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 .
Claims 1 – 20 are pending in this Office Correspondence.
Priority
This application is a continuation of U.S. Patent App. Ser. No. 18/433,318, filed on February 5, 2024, now U.S. Patent No. 12292898, which is a continuation of U.S. Patent App. Ser. No. 17/866,091 filed on July 15, 2022, now U.S. Patent No. 11,893,039, which is a continuation of U.S. Patent App. Ser. No. 16/944,064 filed on July 30, 2020, now U.S. Patent No. 11,397,746.
Drawings
The Drawing filed on May 6, 2025 have been considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: The claims 1 recites a “method for detecting a user input. . ..; concurrently displaying . . . an insight item generated. . . ; insight item includes an insight visualization. . . ; and a relevance of one or more trends. . .” the claim(s) recites a series of steps and, therefore, is a process
Step 2A Prong One:
Claim 1 recites the limitations “detecting” and “generated”, which specifically recite
"detecting a user input to display a first visualization” and “an insight item generated based on the data from the data source.” These limitations are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting a "database" or "processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “detecting and generated (or generating)” in the context of this claim encompasses a user mentally, and with the aid of pen and paper, within the plurality of command sets, process the steps of detecting a user input to display a first visualization; and an insight item generated based on the data from the data source - so as allow a user to quickly and easily analyze the data in an efficient manner, and to allow the user to efficiently generate reports that capture the insights discovered during data analysis in an easy manner..
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements "displaying, in a graphical user interface (GUI), the first visualization." These limitations amount to a data gathering step and a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (see MPEP 2106.05(g)) The limitations represents an extra-solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presentation of collected and analyzed data. (See MPEP 2106.05(g)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: The limitation "displaying” is recognized by the courts as well-understood, routine , and conventional activities when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)(iv) Storing and retrieving information in memory, Versata Dev. Group Inc....; Receiving or transmitting data over a network, e.g., using the Internet to gather data, buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); (v) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Therefore, the claim is not patent eligible.
Accordingly, claims 8 and 15 are rejected for the same rational under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Therefore, claims 1, 8 and 15 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Further the limitations in the dependent claims 2 – 7, 9 – 14 and 16 – 20, respectively, merely specify the type of the data gathered and analyzed without adding significantly more. Analysis of the dependent claims is shown below.
Claim 2 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the insight item is based on the first visualization; the insight visualization is based on one or more data fields from the data source that are used in the first visualization; and the insight visualization shows trends for the one or more data fields that are used in the first visualization”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 9 and 16, since they also recite limitations that further elaborate on the abstract idea.
Claim 3 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the insight item is based on the first visualization; and the insight visualization is based on one or more data fields from the data source that are derived from the one or more data fields that are used in the first visualization”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limit the claim. Same rationale applies to claim 10 and 17, since they also recite limitations that further elaborate on the abstract idea.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “generating one or more candidate insight items associated with one or more insight scores; and determining the insight item based on the one or more candidate insight items having the one or more insight scores that exceed a threshold value”, which further elaborates on the abstract idea, since analyzing of information is a mental process, and therefore, does not meaningfully limits the claim.
Same rationale applies to claim 11 and 18, since they also recite limitations that further elaborate on the abstract idea.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim recites the additional limitation of “in response to selecting the insight item, displaying a second visualization in the GUI instead of the first visualization, wherein the second visualization is distinct from the first visualization”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 12 and 19, since they also recite limitations that further elaborate on the abstract idea.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim recites the additional limitation of “in response to selecting the insight item, displaying a scratch item that includes a thumbnail view of the first visualization for display in a scratch pane, wherein the scratch item, when selected, causes the computer system to display the first visualization in the GUI”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claims 13 and 20, since they also recite limitations that further elaborate on the abstract idea.
Claim 7 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional limitation of “the natural language narrative is displayed in the GUI, and provides context information for the insight item that is displayed in the GUI”, which is equivalent to merely saying “apply it”, and amounts to no more than mere instructions to implement the abstract idea on a computer. Mere instructions to apply an exception using a generic computer does not amount to significantly more. Same rationale applies to claim 14, since they also recite limitations that further elaborate on the abstract idea.
Therefore, claims 1 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more than the abstract idea.
