DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is in response to the communication filed on 02/28/2023. Claims 1-20 are pending in this application.
Priority
This application claims are effectively filed 02/28/2023. The assignee of record is International Business Machines Corporation. The listed inventor(s) is/are: Saha, Avirup; Gantayat, Neelamadhav; Sindhgatta Rajan, Renuka; Arunachalam, Ravi Shankar; George, Geomy; Dechu, Sampath.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 02/28/2023 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) is/are being considered by the examiner.
Claim Objections
Claims 1, 7, 11, 16 and 20 are objected to because of the following informalities:
Claims 1, 11 and 20 recite the limitation “IT” in line 7, line 11 and line 13 respectively with an acronym without properly defined when it is first introduced. For examination purpose, “IT” will read as "information technology (IT)."
Claims 7 and 16 recite the limitation “the one or more impact analyzes” in line 1. For examination purpose, “the one or more impact analyzes” will read as “the one or more impact analyzers.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites the limitation "the input data" in line 4 and then recites the limitation “input data” in line 12. There is insufficient antecedent basis for this limitation in the claim. For examination purpose, “the input data” recited in line 4 will read as “input data,” and “input data” recited in line 12 will read as “the input data.”
Claim 4 recites the limitation “key entities” in line 6. It is unclear to the examiner if the limitation refers to the limitation “key entities” recited in line 2. For examination purpose, “key entities” recited in line 6 will read as “the key entities.”
Claim 4 recites the limitation “service APIs” in line 7. It is unclear to the examiner if the limitation refers to the limitation “service APIs” recited in line 5. For examination purpose, “service APIs” recited in line 7 will read as “the service APIs.”
Claim 11 recites the limitation "the input data" in line 8 and then recites the limitation “input data” in line 16. There is insufficient antecedent basis for this limitation in the claim. For examination purpose, “the input data” recited in line 8 will read as “input data,” and “input data” recited in line 16 will read as “the input data.”
Claim 20 recites the limitation "the input data" in line 9 and then recites the limitation “input data” in line 17. There is insufficient antecedent basis for this limitation in the claim. For examination purpose, “the input data” recited in line 9 will read as “input data,” and “input data” recited in line 17 will read as “the input data.”
The dependent claims of the above rejected claims are rejected due to their dependencies.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1, 5-6, 8, 11, 15, 17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11366842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur).
For Claim 1, Swaminathan teaches a method (Swaminathan, col. 62, ll. 50-55 “… Such an advanced monitoring system, particularly one for monitoring the services operationally delivered by an information technology (IT) environment, may implement an embodiment of KPI prediction and/or impactor determination …”), comprising:
executing machine learning training of one or more machine learning (ML) computer models ( Swaminathan, FIG. 20 exemplifies prediction models) based on historical data representing logged events and key performance indicators (KPIs) of organizational processes (Swaminathan, FIG. 20 block 2050 exemplifies receiving historical KPI and health score data to start the prediction process), wherein the one or more ML computer models are trained to forecast a KPI impact given events in input data (Swaminathan, col. 66, ll. 39-59 “… FIG. 20 depicts a system processing flow diagram for KPI prediction and impactor identification … The depicted example embodiment determines future KPI values by processing current or recent data in accordance with a prediction model … The processing depicted by FIG. 20 includes processing to generate feature data for the training, testing, and use of the models, processing to create the set of models used in the embodiment, processing to test those models, and processing to apply those models to produce useful predictive values. The processing depicted by FIG. 20 further includes processing to utilize those predictive values to identify KPIs …”; col. 66, l. 60 – col. 72, l. 2 teaches details of prediction models training and predicting KPI impact processes, especially “… At block 2012, trained models are produced for each KPI involved …” and “… Processing flow 2000 of FIG. 20 as depicted indicates that the predicted KPI values 2052 are an input to the processing of block 2022 that identifies impactors. Similarly, a representation of identified impactor KPIs in computer storage as indicated by block 2054 is an output of the processing of block 2022 …”; Please see Swaminathan FIG. 20 screen shot below, thank you:
