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 .
Claim Rejections - 35 USC § 102
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,6,8,9,11,14,15,17,18,21, and 23 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by US 20150082224 A1; HATHAWAY; WILLIAM M. et al. (hereinafter Hathaway)
Regarding claim 1, Hathaway teaches A visual data analysis method, comprising: obtaining multiple types of data sources, and establishing a connection with each type of data source, wherein the type of data source is used to represent a source from which data is obtained; (Hathaway [0004] data analyses at different stages of projects, for different projects, and different tasks. Providing support for such projects, the software application may be required to provide an array of data analytical tools and to access and store different types of data at different times and points of a project. [0033] the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study. [0046] a data source is understood to contain one or more sets of data or information just as a spreadsheet may contain specific collections of data arranged in rows under a column. The data identified to a set will be of a specific type or category [49-56] elaborates on the matter [FIG.1A-B in conjunction with FIG.8B] shows obtaining multiple types of data sources, and establishing a connection with each type of data source, wherein the type of data source is used to represent a source from which data is obtained) displaying, through a visual page, each piece of table information contained in each type of data source with which the connection is made; (Hathaway [0011] displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type. Preferably, the visual attributes include a first reflective of data type and a second containing a graphical display derived from the associated data set.[0045] the collection of data sources, is logically arranged and accessible in the software (e.g., via the user interface 310). In most operating modes, a user typically deals with, and makes active, a specific Project in the Projects Database 312 (which is shown in shade in FIG. 3A). Thereafter, computing device 216 communicates specifically with Data Sources 320 identified to the active Project. This is illustrated in FIG. 3B, wherein Data Sources 320 identified to the active Project are arranged and displayed in a Data Source Database 322, and made accessible to computing device 216 and to the user via user interface 310. [62-64] further elaborates on the matter [FIG.1A-B in conjunction with FIG.8B] shows displaying, through a visual page, each piece of table information contained in each type of data source with which the connection is made) in response to an association operation of a user on multiple tables that are displayed, generating a target dataset according to an association relationship between the multiple tables indicated by the association operation; (Hathaway [0033] access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study.[0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application...arranges data in rows and columns that define spreadsheet cells, and users may be given various options for interacting with and editing the data. New data are entered by highlighting a cell and typing into a window that appears above the spreadsheet. Also, the user may globally replace data, make computations on the data, or perform a number of functions [0038] The user then finds and enables the pareto analysis tool in the menu 118, thereby opening a new data selection window 122 for preparing the pareto analysis. See FIG. 1B. Another window 124 inside the data selection window 122 lists the data sets in the worksheet 112 by name and type. The user scrolls through the list and finds the data set "Medication Error" among the eleven other data sets on the list, as it is aptly named and selects it for analysis. The user also highlights the data set "Frequency" which he thinks corresponds to the "Medication Error" data set. If the user is correct in his selection of data variables, the pareto chart 128 in FIG. 1C is created and appears in a new window 130 over the worksheet 112 [0054] FIG. 4 illustrates an exemplary method of performing data analysis and/or generating a project study according to the present disclosure. The method chosen for illustration is one that utilizes the various system elements identified in FIGS. 2 and 3 by way of a graphical user interface and from the perspective of the user at a client station. The method may be initiated by the user selecting a tool function (408), which in actuality entails the user enabling a tool object on the user interface. In some embodiments described, the user interface requires the user to first select a Project phase [62-67] elaborate on the matter [FIG.1A-B in conjunction with FIG.4] shows in response to an association operation of a user on multiple tables that are displayed, generating a target dataset according to an association relationship between the multiple tables indicated by the association operation) and displaying the target dataset on the visual page by means of a chart. (Hathaway[0006] a graphical user interface containing a plurality of graphical objects controllable by a controller (of the computing apparatus). Using at least one stored data set as input variables, a data analysis function is initiated to generate a graphical element output (e.g., a graph, chart, summary table, etc). A stored study object is also generated, which corresponds to the graphical element output and identified with the data analysis function and the at least one stored data set, whereby the stored study object is controller engageable to regenerate the graphical element on the user interface[0007] Initiating the data analysis function preferably generates a study object in an engaged or open mode, which includes displaying a data object element associated with the at least one stored data set, a data analysis function object element (e.g., a data analysis tool object element) and the graphical element output. [0033] access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study. [0080] On engagement of data variables object 738 with study object 750, a window 770 associated with the Study and with the study object 750 is outputted. The window 770 functions as a workpad that provides the data analysis tools available. The workpad 770 also uses color coordination to show which object is associated with the displayed tools FIG. 7D provides results in another window 772 using a first data variables object, which include charts and data summaries. It should be noted also that the output will automatically change when the first data variable FIG. 7D also shows that the results of the study are automatically changed when the data variables object 754 entered is replaced by another. [64-68] elaborate on the matter[FIG.1A-B in conjunction with FIG.8B] shows displaying the target dataset on the visual page by means of a chart.)
Regarding claim 6, Hathaway teaches The method according to claim 1,wherein the establishing a connection with each type of data source, comprises: establishing a connection with each type of data source according to connection information of each type of data source. (Hathaway [0033] As used herein, the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. [0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. For example, a Project may represent a quality improvement effort directed to a specific process in the user's organization. In this web-based system 350, computing device 216 generally extracts data from the Projects Database 312 and directs the data as input to a Tool function selected from the Tools Library 314. In exemplary embodiments, the selected Tool function will perform data analysis on the data and deliver an output to or through computing device 216. Computing device 216 may also deliver output to Studies Database 316 or some other external facility, such as a printer, data storage, or another client station. As shown in FIG. 3A, computing device 216 may receive data and information from the Tools Library 314 and from Studies Database 316.[0061] FIG. 5A depicts ...the data analysis software application and for implementing steps and methods previously described in respect to FIGS. 3-4. As with most computer user interfaces, the user interface 512 of this software application may be navigated, engaged, and changed through use of a keyboard and control pointer such as a mouse, cursor, or equal (not shown). These control devices are specifically used to manipulate a plurality of graphical user interface elements or widgets. In one aspect of the disclosure, the user interface 512 preferably employs several types of widgets to facilitate the user's management of projects and data sources and selection and employment of an array of data analysis tools. These widgets include menus, toolbars, containers such as windows, panels, and palettes, icons, buttons, and other common user interface elements. The present graphical user interface favors an object-oriented design (i.e., as opposed to an application-oriented design), whereby the user interacts explicitly with objects that are intuitive representations of the entities in the domain relevant to the application. In the embodiments described herein, these user interface objects may represent projects, data sources or data sheets, data sets, tools and tool functions, and studies, among other things. [63-73] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] show the corresponding visual)
Regarding claim 8, Hathaway teaches The method according to claim 6, wherein when the data source is a data source of a database type, the establishing a connection with each type of data source according to connection information of each type of data source, comprises: establishing a connection with the data source of the database type according to a database parameter, wherein the database parameter represents a parameter required to connect with a database. (Hathaway [0009] user interface to initiate the data analysis function, whereby the at least one stored data set are input variables to the data analysis function and a graphical element (e.g., a chart or graphical representation) is displayed on the user interface as output to the initiated data analysis function. [0011] The graphical user interface includes a data object selection region containing at least one user interface data object associated with a stored data set and displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. [0047] In this embodiment, Study 324 refers to a stored computing event--the initiation of the Tool function to perform data analysis on target data. Thus, a Study 324 stored in Studies Database may include the Tool function selected and any relevant parameters, the target data, and the results or Output,[0049] FIGS. 3C and 3D illustrate features of an exemplary user interface environment of the disclosure, and how the relationships of system elements in FIGS. 3A-3B are translated onto the user interface 310. Data Source Database 322 and Data Sources 320 from FIGS. 3A-3B are represented as user interface objects that the user can manipulate to translate the data among system elements and across the user interface environment, and to initiate action. In this example, Database 322 is represented by a user-activated container panel 332 while Data Sources 320 identified to the Database [53-56] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] shows wherein when the data source is a data source of a database type, the establishing a connection with each type of data source according to connection information of each type of data source, comprises: establishing a connection with the data source of the database type according to a database parameter, wherein the database parameter represents a parameter required to connect with a database.)