Double Patenting
Claim 1 – 20 of this application is patentably indistinct from claims 1 – 20 of Application No. 18/433,318, now U.S. Patent 12,292,898. Pursuant to 37 CFR 1.78(f) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
The subject matter claimed in the instant application is fully disclosed in the co-pending application and is covered by the co-pending application since the co-pending application and the application are claiming common subject matter, as follows:
Instant application 19/200,603
Co-pending Application 18/433,318
USP 12,292,898
1. A method comprising:
at a computer system including one or more processors and memory storing one or more programs:
detecting a user input to display a first visualization;
in response to detecting the user input to display the first visualization:
concurrently displaying, in a graphical user interface (GUI), the first visualization that is associated with data from a data source and
an insight item generated based on the data from the data source,
wherein the insight item includes an insight visualization and an associated natural language narrative that explains a meaning for the insight item and a relevance of one or more trends associated with the insight item.
2. The method of claim 1, wherein: the insight item is based on the first visualization;
the insight visualization is based on one or more data fields from the data source that are used in the first visualization; and
the insight visualization shows trends for the one or more data fields that are used in the first visualization.
3. The method of claim 1, wherein: the insight item is based on the first visualization; and the insight visualization is based on one or more data fields from the data source that are derived from the one or more data fields that are used in the first visualization.
4. The method of claim 1, wherein generating the insight item comprises:
generating one or more candidate insight items associated with one or more insight scores; and
determining the insight item based on the one or more candidate insight items having the one or more insight scores that exceed a threshold value.
The method of claim 1, further comprising: in response to selecting the insight item, displaying a second visualization in the GUI instead of the first visualization, wherein the second visualization is distinct from the first visualization.
The method of claim 5, further comprising: in response to selecting the insight item, displaying a scratch item that includes a thumbnail view of the first visualization for display in a scratch pane, wherein the scratch item, when selected, causes the computer system to display the first visualization in the GUI.
7. The method of claim 1, wherein: the natural language narrative is displayed in the GUI, and provides context information for the insight item that is displayed in the GUI.
8. A non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by an electronic device that is communicatively coupled to a display generation component, cause the electronic device to:
detect a user input to display a first visualization;
in response to detecting the user input to display the first visualization:
concurrently display, in a graphical user interface (GUI), the first visualization that is associated with data from a data source and an insight item generated based on the data from the data source,
wherein the insight item includes an insight visualization and an associated natural language narrative that explains a meaning for the insight item and a relevance of one or more trends associated with the insight item.
9. The non-transitory computer readable storage medium of claim 8, wherein: the insight item is based on the first visualization; the insight visualization is based on one or more data fields from the data source that are used in the first visualization; and the insight visualization shows trends for the one or more data fields that are used in the first visualization.
10. The non-transitory computer readable storage medium of claim 8, wherein: the insight item is based on the first visualization; and the insight visualization is based on one or more data fields from the data source that are derived from the one or more data fields that are used in the first visualization.
11. The non-transitory computer readable storage medium of claim 8, wherein generating the insight item comprises: generating one or more candidate insight items associated with one or more insight scores; and determining the insight item based on the one or more candidate insight items having the one or more insight scores that exceed a threshold value.
12. The non-transitory computer readable storage medium of claim 8, wherein the one or more programs include instructions that, when executed by the computer system, cause the computer system to:
in response to selecting the insight item, display a second visualization in the GUI instead of the first visualization, wherein the second visualization is distinct from the first visualization.
13. The non-transitory computer readable storage medium of claim 12, wherein the one or more programs include instructions that, when executed by the computer system, cause the computer system to:
in response to selecting the insight item, display a scratch item that includes a thumbnail view of the first visualization for display in a scratch pane, wherein the scratch item, when selected, causes the computer system to display the first visualization in the GUI.
14. The non-transitory computer readable storage medium of claim 8, wherein: the natural language narrative is displayed in the GUI, and provides context information for the insight item that is displayed in the GUI.
15. A computer system, comprising: a display generation component that is communicatively coupled to the computer system; one or more processors; and
memory; wherein the memory stores one or more programs configured for execution by the one or more processors, and the one or more programs comprising instructions for:
detecting a user input to display a first visualization;
in response to detecting the user input to display the first visualization:
concurrently displaying, in a graphical user interface (GUI), the first visualization that is associated with data from a data source and an insight item generated based on the data from the data source,
wherein the insight item includes an insight visualization and an associated natural language narrative that explains a meaning for the insight item and a relevance of one or more trends associated with the insight item.