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generating at least one correlation … data structure that maps at least one of events to IT computing resources, or KPI impacts to organizational processes (Swaminathan teaches mapping a KPI impact score to a service health score using a correlation data structure; FIG. 23, col. 78, ll. 49-63, “… At block 2330, a KPI impact score (KPIimpact) is determined for the KPI currently being processed. In an embodiment, the KPI impact score represents some measure indicating the prospective influence the KPI, and automated or other work expended to change it, may have on some reference health score or similar service measure, such as a predicted future service health score …”; col. 79, l. 57 – col. 80, l. 14, “… At the completion of example processing flow 2300 of FIG. 23 a set of impactor KPIs is identifiable using the output 2054b of processing block 2332. Many data structures, formats, representations, and such, may be used to reflect the output of the Determine Impactor(s) block 2332 …”);
generating a unified model of organizational processes and IT resources, wherein the unified model executes to predict affected IT resources given a KPI impact to an organizational process (Swaminathan FIG. 23 exemplifies calculating KPI impact scores associated with a service health score using regression models, FIG. 24 and FIG. 25 exemplifies predicting the KPI impact scores associating with IT resources such as database services, memory, storage and CPU; col. 77, l. 18 – col. 78, l. 9 “… processing of block 2310 fits a regression model to predict service health score, for example, to historic service health score and KPI data 1932 with the KPI data as the model input features. The resulting model, in one embodiment, includes a positive or negative coefficient for each KPI feature. The sign of that coefficient, positive or negative, represents the directionality of the KPI. In one embodiment, the processing of block 2310 may extract sign data from the fitted regression model 2040 and reflect it in computer storage in association with other KPI-specific values as shown at 2042 …”);
processing, by the one or more trained ML computer models and the unified model, the input data to generate a forecast output, wherein the forecast output specifies at least one of a forecasted IT event or a forecasted KPI impact (Swaminathan FIG. 20 and FIG. 23 exemplify generating predicted KPI impact scores using trained ML models and regression models; col. 79, l. 57 – col. 80, l. 14, “… At the completion of example processing flow 2300 of FIG. 23 a set of impactor KPIs is identifiable using the output 2054b of processing block 2332. Many data structures, formats, representations, and such, may be used to reflect the output of the Determine Impactor(s) block 2332 … the impactor list represents a triaged or prioritized version or subset of the greater KPI list, which impactor list identifies the KPIs to which corrective attention by automated, human, or hybrid means, have the relative highest likelihoods by some measure of preventing, postponing, diminishing, or otherwise reducing, an unfavorable future service performance state or condition as measured by a service health score in one embodiment. Each KPI on such an impactor list is identified as an impactor or impactor-KPI …”);
correlating the forecasted output with at least one of an IT computing resource or an organizational process, at least by applying the at least one correlation … data structure to the forecast output to generate a correlation output (Swaminathan FIG. 20 and FIG. 23 exemplify the correlation of the predicted KPI impact scores with a service health score, FIG. 24 and FIG. 25 exemplify the correlation of the predicted KP impact scores with IT computing resources such as database services, memory, storage and CPU; FIG. 23, col. 79, l. 57 – col. 80, l. 14, “… At the completion of example processing flow 2300 of FIG. 23 a set of impactor KPIs is identifiable using the output 2054b of processing block 2332. Many data structures, formats, representations, and such, may be used to reflect the output of the Determine Impactor(s) block 2332 …”; FIG. 24, col. 80, l. 44 – col. 81, l. 12 “… For example, such an embodiment may have determined the KPI names/identifiers to incorporate into the content of 2420, such as 2424, by referencing such stored names/identifiers directly from the impactor list or by indirectly referencing such stored names/identifiers via the impactor list … such an embodiment may have determined the content of the star value indicator bands, such as 2426, based at least in part on the impact score of the impactor list, perhaps in view of a set of one or more thresholds used to map impactor scores to star ratings …”); and
generating a remedial action recommendation based on the forecast output and correlation output, wherein the remedial action recommendation has an associated resource allocation (Swaminathan, col. 62, ll. 21-49 “… Implementations of data aggregations of key performance indicators (KPI's) and Health Score values for the monitored services of an IT environment may be useful to automated processes that adjust the size of an elastic execution environment or that generate information presentations of current and historic states and trends … Aspects of the service monitoring system operation that generate and utilize such transformations may include … remedial aspects that may themselves include investigative aspects that automatically identify, gather, collect, concentrate, transform, visualize, present, or otherwise process information for investigative processes and remediation aspects that automatically invoke system actions or workflow steps to remedy, correct, improve, ameliorate, or otherwise adjust some suboptimal condition …”).
