DETAILED ACTION
Status of the Application
This Final Office Action is in response to Application Serial 18/118,274. In response to Examiner’s action mail dated January 29, 2025, Applicant submitted arguments and amendments that are mail dated February 11, 2026. Claims 1-20 are pending.
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 . 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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted February 11, 2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
Claims 1 -20 are pending in this application. The claim(s) 1,8, 9, and 15 are amended.
Regarding the 35 U.S.C. 101 rejection, the amendments are not persuasive. The claims 1-20 are rejected under 35 U.S.C. 101, see below.
Regarding the 35 U.S.C. 103 rejection, the amendments to claims are not persuasive for claims 1-20. The Applicant’s amendments necessitate grounds for a new rejection.
Response to Arguments
Response to arguments dated February 11, 2026 have been fully considered but they are not persuasive and/or are moot in view of the revised rejections. Applicant’s arguments will be addressed herein below.
Claim Rejections – 35 USC 101
On pages 9-11 of the Applicant’s 35 U.S.C. 101 arguments, the Applicant traverses, the claims directed to patentable subject matter, and withdrawal of the rejection of the claims under 35 U.S.C. 101 is respectfully requested.
Applicant submits the features of the independent claims such as use of a feature graph constructed from specific data and a user provided definition of a metric they desire to monitor enable the user to improve how a business or health metric is constructed, monitored, and indicated as having changed to a degree a user desires to be made aware of which provides an improvement to conventional approaches. Not previously defined metrics may be monitored. Due to the volume of data and its addition to the feature graph at varying times by one or more processes, these steps or processes are not capable of being performed in the human mind in a practical way.
The claims do not integrate the inventive data into a practical application. The application in one enabling a user to define a metric of interest based on the data used to construct a feature graph (which is statistically relevant data found to be associated with a topic). The claims embodiments represent an improvement to the techniques used to define and monitor a metric that can be used to evaluate the condition of a business function.
It is submitted that the claims are directed to patentable subject matter, and withdrawal of the rejections of the claims under 35 U.S.C. 101 is requested.
Examiner respectfully disagrees with Applicant’s 35 U.S.C. 101 arguments. The claims recite monitoring business metrics, especially KPIs, to determine a data and statistical relationship using a feature graph. Modeling statistical data, such as frequency of use and ranking, can be completed using pen and paper. The claims recite a mental concept. Because the claims recite a mental concept the claims are directed to a judicial exception at Step 2A prong one.
At Step 2A prong two, the claims recite processors, an interface, and machine learning to conduct the modeling of the abstract idea. The claims recite using a computer to conduct the abstract idea, and thus, the claims are merely applying a computer to complete the abstract idea.
Regarding an improvement, the claims are using graphing techniques to model the statistics. Graphing and tallying counts can be completed using a pen and paper. As recited the claims are using a computer to complete the statistics and display the statistical data. The claims do not recite an improvement that is rooted in technology. Applicant is encouraged to clarify the relationship of claim 9 and claim 13.
At Step 2A prong two the claims are not integrated into a practical application.
At Step 2B, the claims when considered as a whole are using a computer to conduct the abstract idea. Furthermore, the claims are merely using a computer to process computer data and instructions. The claims are not patent eligible.
The claims are directed to a judicial exception, are not integrated into a practical application, and do not amount to significantly more.
Claim Rejections – 35 USC 102
On pages 11-13 of the Applicant’s 35 U.S.C. 102 arguments, the Applicant traverses, the are directed to assisting a user to define a metric or value they desire to monitor and not simply to specify an existing or previously define quantity to monitor. Applicant submits the prior art Agarwal differs from the claims in the pending application. Additionally, Agarwal does not mention fa recommendation algorithm for the creation of rules or generating algorithms.
Examiner acknowledges Applicant arguments. Applicant’s amendments necessitate grounds for a new rejection see below.
Furthermore, claims 1, 2, 4, 7, 8, 9,10, 12, 13, 14, 16, 19 are Markush claims. Therefore, the claims were previously rejected by prior art that taught statistical modeling. Examiner rejected the amended claims to illustrate the Markush rejection.
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 1-8 are process.
Claims 9-14 are machine.
