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 .
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
3. The information disclosure statement (IDS) submitted on 02/11/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. 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.
7. Claim(s) 1, 2, 13, 14 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Talbot et al. (US 2023/0385341 A1) in view of Ding et al. (US 2023/0012578 A1).
8. With reference to claim 1, Talbot teaches A data analysis method, comprising: displaying a target data page, wherein the target data page is used to display target data; (“Thus methods, systems, and graphical user interfaces are provided for interactive visual analysis of a data set.” [0029] “a web server 320 (such as an HTTP server), which receives web requests from users and responds by providing responsive web pages or other resources; a data visualization web application 322, which may be downloaded and executed by a web browser 220 on a user's computing device 200.” [0075-0076] “a data visualization generator 290, which generates and displays data visualizations according to user-selected data sources and data fields, as well as one or more object models that describe the data sources 106. The operation of the data visualization generator is described above with respect to a computing device 200;” [0079] “the data visualization region 412 also has a large space for displaying a visual graphic. … The data visualization application 222 (or web application 322) displays the generated graphic 428 in the data visualization region 412.” [0089-0090]) Talbot also teaches displaying a first intelligent object page in response to a trigger operation on a first intelligent object in the target data page; (“Some implementations use an object model 108 to build the appropriate data visualizations. In some instances, an object model applies to one data source (e.g., one SQL database or one spreadsheet file), but an object model may encompass two or more data sources. …as a user adds data fields to the visual specification (e.g., indirectly by using the graphical user interface to place data fields onto shelves), the data visualization application 222 (or web application 322) groups (110) together the user-selected data fields according to the object model 108. … “The data visualization application 222 (or web application 322) queries (112) the data sources 106 for the first data field set 294, and then generates a first data visualization 122 corresponding to the retrieved data. The first data visualization 122 is constructed according to the visual variables 282 in the visual specification 104 that have assigned data fields 284 from the first data field set 294. When there is only one data field set 294, all of the information in the visual specification 104 is used to build the first data visualization 122. When there are two or more data field sets 294, the first data visualization 122 is based on a first visual sub-specification consisting of all information relevant to the first data field set 294.” [0046-0048] “one or more object models 108, which identify the structure of the data sources 106. In an object model, the data fields (attributes) are organized into classes, where the attributes in each class have a one-to-one correspondence with each other.” [0064] “a web server 320 (such as an HTTP server), which receives web requests from users and responds by providing responsive web pages or other resources; a data visualization web application 322, which may be downloaded and executed by a web browser 220 on a user's computing device 200. … one or more object models 108, as described above for a computing device 200; a data visualization generator 290, which generates and displays data visualizations according to user-selected data sources and data fields, as well as one or more object models that describe the data sources 106. The operation of the data visualization generator is described above with respect to a computing device 200;” [0075-0079] “the user interface 102 includes a schema information region, which is also referred to as a data pane. The schema information region provides named data elements (e.g., field names) that may be selected and used to build a data visualization. In some implementations, the list of field names is separated into a group of dimensions and a group of measures (typically numeric quantities). Some implementations also include a list of parameters. The graphical user interface 102 also includes a data visualization region 412. … the data visualization region 412 also has a large space for displaying a visual graphic.” [0088-0089]) Talbot further teaches displaying a target dashboard page in response to target information entered on the first intelligent object page and used to create a dashboard for the target data, wherein the target dashboard page comprises at least one data chart, (“The data visualization application 222 (or web application 322) queries (112) the data sources 106 for the first data field set 294, and then generates a first data visualization 122 corresponding to the retrieved data. The first data visualization 122 is constructed according to the visual variables 282 in the visual specification 104 that have assigned data fields 284 from the first data field set 294. When there is only one data field set 294, all of the information in the visual specification 104 is used to build the first data visualization 122. When there are two or more data field sets 294, the first data visualization 122 is based on a first