Swaminathan does not explicitly teach generating at least one correlation graph data structure; and applying the at least one correlation graph data structure.
but Hampapur teaches generating at least one correlation graph data structure (Hampapur, FIG. 9, ¶ 0081 “… At step 906, the processed data is analyzed by the multiple engines. At step 908, multiple value graphs is generated based on the analysis. The value graph may include a measure of the key performance indicator associated with multiple tasks and the processes …”); and
applying the at least one correlation graph data structure (Hampapur, FIG. 3, FIG. 7; ¶ 0059 “… the big data integration engine 308 in cooperation with the machine learning framework 306, the predictive analysis and modeling engine 304A, the analytics and visualization engine 304E, and the trend and anomaly analysis engine 304L may execute operations to determine and perform demand analysis and forecasting …”; ¶ 0077 “… The integrated system 704 is the same one as the integrated system 304, as shown and described in FIG. 3 …”; ¶ 0078 “… In an embodiment, the engines and/or models in the integrated system 706 may execute operations to identify opportunities that may be associated with the processes, tasks, and/or services that may be optimized and drive value for the hospital ecosystem. …”; Please see Hampapur screen shot of FIG. 7 below, thank you:
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Swaminathan and Hampapur are analogous art because they are both related to analyzing input data using models and generating KPI impact factors to measure service performance in the organizations.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the value graph techniques of Hampapur with the system of Swaminathan to “aid, or support decision making to implement or modify processes in the enterprise thereby optimizing metrics or values for the enterprise (Hampapur ¶ 0002).”
For Claim 5, Swaminathan teaches the method of claim 1, wherein generating a remedial action recommendation comprises performing a lookup operation in a site reliability engineering database of remediation actions corresponding to at least one of the one or more IT computing resources or one or more organizational processes (Swaminathan, FIG. 23, col. 79, ll. 21-56 “… the same sorted table (2054a) may be used with additional external information such as a correctability score for each KPI that indicates some measure of the speed and/or ease with which successful anticipatory remediation has been accomplished in the past in relation to that KPI …”).
For Claim 6, Swaminathan-Hampapur teaches the method of claim 1, wherein generating a remedial action recommendation based on the forecast output and correlation output comprises:
executing one or more impact analyzers to predict a number of resources to allocate to one of IT systems or organizational process operations based on one or more machine learning computer model (Hampapur, FIG. 1, FIG. 3, ¶ 0049 “… The optimization engine 304J may work in cooperation with the automation engine 304H, and the machine learning engine 304G, to execute operations to determine various metrics and identify opportunities that may be optimized. For example, when the enterprise 102 is associated with a retail industry related to fast moving consumer goods (FMCGs), the optimization engine 304J may be configured to determine opportunities that may be related to an inventory availability, a number of reorders of specific goods, etc., that may be optimized …”); and
executing, by an orchestrator computing tool, an allocation of the predicted number of resources to one of the IT systems or organizational process operations based on the prediction (Hampapur, FIG. 1, FIG. 3, ¶ 0049 “… The optimization engine 304J may work in cooperation with the optimal resource allocation engine 304T to determine an optimal allocation of the resources for executing the specific operations or functions …”). See motivation to combine for claim 1.