Claims 15-20 are manufacture.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim 1 (and similarly claim 9 and 15) recite, “… . A method for monitoring one or more metrics, comprising: constructing or accessing a feature graph, the feature graph including a set of nodes and a set of edges, wherein each edge in the set of edges connects a node in the set of nodes to one or more other nodes, and further, wherein each node represents a variable found to be statistically associated with a topic and each edge represents a statistical association between a node and the topic or between a first node and a second node; generating … to enable a user to perform one or more of identifying a metric for monitoring, wherein the identified metric represents a quantity based on data obtained from a business, a health-related condition , or a scientific investigation; defining a rule that describes when an alert regarding the behavior of the identified metric should be generated; defining how the result of applying the rule is indicated on …; and allowing the user to select a metric for which an alert has been generated and in response, provide information regarding one or more of the metric's changes in value over time, the rule that resulted in the alert, the metric's relationship to other metrics, and information regarding the datasets, machine learning models, rules, or factors used to generate the metric, thereby enabling the user to define a metric based on one or more of the variables represented by the nodes of the feature graph and monitor the metric’s changes as a value or values of the one or more variable change ”. Claims 1-20 in view of the claims limitations, are related to the abstract idea of … monitoring business metrics, especially KPIs, to determine a data and statistical relationship using a feature graph. Modeling statistical data, such as frequency of use and ranking, can be completed using pen and paper. The claims recite a mental concept. Because the claims recite a mental concept the claims are directed to a judicial exception at Step 2A prong one.
This judicial exception are not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of, “a user interface display and user interface tools”, “machine learning models” in claim 1; “A system, comprising: one or more electronic processors configured to execute a set of computer-executable instructions; and one or more non-transitory computer-readable media containing the set of computer- executable instructions, wherein when executed, the instructions cause the one or more electronic processors or an apparatus or device containing the processors”, “a user interface display and user interface tools”, “indicated on the user interface display”, “machine learning model”, in claim 9; “One or more non-transitory computer-readable media comprising a set of computer-executable instructions that when executed by one or more programmed electronic processors, cause the processors or an apparatus or device containing the processors to construct or access a feature graph”, “a user interface display and user interface tools”, “on the user interface display”, “machine learning model”, in claim 15; however, when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, each of the additional elements are computing elements recite adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer or merely uses a computer as a tool to perform an abstract idea – see MPEP 2106.05 (f).
At Step 2A prong two, the claims recite processors, an interface, and machine learning to conduct the modeling of the abstract idea. The claims recite using a computer to conduct the abstract idea, and thus, the claims are merely applying a computer to complete the abstract idea. Furthermore, machine learning is a regressive model that could be mathematical. Applicant is encouraged to clarify the relationship of claim 9 and claim 13.
Regarding an improvement, the claims are using graphing techniques to model the statistics. Graphing and tallying counts can be completed using a pen and paper. As recited the claims are using a computer to complete the statistics and display the statistical data. The claims do not recite an improvement that is rooted in technology. Applicant is encouraged to clarify the relationship of claim 9 and claim 13.
At Step 2B, The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims are using a computer to conduct the judicial exception. See MPEP 2106.05 (f).
At step 2B, it is MPEP 2106.05 (d) – Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Regarding an improvement, the claims are improving the judicial exception. As disclosed in the claims, the data from a model could be statistical data or machine learning model that is applied to identify metrics and define metrics. In machine learning, filtering, ranking, and classifying data are steps that a machine model conduct as basic functioning. See Eidelman (US 2023/0,214,754 A1) and other pertinent art of record (Nguyen 2021 Introduction to Machine Learning with Graphs and Knowledge Hub, 2021 What is a Knowledge Graph). In Eidelman, the data may generate information that are outliers, anomalies, or clusters of data. Eidelman teaches machine learning, nodes, edges, graph modeling, and calculated metrics used to suggest relevant issues. See Eidelman (US 2023/0,214,754 A1) at [0361] –[0369], [0369], [Figure 35]. The claims are using the technology to improve the abstract concept.
The claims do not amount to significantly more at Step 2B.
Dependent claims 2- 8 further narrow the abstract idea of independent claim 1. Dependent claims 10 -14 further narrow the abstract idea of independent claim 9. The claims 16-20 further narrow the abstract idea of independent claim 15. Claim 1-20 are not patent eligible.
Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2 -8, & 10-14 & claims 16-20 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Agarwal (US 11347622B1) in view of Eidelman (US 2023/0,214,754 A1.
