DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Applicant's submission filed on 12/15/2025 has been entered.
Status of the Claims
Claims 1, 7, 8, 15, 17 have been amended. Claims 1-20 are pending.
Claim Construction
Independent claims recite limitation –
“determine a set of key performance indicators, wherein the set of key performance indicators are used to identify a first threshold level and a second threshold level of usage for the processed data representation determine a level of usage of a dataset.” The limitation is examined and construed in view of the specification, paragraphs [0024]-[0025].
A Key Performance Indicator (KPI) by definition is a quantifiable, high-level measurement used to evaluate an organization’s success in reaching specific, long-term operational or strategic goals. Thus, the KPI can vary by business and organization and depends on objectives set forth by such organizations The present application does not define exact KPI objectives, such as operational or strategic goals. Instead, the specification [0025] discloses –
“The KPIs may include a subset of the processed data representation (e.g., one or more metrics) that the data processing system 102 determines are associated with a threshold relevance or a threshold correlation to a particular result. … process information relating to the one or more datasets using a machine learning algorithm or an artificial intelligence algorithm to identify one or more metrics with a threshold correlation with a failure occurring, a resource shortage occurring, a trouble ticket being submitted, or another result. In this case, the data processing system 102 may designate the one or more metrics as KPIs for the one or more results and may set one or more thresholds for measuring deviation of the KPIs”, [0026] “generate a visualization of the data ecosystem metrics, the health metrics, the data ecosystem costs, the data usage metrics, or the KPIs.”
I.e. the specification shows that the KPI does not particularly refers to the organization’s operational or strategic goals and instead designates performance metrics, such as usage, as the KPIs.
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.
◊ Claims 1-2, 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ellis et al. (US 20180131803) in view of Cherubini et al. (US 11188236) and in further view of Bly et al. (20230289698).
Regarding claim 8, Ellis teaches a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a system, cause the system to:
receive information identifying a set of data reports generated from a group of datasets ([0042]-[0043], wherein “insight objects” are reports, [0059]);
request, from a data source storing the group of datasets, source data associated with the set of data reports based on receiving the information identifying the set of data reports ([0063]);
receive, from the data source, the source data associated with the set of data reports ([0086] “content of various data sources and data files to determine … metadata and other contextual information related to the data sources to provide further … such as organizational policies and related data, organizational profiles, security properties, usage rights information”, [0087]);
associate the source data with data lineage information identifying a set of connections between the group of datasets ([0041], [0066] “trends determined for the datasets and from past insight analyses or other dataset”, [0106]);
process, based on associating the source data with the data lineage information, the source data, the data lineage information ([0041] “comprise dynamic objects with a processing lineage and/or analysis summary associated therewith”, “maintain a chain of inference among datasets”, “include … data sources, and other rationale or history behind the generation and establishment of the insight objects”), and a set of usage metrics associated with the set of data reports to generate a processed data representation ([0083]-[0086], [0088]),
wherein the processed data representation includes information identifying a set of relationships associated with the group of datasets and the set of data reports ([0027], [0039]-[0040], [0070], [0088]);
receive information identifying a dataset, of the group of datasets ([0048] “insight interface that can be selected by a user to trigger processing of the dataset”, [0052], [0054]);
generate, using the processed data representation, a visualization relating to usage of the dataset in connection with the set of reports ([0046], [0052], [0107]);
provide, for display via a user interface of a client device, information identifying the visualization ([0046], [0049]-[0051], [0129]);
determine a set of key performance indicators, wherein the set of key performance indicators ([0089] “determine key activity and action hubs … of user/organization activities or actions. Key hubs are assigned confidence levels”, [0090] “identifies operational knowledge that is relevant to a user or organization”) are used to identify a first threshold level and a second threshold level of usage for the processed data representation ([0080], [0089] “Key hubs are assigned confidence levels or ranked based on relevance or knowledge confidence levels, and can vary over time”, [0106]) (see NOTE)
Ellis does not explicitly teach, however Cherubini discloses determine a level of usage of the dataset in connection with resources (C3L62-67, C4L10-18, C8L1-12), when the level of usage of the dataset satisfies a first threshold level of usage (C4L36-40): automatically allocate additional resources to support the dataset (C15L9-20, C16L41-45), and when the level of usage of the dataset does not satisfy a second threshold level of usage: automatically remove the dataset from the group of datasets (C13L40-55, C14L1-17, 25-35), and automatically reallocate one or more resources associated with storing the dataset toward another purpose (C14L37-65, C16L28-30).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Ellis to include resource allocation based on usage of the data sets as disclosed by Cherubini. Doing so would provide much higher storage efficiency (Cherubini C1261).