Regarding claim 11, Hathaway teaches The method according to claim 9 wherein the data source parameter comprises at least one of a data source identifier, a type of data source, a library field, a table field, a column field, or a field type of a column field. (Hathaway [0011] The graphical user interface includes a data object selection region containing at least one user interface data object associated with a stored data set and displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type. Preferably, the visual attributes include a first reflective of data type and a second containing a graphical display derived from the associated data set. For example, the data object may include a window having a histogram of the data set. [0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application. The user interface 110 depicted reflects features of a proprietary desktop-based application called Engine Room.RTM. data analysis software available from MoreSteam.com LLC in Ohio. The software is available as an add-in to a Microsoft Excel.RTM. platform. This software application is designed to support users implementing the quality improvement process termed DMAIC, which is a data-driven improvement cycle used to improve, optimize, and stabilize business processes and designs. With this application, a spreadsheet or worksheet provides a platform on which data are stored, edited, and arranged. As the application is intended to support a quality improvement process, the user interface 110 is particularly directed to user implementation of certain fundamental problem-solving tools commonly used to support process improvement efforts. As generally known, the worksheet 112 arranges data in rows and columns that define spreadsheet cells, and users may be given various options for interacting with and editing the data. New data are entered by highlighting a cell and typing into a window that appears above the spreadsheet. Also, the user may globally replace data, make computations on the data, or perform a number of functions. [0073] With some information on the properties of the data variables (i.e., numeric type) required for Tool function input, the user navigates the pointer to a data source object 528, thereby enabling it and prompting the associated Data Variables Object Panel 536. The user looks to and reviews this Panel 536 to match possible data variables to the active Study. [61-66] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] show the corresponding visual flow)
Regarding claim 14, Hathaway teaches The method according to claim 1,wherein the establishing a connection with each type of data source, comprises:building a shared data source application according to a connection pool of each data source contained in each type of data source; (Hathaway [0039] The present disclosure provides general and specific improvements and enhancements to the system and methods associated with data analysis software applications of the type described above and in respect to FIGS. 1A-1C. These improved and enhanced systems and methods are well suited for the computing architecture and system 210 depicted in FIG. 2. Specifically, the systems and methods are preferably implemented with a web-based system architecture that takes advantage of one or more networks 212 having one or more servers 214 and one or more client stations 216. The system 210 may also include a dedicated database server 218 for storing and manipulating client data and a dedicated server 220 for programs responsible for analytical processes and support. The data and program logic and functionalities of the software application may be shared among the various client stations and server devices in real time. Accordingly, the system may be described as employing cloud computing capabilities to perform or support the data manipulation and computation required by these software applications. [0040] Preferably, details of the configuration and dynamic processes of the system 210 will not be apparent to the user at the client station 216. The web-based software application may execute and launch in a web browser on the user's client station 216, with minimal or no download and management of software modules. The user client station 216 preferably includes one or more processors 230 with memory to handle and share in the operating tasks of the software application, data storage or secondary memory, and a display 232 on which the user may interact with a graphical user interface for the software application. The user client station 216 also includes a control pointer for interacting with the user interface. Such a control pointer may be provided by a keyboard, a mouse, a touch-screen, a touch-pad, joystick, and other common devices, and various combinations of these devices. The client station in FIG. 2 is shown with a standard computer keyboard 234, but preferably will also include a mouse or other control device particularly adept at direct manipulation of objects on a graphical user interface. [0082] In FIG. 7F, the study object 750 is modified to include a window 782 that contains a link to a file or other artifact identified to the study that then becomes associated with the Study. Window 782, and the artifact within window 782, is yet another object element added (by the user in this case) to the cluster of objects that make up the study object 750. In some applications, the file may be a report with detailed interpretations or conclusions applicable to the data analysis output or the project. The file may be intended for the user's future use or use by another user or third party. In further embodiments, the uploaded or uploadable artifacts can be image files, videos, or documents, among other things. Such user-added information provides further context and helps users comprehend the practical significance of any conclusions reached from the study output--especially in a shared environment where multiple users may participate in the analysis and contribute to the critical thinking. As again shown in these examples, the visual object-oriented approach facilitates communication and transitions among groups who are working on a project, sharing data, and/or jointly performing the analytical work. [FIG.1 in conjunction with FIG.7F] show the building a shared data source application according to a connection pool of each data source contained in each type of data source; and establishing a connection between each business system and each type of data source through the shared data source application, wherein the shared data source application provides a service for each business system to connect with each type of data source through an ability for integrating a connection with each type of data source.) and establishing a connection between each business system and each type of data source through the shared data source application, wherein the shared data source application provides a service for each business system to connect with each type of data source through an ability for integrating a connection with each type of data source. (Hathaway [0039] The present disclosure provides general and specific improvements and enhancements to the system and methods associated with data analysis software applications of the type described above and in respect to FIGS. 1A-1C. These improved and enhanced systems and methods are well suited for the computing architecture and system 210 depicted in FIG. 2. Specifically, the systems and methods are preferably implemented with a web-based system architecture that takes advantage of one or more networks 212 having one or more servers 214 and one or more client stations 216. The system 210 may also include a dedicated database server 218 for storing and manipulating client data and a dedicated server 220 for programs responsible for analytical processes and support. The data and program logic and functionalities of the software application may be shared among the various client stations and server devices in real time. Accordingly, the system may be described as employing cloud computing capabilities to perform or support the data manipulation and computation required by these software applications. [0040] Preferably, details of the configuration and dynamic processes of the system 210 will not be apparent to the user at the client station 216. The web-based software application may execute and launch in a web browser on the user's client station 216, with minimal or no download and management of software modules. The user client station 216 preferably includes one or more processors 230 with memory to handle and share in the operating tasks of the software application, data storage or secondary memory, and a display 232 on which the user may interact with a graphical user interface for the software application. The user client station 216 also includes a control pointer for interacting with the user interface. Such a control pointer may be provided by a keyboard, a mouse, a touch-screen, a touch-pad, joystick, and other common devices, and various combinations of these devices. The client station in FIG. 2 is shown with a standard computer keyboard 234, but preferably will also include a mouse or other control device particularly adept at direct manipulation of objects on a graphical user interface. [0082] In FIG. 7F, the study object 750 is modified to include a window 782 that contains a link to a file or other artifact identified to the study that then becomes associated with the Study. Window 782, and the artifact within window 782, is yet another object element added (by the user in this case) to the cluster of objects that make up the study object 750. In some applications, the file may be a report with detailed interpretations or conclusions applicable to the data analysis output or the project. The file may be intended for the user's future use or use by another user or third party. In further embodiments, the uploaded or uploadable artifacts can be image files, videos, or documents, among other things. Such user-added information provides further context and helps users comprehend the practical significance of any conclusions reached from the study output--especially in a shared environment where multiple users may participate in the analysis and contribute to the critical thinking. As again shown in these examples, the visual object-oriented approach facilitates communication and transitions among groups who are working on a project, sharing data, and/or jointly performing the analytical work. [FIG.1 in conjunction with FIG.7F] show the building a shared data source application according to a connection pool of each data source contained in each type of data source; and establishing a connection between each business system and each type of data source through the shared data source application, wherein the shared data source application provides a service for each business system to connect with each type of data source through an ability for integrating a connection with each type of data source.)