Regarding Claim 1 [and similarly claim 9 and 16] (Currently Amended)
A method for monitoring one or more metrics, comprising: constructing or accessing a feature graph, the feature graph including a set of nodes and a set of edges, wherein each edge in the set of edges connects a node in the set of nodes to one or more other nodes, and further, wherein each node represents a variable found to be statistically associated with a topic and each edge represents a statistical association between a node and the topic or between a first node and a second node;
Agarwal discloses tools provide the capability to visualize different levels of a client's application …collect values of monitored or tracked metrics at those different levels, and visualize values of the metrics at those levels., Agarwal [abstract]
generating a user interface display and user interface tools to enable a user to perform one or more of identifying a metric for monitoring;
Agarwal discloses an on-screen GUI comprising an interactive topology graph for an application created from the aggregated metric events data and FIG. 10 illustrates an on-screen GUI comprising an interactive full-context service graph 1000, which is constructed for an example microservices-based application using the metrics data generated in connection with the metric events modality. Each circular node (e.g., nodes associated with services 1002, 1004 and 1006 of FIG. 10) represents a single microservice., Agarwal [Figure 10].
….
defining a rule that describes when an alert regarding the behavior of the identified metric should be generated;
Agarwal discloses microservices (e.g., the product catalog service 2206) and the monolithic application 2270 can each be abstracted or logically represented as a collection of two or more components. The components can be defined in advance based on, for example, the functions they perform (e.g., business logic, customer user interface, etc.) and/or the type of metrics that are to be monitored or tracked., Agarwal[column 43 lines 35-41]
defining how the result of applying the rule is indicated on the user interface display;
Agarwal discloses having identified a problem either by manual monitoring of RED metrics or through an automatically generated alert, the user may be able to traverse deeper using the metric events data set and access relevant traces to receive more specific information regarding the problem. Also, the metric events mode allows the user to run an arbitrary analysis on the traces., Agarwal[column 35 lines 12-24]
and allowing the user to select a metric for which an alert has been generated and in response, provide information regarding one or more of the metric's changes in value over time, the rule that resulted in the alert, the metric's relationship to other metrics, and information regarding the datasets, machine learning models, rules, or factors used to generate the metric.
Agarwal discloses the metric time series provide valuable real-time information pertaining to services or endpoints within an application and also allow alerts to be configured to manage anomalous behavior on the endpoints.., Agarwal[column 12 lines 39-44] and Agarwal discloses key performance metrics (KPIs) can be extracted directly from the metric time series in real-time and reported to a user. Agarwal [column 13 lines 5-10], [Figure 8].
Examiner relies on Eidelman to teach:
… identifying a metric for monitoring, wherein the identified metric represents a quantity based on data obtained from a business, a health related-condition, or scientific investigation …. allowing the user to select a metric for which an alert has been generated and in response, provide information regarding one or more of the metric changes in value over time, the rule that resulted in the alert, the metric’s relationship to other metrics, and information regarding the datasets, machine learning models, rules, or factors used to generate the metric, thereby enabling the user to define a metric based on one or mote of the variables represented by the nodes of the feature graph and monitor the metric’s changes as a value or values of the one or more variables change.
Eidelman at [0339], [0381] teaches machine learning; Eidelman [0363] teaches the issue graph of each of the agenda issues selected as being of interest to the organization may be presented with the one or more calculated metrics selected by the user. Eidelman [0359] teaches generating and analyzing policy, policymaker and organizational entities and relationships through construction and inference in an issue-based graph modeling framework, analyze policy, policymaker and organizational issue graphs, automatically identify connections between one or more entities, make new inferences from these connections, and display one or more connections., Eidelman [0339], [0359] –[0369], [Figure 35].
Agarwal collects values of monitored or tracked metrics at those different levels, and visualize values of the metrics at those levels. Eidelman relates to systems and methods for generating and analyzing policy, policymaker, and organizational entities and relationships through the construction of issue-based knowledge graphs. It would have been obvious to combine before the effective filing date, tracking the performance of an application, as taught by Agarwal with tracking analytics, as taught by Eidelman, to calculate and maintain complex relationships between the unstructured and structured data. Eidelman [005].