NOTE Ellis does not explicitly teach determine a set of key performance indicators (KPIs). Instead Ellis discloses determination of operational knowledge [0082], which “include metrics and data related to how an organization typically uses data, what data insight objects are preferred or employed often, and what analysis has been performed in the past for various users and members of an organization” [0083]. The metrics of “operational knowledge, or global knowledge” [0073] of a company or organization, which determine confidence levels for “key activity and action hubs” is obviously analogous to the claims KPIs.
However, to merely obviate such reasoning, Bly discloses determine a set of key performance indicators, wherein the set of key performance indicators are used to identify a first threshold level and a second threshold level of usage for the processed data representation determine a level of usage of a dataset, of the group of datasets ([0241], [0244], [0252], [0254], [0257], [0303]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Ellis to include KPIs as disclosed by Bly. Doing so would provide users with the ability to monitor business related metrics and more efficiently evaluate the quality of the underlying data used to generate those metrics (By [0241).
◊ Claim 1-2 and 15 recites substantially the same limitations as claim 8, and is rejected for substantially the same reasons. Further, with respect to Claims 1 and 15, Ellis further teaches transmit, to a client device, information identifying the processed data representation and resource allocation (C15L9-20, C16L24-47).
◊ Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Reynolds et al.(US 20170364568) in view of Spitz et al. (US 20160364434), ZAROO et al. (US 20230103011) and in further view of James et al. (US 20220156267).
Regarding claim 1, Reynolds teaches a system for data processing, the system comprising: one or more memories; and one or more processors, communicatively coupled to the one or more memories, configured to:
receive information identifying a set of data reports generated from a group of datasets ([0056], wherein “Insight information” “in summary form” is a report, [0117], [0175], [0177]);
request, from a data source storing the group of datasets, source data associated with the set of data reports based on receiving the information identifying the set of data reports ([0059], [0061], [0147]);
receive, from the data source, the source data associated with the set of data reports ([0093] “data from a number of sources may include dataset metadata (e.g., descriptive data or information specifying dataset attributes”, [0097], [0171]);
associate the source data with data
process, based on associating the source data with the data
determine a level of usage of a dataset, of the group of datasets ([0068] “information about datasets over time to predict or estimate patterns of dataset interactions and usage”, [0122])
transmit, to a client device, information identifying the processed data representation
Reynolds does not explicitly teach, however Spitz discloses the source data with data lineage information and process ([0046]-[0047], [0133]); process, based on associating the source data with the data lineage information, the source data, the data lineage information, and a set of usage metrics associated with the set of data reports to generate a processed data representation ([0073]-[0074], [0077], [0087]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to include source data with data lineage information as disclosed by Spitz. Doing so would provide information that describes the life cycle of data records that are processed by a data processing system to help identify quality issues(Spitz [0040]).