16. The computer system of claim 15, wherein: the insight item is based on the first visualization; the insight visualization is based on one or more data fields from the data source that are used in the first visualization; and the insight visualization shows trends for the one or more data fields that are used in the first visualization.
17. The computer system of claim 15, wherein: the insight item is based on the first visualization; and the insight visualization is based on one or more data fields from the data source that are derived from the one or more data fields that are used in the first visualization.
18. The computer system of claim 15, wherein generating the insight item comprises: generating one or more candidate insight items associated with one or more insight scores; and determining the insight item based on the one or more candidate insight items having the one or more insight scores that exceed a threshold value.
19. The computer system of claim 15, wherein the one or more programs comprising instructions for: in response to selecting the insight item, displaying a second visualization in the GUI instead of the first visualization, wherein the second visualization is distinct from the first visualization.
20. The computer system of claim 19, wherein the one or more programs comprising instructions for: in response to selecting the insight item, displaying a scratch item that includes a thumbnail view of the first visualization for display in a scratch pane, wherein the scratch item, when selected, causes the computer system to display the first visualization in the GUI.
1. A method for managing insights in reports and providing explanations for the insights, performed at a computing device having a display, one or more processors and memory storing one or more programs configured for execution by the one or more processors, the method comprising:
providing a primary report that is associated with a data model in a graphical user interface (GUI); and
in response to a user's interaction with the primary report or the user opening the primary report, performing actions:
generating an insight item and an associated natural language narrative based on the data model,
wherein the insight item includes an insight visualization and the associated natural language narrative that explains a meaning for the insight item and a relevance for one or more of a trend or an insight associated with the insight item;
employing the GUI to display an insights pane that provides an interactive display of the insight item and the associated natural language narrative; in response to a user's interaction with the insights pane displayed by the GUI, selecting the insight item; and displaying the insight item in the GUI.
2. The method of claim 1, further comprising: generating one or more insight items based on the primary report and the data model, wherein the one or more insight items correspond to one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
3. The method of claim 1, further comprising: generating one or more narratives that are displayed in the insights pane, wherein the one or more narratives explain a context for the displayed insight item.
7. The method of claim 1, wherein generating the insight item further comprises: employing one or more assessment models to generate one or more candidate insight items associated with one or more insight scores; and determining the insight item based on those candidate insight items having the one or more insight scores that exceed a threshold value.
4. The method of claim 1, further comprising: providing a correspondence between the insight item and one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
5. The method of claim 1, further comprising: in response to selecting the insight item, generating another report for display in the GUI instead of the primary report; and generating a scratch item that includes a thumbnail view of the primary report for display in a scratch pane.
6. The method of claim 1, further comprising: generating another report in response to selection of one of another insight item displayed in the insight pane or a scratch item displayed in a scratch pane, wherein the other report is displayed in the GUI instead of a currently displayed report.
8. A network computer for managing insights in reports and providing explanations for the insights, comprising: a memory that stores at least instructions; and one or more processors that execute instructions that are configured to cause performance of actions, including: providing a primary report that is associated with a data model in a graphical user interface (GUI); and in response to a user's interaction with the primary report or the user opening the primary report, performing actions: generating an insight item and an associated natural language narrative based on the data model, wherein the insight item includes an insight visualization and the associated natural language narrative that explains a meaning for the insight item and a relevance for one or more of a trend or an insight associated with the insight item; employing the GUI to display an insights pane that provides an interactive display of the insight item and the associated natural language narrative; in response to a user's interaction with the insights pane displayed by the GUI, selecting the insight item; and displaying the insight item in the GUI.
9. The network computer of claim 8, further comprising: generating one or more insight items based on the primary report and the data model, wherein the one or more insight items correspond to one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
10. The network computer of claim 8, further comprising: generating one or more narratives that are displayed in the insights pane, wherein the one or more narratives explain a context for the displayed insight item.
11. The network computer of claim 8, further comprising: providing a correspondence between the insight item and one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
12. The network computer of claim 8, further comprising: in response to selecting the insight item, generating another report for display in the GUI instead of the primary report; and generating a scratch item that includes a thumbnail view of the primary report for display in a scratch pane.
13. The network computer of claim 8, further comprising: generating another report in response to selection of one of another insight item displayed in the insight pane or a scratch item displayed in a scratch pane, wherein the other report is displayed in the GUI instead of a currently displayed report.