For Claim 8, Swaminathan-Hampapur teaches the method of claim 6, wherein the one or more impact analyzers comprises a non-human organizational resource allocation impact analyzer that predicts a quantum of organizational resources that can be deallocated (Hampapur, ¶ 0049 discloses an optimal allocation of the resources) without worsening a severity of the predicted KPI impact based on an excepted cumulative load on a resource allocation operation (Hampapur, FIG. 1, FIG. 3, ¶ 0049 “… The optimization engine 304J may work in cooperation with the optimal resource allocation engine 304T to determine an optimal allocation of the resources for executing the specific operations or functions …”). See motivation to combine for claim 1.
For Claim 11, the claim is substantially similar to claim 1 and therefore is rejected for the same reasoning set forth above. Additionally, Swaminathan-Hampapur teaches a computer program product, comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed by a data processing system, causes the data processing system to (Swaminathan, col. 83, ll. 20-30 “… Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a computer readable medium for execution by, or to control the operation of, data processing apparatus …”).
For Claim 15, the claim is substantially similar to claim 6 and therefore is rejected for the same reasoning set forth above.
For Claim 17, the claim is substantially similar to claim 8 and therefore is rejected for the same reasoning set forth above.
For Claim 20, the claim is substantially similar to claim 1 and therefore is rejected for the same reasoning set forth above. Additionally, Swaminathan-Hampapur teaches an apparatus comprising: at least one processor; and at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to (Swaminathan, FIG. 9, col. 83, ll. 44-67 “… The term ‘data processing apparatus’ encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers … A computer program (also known as a program, software, software application, script, or code) typically includes one or more instructions set at various times in various memory devices of a computing device, which, when read and executed by at least one processor (e.g., processing device(s) 9152), will cause a computing device to execute functions involving the disclosed techniques …”).
Claim Rejections - 35 USC § 103
Claims 2-3 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11,366,842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur), and in further view of Bobak et al. (US 20090171731 A1, published 07/02/2009; hereinafter Bobak).
For Claim 2, Swaminathan-Hampapur teaches the method of claim 1, wherein the at least one correlation graph data structure comprises an organizational process (OP) correlation graph data structure that correlates different types of OP operations with corresponding KPIs (Hampapur, FIG. 9, ¶ 0081 “… At step 906, the processed data is analyzed by the multiple engines. At step 908, multiple value graphs is generated based on the analysis. The value graph may include a measure of the key performance indicator associated with multiple tasks and the processes …”) and, …
Swaminathan-Hampapur does not explicitly teach, but Bobak teaches an IT correlation graph data structure that correlates an IT topology with corresponding IT events (Bobak, FIG. 5B, ¶ 0086 “… Ability to configure customized scopes of recovery, based on topologies of resources and their relationships, called Recovery Segments (RSs) …”; ¶ 0166 “… The topology is reflected by a RS, as shown in the screen display of FIG. 5B. In FIG. 5B, a Recovery Segment 550 is depicted, along with a list of one or more topology resources 552 of the RS …”; ¶ 0307 “… Definition of a RS may use a representation of resources in a topology graph as described herein …”).
Bobak and Swaminathan-Hampapur are analogous art because they are both related to business/organization event recovery/remediation analysis corresponding to IT infrastructure.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the graph techniques of Bobak with the system of Swaminathan-Hampapur to achieve predictable recovery objective prior to time of failure for business resiliency (Bobak ¶ 0006).
For Claim 3, Swaminathan-Hampapur-Bobak teaches the method of claim 2, wherein correlating the forecasted output with at least one of an IT computing resource or an organizational process, comprises at least one of: identifying, in the OP correlation graph data structure, at least one OP operation affected by the forecasted KPI impact (Hampapur, ¶ 0021 “… the KPIs may be associated with multiple processes, services, tasks, etc., that are delivered by the enterprise. The value graphs may include multiple connections and a corresponding weight for each connection. The multiple connections may represent multiple links between factors and corresponding parameters influencing the measure of KPIs. Based on the generated value graphs, the integrated system may be configured to determine multiple tasks and opportunities that may be optimized …”); or identifying, in the IT correlation graph data structure, at least one IT topology component correlated with the forecasted IT event.