Reynolds does not explicitly teach, however ZAROO discloses determine a set of key performance indicators, wherein the set of
when the level of usage of the dataset satisfies the first threshold level of usage: automatically allocate additional resources to support the dataset ([0077] “different storage tiers can be associated with different latencies, availabilities, costs, and/or other performance-related attributes”; move “higher cost to a second storage tier with higher latency, lower availability, and/or lower cost”; “and/or allow the first storage tier to accommodate new data”)(see NOTE below), and when the level of usage of the dataset does not satisfy the second threshold level of usage: automatically remove the dataset from the group of datasets, and automatically reallocate one or more resources associated with storing the dataset toward another purpose ([0077]-[0078], [0080]-[0083]); and transmit, to a client device, information identifying the processed data representation and resource allocation ([0044]. [0071]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to include resource allocation based on usage of the data sets as disclosed by ZAROO. Doing so would improve the storage utilization and/or query overhead associated with a data store (ZAROO [0020]).
Reynolds as modified by ZAROO does not explicitly teach a set of key performance indicators. Instead, ZAROO discloses performance indicators. However, as indicated by the applicant’s own specification, performance indicators can obviously be used as KPIs.
However, to merely obviate such reasoning, James discloses determine a set of key performance indicators, wherein the set of key performance indicators are used to identify a first threshold level and a second threshold level of usage for the processed data representation determine a level of usage of a dataset, of the group of datasets ([0517], [1044]-[1045]).
NOTE James further discloses automatically allocate additional resources to support the dataset ([0507], [0509]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Ellis to include KPIs and automatically allocating additional resources as disclosed by James. Doing so would provide insight into the IT environment, thereby improving the performance of components in the IT environment (James [0163).
Regarding claim 2, Reynolds as modified teaches the system of claim 1, wherein the one or more processors are further configured to:
generate a set of user interface visualizations of the processed data representation (Reynolds [0183], [0182], [0187]-[0188], [0189], Spitz [0090]-[0091]); and
wherein the one or more processors, to transmit the information identifying the processed data representation, are to: provide the set of user interface visualizations for display via a user interface of the client device (Reynolds [0185]-[0186], Spitz [0090]-[0091]).
Regarding claim 3, Reynolds as modified teaches the system of claim 1, wherein the one or more processors are further configured to:
generate a graph representation of the processed data representation, wherein the graph representation includes a set of nodes and a set of edges, the set of nodes representing a set of reports or tables, the set of edges representing a set of linkages between the reports or the tables (Reynolds [0070], [0176], [0180], F1, Spitz [0040], [0139] “dataflow graphs that include vertices (representing data processing components or datasets) connected by directed links (representing flows of work elements, i.e., data) between the vertices”);
generate a visualization of the graph representation (Reynolds [0054], [0070] “data points may be linked to each other, data arrangement may be represented as a graph, whereby the converted dataset (i.e., atomized dataset) forms a portion of the graph”, [0073], “atomized datasets may be represented as graphs”, [0078], [0182], Spitz [0040], [0139]); and
wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide the visualization of the graph representation for display via a user interface of the client device (Reynolds [0185]-[0186], Spitz [0090]-[0091]).
Regarding claim 4, Reynolds as modified teaches the system of claim 1, wherein the one or more processors are further configured to:
determine a status of one or more queries associated with the processed data representation (Reynolds [0065], [0066] “data specifying one or more of the following: a new dataset is created, a dataset is queried”, [0067] “total number of queries per unit time”, Spitz [0068]-[0069], [0085]-[0086]); and wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide information identifying the status of the one or more queries (Reynolds [0065], [0066], [0067], F19:1950).
Regarding claim 5, Reynolds as modified teaches the system of claim 1, wherein the one or more processors are further configured to:
determine a resource utilization associated with the set of data reports (Reynolds [0067] see “total number of queries per unit time”, [0078] “dataset in one operational state (of a number of operational states), and can be partitioned in another operational state … be linked together when loaded into a data store to provide resources for a query”, Spitz [0041] “data is available and system resources allow” [0080] “profile data [reports] can be analyzed … tracking engine has computing resources free for the analysis”); and
wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide information identifying the resource utilization ([0079] “provide resources for a query”, [0083]).