Regarding claim 15, Hathaway teaches The method according to claim 14, wherein the establishing a connection between each business system and each type of data source through the shared data source application, comprises: establishing a connection between the shared data source application and each type of data source according to connection information of each data source described in a metadata; and establishing a connection between each type of data source connected with the shared data source application and each business system through the shared data source application; or… (Hathaway [0033] As used herein, the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. [0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. For example, a Project may represent a quality improvement effort directed to a specific process in the user's organization. In this web-based system 350, computing device 216 generally extracts data from the Projects Database 312 and directs the data as input to a Tool function selected from the Tools Library 314. In exemplary embodiments, the selected Tool function will perform data analysis on the data and deliver an output to or through computing device 216. Computing device 216 may also deliver output to Studies Database 316 or some other external facility, such as a printer, data storage, or another client station. As shown in FIG. 3A, computing device 216 may receive data and information from the Tools Library 314 and from Studies Database 316. [0039] The present disclosure provides general and specific improvements and enhancements to the system and methods associated with data analysis software applications of the type described above and in respect to FIGS. 1A-1C. These improved and enhanced systems and methods are well suited for the computing architecture and system 210 depicted in FIG. 2. Specifically, the systems and methods are preferably implemented with a web-based system architecture that takes advantage of one or more networks 212 having one or more servers 214 and one or more client stations 216. The system 210 may also include a dedicated database server 218 for storing and manipulating client data and a dedicated server 220 for programs responsible for analytical processes and support. The data and program logic and functionalities of the software application may be shared among the various client stations and server devices in real time. Accordingly, the system may be described as employing cloud computing capabilities to perform or support the data manipulation and computation required by these software applications. [0040] Preferably, details of the configuration and dynamic processes of the system 210 will not be apparent to the user at the client station 216. The web-based software application may execute and launch in a web browser on the user's client station 216, with minimal or no download and management of software modules. The user client station 216 preferably includes one or more processors 230 with memory to handle and share in the operating tasks of the software application, data storage or secondary memory, and a display 232 on which the user may interact with a graphical user interface for the software application. The user client station 216 also includes a control pointer for interacting with the user interface. Such a control pointer may be provided by a keyboard, a mouse, a touch-screen, a touch-pad, joystick, and other common devices, and various combinations of these devices. The client station in FIG. 2 is shown with a standard computer keyboard 234, but preferably will also include a mouse or other control device particularly adept at direct manipulation of objects on a graphical user interface. [0082] In FIG. 7F, the study object 750 is modified to include a window 782 that contains a link to a file or other artifact identified to the study that then becomes associated with the Study. Window 782, and the artifact within window 782, is yet another object element added (by the user in this case) to the cluster of objects that make up the study object 750. In some applications, the file may be a report with detailed interpretations or conclusions applicable to the data analysis output or the project. The file may be intended for the user's future use or use by another user or third party. In further embodiments, the uploaded or uploadable artifacts can be image files, videos, or documents, among other things. Such user-added information provides further context and helps users comprehend the practical significance of any conclusions reached from the study output--especially in a shared environment where multiple users may participate in the analysis and contribute to the critical thinking. As again shown in these examples, the visual object-oriented approach facilitates communication and transitions among groups who are working on a project, sharing data, and/or jointly performing the analytical work. [FIG.1 in conjunction with FIG.7F] show the shared data connection)
Regarding claim 17, Hathaway teaches The method according to claim 14, wherein after the establishing a connection between each business system and each type of data source through the shared data source application, the method further comprises:receiving an operation instruction sent by the business system in a form of a metadata through the shared data source application; (Hathaway [0039] The present disclosure provides general and specific improvements and enhancements to the system and methods associated with data analysis software applications of the type described above and in respect to FIGS. 1A-1C. These improved and enhanced systems and methods are well suited for the computing architecture and system 210 depicted in FIG. 2. Specifically, the systems and methods are preferably implemented with a web-based system architecture that takes advantage of one or more networks 212 having one or more servers 214 and one or more client stations 216. The system 210 may also include a dedicated database server 218 for storing and manipulating client data and a dedicated server 220 for programs responsible for analytical processes and support. The data and program logic and functionalities of the software application may be shared among the various client stations and server devices in real time. Accordingly, the system may be described as employing cloud computing capabilities to perform or support the data manipulation and computation required by these software applications. [0040] Preferably, details of the configuration and dynamic processes of the system 210 will not be apparent to the user at the client station 216. The web-based software application may execute and launch in a web browser on the user's client station 216, with minimal or no download and management of software modules. The user client station 216 preferably includes one or more processors 230 with memory to handle and share in the operating tasks of the software application, data storage or secondary memory, and a display 232 on which the user may interact with a graphical user interface for the software application. The user client station 216 also includes a control pointer for interacting with the user interface. Such a control pointer may be provided by a keyboard, a mouse, a touch-screen, a touch-pad, joystick, and other common devices, and various combinations of these devices. The client station in FIG. 2 is shown with a standard computer keyboard 234, but preferably will also include a mouse or other control device particularly adept at direct manipulation of objects on a graphical user interface. [0082] In FIG. 7F, the study object 750 is modified to include a window 782 that contains a link to a file or other artifact identified to the study that then becomes associated with the Study. Window 782, and the artifact within window 782, is yet another object element added (by the user in this case) to the cluster of objects that make up the study object 750. In some applications, the file may be a report with detailed interpretations or conclusions applicable to the data analysis output or the project. The file may be intended for the user's future use or use by another user or third party. In further embodiments, the uploaded or uploadable artifacts can be image files, videos, or documents, among other things. Such user-added information provides further context and helps users comprehend the practical significance of any conclusions reached from the study output--especially in a shared environment where multiple users may participate in the analysis and contribute to the critical thinking. As again shown in these examples, the visual object-oriented approach facilitates communication and transitions among groups who are working on a project, sharing data, and/or jointly performing the analytical work. [FIG.1 in conjunction with FIG.7F] show the building a shared data source application according to a connection pool of each data source contained in each type of data source; and establishing a connection between each business system and each type of data source through the shared data source application, wherein the shared data source application provides a service for each business system to connect with each type of data source through an ability for integrating a connection with each type of data source.) and performing at least one operation of aggregation, filtering, or query on a data source corresponding to the operation instruction. (Hathaway [0058] When the study object has engaged all required data set objects, the tool function is initiated and the output is displayed. The user can review the output on the user interface, and if not satisfied, modify the study by adding, deleting, and\or substituting data set objects. In any case, the study object is automatically updated, including the output associated with the study object. By closing the study object, it is automatically saved in the Studies Panel (420). The study object (and study) may also be saved by closing the study object at any time after tool function selection. [0075] In FIG. 6G, two more data sets have been selected for data analysis by attaching their corresponding data variables objects 638 on study object 650. Almost intuitively, the multiple data variables objects 538 attach together and are of the same color, signifying their common data type and input to the tool function. The objects 638 are described as being in contiguous positional relationships, in respect to one another and with tool object 652. In the alternative, replacing a previously integrated data variables field object with another data variables field object automatically updates and replaces the analytical results. It should be noted that at the end of the user's data analysis exercise, the new study object 650 is transformed to a cluster of dynamic objects--or aggregates of information associated with a study event(s). The new study object 650 and all of its associated objects and information may be accessible and reproducible by the user or a different user in the future. The study object of the present disclosure presents, therefore, a cluster of objects that provided a durable visual context of historical, present, and ongoing analyses [FIG.6G in conjunction with FIG.7F] show performing at least one operation of aggregation, filtering, or query on a data source corresponding to the operation instruction.)
