric [a computer program product]. The models are stored in a centralized object database and they can be pulled and updated by model training and inference workers. A centralized model server maintains the life cycle of the GNN model, including model creation, storage, update, inheritance and retirement [the computer program product being tangibly embodied on a computer readable medium].” Gan further contains data collected from an implemented version of the computer program product and information on hardware details; see Table 2, Table 3, and Figures 5-8.)
Gan teaches
and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to: input a situation event graph and a corresponding scenario into a neural network model, the neural network model including a plurality of scenarios, wherein the situation event graph represents a situation and the corresponding scenario represents a plurality of situations similar to the situation;
(On Gan page 5, “To address the limitations of tree-based distance metrics, we propose a new metric based on the Jaccard-index similarity. This metric converts the similarity of two trees into a similarity measure between two weighted sets of spans, with a time complexity of O(m) to computer distances […] We used the HDBSCAN algorithm with the distance defined above to cluster traces. The algorithm is a density-based clustering algorithm that does not require the number of clusters to be known a priori [the clustering algorithm may produce more than one cluster, or a plurality of scenarios]. The hyperparameters of the algorithm, namely, min_cluster_size, min_samples and cluster_selection_epsilon, are initialized with 10,5, and 1, respectively. These hyperparameters are then adjusted according to the number and variation of the traces to be clustered. After clustering, we chose the trace with the minimum sum of distances to all other traces within the cluster (i.e. geometric median of that cluster) as the representatives of this cluster. If the algorithm can accurately classify traces with different root causes into different clusters [the corresponding scenario represents a plurality of situations similar to the situation], then the root causes of the representative traces can be derived across the entire cluster [a corresponding scenario]”.
on Gan page 6, “In Sleuth, the CBN [causal Bayesian network] of a trace is defined as a DAG [directed acyclic graph] G = (V, E, X) where V is a list of nodes (i.e. spans), E is the edge list, and X ϵ ℝ |V|×d is a matrix of node attributes (i.e. span attributes). [a situation event graph; the specification of the instant application describes in [00387] that “A situation event graph is a causal event graph of all events that are causally related, starting from the root cause and leading up to symptomatic events.”] Because of the Markov property of the causal DAG, Sleuth only needs one DNN layer [input into a neural network model] to model the causality from child spans to the parent span.”)
Processing, by the neural network model, the situation event graph and the corresponding scenario to determine a causal impact of the situation. (On Gan page 6, “Similar to Sage, Sleuth uses counterfactual queries to locate the root instance [determining a causal impact] of anomaly traces.”
Regarding claim 3 and analogous claim 16:
Gan teaches the computer program product of claim 1, wherein the causal impact of the situation includes a node representing an event and a time indicator representing timing related to the event (On Gan page 3, “The RCA system uses GNN to predict the duration [a time indicator representing timing related to the event] and error status of the trace and determine the instances in the trace that result in anomalies [an event]”).
Regarding claim 7 and analogous claim 20:
Gan teaches the computer program product of claim 1, wherein the situation event graph and the corresponding scenario are grouped as similar based on a similarity estimate (On Gan page 5, “To address the limitations of tree-based distance metrics, we propose a new metric based on the Jaccard-index similarity. This metric converts the similarity of two trees into a similarity measure between two weighted sets of spans, with a time complexity of O(m) to computer distances [based on a similarity estimate] […] We used the HDBSCAN algorithm with the distance defined above to cluster traces [wherein the situation event graph and the corresponding scenario are grouped as similar].”)
Regarding claim 8 and analogous claim 21:
Gan teaches the computer program product of claim 1, wherein the neural network model comprises a graph neural network (GNN) model. (On Gan page 3, “The RCA system uses GNN to predict the duration and error status of the trace and determine the instances in the trace thar result in anomalies).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 4-6, and 17-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gan, further in view of Garapati et al. (Garapati SE, Giral E, Antonijević S. Rethinking Monitoring for Cloud Environments: BMC Software AIOps Case Study. In European Conference on Service-Oriented and Cloud Computing 2022 Mar 22 (pp. 109-115). Cham: Springer Nature Switzerland. Hereafter referred to as Garapati.)