visual sub-specification consisting of all information relevant to the first data field set 294.” [0048] “Returning to the example view shown in FIG. 1B, sales for two product dimensions (Category 136 and Sub-Category 138) are displayed as a bar chart 140. Suppose a user wants to view data for the largest marks in each pane. In some implementations (e.g., the desktop version), the user selects the marks, right-clicks (or control-clicks) in the view, and selects ‘View Data’ on the context menu. Alternatively, a user selects Analysis, then selects the View Data menu item. In some implementations (e.g., online or server versions), the user selects the marks and clicks ‘View Data’ on the Tooltip menu.” [0051] “a data visualization application 222, which provides a graphical user interface 102 for a user to construct visual graphics (e.g., an individual data visualization or a dashboard with a plurality of related data visualizations). In some implementations, the data visualization application 222 executes as a standalone application (e.g., a desktop application). In some implementations, the data visualization application 222 executes within the web browser 220 (e.g., as a web application 322); a graphical user interface 102, which enables a user to build a data visualization by specifying elements visually, as illustrated in FIG. 4 below; in some implementations, the user interface 102 includes a plurality of shelf regions 250, which are used to specify characteristics of a desired data visualization. In some implementations, the shelf regions 250 include a columns shelf 230 and a rows shelf 232, which are used to specify the arrangement of data in the desired data visualization. In general, fields that are placed on the columns shelf 230 are used to define the columns in the data visualization (e.g., the x-coordinates of visual marks). Similarly, the fields placed on the rows shelf 232 define the rows in the data visualization (e.g., the y-coordinates of the visual marks).” [0060-0062] “a web server 320 (such as an HTTP server), which receives web requests from users and responds by providing responsive web pages or other resources; a data visualization web application 322, which may be downloaded and executed by a web browser 220 on a user's computing device 200. … one or more object models 108, as described above for a computing device 200; a data visualization generator 290, which generates and displays data visualizations according to user-selected data sources and data fields, as well as one or more object models that describe the data sources 106. The operation of the data visualization generator is described above with respect to a computing device 200;” [0075-0079] “In the example shown in FIGS. 7A-7C, the data visualization 702 is a bar chart for SUM(Sales−Profits) for different products. As shown, the referenced calculation is shown in a separate tab 704 (similar to how measures are displayed in separate tabs, as explained above in reference to FIGS. 5A-5D).” [0102])
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Talbot does not explicitly teach the at least one data chart is obtained by analyzing the target data based on analysis dimension information, and the analysis dimension information is determined based on a data dimension in the target data. This is what Ding teaches (“A user of the user computing device 102 may request access to a multi-dimensional data structure stored in the data store 104. This request may be in the form of interaction with a webpage produced by the processor 108 via the network interface 114. The processor 108 upon execution of the insight engine 112 performs insight analysis of one or more datasets of the requested multi-dimensional data structure or other unrequested multi-dimensional data structures. Upon completion of the insight analysis, one or more insights may be presented via the display 120 to the user at the time soon after the user making the request via the user interface 122.” [0022] “Multi-dimensional data is conceptually organized in a tabular format (i.e., multi-dimensional table) that includes a set of records as rows in the table, and each record is represented by a set of properties as columns in the table.” [0024] “Dimensions represent basic and intrinsic properties of records in the table, e.g., “Brand” and “Year” for the car sales dataset. Dimensions are used to group and filter the records, based on equality and inequality of the dimension values. … Categorical and ordinal are used to categorize dimensions mainly for characterizing their intrinsic ability to reflect ordering, but not to limit their usage scenarios. There may be ordinal dimensions with non-numerical values (e.g., an “Age” dimension could also take “Infants”, “Children”, “Teens”, and etc. as values). When ordering is not an important aspect in the analysis task, ordinal dimensions may be used just as categorical dimensions.” [0026-0027] “So based on the preceding discussion about multi-dimensional data, attributes and attribute pipeline, an example insight definition is as follows: Definition 1 Basic insight—given a multi-dimensional dataset as analysis context, a basic insight is a fact of Single insight—a subspace with an significantly uncommon value (based on a threshold value/setting) of attribute compared to its sibling subspaces or parent subspaces; Compound insight—a significantly uncommon relationship among some subspaces.” [0042-0045]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ding into Talbot, in order to approach desirable analysis results more quickly.