Regarding claim 18, Hathaway teaches The method according to claim 1, wherein in response to an association operation of a user on multiple tables that are displayed, the generating a target dataset according to an association relationship between the multiple tables indicated by the association operation, comprises:in response to a dragging instruction of the user for the multiple tables displayed, determining table information of each target table corresponding to the dragging instruction; (Hathaway [0006] Described herein, in respect to various systems and methods of process mapping on a graphical user interface, is a computer display of a computing apparatus configured to present a graphical user interface containing a plurality of graphical objects controllable by a controller (of the computing apparatus). Using at least one stored data set as input variables, a data analysis function is initiated to generate a graphical element output (e.g., a graph, chart, summary table, etc). A stored study object is also generated, which corresponds to the graphical element output and identified with the data analysis function and the at least one stored data set, whereby the stored study object is controller engageable to regenerate the graphical element on the user interface.[0033] As used herein, the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study.[0047] In this embodiment, Study 324 refers to a stored computing event--the initiation of the Tool function to perform data analysis on target data. Thus, a Study 324 stored in Studies Database may include the Tool function selected and any relevant parameters, the target data, and the results or Output, user-associated data or information (including relevant conclusions), and time-stamped history. In certain embodiments, information in a Study will simply include reference to the Tool function and target data such that the Output may be readily reproduced by the original user or a new user. Output may include charts, graphs, tables, listing, and other arrangement or display of information resulting from the data analysis. The study may also include user-associated information such as notes, conclusions, or user information. [FIG.1A-B in conjunction with FIG.4] shows in response to a dragging instruction of the user for the multiple tables displayed, determining table information of each target table corresponding to the dragging instruction) and receiving an association relationship between multiple target tables input by the user, and generating a target dataset according to the table information of each target table and the association relationship. (Hathaway [0033] access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study.[0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application...arranges data in rows and columns that define spreadsheet cells, and users may be given various options for interacting with and editing the data. New data are entered by highlighting a cell and typing into a window that appears above the spreadsheet. Also, the user may globally replace data, make computations on the data, or perform a number of functions [0038] The user then finds and enables the pareto analysis tool in the menu 118, thereby opening a new data selection window 122 for preparing the pareto analysis. See FIG. 1B. Another window 124 inside the data selection window 122 lists the data sets in the worksheet 112 by name and type. The user scrolls through the list and finds the data set "Medication Error" among the eleven other data sets on the list, as it is aptly named and selects it for analysis. The user also highlights the data set "Frequency" which he thinks corresponds to the "Medication Error" data set. If the user is correct in his selection of data variables, the pareto chart 128 in FIG. 1C is created and appears in a new window 130 over the worksheet 112 [0054] FIG. 4 illustrates an exemplary method of performing data analysis and/or generating a project study according to the present disclosure. The method chosen for illustration is one that utilizes the various system elements identified in FIGS. 2 and 3 by way of a graphical user interface and from the perspective of the user at a client station. The method may be initiated by the user selecting a tool function (408), which in actuality entails the user enabling a tool object on the user interface. In some embodiments described, the user interface requires the user to first select a Project phase [62-67] elaborate on the matter [FIG.1A-B in conjunction with FIG.4] shows receiving an association relationship between multiple target tables input by the user, and generating a target dataset according to the table information of each target table and the association relationship)
Regarding claim 21, Hathaway teaches The method according to claim 1, wherein the displaying the target dataset on the visual page by means of a chart, comprises:determining a chart type specified by the user and a target data column in the target dataset; (Hathaway [0004] data analyses at different stages of projects, for different projects, and different tasks. Providing support for such projects, the software application may be required to provide an array of data analytical tools and to access and store different types of data at different times and points of a project. [0033] the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study. [0046] a data source is understood to contain one or more sets of data or information just as a spreadsheet may contain specific collections of data arranged in rows under a column. The data identified to a set will be of a specific type or category [0066] FIG. 3D, the data variables object 538 is a tile that serves as the platform for multiple descriptive elements. First, the tile showcases a hexagon icon with a graphical symbol signifying a data type--numeric, date and time, or text. Secondly, the tile includes text providing a name description, data count, and data type of the underlying data set. So, with this view of the Panel 536, the user may be able to eliminate data sets from consideration or determine the viability of a data set for tool implementation and study. [49-56] elaborates on the matter [FIG.1A-B in conjunction with FIG.8B] shows determining a chart type specified by the user and a target data column in the target dataset) using the target data column as chart data corresponding to the chart type, and using a chart component to draw a chart corresponding to the chart type; (Hathaway [0033] As used herein, the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study. [0047] In this embodiment, Study 324 refers to a stored computing event--the initiation of the Tool function to perform data analysis on target data. Thus, a Study 324 stored in Studies Database may include the Tool function selected and any relevant parameters, the target data, and the results or Output, user-associated data or information (including relevant conclusions), and time-stamped history. In certain embodiments, information in a Study will simply include reference to the Tool function and target data such that the Output may be readily reproduced by the original user or a new user. Output may include charts, graphs, tables, listing, and other arrangement or display of information resulting from the data analysis. The study may also include user-associated information such as notes, conclusions, or user information. [0070] configured to draw and accept another hexagon object [49-56] elaborates on the matter [FIG.1A-B in conjunction with FIG.8B] shows using the target data column as chart data corresponding to the chart type, and using a chart component to draw a chart corresponding to the chart type) and displaying the drawn chart on the visual page. (Hathaway[0006] a graphical user interface containing a plurality of graphical objects controllable by a controller (of the computing apparatus). Using at least one stored data set as input variables, a data analysis function is initiated to generate a graphical element output (e.g., a graph, chart, summary table, etc). A stored study object is also generated, which corresponds to the graphical element output and identified with the data analysis function and the at least one stored data set, whereby the stored study object is controller engageable to regenerate the graphical element on the user interface[0007] Initiating the data analysis function preferably generates a study object in an engaged or open mode, which includes displaying a data object element associated with the at least one stored data set, a data analysis function object element (e.g., a data analysis tool object element) and the graphical element output. [0033] access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. The presentation of the new information embodies the "results" of the data analysis, and may be conveyed through charts, graphs, and\or textual write-ups, all of which are considered "output." Moreover, as used herein, the term "Study" or "Studies", as it relates to "data analysis", refers to the means by which or the tool(s) used to perform the data analysis, including the parameters of the analysis, the target data that is the subject of analysis. A "study" preferably includes output of the data analysis and/or user-added information associated with the study. [0080] On engagement of data variables object 738 with study object 750, a window 770 associated with the Study and with the study object 750 is outputted. The window 770 functions as a workpad that provides the data analysis tools available. The workpad 770 also uses color coordination to show which object is associated with the displayed tools FIG. 7D provides results in another window 772 using a first data variables object, which include charts and data summaries. It should be noted also that the output will automatically change when the first data variable FIG. 7D also shows that the results of the study are automatically changed when the data variables object 754 entered is replaced by another. [64-68] elaborate on the matter[FIG.1A-B in conjunction with FIG.8B] shows d displaying the drawn chart on the visual page)
Regarding claim 23, Hathaway teaches A visual data analysis device, comprising: a processor and a memory, wherein the memory is configured to store programs executable by the processor, and the processor is configured to read the programs in the memory and execute steps of the method according to claim 1. (Hathaway [0002] The present disclosure relates generally to a computer-human user interface for a software application, and systems and methods of implementing same. The disclosure also relates to a system, method, and computing environment for data analysis, and\or for a data analysis software application, program, or portion or module thereof. The disclosure relates further to a user interface and method that includes or incorporates object-oriented elements and/or steps, particularly in interfacing a user with a data analysis software application. Further yet, the disclosure relates to systems and methods for project activity tracking ("project mapping") and more particularly, process tracking, thought process mapping, critical question mapping, and/or critical path mapping. [0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application. The user interface 110 depicted reflects features of a proprietary desktop-based application called Engine Room.RTM. data analysis software available from MoreSteam.com LLC in Ohio. The software is available as an add-in to a Microsoft Excel.RTM. platform. This software application is designed to support users implementing the quality improvement process termed DMAIC, which is a data-driven improvement cycle used to improve, optimize, and stabilize business processes and designs. With this application, a spreadsheet or worksheet provides a platform on which data are stored, edited, and arranged.[0061] FIG. 5A depicts a computer display 510 and a graphical user interface 512 presented thereon, which are suitable for the data analysis software application and for implementing steps and methods previously described in respect to FIGS. 3-4. As with most computer user interfaces, the user interface 512 of this software application...[88-90] elaborate on the matter)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Hathaway in view of US 20220076165 A1; Minkin; Andrew M. et al. (hereinafter Minkin)