Regarding claim 4 and analogous claim 17:
Gan teaches the computer program product of claim 3, and
Gan does not explicitly teach
Wherein the instructions are further configured to cause the at least one computing device to:
Generate and output a visualization to a user interface, the visualization indicating the event and a predicted incident priority.
Garapati does teach
Wherein the instructions are further configured to cause the at least one computing device to:
Generate and output a visualization to a user interface, the visualization indicating the event and a predicted incident priority. (On page 7 of Garapati, “Each of these Situations is presented in the user interface (UI) [user interface] as shown below (Fig. 4). When users investigate each Situation, they see all the symptomatic events [the visualization indicating the event] and probable root causes grouped together in the sorting order of a RCA score [a predicted event priority], which signifies the probability of that event being the root cause candidate.”)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Garapati to Gan in order to improve user experience by clearly displaying information (On page 7 of Garapati, “Probable Cause Analysis was very well received and evaluated as a ‘major saver’”).
Regarding claim 5 and analogous claim 18:
Gan and Garapati teach the computer program product of claim 4.
Garapati further teaches
Wherein the instructions are further configured to cause the at least one computing device to:
Order the event in relation to other events using the predicted event priority (On page 7 of Garapati, “When users investigate each Situation, they see all the symptomatic events and probable root causes grouped together in the sorting order of a RCA score [order the event in relation to other events using the predicted event priority].”
Gan and Garapati are combinable for the same rationale as set forth above with respect to claim 4.
Regarding claim 6 and analogous claim 19:
Gan and Garapati teach the computer program product of claim 5.
Gan further teaches
Wherein the instructions are further configured to cause at least one computing device to:
Input a new situation graph and a corresponding new scenario into the neural network model, wherein the new situation event graph represents a new situation;
Process, by the neural network model, the new situation event graph and the corresponding new scenario to determine a new causal impact of the new situation, the new casual impact including a new node representing a new event; and (These are performed on new data, but are the same steps described in claim 3 and are anticipated by Gan in the same ways.)
Garapati further teaches
Output and reorder the visualization to include the new event and a new predicted priority (These are performed on new data, but are the same steps described in claim 5 and are anticipated by Lu in the same ways.)
Gan and Garapati are combinable for the same rationale as set forth above with respect to claim 4.
Claims 2, 9, 13, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Gan further in view of Li and Liu (Li Y, Liu W. Sudden event prediction based on event knowledge graph. Applied Sciences. 2022 Nov 4;12(21):11195. Hereafter referred to as Li).
Regarding claim 2 and analogous claim 15
Gan teaches the computer program product of claim 1
Gan fails to teach wherein the situation event graph and corresponding scenario include topology data and knowledge graph data.
Li teaches wherein the situation event graph and corresponding scenario include topology data and knowledge graph data. (On page 7 of Li, “The event knowledge graph is an event-based knowledge base built for different application domains, containing an event ontology and a large number of event instances. […] The event ontology includes: (1) class concepts, [topology data] (2) event class relations, and (3) assertion and inference rules for the event classes.” On page 8 of Li, “guided by the scenario model [corresponding scenario] we annotated event instances and their relations [situation event graph] on a corpus of more than one hundred news items to obtain a knowledge graph of events in the transportation domain [knowledge graph data], which contained 510 events and 500 event relation pairs. […] The central nodes of all event instances were connected into a subgraph according to the relations in the event knowledge graph. We used the generated subgraphs as the inputs to the graph neural network.”)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Li to Gan in order to allow machine learning models to learn patterns from past events (On page 2 of Li, “The logic of subsequent events occurring is then captured by extracting the features of the initial event and its concurrent events. When a new event occurs, the machine quickly makes a reliable prediction based on past events with similar features.”)