Regarding claim 6, Reynolds as modified teaches the system of claim 1, wherein the one or more processors are further configured to:
generate a set of visualizations of the set of usage metrics (Reynolds [0066], F1:134, F19:1950); and wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide information identifying the set of visualizations of the set of usage metrics (Reynolds [0066], F1:134, F19:1950).
Regarding claim 8, Reynolds teaches a non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a system, cause the system to:
receive information identifying a set of data reports generated from a group of datasets ([0056], wherein “Insight information” “in summary form” is a report, [0117], [0175], [0177]);
request, from a data source storing the group of datasets, source data associated with the set of data reports based on receiving the information identifying the set of data reports ([0059], [0061], [0147]);
receive, from the data source, the source data associated with the set of data reports ([0093] “data from a number of sources may include dataset metadata (e.g., descriptive data or information specifying dataset attributes”, [0097], [0171]);
associate the source data with data
process, based on associating the source data with the data
wherein the processed data representation includes information identifying a set of relationships associated with the group of datasets and the set of data reports ([0147]);
receive information identifying a dataset, of the group of datasets ([0192], [0194]);
generate, using the processed data representation, a visualization relating to usage of the dataset in connection with the set of reports ([0183], [0182], [0187]-[0188], [0189]);
provide, for display via a user interface of a client device, information identifying the visualization ([0185]-[0186])
determine a level of usage of the dataset ([0068] “information about datasets over time to predict or estimate patterns of dataset interactions and usage”, [0122])
Reynolds does not explicitly teach, however Spitz discloses the source data with data lineage information and process ([0046]-[0047], [0133]); process, based on associating the source data with the data lineage information, the source data, the data lineage information, and a set of usage metrics associated with the set of data reports to generate a processed data representation ([0073]-[0074], [0077], [0087]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to include source data with data lineage information as disclosed by Spitz. Doing so would provide information that describes the life cycle of data records that are processed by a data processing system to help identify quality issues(Spitz [0040]).
Reynolds does not explicitly teach, however ZAROO discloses determine a set of key performance indicators, wherein the set of
when the level of usage of the dataset satisfies the first threshold level of usage: automatically allocate additional resources to support the dataset ([0077] “different storage tiers can be associated with different latencies, availabilities, costs, and/or other performance-related attributes”; move “higher cost to a second storage tier with higher latency, lower availability, and/or lower cost”; “and/or allow the first storage tier to accommodate new data”)(see NOTE below), and when the level of usage of the dataset does not satisfy the second threshold level of usage: automatically remove the dataset from the group of datasets, and automatically reallocate one or more resources associated with storing the dataset toward another purpose ([0077]-[0078], [0080]-[0083]); and transmit, to a client device, information identifying the processed data representation and resource allocation ([0044]. [0071]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to include resource allocation based on usage of the data sets as disclosed by ZAROO. Doing so would improve the storage utilization and/or query overhead associated with a data store (ZAROO [0020]).
Reynolds as modified by ZAROO does not explicitly teach a set of key performance indicators. Instead, ZAROO discloses performance indicators. However, as indicated by the applicant’s own specification, performance indicators can obviously be used as KPIs.
However, to merely obviate such reasoning, James discloses determine a set of key performance indicators, wherein the set of key performance indicators are used to identify a first threshold level and a second threshold level of usage for the processed data representation determine a level of usage of a dataset, of the group of datasets ([0517], [1044]-[1045]).
NOTE James further discloses automatically allocate additional resources to support the dataset ([0507], [0509]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Ellis to include KPIs and automatically allocating additional resources as disclosed by James. Doing so would provide insight into the IT environment, thereby improving the performance of components in the IT environment (James [0163).