Regarding claim 2, Hathaway teaches The method according to claim 1, wherein obtaining multiple types of data sources through any one or more of following manners:receiving parameter information input by the user, and obtaining a data source of a corresponding type according to the parameter information; (Hathaway [0009] user interface to initiate the data analysis function, whereby the at least one stored data set are input variables to the data analysis function and a graphical element (e.g., a chart or graphical representation) is displayed on the user interface as output to the initiated data analysis function. [0011] The graphical user interface includes a data object selection region containing at least one user interface data object associated with a stored data set and displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. [0047] In this embodiment, Study 324 refers to a stored computing event--the initiation of the Tool function to perform data analysis on target data. Thus, a Study 324 stored in Studies Database may include the Tool function selected and any relevant parameters, the target data, and the results or Output,[0049] FIGS. 3C and 3D illustrate features of an exemplary user interface environment of the disclosure, and how the relationships of system elements in FIGS. 3A-3B are translated onto the user interface 310. Data Source Database 322 and Data Sources 320 from FIGS. 3A-3B are represented as user interface objects that the user can manipulate to translate the data among system elements and across the user interface environment, and to initiate action. In this example, Database 322 is represented by a user-activated container panel 332 while Data Sources 320 identified to the Database [53-56] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] shows receiving parameter information input by the user, and obtaining a data source of a corresponding type according to the parameter information) Hathaway lacks explicitly and orderly teaching obtaining a data source of a corresponding type through a file transfer protocol; or using an executed structured query language (SQL) statement as an obtained data source of a corresponding type. However Minkin teaches obtaining a data source of a corresponding type through a file transfer protocol; or using an executed structured query language (SQL) statement as an obtained data source of a corresponding type. (Minkin [0163] FIG. 4 shows an example embodiment of system architecture diagram 200. In the example embodiment, client browsers on client user devices 202 can access an AC Portal 204 and a DSL Workbench portal 206. DSL Workbench portal 206 can exchange data with a workspace manager or other system engine 208 which can exchange data with one or more of various cluster nodes 210, one of which may be a cluster master 212. Each node of 210 can have a Spark node 214 which may be master or slave depending on its configuration. Each node can also have Hadoop 216, Mesos/YARN 218, and HDFS 220. Nodes 210 can also interact with Interface Layer 222 via Stream protocol, HTTP, and FTP to enable access to external storage such as S3 224. [FIG.4] shows obtaining a data source of a corresponding type through a file transfer protocol; or using an executed structured query language (SQL) statement as an obtained data source of a corresponding type. ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take all prior methods and make the addition of Minkin's analytical workflows in order to optimize the system via machine learning methods (Minkin [0160] FIG. 3 shows an example embodiment of a Lambda Big Data architecture and its mapping to a physical architecture diagram 150. As shown in the example embodiment, one or more data sources, feeds, streams, or integrations 152. This type of data-processing architecture can handle massive quantities of data by taking advantage of both batch- and stream-processing methods balance latency, throughput, and fault-tolerance by using batch processing 156 to provide comprehensive and accurate views of batch data 160, while simultaneously using the speed of real-time stream processing with speed sentry module 154 to provide queried views of online data. Speed sentry module 154 and batch module 156 can exchange data with a “query” Auto-Curious module or system 158, while batch module can send data to or have data retrieved from it by a “serving” module 160. Speed sentry module 154 can also exchange data with serving module 160. Additionally, query module 158 can exchange data with serving module 160 and can be joined before presentation. [0187] An example of a complex and real-world data science workflow is the IHS multiclass classification problem of determining the destination ports of oils vessels. The workflow has historical data that users can understand better and generate analytic content by using Source nudges 701. Users can enhance semantic understanding through friendly labels and relationships that Auto-curious can use to find analytic domain entities that map to their analytic content 702. In order to apply semantic suggestions for the machine learning workflow, aggregations, unsupervised clustering and multi-resolution feature engineering by Schema nudges 703. Based on the metaspace pints generated on additional schematization, Auto-curious can review the analytic content and context and start building machine learning models by Analytic nudges 704. The details of the model performance, resource optimization and all audit features, including visual analytic workflows that answer specific questions not stored in the exact format needed by Insight nudges 705. [0197] FIG. 10A shows an example embodiment of processes of ingesting source analytic assets, including analytic context from a corpus of documents and code, processing to generate metaspace points that map user domains to analytic domains and drive autonomous machine learning workflows as expressed in a high level architectural diagram 4000. Gestalt Modeling Progressive modeling as a formalized model optimization technique of iteration. Gestalt Modeling and the use of Overkill Analytics as a Scout style workflow for improving automated workflows. Gestalt Modeling and the use of Overkill Analytics as a Scout style workflow for suggesting new workflow. Gestalt Modeling and the use of Overkill Analytics as a Sentry style workflow for improving automated workflows. )
Regarding claim 2, Hathaway and Minkin teach The method according to claim 2, wherein obtaining a data source of a corresponding type according to the parameter information through any one or more of following manners:receiving a database parameter input by the user, and obtaining a data source of a database type according to the database parameter; or,receiving an interface parameter input by the user, and obtaining a data source of an interface type according to the interface parameter; or,obtaining text data uploaded by the user, and determining text data named by the user as a data source of a text type; or, receiving a Redis parameter input by the user, and obtaining a data source of a Redis cache type according to the Redis parameter; or,receiving a SQL statement input by the user, and determining the SQL statement input as a data source of a SQL statement type. (Hathaway [0009] user interface to initiate the data analysis function, whereby the at least one stored data set are input variables to the data analysis function and a graphical element (e.g., a chart or graphical representation) is displayed on the user interface as output to the initiated data analysis function. [0011] The graphical user interface includes a data object selection region containing at least one user interface data object associated with a stored data set and displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. [0047] In this embodiment, Study 324 refers to a stored computing event--the initiation of the Tool function to perform data analysis on target data. Thus, a Study 324 stored in Studies Database may include the Tool function selected and any relevant parameters, the target data, and the results or Output,[0049] FIGS. 3C and 3D illustrate features of an exemplary user interface environment of the disclosure, and how the relationships of system elements in FIGS. 3A-3B are translated onto the user interface 310. Data Source Database 322 and Data Sources 320 from FIGS. 3A-3B are represented as user interface objects that the user can manipulate to translate the data among system elements and across the user interface environment, and to initiate action. In this example, Database 322 is represented by a user-activated container panel 332 while Data Sources 320 identified to the Database [53-56] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] shows wherein obtaining a data source of a corresponding type according to the parameter information through any one or more of following manners: receiving a database parameter input by the user, and obtaining a data source of a database type according to the database parameter)
Regarding claim 4, Hathaway and Minkin teach The method according to claim 2, wherein the obtaining a data source of a corresponding type through a file transfer protocol, comprises: obtaining a file in a file transfer protocol (FTP) server by means of a secret file transfer protocol (SFTP), and determining the file obtained as a data source of a FTP type. (Minkin [0163] FIG. 4 shows an example embodiment of system architecture diagram 200. In the example embodiment, client browsers on client user devices 202 can access an AC Portal 204 and a DSL Workbench portal 206. DSL Workbench portal 206 can exchange data with a workspace manager or other system engine 208 which can exchange data with one or more of various cluster nodes 210, one of which may be a cluster master 212. Each node of 210 can have a Spark node 214 which may be master or slave depending on its configuration. Each node can also have Hadoop 216, Mesos/YARN 218, and HDFS 220. Nodes 210 can also interact with Interface Layer 222 via Stream protocol, HTTP, and FTP to enable access to external storage such as S3 224. [0171] As shown, system data and ML services 452 can include system tables 456; ingestion 458; transformation and query 460; streaming, graph, and search 462; machine learning 464; DSL workbench 468; system DSL 470; and others. Examples of system tables 456 can include H* Dense/Sparse, C* Lookup and TimeSeries, C*+ES Indexed Lookup, and others. Ingestion 458 can include load http/sftp/S3/json/paquet/av ro/tsv/csv/api, push2stream, stream producers: tcp/twitter/ubix_table, insert C*, index ES, direct Kafka/Hive, and others. Transformation and query 460 can include filter, join, groupby, sort, expr, transpose, factor, wf, span, describe, variance, as, append, update, create/drop/generate, min, max, stddev, sum, count, pipe, fetch, sample, stream ws, and others. Streaming, graph, search 462 can include stream process/listen/pyMap, emit sns, smtp, rabbitmq, kafka index, search, graph, subgraph, vertices, edges, and others. Machine learning 464 can include train, predict evaluate, regression in linear or log, classification in bin or multi, clustering in kmeans or gmm, topic discovery in Ida, feature selection, Spark MILib and ML, VW, R in rMap and rubix, python in PyMap, upyx, gbt, rf, dt, nb, ridge, lasso, svm, and others. System DSL can include http, ws, akka API, and others. [FIG.4] shows corresponding system)
Regarding claim 5, Hathaway and Minkin teach The method according to claim 2, wherein the using an executed SQL statement as an obtained data source of a corresponding type, comprises:receiving a SQL statement executed by the user on a data source with which a connection is made, and determining the executed SQL statement as a data source of a SQL statement type. (Minkin [0005] This knowledge is typically applied using a labor intensive “manual” data science process in the prior art at present. Various data science technologies may automate small parts or portions of a particular process, such as searching for parameters for a given machine learning algorithm or using relational database software to build queries for extraction, transformation, and loading. The prior art is currently deficient in automating an entire data science analytical process on any sort of a larger scale. [0159] FIG. 2 shows an example embodiment of an idealized partial system architecture diagram 100. In the example embodiment, real time data 102 can be received by the system and stored in one or more databases 104 in non-transitory computer readable media. In some embodiments these can be Tachyon HDFS databases. The system can also exchange data with other databases 106 and systems such as enterprise data via extraction, transform, load (ETL), S3 data via long term (LT)-Storage and Hadoop Distributed Filing System (HDFS) data via HDFS importing. A Spark/Query Language (QL) sub-system [264] TABLE-US-00002 pipe Investment | where Location = ‘BeiJing, Capitol of China’ | describe distribution | sql-expr -n Location “‘BeiJing, Capitol of China’” | sql-expr -n Topic ‘Location’ | sql-expr -n Term “‘BeiJing, Capitol of China’” | sql...[FIG.2] shows wherein the using an executed SQL statement as an obtained data source of a corresponding type, comprises:receiving a SQL statement executed by the user on a data source with which a connection is made, and determining the executed SQL statement as a data source of a SQL statement type.)