Regarding claim 9:
Gan in view of Li teaches
a computer program product, the computer program product being tangibly embodies on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least computing device to:
Input a situation event graph, topology associated with the situation event graph, and a knowledge graph associated with the situation event graph into a neural network model;
Generate a predicted causal impact for the situation event graph using the neural network model.
The above limitations of claim 9 are shared with claim 2 and rejected under the same rationale.
Li further teaches
Compare the predicted causal impact to an actual causal impact for the situation event graph to determine a loss; and input the loss as feedback to the neural network model.
(On page 10 of Li, “we formulated the training task as a multilabel classification task, with the event class to which all tail events belonged as the true labels G [to an actual causal impact] for comparison with our prediction results P [compare the predicted causal impact]. The model was optimized using MultiLabelSoftMarginloss as the loss function to reduce the prediction of mislabeling [input the loss as feedback to the neural network model]”)
Gan and Li are combinable for the same rationale set forth above with respect to claim 2.
Regarding claim 13:
Gan in view of Li teaches
The computer program product of claim 9.
Gan further teaches
wherein the neural network model includes a graph neural network model (On Gan page 3, “The RCA system uses GNN to predict the duration and error status of the trace and determine the instances in the trace thar result in anomalies.”)
Gan and Li are combinable for the same rationale set forth above with respect to claim 2.
Claims 10, 11, and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Gan in view of Li and Garapati.
Regarding claim 10:
Gan in view of Li teaches
The computer program product of claim 9.
Gan in view of Li fails to teach
Wherein the instructions are further configured to cause the at least one computing device to:
Determine the actual causal impact for the situation event graph using a chronological order of events and topology data changes related to the chronological order of events
Garapati teaches
Wherein the instructions are further configured to cause the at least one computing device to:
Determine the actual causal impact for the situation event graph using a chronological order of events and topology data changes related to the chronological order of events (on pages 4, 5, and 6 of Garapati, “the MBC team built a novel clustering algorithm that leverages observed topologies to reason about causal distances of event pairs by relying on observed and ontological distances of the sources of those events to cluster causally linked events together. […] A situation/cluster comprises events associated with the same or different host that are aggregated based on their occurrence [a chronological order of events], message, topology or a combination of those factors [topology data changes related to the chronological order of events] […] If we can identify the noise into a group and identify the root cause [determine the actual causal impact for the situation event graph], we consider it a success, as mentioned earlier. […] finding the root cause is essentially a network centrality check that maximizes causality, and we use a trivial derivative of the PageRank algorithm to look up the root cause”)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Garapati to Gan and Li in order to improve the training of the neural network by determining causal impact in a way which makes intuitive sense to users and researchers (On page 6 of Garapati, “we will be able to identify directed clusters at different causal distances according to underlying event distribution in a much more intuitive and natural way”).
Regarding claim 11:
Gan in view of Li teaches
The computer program product of claim 9
Gan in view of Li fails to teach
wherein the predicted causal impact includes a predicted event and a predicted incident priority
Garapati teaches
wherein the predicted causal impact includes a predicted event and a predicted incident priority (On page 7 of Garapati, “When users investigate each Situation, they see all the symptomatic events and probable root causes [wherein the predicted causal impact includes a predicted event] grouped together in the sorting order of a RCA score [a predicted incident priority], which signifies the probability of that event being the root cause candidate.”)
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Garapati to Gan and Li in order to improve user experience by clearly organizing information on which of several probable root causes to prioritize (On page 7 of Garapati, “Probable Cause Analysis was very well received and evaluated as a ‘major saver’”).
Regarding claim 12:
Gan in view of Li teaches
The computer program product of claim 9
Gan in view of Li fails to teach
wherein the actual causal impact includes an actual event and an actual predicted incident priority.
Garapati teaches
Wherein the actual causal impact includes an actual event and an actual predicted incident priority
It would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Garapati to Gan and Li in order to improve the training of the neural network by including information on which of several probable root causes to prioritize (On page 7 of Garapati, “Probable Cause Analysis was very well received and evaluated as a ‘major saver’”).
Conclusion
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/AT/Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
Prosecution Insights