14. The network computer of claim 8, wherein generating the insight item further comprises: employing one or more assessment models to generate one or more candidate insight items associated with one or more insight scores; and determining the insight item based on those candidate insight items having the one or more insight scores that exceed a threshold value.
15. A processor readable non-transitory storage media that includes instructions that are configured to cause management of insights in reports and providing explanations for the insights, wherein execution of the instructions by one or more processors, enables performance of actions, comprising: providing a primary report that is associated with a data model in a graphical user interface (GUI); and in response to a user's interaction with the primary report or the user opening the primary report, performing actions: generating an insight item and an associated natural language narrative based on the data model, wherein the insight item includes an insight visualization and the associated natural language narrative that explains a meaning for the insight item and a relevance for one or more of a trend or an insight associated with the insight item; employing the GUI to display an insights pane that provides an interactive display of the insight item and the associated natural language narrative; in response to a user's interaction with the insights pane displayed by the GUI, selecting the insight item; and displaying the insight item in the GUI.
16. The processor readable non-transitory storage media of claim 15, further comprising: generating one or more insight items based on the primary report and the data model, wherein the one or more insight items correspond to one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
17. The processor readable non-transitory storage media of claim 15, further comprising: generating one or more narratives that are displayed in the insights pane, wherein the one or more narratives explain a context for the displayed insight item.
18. The processor readable non-transitory storage media of claim 15, further comprising: providing a correspondence between the insight item and one or more of analytical information, another data model, or a data source related to one or more reports that share one or more portions of the data model.
19. The processor readable non-transitory storage media of claim 15, further comprising: in response to selecting the insight item, generating an alternate report for display in the GUI instead of the primary report; and generating a scratch item that includes a thumbnail view of the primary report for display in a scratch pane.
20. The processor readable non-transitory storage media of claim 15, further comprising: generating another report in response to selection of one of another insight item displayed in the insight pane or a scratch item displayed in a scratch pane, wherein the other report is displayed in the GUI instead of a currently displayed report.
Claims 1 – 20 are rejected under the judicially created doctrine of obviousness-type double patenting as being unpatentable over claims 1 – 20 of co-pending application 18/433,318, now U.S. Patent 12,292,898.
Although the conflicting claims are not identical, they are not patentably distinct from each other because of corresponding language that recites virtually all of the same elements and functions claimed in the claim 1 of instant application and claim 1 of the copending invention, e.g., “an insight item generated based on the data from the data source, wherein the insight item includes an insight visualization and an associated natural language narrative that explains a meaning for the insight item and a relevance of one or more trends associated with the insight item.”
The claimed differences would be obvious to a programmer of ordinary skill because the instant claims are merely broader and/or alternate variations of the claims recited in the co-pending application.
Because the instant claims merely add/delete/modify the additional elements from the set of elements and functions claimed in the parent application, such modifications would be readily apparent to a programmer of ordinary skill.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the invention was made to omit/delete/modify the additional elements of claim 1 to arrive at the claim 1 of the instant application because the person would have realized that the remaining element would perform the same functions as before.
It would have been obvious to modify instant claims in order providing the interactive interface for data analysis and report generation in an effective manner and to allow a user to quickly and easily analyze the data in an efficient manner, and to allow the user to efficiently generate reports that capture the insights discovered during data analysis in an easy manner.
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.
Claims 1 – 20 are rejected under 35 U.S.C. 103 as being unpatentable over USPGPUB 2016/0103908 issued to Tristan Fletcher et al. (hereinafter “Fletcher”) and in view of USPGPUB 2018/0210936 issued to Shad Reynolds et al. (hereinafter “Reynolds”).