Regarding claim 6, Hathaway teaches The method according to claim 6 Hathaway lacks explicitly and orderly teaching wherein the establishing a connection with each type of data source according to connection information of each type of data source, comprises:writing the connection information of each type of data source into a configuration file of a distributed query engine; and when starting the distributed query engine, establishing, according to the connection information of each type of data source in the configuration file, the connection with each type of data source. However Minkin teaches wherein the establishing a connection with each type of data source according to connection information of each type of data source, comprises:writing the connection information of each type of data source into a configuration file of a distributed query engine; and when starting the distributed query engine, establishing, according to the connection information of each type of data source in the configuration file, the connection with each type of data source. (Minkin [0113] Metafeatures are synonymous with metaspace points and covering entire workflows, including transforms, user queries, model configuration and testing, exploring “dead ends” in research for further usage later and training models beyond the initial scope of predictive model algorithm choices.[0159] FIG. 2 shows an example embodiment of an idealized partial system architecture diagram 100. In the example embodiment, real time data 102 can be received by the system and stored in one or more databases 104 in non-transitory computer readable media. In some embodiments these can be Tachyon HDFS databases. The system can also exchange data with other databases 106 and systems such as enterprise data via extraction, transform, load (ETL), S3 data via long term (LT)-Storage and Hadoop Distributed Filing System (HDFS) data via HDFS importing. A Spark/Query Language (QL) sub-system 108 can exchange data over a system control plane 110 with a system layer 112 analytics platform, such as an engine that can interact with Hive, GraphX, and other libraries before using a visualization engine to prepare and distribute results for display of information to a user via a browser 114. Data in the system can also be used by an internal sub-system 114 of combined or separate engines Hadoop or Spark to export real time data 116 out of the system via Pub/Sub. [0163] FIG. 4 shows an example embodiment of system architecture diagram 200. In the example embodiment, client browsers on client user devices 202 can access an AC Portal 204 and a DSL Workbench portal 206. DSL Workbench portal 206 can exchange data with a workspace manager or other system engine 208 which can exchange data with one or more of various cluster nodes 210, one of which may be a cluster master 212. Each node of 210 can have a Spark node 214 which may be master or slave depending on its configuration. Each node can also have Hadoop 216, Mesos/YARN 218, and HDFS 220. Nodes 210 can also interact with Interface Layer 222 via Stream protocol, HTTP, and FTP to enable access to external storage such as S3 224.[246-251] further elaborate [0277] FIGS. 25A-25D show an example embodiment of AC's persistence schema. As shown, the persistence schema for AC's architecture can include a knowledge base, configuration, agent, metaspace and world model. In this implementation, the non-relational schema is realized using a low latency noSQL DB such as Cassandra. [FIG.2 & 4] shows wherein the establishing a connection with each type of data source according to connection information of each type of data source, comprises:writing the connection information of each type of data source into a configuration file of a distributed query engine and when starting the distributed query engine, establishing, according to the connection information of each type of data source in the configuration file, the connection with each type of data source.) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take all prior methods and make the addition of Minkin's analytical workflows in order to optimize the system via machine learning methods (Minkin [0160] FIG. 3 shows an example embodiment of a Lambda Big Data architecture and its mapping to a physical architecture diagram 150. As shown in the example embodiment, one or more data sources, feeds, streams, or integrations 152. This type of data-processing architecture can handle massive quantities of data by taking advantage of both batch- and stream-processing methods balance latency, throughput, and fault-tolerance by using batch processing 156 to provide comprehensive and accurate views of batch data 160, while simultaneously using the speed of real-time stream processing with speed sentry module 154 to provide queried views of online data. Speed sentry module 154 and batch module 156 can exchange data with a “query” Auto-Curious module or system 158, while batch module can send data to or have data retrieved from it by a “serving” module 160. Speed sentry module 154 can also exchange data with serving module 160. Additionally, query module 158 can exchange data with serving module 160 and can be joined before presentation. [0187] An example of a complex and real-world data science workflow is the IHS multiclass classification problem of determining the destination ports of oils vessels. The workflow has historical data that users can understand better and generate analytic content by using Source nudges 701. Users can enhance semantic understanding through friendly labels and relationships that Auto-curious can use to find analytic domain entities that map to their analytic content 702. In order to apply semantic suggestions for the machine learning workflow, aggregations, unsupervised clustering and multi-resolution feature engineering by Schema nudges 703. Based on the metaspace pints generated on additional schematization, Auto-curious can review the analytic content and context and start building machine learning models by Analytic nudges 704. The details of the model performance, resource optimization and all audit features, including visual analytic workflows that answer specific questions not stored in the exact format needed by Insight nudges 705. [0197] FIG. 10A shows an example embodiment of processes of ingesting source analytic assets, including analytic context from a corpus of documents and code, processing to generate metaspace points that map user domains to analytic domains and drive autonomous machine learning workflows as expressed in a high level architectural diagram 4000. Gestalt Modeling Progressive modeling as a formalized model optimization technique of iteration. Gestalt Modeling and the use of Overkill Analytics as a Scout style workflow for improving automated workflows. Gestalt Modeling and the use of Overkill Analytics as a Scout style workflow for suggesting new workflow. Gestalt Modeling and the use of Overkill Analytics as a Sentry style workflow for improving automated workflows. )
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Hathaway in view of US 20230127193 A1; Mukherjee; Maharaj et al. (hereinafter Mukherjee)
Regarding claim 12, Hathaway teaches The method according to claim 6 … establishing a connection with the data source of the SQL statement type according to the SQL statement and the table information in the SQL statement. (Hathaway [0033] As used herein, the term "data analysis" means the execution of a computer program or algorithm to access a target collection of data or information ("data source`) and to evaluate, manipulate, or organize the target data, so as to derive or extract useful information from the data and present the useful information in a form or format different from the original target collection. [0036] FIG. 1 depicts a user interface 110 for a prior art data analysis software application that may serve as background information for the present disclosure. An appreciation and understanding of the present disclosure's particular contribution to the art may be gained with reference to methods and functions associated with this type of data analysis software application. [0044] The Projects Database 312 in this case is merely a collection of discrete data sources each of which is commonly identified to and grouped by Project 322. Each Project is defined by parameters relevant to the user. For example, a Project may represent a quality improvement effort directed to a specific process in the user's organization. In this web-based system 350, computing device 216 generally extracts data from the Projects Database 312 and directs the data as input to a Tool function selected from the Tools Library 314. In exemplary embodiments, the selected Tool function will perform data analysis on the data and deliver an output to or through computing device 216. Computing device 216 may also deliver output to Studies Database 316 or some other external facility, such as a printer, data storage, or another client station. As shown in FIG. 3A, computing device 216 may receive data and information from the Tools Library 314 and from Studies Database 316.