Reynolds does not explicitly teach, however Cherubini discloses determine a level of usage of the dataset in connection with resources (C3L62-67, C4L10-18, C8L1-12), when the level of usage of the dataset satisfies a first threshold level of usage (C4L36-40):
automatically allocate(C16L41-45) additional resources to support the dataset ([0031] “cache memory is allocated to store the data set, for the time interval, that corresponds to the high priority value”)t, and when the level of usage of the dataset does not satisfy a second threshold level of usage:
automatically remove the dataset from the group of datasets (C13L40-55, C14L1-17, 25-35), and automatically reallocate one or more resources associated with storing the dataset toward another purpose (C14L37-65, C16L28-30) and transmit, to a client device, information identifying the processed data representation and resource allocation (C15L9-20, C16L24-47).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to include resource allocation based on usage of the data sets as disclosed by Cherubini. Doing so would provide much higher storage efficiency (Cherubini C1261).
Claim 15 recites substantially the same limitations as claim 1, and is rejected for substantially the same reasons.
Regarding claims 7 and 17, Reynolds as modified teaches the system and the method, wherein the one or more processors are further configured to:
wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide information identifying the set of key performance indicators (Spitz [0070], [0081], [0083]-[0084], [0128]-[0130] [0130], James [0517], [1044]-[1045], ZAROO [0077]-[0078], [0080]-[0083]).
Regarding claims 9 and 18, Reynolds as modified teaches the medium and the method, wherein the one or more instructions further cause the system to:
determine the data lineage information for the group of datasets (Spitz [0023] “based on data lineage information relating to the downstream dataset, one or more upstream datasets that affect the data element”, [0047], [0049], [0068], Reynolds [0166]).
Regarding claims 10 and 19, Reynolds as modified teaches the medium and the method, wherein the one or more instructions, that cause the system to determine the data lineage information, cause the system to:
identify a first one or more datasets, of the group of datasets, that are an input to a process (Reynolds [0071], [0088], [0093], Spitz [0040] “upstream dataset can be a dataset input into the data processing system”, [0053]);
identify a second one or more datasets, of the group of datasets, that are an output of the process (Reynolds [0091]-[0092], Spitz [0040], [0046], [0049]); and
generate an association between the first one or more datasets and the second one or more datasets (Reynolds [0048] “atomized dataset may be linked … to other datasets, such as public datasets and, to form a collaborative dataset”, [0050] “dataset then may be supplemented by linking to protected data to form a larger atomized dataset that includes data from datasets”, Spitz [0047], [0133]-[0134]).
Regarding claim 11, Reynolds as modified teaches the non-transitory computer-readable medium of claim 10, wherein the one or more instructions, that cause the system to generate the visualization, cause the one or more instructions to:
generate the visualization based on one or more generated associations of the data lineage information (Reynolds [0185]-[0186], Spitz [0090]-[0091], [0133]-[0134]).
Regarding claims 12 and 20, Reynolds as modified teaches the medium and the method, wherein the one or more instructions further cause the system to:
identify a set of compliance requirements for the dataset (Reynolds [0070] as in formatting requirements, [0083] (see [0089] “atomized dataset may be formed as triples compliant with an RDF specification”), [0079] as in security requirements, [0082] “without authorization data may be rejected or invalidated”, [0118] see legal requirements for the dataset);
determine, based on the processed data representation, whether the set of compliance requirements is satisfied for the dataset (Spitz [0055], [0058], [0060], [0096], [0129]); and
wherein the one or more instructions, that cause the system to transmit information identifying the processed data representation, cause the system to: transmit information indicating whether the set of compliance requirements is satisfied for the dataset (Spitz [0055], [0058], [0060], [0096], [0129]).
Regarding claim 13, Reynolds as modified teaches the non-transitory computer-readable medium of claim 8, wherein the one or more instructions further cause the system to: identify an error associated with the dataset based on the processed data representation (Reynolds [0063]-[0064], [0090], [0093], [0120], Spitz [0049], [0051], [0134]); transform the dataset, using a set of data transformation rules, to generate a transformed dataset (Reynolds [0142], Spitz [0090]-[0091]); and update the group of datasets to include the transformed dataset (Reynolds [0070], [0124], [0142], Spitz [0133]).