With respect to claims 1, 8 and 15, Fletcher teaches a method, a program product and a system, comprising: at a computer system including one or more processors and memory storing one or more programs:
detecting a user input to display a first visualization (Fletcher, Para [0230]: a dashboard-creation graphical interface can be provided to define a service-monitoring dashboard based on user input allowing different users to each create a customized service-monitoring dashboard. Users can select an image for the service-monitoring dashboard (e.g., image for the background of a service-monitoring dashboard, image for an entity and/or service for service-monitoring dashboard);
in response to detecting the user input to display the first visualization: concurrently displaying, in a graphical user interface (GUI), the first visualization that is associated with data from a data source and an insight item generated based on the data from the data source (Fletcher, ¶ [0265], teaches deep dive module works in connection with UI module to present a wizard for creation and editing of the deep dive visual interface, to generate the deep dive visual interface in response to user input, and to cause display of the deep dive visual interface including the one or more graphical visualizations; ¶ [0551], teaches an example of a GUI of a service monitoring system for defining a search query for a KPI using a data model, in accordance with one or more implementations of the present disclosure. GUI can facilitate user input specifying a name and optionally a description for a KPI, reports for a service. For example, the aspect of the service to monitor can be CPU utilization, and the KPI name can be CPU Usage. If button is selected, GUI displays buttons for defining the search query for the KPI using a data model; and ¶ [0638], a specified action (e.g., generate alert, add to report) will be triggered; create notable event, generate alert, add to incident report)),
wherein the insight item includes an insight visualization and an associated [natural language narrative] that explains a meaning for the insight item and a relevance of one or more trends associated with the insight item (Fletcher, ¶ [0168], teaches FIG. 60 illustrates an example of a GUI for selecting a data model corresponding to a graphical visualization along a time-based graph lane in a visual interface, Fletcher, ¶ [0265], teaches deep dive module works in connection with UI module to present a wizard for creation and editing of the deep dive visual interface, to generate the deep dive visual interface in response to user input, and to cause display of the deep dive visual interface including the one or more graphical visualizations; and ¶ [0638], a specified action (e.g., generate alert, add to report) will be triggered; create notable event, generate alert, add to incident report) and (Fletcher, ¶ [0557], teaches a user may request the service-monitoring dashboard to be displayed, and the computing machine can cause the search query for the KPI to execute in response to the request to produce the value for the KPI).
Fletcher teaches claimed invention substantially as claimed. However, Fletcher does not explicitly teach an associated natural language narrative.
Reynolds discloses an associated natural language narrative in ¶ [0117], User interface element may be configured as a user input to generate data signals to initiate inclusion of another set of data for creating a new dataset. User interface element may be configured to generate data signals to associate a narrative to the dataset, whereby the narrative may be of sufficient size to convey sufficient detail to those potential collaborators that may be interested in using the dataset for their own or different purposes.
Both Fletcher and Reynolds are same field of endeavor and they are both in the data processing art and therefore, are combinable/modifiable.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the teachings of Fletcher's performing a search query in response to detecting a scheduled time for a key performance indicator by associating values in machine data including disparate field names in accordance with disparate schemas with a field identifier specified in a data model with the teachings of Reynolds' user interface that enhance dataset attribute data for dataset in order to provide natural language narratives that may be employed in user interfaces or reports explain the meaning or context of an insight item.
Modification would perform statistical analysis or machine learning may be configured to facilitate curation of datasets, as well as assisting in classifying and tagging data with relevant datasets attributes.
In a modified system a user can correct or otherwise provide for enhanced accuracy in atomized dataset generation during the dataset ingestion and/or graph formation processes.
As to claims 2, 9 and 16, the insight item is based on the first visualization; the insight visualization is based on one or more data fields from the data source that are used in the first visualization; and the insight visualization shows trends for the one or more data fields that are used in the first visualization (Fletcher, ¶ [0551], teaches an example of a GUI of a service monitoring system for defining a search query for a Using Key Performance Indicator (KPI using a data model (Data sources equate model, record, etc.) GUI can facilitate user input specifying a name and optionally a description (insight item(s)) for a KPI for a service (name is “field”). For example, the aspect of the service to monitor can be CPU utilization, and the KPI name can be CPU Usage. If button is selected, GUI displays (visualization equates figure, image display) buttons for defining the search query for the KPI (may also be insight item(s)) using a data model).