Within claim 1, Eidelman teaches generating and analyzing policy, policymaker, organizational relationships through construction and inference using an issue-based graph modeling framework, and thus, Eidelman discloses provide information regarding one or more of the metric changes in value over time,. Claim 1 a "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
Within claim 1, Eidelman teaches generating and analyzing policy, policymaker, organizational relationships through construction and inference using an issue-based graph modeling framework, and thus, Eidelman discloses identified metric represents a quantity based on data obtained from a business and the metric’s relationship to other metrics. Claim 1 a "Markush" claim recites a list of alternatively useable members. In re Harnisch, 631 F.2d 716, 719-20, 206 USPQ 300, 303 (CCPA 1980); Ex parte Markush, 1925 Dec. Comm'r Pat. 126, 127 (1924). The listing of specified alternatives within a Markush claim is referred to as a Markush group or a Markush grouping. Abbott Labs v. Baxter Pharmaceutical Products, Inc., 334 F.3d 1274, 1280-81, 67 USPQ2d 1191, 1196 (Fed. Cir. 2003) (citing to several sources that describe Markush groups)- See MPEP 706.03.
(Examiner notes Applicant had two Markush clauses in the independent claims.)
Regarding Claim 2, [and similarly claim 10] (Original)
The method of claim 1, further comprising generating a recommendation for the user regarding one or more of a different metric or set of metrics to monitor, a dataset that may be useful to examine, metadata that may be relevant to a metric, or an aspect of the underlying data or metrics.
Agarwal discloses the metric events modality can comprise higher-cardinality metrics information because a higher number of tags may be indexed for the metric events data set as compared to the dimensions associated with the metric time series. However, the metric time series modality may provide higher-fidelity information because it retains metadata associated with incoming spans (e.g., service name, operation name, count values, etc.) that are not collected in the metric events modality., Agarwal[column 36 lines 33-41].
Regarding Claim 3, [and similarly claim 11] (Original)
The method of claim 1, wherein constructing the feature graph further comprises: accessing one or more sources, wherein each source includes information regarding a statistical association between a topic discussed in the source and one or more variables considered in discussing the topic; processing the accessed information from each source to identify the one or more variables considered, and for each variable, to identify information regarding the statistical association between the variable and the topic;
Agarwal discloses the root cause information 2016 includes, for example, the number of errors for which the selected team of microservices was the originator, the associated error rate, and the percentage of the total number of requests that represents. In this way, in addition to providing visual cues for identifying root cause error originators at the team level, meaningful and accurate team-level quantitative information is provided, to help clients distinguish between root cause-related errors and errors associated with downstream causes., Agarwal [column 41 lines 13-29].
and storing the results of processing the accessed source or sources in a database, the stored results including, for each source, a reference to each of the one or more variables, a reference to the topic, and information regarding the statistical association between each variable and the topic.
Agarwal discloses metrics data can be collected and processed at the microservices level and then aggregated into team-level data, or metrics data can be collected and processed at the team level. In either case, once a team is defined, the definition of the team (e.g., a team ID and IDs of the microservices in the team) can be stored in computer system memory., Agarwal[column 39 lines 15-21] and Agarwal discloses the data is stored in parquet-formatted batches of full traces in an unstructured format (e.g., blob storage) along with some metadata. The metadata may comprise the tag., Agarwal[column 36 lines 56-60].
Regarding Claim 4, [and similarly claim 12] (Original)
The method of claim 3, further comprising storing an element to enable access to a dataset, wherein the dataset includes data used to demonstrate the statistical association between each variable and the topic or data representing a measure of one or more of the variables.
Agarwal discloses the service graph 1801 may also be generated using metrics data generated in connection with the metric events modalilty., Agarwal[column 38 lines 5-12] and Agarwal discloses each edge in the service graph 1801 (e.g., the edges 1822, 1824, and 1826) represents a cross-service dependency (or a cross-service call)., Agarwal[column 38 lines 27-30].
(Examiner points out to Applicant the limitations “… demonstrate the statistical association between each variable and the topic or data representing a measure of one or more of the variables…” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 5, [and similarly claim 13] (Original)
The method of claim 4, further comprising: traversing the feature graph to identify a dataset or datasets associated with one or more variables that are statistically associated with a topic of interest to a user or are statistically associated with a topic semantically related to the topic of interest; filtering and ranking the identified dataset or datasets; and presenting the result of filtering and ranking the identified dataset or datasets to the user.