9. With reference to claim 2, Talbot teaches the displaying a target dashboard page in response to target information entered on the first intelligent object page and used to create a dashboard for the target data comprises: displaying first prompt information on the first intelligent object page in response to the target information entered on the first intelligent object page and used to create the dashboard for the target data, wherein the first prompt information comprises the information corresponding to a data chart in the dashboard to be created; and displaying the target dashboard page based on the target data and information in response to a confirmation creation operation on the first intelligent object page. (“The data visualization application 222 (or web application 322) queries (112) the data sources 106 for the first data field set 294, and then generates a first data visualization 122 corresponding to the retrieved data. The first data visualization 122 is constructed according to the visual variables 282 in the visual specification 104 that have assigned data fields 284 from the first data field set 294. When there is only one data field set 294, all of the information in the visual specification 104 is used to build the first data visualization 122. When there are two or more data field sets 294, the first data visualization 122 is based on a first visual sub-specification consisting of all information relevant to the first data field set 294.” [0048] “Returning to the example view shown in FIG. 1B, sales for two product dimensions (Category 136 and Sub-Category 138) are displayed as a bar chart 140. Suppose a user wants to view data for the largest marks in each pane. In some implementations (e.g., the desktop version), the user selects the marks, right-clicks (or control-clicks) in the view, and selects ‘View Data’ on the context menu. Alternatively, a user selects Analysis, then selects the View Data menu item. In some implementations (e.g., online or server versions), the user selects the marks and clicks ‘View Data’ on the Tooltip menu.” [0051] “a data visualization application 222, which provides a graphical user interface 102 for a user to construct visual graphics (e.g., an individual data visualization or a dashboard with a plurality of related data visualizations). In some implementations, the data visualization application 222 executes as a standalone application (e.g., a desktop application). In some implementations, the data visualization application 222 executes within the web browser 220 (e.g., as a web application 322); a graphical user interface 102, which enables a user to build a data visualization by specifying elements visually, as illustrated in FIG. 4 below; in some implementations, the user interface 102 includes a plurality of shelf regions 250, which are used to specify characteristics of a desired data visualization. In some implementations, the shelf regions 250 include a columns shelf 230 and a rows shelf 232, which are used to specify the arrangement of data in the desired data visualization. In general, fields that are placed on the columns shelf 230 are used to define the columns in the data visualization (e.g., the x-coordinates of visual marks). Similarly, the fields placed on the rows shelf 232 define the rows in the data visualization (e.g., the y-coordinates of the visual marks).” [0060-0062] “a web server 320 (such as an HTTP server), which receives web requests from users and responds by providing responsive web pages or other resources; a data visualization web application 322, which may be downloaded and executed by a web browser 220 on a user's computing device 200. … one or more object models 108, as described above for a computing device 200; a data visualization generator 290, which generates and displays data visualizations according to user-selected data sources and data fields, as well as one or more object models that describe the data sources 106. The operation of the data visualization generator is described above with respect to a computing device 200;” [0075-0079] “In the example shown in FIGS. 7A-7C, the data visualization 702 is a bar chart for SUM(Sales−Profits) for different products. As shown, the referenced calculation is shown in a separate tab 704 (similar to how measures are displayed in separate tabs, as explained above in reference to FIGS. 5A-5D).” [0102])
Talbot does not explicitly teach the analysis dimension information. This is what Ding teaches (“A user of the user computing device 102 may request access to a multi-dimensional data structure stored in the data store 104. This request may be in the form of interaction with a webpage produced by the processor 108 via the network interface 114. The processor 108 upon execution of the insight engine 112 performs insight analysis of one or more datasets of the requested multi-dimensional data structure or other unrequested multi-dimensional data structures. Upon completion of the insight analysis, one or more insights may be presented via the display 120 to the user at the time soon after the user making the request via the user interface 122.” [0022] “Multi-dimensional data is conceptually organized in a tabular format (i.e., multi-dimensional table) that includes a set of records as rows in the table, and each record is represented by a set of properties as columns in the table.” [0024] “Dimensions represent basic and intrinsic properties of records in the table, e.g., “Brand” and “Year” for the car sales dataset. Dimensions are used to group and filter the records, based on equality and inequality of the dimension values. … Categorical and ordinal are used to categorize dimensions mainly for characterizing their intrinsic ability to reflect ordering, but not to limit their usage scenarios. There may be ordinal dimensions with non-numerical values (e.g., an “Age” dimension could also take “Infants”, “Children”, “Teens”, and etc. as values). When ordering is not an important aspect in the analysis task, ordinal dimensions may be used just as categorical dimensions.” [0026-0027]) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ding into Talbot, in order to approach desirable analysis results more quickly.