As to claims 3, 10 and 17, the insight item is based on the first visualization; and the insight visualization is based on one or more data fields from the data source that are derived from the one or more data fields that are used in the first visualization (Fletcher, ¶ [0551], teaches an example of a GUI of a service monitoring system for defining a search query for a KPI using a data model, in accordance with one or more implementations of the present disclosure. GUI can facilitate user input specifying a name and optionally a description for a KPI for a service. For example, the aspect of the service to monitor can be CPU utilization, and the KPI name can be CPU Usage. If button is selected, GUI displays buttons for defining the search query for the KPI using a data model; and Fletcher, ¶ [0557], teaches a user may request the service-monitoring dashboard to be displayed, and the computing machine can cause the search query for the KPI to execute in response to the request to produce the value for the KPI and the produced value can be displayed in the service-monitoring dashboard); and generating a scratch item that includes a thumbnail view of the primary visualization for display in a scratch panel (Fletcher, ¶ [0885], teaches when drop-down menu is selected by a user, GUI can be displayed. GUI for facilitating user input specifying a time range to use when executing a search query defining a KPI. For real-time execution, for example, used to update the service-monitoring dashboard in real-time, the time range for machine data can be a specified time window (e.g., 30-second window, 1-minute window, 1-hour window, etc.) from the execution time (e.g., each time the query is executed, the events with timestamps within the specified time window from the query execution time will be used). For relative execution, the time range can be historical (e.g., yesterday, previous week, etc.) or based on a specified time window from the requested time or scheduled time (e.g., last 15 minutes, last 4 hours, etc.). For example, the historical time range “Yesterday” can be selected for relative execution. In another example, the window time range “Last 15 minutes” can be selected for relative execution).
As to claims 4, 11 and 18, generating the insight item comprises: generating one or more candidate insight items associated with one or more insight scores (Fletcher, ¶ [0628], teaches the computing machine derives one or more values for each of the identified KPIs. The search query for each KPI to execute to produce a corresponding value and the search query for a particular KPI is executed based on a frequency of monitoring assigned to the particular KPI. When the frequency of monitoring for a KPI is set to a time period, for example, High Frequency (e.g., 2 minutes), a value for the KPI is derived each time the search query defining the KPI is executed every 2 minutes. The derived value(s) for each KPI can be stored in an index. In one implementation, when a KPI is assigned a frequency of monitoring of do not measure or is assigned a zero frequency (no frequency), no value is produced (the search query for the KPI is not executed) for the respective KPI and no values for the respective KPI are stored in the data store); and
determining the insight item based on the one or more candidate insight items having the one or more insight scores that exceed a threshold value (Fletcher, ¶ [0629-0630], teaches calculates a value for an aggregate KPI score for the service using the value(s) from each of the KPIs of the service. The value for the aggregate KPI score indicates an overall performance of the service. For example, a Web Hosting service may have 10 KPIs and one of the 10 KPIs may have a frequency of monitoring set to Do Not Monitor. The other nine KPIs may be assigned various frequencies of monitoring. The computing machine can access the values produced for the nine KPIs in the data store to calculate the value for the aggregate KPI score for the service. Based on the values obtained from the data store, if the values produced by the search queries for 8 of the 9 KPIs indicate that the corresponding KPI is in a normal state, then the value for an aggregate KPI score may indicate that the overall performance of the service is normal … An aggregate KPI score can be calculated by adding the values of all KPIs of the same service together).
As to claims 5, 12 and 19, in response to selecting the insight item, displaying a second visualization in the GUI instead of the first visualization, wherein the second visualization is distinct from the first visualization (Fletcher, ¶ [0876], teaches implementations of widgets for representing KPIs. In response to a user selection of a style setting for the KPI widget, one or more GUIs can be presented for customizing the selected KPI widget for the KPI. Input can be received via the GUIs to select a label for a KPI widget and the metric unit to be used for the KPI value with the KPI widget. Fletcher, ¶ [0880], teaches the computing machine stores the resulting dashboard template in a data store. The dashboard template can be saved in response to a user request. For example, a request to save the dashboard template may be received upon selection of a save button (e.g., save button 3612 in GUI 3600 of FIG. 36). In one implementation, an image source byte for the resulting dashboard template is stored in a data store. In one implementation, an image source location for the resulting dashboard template is stored in a data store. The resulting dashboard template can be stored in a structure where each item (e.g., widget, line, text, image, shape, connector, etc.) has properties specified by the service-monitoring dashboard creation GUI. Fletcher, ¶ [0992], teaches the visual representations can be automatically updated in response to a specific user request, when the aggregate KPIs and aspect KPIs can be recalculated outside of their normal schedules specifically for the purpose of updating service-monitoring page. In yet another implementation, the visual representations can be static such that they do not change once initially displayed. The aggregate KPIs and aspect KPIs can be determined in response to the initial user request to view the service-monitoring page, and then displayed and refreshed at predefined time intervals or in real time once new values are calculated based on KPI monitoring parameters discussed above. Alternatively, the aggregate KPIs and aspect KPIs can be displayed, but not updated until a subsequent request to view the service-monitoring page is received).