Agarwal discloses for a given tag, the analysis system calculates a p-value indicating the likelihood that the ranks of the spans for that tag in the distribution arose by chance. In particular, the analysis system may calculate a p-value of the Mann-Whitney U-statistic comparing the ranks of the durations of the traces having the tag to the other traces in the distribution. … The analysis system sorts the tags by the associated p-value (e.g., in ascending order) and returns those with p-value less than or equal to some threshold, e.g., 0.01, Agarwal[column 21 lines 36-60]
(Examiner points out to Applicant the limitations “… traversing the feature graph to identify a dataset or datasets associated with one or more variables that are statistically associated with a topic of interest …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 6, (Original)
The method of claim 3, wherein the one or more sources include at least one source containing proprietary data.
Agarwal [Figure 1B item 106 and the associated text]
Regarding Claim 7, [and similarly claim 14] (Original)
The method of claim 6, wherein the proprietary data is obtained from a business, a study, or an experiment.
Agarwal [Figure 1B item 106] illustrates retail service, retail database which is a business, therefore, Agarwal teaches proprietary data.; Agarwal discloses microservices-based architecture involves the building of modules (e.g., modules 104, 106 and 108) that address a specific task or business objective., Agarwal [column 6 lines 62-68]
(Examiner points out to Applicant the limitations “… obtained from a business, a study, or an experiment …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 8, (Currently Amended)
The method of claim 2, wherein the recommendation is generated by one or more of a trained model or a statistical analysis.
Agarwal [column 21 lines 36-60] discloses Mann-Whitney U-statistic.
(Examiner points out to Applicant the limitations “… of a trained model or a statistical analysis …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 18, (Original)
The non-transitory computer-readable media of claim 17, further comprising storing an element to enable access to a dataset, wherein the dataset includes data used to demonstrate the statistical association between each variable and the topic or data representing a measure of one or more of the variables.
Agarwal [column 21 lines 36-60] – see above
(Examiner points out to Applicant the limitations “… the dataset includes data used to demonstrate the statistical association between each variable and the topic or data representing a measure of one or more of the variables …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 19, (Original)
The non-transitory computer-readable media of claim 18, wherein the instructions cause the one or more electronic processors or an apparatus or device containing the processors to: traverse the feature graph to identify a dataset or datasets associated with one or more variables that are statistically associated with a topic of interest to a user or are statistically associated with a topic semantically related to the topic of interest; filter and rank the identified dataset or datasets; and present the result of filtering and ranking the identified dataset or datasets to the user.
Similar to claim 5 – Agarwal teaches traces are traversed to generate values of metrics for the team., Agarwal [column 39 lines 30-36] and Agarwal[column 21 lines 36-60]
(Examiner points out to Applicant the limitations “… traverse the feature graph to identify a dataset or datasets associated with one or more variables that are statistically associated with a topic of interest to a user or are statistically associated with a topic semantically related to the topic of interest …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Regarding Claim 20, (Original)
The non-transitory computer-readable media of claim 17, wherein the one or more sources include at least one source containing proprietary data, and further, wherein the proprietary data is obtained from a business, a study, or an experiment.
Similar to claim 6 and claim 7 – Agarwal [column 6 lines 62-68], [Figure 1B item 106 and the associated text]
(Examiner points out to Applicant the limitations “… obtained from a business, a study, or an experiment …” are a Markush. Because the independent claims has two Markush clauses, and the claim is dependent on the independent claim, Examiner recommends the Applicant clarify an embodiment for examination.)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Modani US 10009363 B2 a data graph is generated that represents various metrics datasets (e.g., sets of data values for a metric over a given time period). Each node in the graph represents a given metric (e.g., “page views,” “website visits,” etc.). Each edge represents a correlation between a given pair of metrics datasets.
Knowledge Hub (January 2022, What is a Knowledge Graph?) https://www.ontotext.com/knowledgehub/fundamentals/what-is-a-knowledge-graph. Knowledge graphs put data in context via linking and semantic metadata.
Nguyen (2021, Introduction to Machine Learning with Graphs) machine learning with graphs and the tasks. Teaches By connecting nodes with edges, we will end up with a graph (network) of nodes.
https://towardsdatascience.com/introduction-to-machine-learning-with-graphs-f3e73c38d4f8?gi=6d3de7d035f0
Le Biannic (US 2016/0,371,288 A1) teaching a plurality of statistical metrics for the query context, the statistical metrics being computed using information obtained from datasets associated with the query context.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/THEA LABOGIN/Examiner, Art Unit 3624 /HAMZEH OBAID/Primary Examiner, Art Unit 3624 May 28, 2026
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