10. Claim 13 is similar in scope to claim 1, and thus is rejected under similar rationale. Talbot additionally teaches An electronic device, comprising: a storage apparatus, having a computer program stored thereon; and a processing apparatus, configured to execute the computer program in the storage apparatus to implement a data analysis method, (“a system for facilitating visualization of object models for data sources includes one or more processors, memory, and one or more programs stored in the memory. The programs are configured for execution by the one or more processors.” [0027] “FIG. 2 is a block diagram illustrating a computing device 200 that can execute the data visualization application 222 or the data visualization web application 322 to display a data visualization 122. … A computing device 200 typically includes one or more processing units/cores (CPUs) 202 for executing modules, programs, and/or instructions stored in the memory 214 and thereby performing processing operations; one or more network or other communications interfaces 204; memory 214; and one or more communication buses 212 for interconnecting these components.” [0055])
11. Claim 14 is similar in scope to claim 2, and thus is rejected under similar rationale.
12. Claim 20 is similar in scope to claim 1, and thus is rejected under similar rationale. Talbot additionally teaches A non-transient computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processing apparatus, implements a data analysis method, (“a non-transitory computer readable storage medium stores one or more programs configured for execution by a computer system having one or more processors. The one or more programs include instructions for performing any of the methods described herein.” [0028] “FIG. 2 is a block diagram illustrating a computing device 200 that can execute the data visualization application 222 or the data visualization web application 322 to display a data visualization 122. … A computing device 200 typically includes one or more processing units/cores (CPUs) 202 for executing modules, programs, and/or instructions stored in the memory 214 and thereby performing processing operations; one or more network or other communications interfaces 204; memory 214; and one or more communication buses 212 for interconnecting these components. … the memory 214 includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. In some implementations, the memory 214 includes one or more storage devices remotely located from the CPUs 202. The memory 214, or alternatively the non-volatile memory devices within the memory 214, comprises a non-transitory computer-readable storage medium. In some implementations, the memory 214, or the computer-readable storage medium of the memory 214, stores the following programs, modules, and data structures, or a subset thereof;” [0055-0056])
Allowable Subject Matter
13. Claims 3-12 and 15-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Regarding claims 3, 9 and 15, the prior art of record fails to either individually or in combination teach the claimed feature of “a dashboard frame is displayed on the initial dashboard page, the dashboard frame comprises at least one chart card, and each chart card corresponds to one data chart to be generated; and displaying the target dashboard page based on the dashboard frame and the target data in response to a confirmation generation operation on the initial dashboard page.”
Claims 4, 11, 12 and 16 are also objected to for depending from claims 3 and 15.
Regarding claims 5, 10 and 17, the prior art of record fails to either individually or in combination teach the claimed feature of “displaying an exploration panel in a suspension manner on an upper layer of a target data chart in response to a trigger operation on the chart exploration control corresponding to the target data chart; and updating display of the target data chart or displaying a result of a target operation on the target data chart in the exploration panel based on the target operation in response to the target operation on the target data chart in the exploration panel.”
Claims 6-8, 18 and 19 are also objected to for depending from claims 5 and 17.
Conclusion
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Chin whose telephone number is (571)270-3697. The examiner can normally be reached on Monday-Friday 8:00 AM-4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Kent Chang can be reached on (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/MICHELLE CHIN/
Primary Examiner, Art Unit 2614
Prosecution Insights