As to claims 6, 13 and 20, in response to selecting the insight item, displaying a scratch item that includes a thumbnail view of the first visualization for display in a scratch pane, wherein the scratch item, when selected, causes the computer system to display the first visualization in the GUI (Fletcher, ¶ [0557], teaches a user may request the service-monitoring dashboard (may be “scratch pane) to be displayed (visualized/visualization), and the computing machine can cause the search query for the KPI (may be scratch item) to execute in response to the request to produce the value for the KPI and the produced value can be displayed in the service-monitoring dashboard. (visualized/visualization)); ¶ [0885], teaches when drop-down menu is selected by a user, GUI can be displayed (visualization). GUI for facilitating user input specifying a time range to use when executing a search query defining a KPI, report. For real-time execution, for example, used to update the service-monitoring dashboard in real-time, the time range for machine data can be a specified time window (e.g., 30-second window, 1-minute window, 1-hour window, etc.) from the execution time (e.g., each time the query is executed, the events with timestamps within the specified time window from the query execution time will be used. Limited or temporary time discussed). For relative execution, the time range can be historical (e.g., yesterday, previous week, etc.) or based on a specified time window from the requested time or scheduled time (e.g., last 15 minutes, last 4 hours, etc.) (here, data files that uses limited time which equates scratch file or scratch items). For example, the historical time range “Yesterday” can be selected for relative execution. In another example, the window time range “Last 15 minutes” can be selected for relative execution. Relates to temporary time for items which is same as scratch time/item(s); ¶ [0981], GUI can include one or more tiles each representing a service-monitoring dashboard. The GUI can also include one or more tiles representing a service-related alarm, or the value of a particular KPI. A tile is a thumbnail image (scratch item) of a service-monitoring dashboard (Scratch pane). When a service-monitoring dashboard is created, a tile representing the service-monitoring dashboard can be displayed in the GUI).
As to claims 7 and 14, the natural language narrative is displayed in the GUI, and provides context information for the insight item that is displayed in the GUI (Fletcher, ¶ [0551], teaches an example of a GUI of a service monitoring system for defining a search query for a KPI using a data model, in accordance with one or more implementations of the present disclosure. GUI can facilitate user input specifying a name and optionally a description for a KPI for a service. For example, the aspect of the service to monitor can be CPU utilization, and the KPI name can be CPU Usage. If button is selected, GUI displays buttons for defining the search query for the KPI using a data model. Fletcher, ¶ [0885], teaches when drop-down menu is selected by a user, GUI can be displayed. GUI for facilitating user input specifying a time range to use when executing a search query defining a KPI, in accordance with one or more implementations of the present disclosure. For real-time execution, for example, used to update the service-monitoring dashboard in real-time, the time range for machine data can be a specified time window (e.g., 30-second window, 1-minute window, 1-hour window, etc.) from the execution time (e.g., each time the query is executed, the events with timestamps within the specified time window from the query execution time will be used). For relative execution, the time range can be historical (e.g., yesterday, previous week, etc.) or based on a specified time window from the requested time or scheduled time (e.g., last 15 minutes, last 4 hours, etc.). For example, the historical time range “Yesterday” can be selected for relative execution. In another example, the window time range “Last 15 minutes” can be selected for relative execution).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Singh (USPAT 7010364), which discloses collecting batch process data comprising measurements of ongoing process, and performing analysis of collection of process data before batch process completion. An indicator of process condition is determined, based on predicated future data from ongoing batch process. The indicator and control region are displayed, in a three-dimensional view to the user.
Toomre (USPGPUB 2014/01562233), discloses analyzing spatial point patterns and visualizing the results is presented. The method includes simulating at least one point set within a region using a point process, dividing the region into a plurality of elements, determining scores for both real data and simulated data for each element by weighting the point sets within a domain of a predetermined kernel, and comparing scores for each element, computing confidence intervals for at least one confidence level having a predetermined statistical significance; and providing a visualization to identify clusters and exclusion zones.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID AL ALAM whose telephone number is (571)272-4030. The examiner can normally be reached M-F 8:00 AM-5:00 PM.
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February 5, 2026
/SHAHID A ALAM/Primary Examiner, Art Unit 2161