[0061] FIG. 5A depicts ...the data analysis software application and for implementing steps and methods previously described in respect to FIGS. 3-4. As with most computer user interfaces, the user interface 512 of this software application may be navigated, engaged, and changed through use of a keyboard and control pointer such as a mouse, cursor, or equal (not shown). These control devices are specifically used to manipulate a plurality of graphical user interface elements or widgets. In one aspect of the disclosure, the user interface 512 preferably employs several types of widgets to facilitate the user's management of projects and data sources and selection and employment of an array of data analysis tools. These widgets include menus, toolbars, containers such as windows, panels, and palettes, icons, buttons, and other common user interface elements. The present graphical user interface favors an object-oriented design (i.e., as opposed to an application-oriented design), whereby the user interacts explicitly with objects that are intuitive representations of the entities in the domain relevant to the application. In the embodiments described herein, these user interface objects may represent projects, data sources or data sheets, data sets, tools and tool functions, and studies, among other things. [63-73] elaborate on the matter [FIG.1A-B in conjunction with FIG.8B] show the corresponding visual) However lacks teaching wherein when the data source is a data source of a SQL statement type, the establishing a connection with each type of data source according to connection information of each type of data source, comprises:performing a syntax verification on a SQL statement, and after determining that the syntax verification passes, parsing the SQL statement to obtain table information in the SQL statement; However Mukherjee teaches wherein when the data source is a data source of a SQL statement type, the establishing a connection with each type of data source according to connection information of each type of data source, comprises:performing a syntax verification on a SQL statement, and after determining that the syntax verification passes, parsing the SQL statement to obtain table information in the SQL statement; (Mukherjee [0076] FIG. 5 provides an illustrative diagram for a machine learning and grammar model based multidialect machine interpretable language transpiler in accordance with one or more example embodiments. Data migration and transformation projects may require adoption of new technologies, and in the process, may require massive efforts in code and logic conversion. SQL is a common language used in applications to code business and reporting logic. It also may exist in many dialects, which may be either proprietary or open source. Typically, a migration effort from one SQL technology to another may involve months of careful code conversion and testing. In some instances, this time and effort may outweigh the potential benefits of migration. Probabilistic machine learning techniques may be used and a custom grammar may be built to interpret a source SQL dialect and build a target dialect to support automatic conversion of dialects and therefore the business logic contained in them. Doing so may obviate the need to develop and test single purpose custom conversion code that may otherwise be needed for a particular migration effort. [0085] As shown in diagram 605, this smart dynamic reversal SQL engine may be implemented as one or more of the RDBS database layers/servers that may intercept queries before they are processed or parsed to the compiler of any specific database target during migration. The smart dynamic reversal SQL engine may be structured to perform a complete SQL language syntax validation, which may include all the keywords that are needed for a query to successfully retrieve accurate data from the source database. [0088] On the second model, if the source database does not exist, the SQL logic engine may review the target SQL query syntax with the source syntax and generate a rule syntax on the target database for the database to accept the new syntax and validate the query for processing. Each time new rules and syntax is created on the database, it may be stored both on the smart dynamic reversal SQL engine and on the database server. [FIG.5] shows corresponding visual flow performing a syntax verification on a SQL statement, and after determining that the syntax verification passes, parsing the SQL statement to obtain table information in the SQL statement ) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take all prior methods and make the addition of Mukherjee in order to create a more efficient system for data verfication and a more accurate output via syntex checks (Mukherjee [0002] most MILP jobs need to be processed in real time, which might not allow time for conversational improvements (as is offered in NLP). Accordingly, it remains difficult to perform translation of machine interpretable language queries in an effective, efficient, timely, and accurate manner.[0076] FIG. 5 provides an illustrative diagram for a machine learning and grammar model based multidialect machine interpretable language transpiler in accordance with one or more example embodiments. Data migration and transformation projects may require adoption of new technologies, and in the process, may require massive efforts in code and logic conversion. SQL is a common language used in applications to code business and reporting logic. It also may exist in many dialects, which may be either proprietary or open source. Typically, a migration effort from one SQL technology to another may involve months of careful code conversion and testing. In some instances, this time and effort may outweigh the potential benefits of migration. Probabilistic machine learning techniques may be used and a custom grammar may be built to interpret a source SQL dialect and build a target dialect to support automatic conversion of dialects and therefore the business logic contained in them. Doing so may obviate the need to develop and test single purpose custom conversion code that may otherwise be needed for a particular migration effort. [0085] As shown in diagram 605, this smart dynamic reversal SQL engine may be implemented as one or more of the RDBS database layers/servers that may intercept queries before they are processed or parsed to the compiler of any specific database target during migration. The smart dynamic reversal SQL engine may be structured to perform a complete SQL language syntax validation, which may include all the keywords that are needed for a query to successfully retrieve accurate data from the source database. )
Regarding claim 13, Hathaway and Mukherjee teach The method according to claim 12, wherein after parsing the SQL statement to obtain table information in the SQL statement, the method further comprises: storing the SQL statement and the table information in the SQL statement in a local database; (Mukherjee [0068] As a particular example, the following input SQL code may be received: “SELECT abc FROM students WHERE class_id=(SELECT id FROM classes WHERE number_of_students=(SELECT MAX number_of_students FROM classes WHERE MAX number_of_TA==2))”. In this example, the available formats and keys may be limited to those illustrated in table 305 of FIG. 3A. Accordingly, it may be evident that the available formats and keys might not include a suitable key for the above input code. However, by using the available formats and templates recursively to create keys and parameters, the used keys and extracted parameters shown in diagram 310 of FIG. 3B may be identified. Using this information, a parse tree may be expanded as illustrated in diagram 315 of FIG. 3C, which may allow the query to be reconstructed. [0076] FIG. 5 provides an illustrative diagram for a machine learning and grammar model based multidialect machine interpretable language transpiler in accordance with one or more example embodiments. Data migration and transformation projects may require adoption of new technologies, and in the process, may require massive efforts in code and logic conversion. SQL is a common language used in applications to code business and reporting logic. It also may exist in many dialects, which may be either proprietary or open source. Typically, a migration effort from one SQL technology to another may involve months of careful code conversion and testing. In some instances, this time and effort may outweigh the potential benefits of migration. Probabilistic machine learning techniques may be used and a custom grammar may be built to interpret a source SQL dialect [0087] The smart dynamic reversal SQL engine may include one or more logic engines and one or more storage memories, which may store rules or instructions and a capabilities model. On the first linear model, the SQL logic engine may be both connected to the old databases and the new database through a secured network channel where by the two engines may communicate with each other through an API. Through that model, if a query submitted does not match the SQL syntax of the target database, the SQL logic engine may re-submit that SQL call to the source database and execute the query to get the data. Once the data has been acquired, the engine may store the data temporally on cache for the other logic engine at the target database to access/review the data. The SQL logic engine may perform one reversal regulatory algorithm function that may return a model scan given the data set in question, which may return a similar SQL query for the data set on the target database tables, thus resulting in a new query that may be provided to the application or the user and stored for further execution. [FIG.5 & 7A] show the corresponding visuals) and generating a nested SQL statement using the stored SQL statement and a SQL statement input by the user, and determining the generated nested SQL statement as an obtained data source of the SQL statement type. (Mukherjee [0015] In accordance with one or more additional or alternative embodiments of the disclosure, a computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may receive a query, formatted in a first format for execution on a first database. The computing platform may translate the query to a second format for execution on a second database, which may include: 1) extracting non-essential parameters from the query to create a query key; 2) storing the non-essential parameters; 3) executing a lookup function on a query library to identify a translated query corresponding to the query key; 4) based on identifying that the query library includes portions of the query key rather than the query key, recursively identifying the translated query by nesting the portions of the query key; and 5) inputting the non-essential parameters into the translated query to create an output query. The computing platform may execute the output query on the second database[0136] The query translation platform 920 may then assemble a translated query in the target dialect using the nested pre-verified query keys corresponding to the nested source query keys to identify their corresponding formatted statements (e.g., as shown in FIG. 3A). [FIG.5 & 7A] show generating a nested SQL statement using the stored SQL statement and a SQL statement input by the user, and determining the generated nested SQL statement as an obtained data source of the SQL statement type.)