For Claim 12, the claim is substantially similar to claim 2 and therefore is rejected for the same reasoning set forth above.
For Claim 13, the claim is substantially similar to claim 3 and therefore is rejected for the same reasoning set forth above.
Claim Rejections - 35 USC § 103
Claims 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11,366,842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur), in view of Dang et al. (US 20140208296 A1, filed 07/24/2014; hereinafter Dang), in view of Swope et al. (US 20200301674 A1, published 0924/2020; hereinafter Swope), in view of Rajagopalan et al. (US 20180314624 A1, published 11/01/2018; hereinafter Rajagopalan), and in further view of Wang et al. (US 20230385175 A1, priority dated 03/23/2021; hereinafter Wang).
For Claim 4, Swaminathan-Hampapur teaches the method of claim 1 and processes performed by an information technology (IT) system.
Swaminathan-Hampapur does not explicitly teach, but Dang teaches wherein generating the unified model comprises:
determining key entities (Dang teaches the object type and the index for each API method call) of a plurality of steps of a process … (Dang teaches determining object types and indexes for API method call sequences during mining API usage patterns; FIG. 1, FIG. 2; ¶ 0023 “… FIG. 2 illustrates an example scheme 200 for mining API usage patterns. In the scheme 200, the code 124 (e.g., a C# file) may be parsed to identify and/or collect a set of calls to API methods. In some embodiments, the parser 112 may parse the code 124 to identify API method calls and match the object type to each API method call. In these instances, the parser may first identify instances of calling the API methods …”; ¶ 0024 “… the parser 112 creates the index for individual API methods. In these instances, an API method may be indexed to include a complete file path, class, method attributes (e.g., return value, method name, parameter list, startLine, endLine, and so on), API name, and line number of the API …”; ¶ 0026 “… After identifying API methods and/or indexing API methods, the parser 112 may collect the set of API method calls and then generate corresponding API method call sequences 202 …”);
grouping a plurality of application program interface (API) calls based on payload and temporal proximities of the API calls, and for corresponding service APIs, extracting the key entities (Dang teaches clustering the API method call sequences based on similarities, and identifying the object types for the API method calls; Examiner notes that the “grouping” step and “determining key entities” step are merely recited here without further relating to other limitations including “generating the unified model” in claim 4, therefore the examination of the “grouping” step and “determining key entities” step is based on the broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art; FIG. 1, FIG. 2, FIG. 3; ¶ 0023 “… the parser may first identify instances of calling the API methods. …”; ¶ 0024 “… the parser may identify the object types of the instances and match the object types to the API method calls …”; ¶ 0027 “… The miner 114 may cluster the API method call sequences 202 to generate first clusters 204 including multiple clusters (e.g., cluster 1, cluster 2 ... cluster N). In some embodiments, the first clusters 204 may be generated using a probability algorithm (e.g, an n-gram model) …”; ¶ 0033 “… the processing engine 106 may collect a set of call sequences (i.e., API method call sequences) that includes the API method, and then perform clustering on the set of call sequences to generate multiple clusters based on the similarity of the sequences …”); …
Dang and Swaminathan-Hampapur are analogous art because they are both related to data mining for a process.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the mining API usage patterns techniques of Dang with the system of Swaminathan-Hampapur to evaluate the metrics to measure the quality of API usage (Dang ¶ 0001).