Regarding claim 14, Reynolds as modified teaches the non-transitory computer-readable medium of claim 8, wherein the one or more instructions further cause the system to: identify an error associated with the dataset based on the processed data representation (Reynolds [0052], [0064], Spitz [0049], [0051], [0134]); transform the dataset, using a machine learning model, to generate a transformed dataset (Reynolds [0098], Spitz [0063]); and update the group of datasets to include the transformed dataset (Reynolds [0070], Spitz [0133]).
NOTE in analogous art US 20190362245 likewise discloses claim 14 in [0042], [0046] and further obviate the teachings Reynolds as modified.
Regarding claim 16, Reynolds as modified teaches the method of claim 15, further comprising: generating a set of user interface visualizations of the processed data representation (F1, 19); and wherein transmitting the information identifying the processed data representation comprises: providing the set of user interface visualizations for display via a user interface of the client device (Reynolds [0065]-[0066], [0185]-[0186], Spitz [0090]-[0091]).
Claim 3 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Reynolds as modified and in further view of Thompson et al. (US 20210334254).
Regarding claim 3, Reynolds as modified teaches the system of claim 1, as disclosed above, Thompson additionally discloses:
generate a graph representation of the processed data representation, wherein the graph representation includes a set of nodes and a set of edges, the set of nodes representing a set of reports or tables, the set of edges representing a set of linkages between the reports or the tables (F6A-8, [0011]); generate a visualization of the graph representation (F6A-8, [0011]); and wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide the visualization of the graph representation for display via a user interface of the client device (F6A-8, [0011]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to provide visualization of the graph representation as disclosed by Thompson. Doing so provides tracking of resource dependency relationships via a resource dependency system and the interactive user interfaces for displaying those resource dependency relationships (Thompson [0002]).
Claim 5 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Reynolds as modified and in further view of Conort et al. or (US 20220076164) or Gusat (US-20220180179).
Regarding claim 5, if Reynolds as modified teaches does not explicitly teach, however Conort discloses determine a resource utilization associated with the set of data reports; and wherein the one or more processors, to transmit information identifying the processed data representation, are to: provide information identifying the resource utilization ([0234]). Gusat teaches the same in [0100].
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to provide information identifying the resource utilization as disclosed by Conort or Gusat. Doing so would efficient allocation of processing resources (Conort [0234]).
Claims 11 and 16 is/are additionally rejected under 35 U.S.C. 103 as being unpatentable over Reynolds as modified and in further view of Weisman (US 20240256576).
Regarding claim 11, Reynolds as modified teaches the non-transitory computer-readable medium of claim 10, as disclosed above, Weisman additionally discloses generate the visualization based on one or more generated associations of the data lineage information ([0179]-[0180]).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to provide visualization based on one or more generated associations of the data lineage information as disclosed by Weisman. Doing so would provide efficient operation of a data processing system that generate technical insights (Weisman [0003], [0007]).
Regarding claim 16, Reynolds as modified teaches the method of claim 15 as disclosed above, Weisman additionally discloses generating a set of user interface visualizations of the processed data representation (F3-8, 9); and wherein transmitting the information identifying the processed data representation comprises: providing the set of user interface visualizations for display via a user interface of the client device (F3-9 and corresponding paragraphs).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify the teachings of Reynolds to provide visualizations as disclosed by Weisman. Doing so would provide efficient operation of a data processing system that generate technical insights (Weisman [0003], [0007]).
Response to Arguments
Applicant’s arguments, filed 12/02/2025, in regard to the presently amended claims, are addressed in the updated rejections to the claims above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is indicated on PTO-892.
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/POLINA G PEACH/Primary Examiner, Art Unit 2165 February 24, 2026