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Hathaway in view of US 20170249318 A1; Mescal; Gerald (hereinafter Mescal)
Regarding claim 19, Hathaway teaches The method according to claim 18 However lacks explicitly and orderly teaching wherein the generating a target dataset according to the table information of each target table and the association relationship, comprises:determining first fields, that are the same, between the multiple target tables and second fields that are retained after the multiple target tables are associated according to the association relationship; and generating a SQL statement according to the table information of each target table, the first fields and the second fields, and executing the SQL statement to obtain the target dataset. However Mescal teaches wherein the generating a target dataset according to the table information of each target table and the association relationship, comprises:determining first fields, that are the same, between the multiple target tables and second fields that are retained after the multiple target tables are associated according to the association relationship; (Mescal [0004] Individual fields are often referred to as columns, because field definitions are the same for each record within a table. Each column in a database table is characterized by a name and a data type. A database may contain multiple tables, each of which may contain multiple rows and/or columns.[0039] The Orbit Form module may comprise a second interface configured to communicate with the database by creating, testing, and executing SQL scripts that include the query option parameters of the call. The second interface may execute SQL script(s) to retrieve from the database records that match values retrieved from the Level 0 target named field, from the Above The Line named fields, and/or from the Below The Line named fields. [107] the entry box may receive the input value as a search variable match or as a selection of a variable present in the target field that the Orbit Form module 105 may offer as a drop-down value choice. The Orbit Form module 105 may use the value entered into the Paging Box to retrieve data from the working data set that matches the filter value from the Paging Box (Block 388), and display those results in a results page (Block 380). [244-254] further elaborate [FIG.4&7] show a visual of the corresponding limitations) and generating a SQL statement according to the table information of each target table, the first fields and the second fields, and executing the SQL statement to obtain the target dataset. (Mescal [0039] The Orbit Form module may comprise a second interface configured to communicate with the database by creating, testing, and executing SQL scripts that include the query option parameters of the call. The second interface may execute SQL script(s) to retrieve from the database records that match values retrieved from the Level 0 target named field, from the Above The Line named fields, and/or from the Below The Line named fields. 0042] A method aspect of the invention may comprise the steps of initiating, using an Orbit Form module, data communication with the database using the database connection string of the call. The method may also include creating SQL scripts that include the query option parameters of the call, and executing the SQL scripts to retrieve records from the database that include structurally-related values retrieved from the Level 0 target named field, from the Above The Line named fields, and from the Below The Line named fields. The method may also include creating and executing SQL scripts to retrieve data from database to show and facilitate the selection of the field value for each approach filter in use....[108] initiate building of a SQL script [109] iterative building of the database query (Block 350), and reactivation/revision of predecessor approach boxes (Blocks 355, 360, 340) may continue as described above until such time that building of the revised database query completes with the entry of a value in the Level 1 Approach Box (A.sub.1) (Block 325). The Orbit Form module 105 may then execute the modified query (e.g., SQL script comprising the combined filters of all approach boxes) as built (Block 370) to create the working data set. [244-254] further elaborate [FIG.4&7] show a visual of the corresponding limitations) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take all pior methods and make the addition Mescal in order to create a more efficeint system (Mescal [0033] With the foregoing in mind, it is therefore en object of the present invention to provide systems and methods for creating and using a database interface. More specifically, the present invention may advantageously provide a generic, stand-alone software tool that may advantageously provide for a tool user to navigate and search objects in a third-party database, and to display data retrieved from the data objects in a meaningful way. The database interface capability of the present invention may advantageously permit an end user to use the systems, methods, and devices described herein to productively and efficiently interact with any SQL-compatible database, regardless of that end user s level of expertise (or lack of expertise) in SQL programming and syntax. [0042] A method aspect of the invention may comprise the steps of initiating, using an Orbit Form module, data communication with the database using the database connection string of the call. The method may also include creating SQL scripts that include the query option parameters of the call, and executing the SQL scripts to retrieve records from the database that include structurally-related values retrieved from the Level 0 target named field, from the Above The Line named fields, and from the Below The Line named fields. The method may also include creating and executing SQL scripts to retrieve data from database to show and facilitate the selection of the field value for each approach filter in use.)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Hathaway in view of US 20210117447 A1; LI; Huaizhi et al. (hereinafter Li)
Regarding claim 20, Hathaway teaches The method according to claim 18 Hathaway lacks explicitly and orderly teaching all of wherein the generating a target dataset according to the table information of each target table and the association relationship, further comprises:receiving a filtering condition input by the user, wherein the filtering condition is used to filter data in multiple target tables; and generating a target dataset according to the filtering condition, table information of the multiple target tables, and the association relationship between the multiple target tables. However Li teaches wherein the generating a target dataset according to the table information of each target table and the association relationship, further comprises:receiving a filtering condition input by the user, wherein the filtering condition is used to filter data in multiple target tables; (Li [0034] FIG. 3 illustrates an exemplary schematic of using a classification model in databases, according to some embodiments of the present disclosure. According to FIG. 3, the classification model takes a database query as input, and outputs a filter column by which data clustering is conducted. After data clustering is performed according to the filter column, the resulting target table should provide the database query a more efficient execution. If the classification model can always produce a target table that is more efficient in executing the database query, the overall execution efficiency of the database can be greatly improved. In some embodiments, the filter column can be used as a table identification (ID) to identify the target table that is produced by performing data clustering on the filter column.[0036] FIG. 4 illustrates an exemplary schematic of using a classification model involving multiple data clustering algorithms in databases, according to some embodiments of the present disclosure. On the basis of FIG. 3, the output of FIG. 4's classification model includes the specific data clustering algorithm in addition to the filter columns. In some embodiments, the filter column and the specific data clustering algorithm together can be used as a table ID to identify the target table that is produced by performing data clustering on the filter column with the specific data clustering algorithm.[0049] Data clustering predictor 342 is configured to load the trained classification model, receive query information of incoming queries from query execution engine 343, and make prediction on the preferred filter column or columns for each incoming query. Data clustering predictor 342 makes predictions by feeding the incoming query into the trained classification model and analyzing trained classification model's output. In some embodiments, after the preferred filter column or columns are identified, data clustering predictor 342 can further identify a target table that is clustered according to the preferred filter column or columns. In some embodiments, in addition to filter columns, data clustering predictor 342 makes prediction on the preferred data clustering algorithms (e.g., output of the classification model in FIG. 4). In some embodiments, there are multiple database queries in the query information. Data clustering predictor 342 can be configured to run some or all of the multiple queries through the trained classification model and identify a number of filter columns or data clustering algorithms for the multiple queries. Data clustering predictor 342 can then select one set of filter columns or data clustering algorithm as a table ID, which points to a target table that is expected to offer a more efficient processing for the database queries. [FIG.1 in conjunction with FIG.5] shows receiving a filtering condition input by the user, wherein the filtering condition is used to filter data in multiple target tables) and generating a target dataset according to the filtering condition, table information of the multiple target tables, and the association relationship between the multiple target tables. (Li [0034] FIG. 3 illustrates an exemplary schematic of using a classification model in databases, according to some embodiments of the present disclosure. According to FIG. 3, the classification model takes a database query as input, and outputs a filter column by which data clustering is conducted. After data clustering is performed according to the filter column, the resulting target table should provide the database query a more efficient execution. If the classification model can always produce a target table that is more efficient in executing the database query, the overall execution efficiency of the database can be greatly improved. In some embodiments, the filter column can be used as a table identification (ID) to identify the target table that is produced by performing data clustering on the filter column.0073] In some embodiments, the table ID pointing to the target table is represented by a filter column or a specific data clustering algorithm (e.g., output of the classification model of FIG. 3 or FIG. 4). In some embodiments, step 4070 can be executed to generate the target table according to the table ID. In step 4070, the target table is generated by performing data clustering on the tables in the database according to the filter column or the specific data clustering algorithm. In some embodiments, there can be multiple filter columns corresponding to multiple tables in the database to represent the table ID. In some embodiments, there can be multiple data clustering algorithms corresponding to multiple tables in the database to represent the table ID. It is appreciated that step 4070 can be performed by compute node 340 (e.g., query execution engine 343) of FIG. 5.[0079] In some embodiments, the table ID pointing to the target table is represented by a filter column or a specific data clustering algorithm (e.g., output of the classification model of FIG. 3 or FIG. 4). In some embodiments, step 4071 can be executed to generate the target table according to the table ID. In step 4071, the target table is generated by performing data clustering on the tables in the database according to the filter column or the specific data clustering algorithm. In some embodiments, there can be multiple filter columns corresponding to multiple tables in the database to represent the table ID. In some embodiments, there can be multiple data clustering algorithms corresponding to multiple tables in the database to represent the table ID. It is appreciated that step 4071 can be performed by compute node 340 (e.g., query execution engine 343) of FIG. 5. [FIG.1 in conjunction with FIG.5] shows generating a target dataset according to the filtering condition, table information of the multiple target tables, and the association relationship between the multiple target tables) Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to take all prior methods and make the addition of Li in order to improve an overall performance of the systems searching capabilities (Li [0022] Many of the modern databases are columnar databases, which store data in columns rather than in rows. Columnar databases can improve an overall performance of analytical queries. For example, columnar databases can reduce input/output cost of queries since generally a query only reads a portion of columns in a table. Moreover, columnar databases can achieve better compression since data in a column is often of a same type.[0023] A column can be divided into blocks of certain sizes. For example, if a block contains 10,000 rows, a column with 1,000,000 rows may consist of 100 blocks. Within a block, statistics may be collected and processed, including the minimum or maximum values of data in the block, histograms of data in the block, etc. These statistics can then be used during query execution to improve query performances.[0026] Different algorithms of data clustering can produce different tables, and each table may produce varying efficiency in executing specific queries on the database. Using FIG. 1 as an example, if 90% of queries on the database are associated with the “id” column (e.g., looking for the specific “id”), then a table produced by a data clustering algorithm that sorts the “id” column (e.g., the algorithm that produced Table 1B) would provide a better average execution efficiency than Table 1A where entries in the “id” column are scattered randomly. Therefore, picking the right data clustering algorithm can have immense impact on the overall performance of query execution on a database [FIG.1 in conjunction with FIG.5] shows receiving a filtering condition input by the user, wherein the filtering condition is used to filter data in multiple target tables)
Conclusion
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/ARYAN D TOUGHIRY/Examiner, Art Unit 2165
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