Swaminathan-Hampapur-Dang does not explicitly teach, but Swope teaches aligning the plurality of steps and the service APIs (Swope teaches mapping software action input variables to API parameters; Examiner notes that the “aligning” step is also an isolated step in claim 4, therefore the examination of the “aligning” step is based on the broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art; ¶ 0003 “… A software-based workflow design tool may enable the specification and execution of workflows, which are specific sequences or series of tasks that, when performed, accomplish one or more goals …”; ¶ 0009 “… The action design tool may parse the API specification and, based thereon, generate input variables for the action that correspond to the inputs of the function. The action design tool may additionally map the input variables to parameters of the API, including URL resource path parameters, URL query parameters, hypertext transfer protocol (HTTP) request header parameters, HTTP request body parameters, and/or HTTP cookie parameters, among other possibilities …”);
Swope and Swaminathan-Hampapur-Dang are analogous art because they are both related to data mining for a process.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the API invoking techniques of Swope with the system of Swaminathan-Hampapur-Dang to “allow an enterprise to take advantage of the computational resources provided by third-party computing systems by integrating these resources into the enterprise's workflows” (Swope ¶ 0004).
Swaminathan-Hampapur-Dang-Swope does not explicitly teach, but Rajagopalan teaches determining key service APIs for the process steps (Rajagopalan teaches prioritizing API call subgraphs/sequences; FIG. 6, FIG. 7; ¶ 0062 “… a large and complex microservices-based application, and associated state transition graph can have thousands of abstract user interface states, edges, and API call subgraphs …”; ¶ 0069 “… ordering component 304 has determined the ordered list 700 such that the order of the annotated edges comprises 602k, 602l, 602i, 602e, 602g, 602c, and 602j from highest priority to lowest priority. Ordering component 304 has added to the ordered list 700 API call subgraphs that correspond to annotated edges 602!, 602!, 602i, 602e, 602g, 602c, and 602}, thus ordering the API call subgraphs from highest priority to lowest priority …”); and …
Rajagopalan and Swaminathan-Hampapur-Dang-Swope are analogous art because they are both related to data mining for a process.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the prioritizing API calls techniques of Rajagopalan with the system of Swaminathan-Hampapur-Dang-Swope to “facilitate improved performance of automated resiliency testing systems that provides for more efficient usage of resiliency test processing resources in a limited available time by reducing redundancy of resiliency testing when testing large applications comprising complex API call graphs spanning across several microservices” (Rajagopalan ¶ 0036).
Swaminathan-Hampapur-Dang-Swope-Rajagopalan does not explicitly teach, but Wang teaches generating the unified model based on the determined key service APIs for the process steps (Wang teaches generating trace flow graphs based on process flows using APIs, the trace flow graphs being used for flow discovery; FIG. 1, FIG. 3; ¶ 0058 “… FIG. 1C is a call flow diagram 170 that illustrates examples of process flows in the example of FIG.1B. In this example, one possible process flow in response to a client request from client 110B that is received by server 120 through API 112B involves server 120 invoking service 130B, which invokes micro-service 132D to access partner service 140 …”; ¶ 0065 “… After pre-processing, trace flow generator 350 can generate trace flow graphs from the aggregated distributed trace data in database 340, which are stored in trace graph database 370 …”; ¶ 0072 “… Based on user input, e.g. selection of elements, navigation inputs, or other data requests, flow discovery module 360 can obtain additional trace flow graph data or performance data from database 370 and provide the additional data in graphical format for display via UIs 356 and 362 …”).
Wang and Swaminathan-Hampapur-Dang-Swope-Rajagopalan are analogous art because they are both related to data mining for a process.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the analyzing API invoked process flows techniques of Wang with the system of Swaminathan-Hampapur-Dang-Swope-Rajagopalan to quickly identify a root cause of a site incident and improve customer experience (Wang ¶ 0041).
For Claim 14, the claim is substantially similar to claim 4 and therefore is rejected for the same reasoning set forth above.
Claim Rejections - 35 USC § 103
Claims 7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11,366,842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur), and in further view of Kaplan et al. (US 20230297907 A1, filed 09/21/2023; hereinafter Kaplan).
For Claim 7, Swaminathan-Hampapur teaches the method of claim 6. Swaminathan-Hampapur does not explicitly teach, but Kaplan teaches wherein the one or more impact analyzers comprises an organizational human staffing impact analyzer that predicts a number of organizational subject matter experts that can be freed up from impacted organizational processes without worsening a severity of the forecasted KPI impact (Kaplan teaches predicting an optimal amount of agents required to keep the quality of service; FIG. 3; ¶ 0040 “… Embodiments of the invention relate generally to a novel method for approximating the quality of service provided by a set of agents for a specific time interval or time period …”; ¶ 0075 “… A search algorithm (block B.2) may suggest an initial/candidate staffing or assignment requirement (block B.3) for a specific interval …”; ¶ 0076 “… A service metric value expected to be achieved by the initial staffing requirement (block B.3) handling the forecasted workload (block B.1) may then be predicted using a machine learning service level prediction model (block B.4) …”; ¶ 0078 “… The predicted service metric value(s) for the interval across all skills and service metrics as received by the machine learning service metrics prediction model may then be compared (block B.6) to the at least one required service metric value(s) (block A.2) as provided by the user …”; ¶ 0079 “… After comparison, the candidate staffing requirement most fitting the required service metrics may be updated (block B.7), for example, by adjusting the initial allocation assignment based on a difference between the expected service metric value and the corresponding at least one required service metric value. If the current candidate staffing assignment is predicted to produce a better outcome (measured in terms of service metrics) than the previously suggested best candidate staffing assignment, then the best staffing assignment may be updated …”).
Kaplan and Swaminathan-Hampapur are analogous art because they are both related to business/organization resource management.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the optimizing staffing assignment techniques of Kaplan with the system of Swaminathan-Hampapur to select the optimal staffing requirement, so that the net staffing will be as low as possible while providing the required service levels (Kaplan ¶ 0040).
For Claim 16, the claim is substantially similar to claim 7 and therefore is rejected for the same reasoning set forth above.
Claim Rejections - 35 USC § 103
Claims 9 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11366842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur), in view of Arzani et al. (US 20210224676 A1, published 07/22/2021; hereinafter Arzani), and in further view of Ghosh et al. (US 20200210924 A1, published 07/02/2020; hereinafter Ghosh).
For Claim 9, Swaminathan-Hampapur teaches the method of claim 6. Swaminathan-Hampapur does not explicitly teach, but Arzani teaches wherein the one or more impact analyzers comprises an IT human resources allocation impact analyzer that predicts a number of Site Reliability Engineers needed to correct an IT issue (Arzani teaches routing the incidents to a team-specific scout (i.e. implying a number of team members in the scout) to resolve the incidents; FIG. 6; ¶ 0080 “… aspects of the disclosure relate to methods for receiving incident-classification predictions from multiple team-specific scouts and, based on the incident-classification predictions, routing an incident to a single team determined to be most likely to resolve an incident …”) and …
Arzani and Swaminathan-Hampapur are analogous art because they are both related to business/organization event recovery/remediation analysis.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the incidents routing techniques of Arzani with the system of Swaminathan-Hampapur to provide accuracy and speed of incident routing (Arzani ¶ 0080).
Swaminathan-Hampapur-Arzani does not explicitly teach, but Ghosh teaches predicting IT effort to bring KPIs back to a predetermined level within a duration of the forecasted KPI impact (Ghosh teaches predicting IT effort and cost to maintain an incident impacted organizational KPI; FIG. 1; ¶ 0043 “… the output 136 may include … a specified organizational key performance indicator, from the specified organization key performance indicators 134, impacted by an incident of the further plurality of incidents 130 …”; ¶ 0045 “… An organizational operation controller 138 … may control, based on the output 136, an operation of a system 140 associated with the identified organization 110. For example, with respect to management of field requests as disclosed herein, some selected requests for field service transactions may be of importance to an organization (e.g., requests for certain emergency responses related to fixing of existing connections in the utilities industry). Incidents related to such requests may be missed within a relatively large number of incidents that are handled on a regular basis. In this regard, the output 136 that identifies an organizational operation impacted by an incident may provide for the identification of the urgency of the exact service requested. The prioritization may be achieved by reading the description of the emergency request, noting key words that highlight the necessity to process such field force requests quickly, and finally re-prioritizing such incidents to modify an existing connection in the utilities industry …”; ¶ 0046 “… The output 136 may also provide a real time understanding of the information technology effort and cost an organization may be undertaking on the maintenance of a critical organizational process index …”).
Ghosh and Swaminathan-Hampapur-Arzani are analogous art because they are both related to business/organization event recovery/remediation analysis.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the incidents control techniques of Ghosh with the system of Swaminathan-Hampapur-Arzani to prioritize the incidents management with respect to availability of personnel (Arzani ¶ 0080).
For Claim 18, the claim is substantially similar to claim 9 and therefore is rejected for the same reasoning set forth above.
Claim Rejections - 35 USC § 103
Claims 10 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Swaminathan et al. (US 11366842 B1, published 06/21/2022; hereinafter Swaminathan), in view of Hampapur et al. (US 20220391815 A1, published 12/08/2022; hereinafter Hampapur), and in further view of Calmon et al. (US 20210064436 A1, published 03/04/2021; hereinafter Calmon).
For Claim 10, Swaminathan-Hampapur teaches the method of claim 6. Swaminathan-Hampapur does not explicitly teach, but Calmon teaches wherein the one or more impact analyzers comprises an IT resources allocation impact analyzer that predicts a quantum of IT resources which can be freed from impacted organizational process operations without worsening a severity of the forecasted KPI impact (Calmon, FiG. 8, ¶ 0124 “… FIG. 8 illustrates a training 800 of a deep neural network (DNN) as an iterative workload, itself comprised of multiple iterative workloads … Assuming that an SLA metric to be controlled is the execution time (et=T), one can feedback the amount of time t it took to complete an epoch and compare this time to the desired time per epoch … if the time t is significantly smaller than T·k/n2, this indicates that the job does not need the current amount of allocated resources and reducing the allocation can decrease cost and even make room for other jobs to run …”).
Calmon and Swaminathan-Hampapur are analogous art because they are both related to optimizing resource allocation for an organization.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to use the resource allocation techniques of Calmon with the system of Swaminathan-Hampapur to use “an infrastructure efficiently to execute workloads while respecting Service Level Agreements (SLAs) and, thus, guaranteeing a specified Quality of Service (QoS) (Calmon ¶ 0004).”
For Claim 19, the claim is substantially similar to claim 10 and therefore is rejected for the same reasoning set forth above.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is listed below, thank you:
i. Cmielowski et al. (US 20210158179 A1, published 05/27/2021; hereinafter Cmielowski) discloses that a computer system determines a key performance value for a key performance indicator and determines a metric value for a metric from a collection of data, identifies a correlation coefficient indicating a correlation between the KPI and the metric, and generates a recommendation based on the correlation coefficient (Cmielowski, Abstract).
ii. Bawcom et al. (US 8805875 B1, published 08/12/2014; hereinafter Bawcom) discloses that a model builder extracts data from a given type of data source (including, without limitation, a relational database system, an application programming interface (API), or the like), and enables that data to be presented to one or more constructs of the search language according to a single unified data model (Bawcom, Abstract).
iii. Lima et al. (US 20200021503 A1, published 01/16/2020; hereinafter Lima) discloses that a system obtains messages with KPI and a threshold and business impact calculation/estimate as notifications of events with discernable impacts on business performance measures, separately ranks the events with discernable impacts on business performance utilizing an impact parameter and the incidents within the technical environment by priority, based on a service level agreement with the entity (Lima, ¶ 0036)
Conclusion
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/Z.D./
Examiner, Art Unit 2444
/SCOTT B CHRISTENSEN/ Primary Examiner, Art Unit 2444