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 .
This Office Action has been issued in response to Applicant’s Communication of application S/N 18/823,465 filed on December 16, 2024. Claims 1-7, 10-11, 13, and 19-20 are pending with the application.
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.
Claim(s) 1-7, 10-11, 13, and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Baird et al. (U.S. Publication No.: US 20170169092 A1) hereinafter Baird, in view of Vaschillo et al. (U.S. Publication No.: US 20050050068 A1) hereinafter Vaschillo, and further in view of Ayachitula et al. (U.S. Publication No.: US 20080005186 A1) hereinafter Ayachitula.
As to claim 1:
A method for generation of semantic layers used to perform data analyses over subject datasets that are described at least in part by subject dataset metadata [Paragraph 0031 teaches an analyst may want to perform analyses, using Tableau, Excel, or QlikView, to generate various instances of database statements to be interpreted on associated datasets on a set of subject data that is stored in a subject database. The structure of the subject database can be specified by certain subject database attributes comprising subject database metadata in a distributed data metastore. Database statements from the analysis tools are conformed to database connectivity statements and are then delivered to a query planner to produce associated instances of subject database statements that can be issued to a distributed data query engine for operation on the subject database. The database statements can be configured to operate on a virtual multidimensional data model (semantic layers). Specifically, the virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes 126) of the subject database. Paragraph 0064, FIG. 6A, FIG. 6B, FIG. 6C, and FIG. 6D depict a series of multidimensional data model design application user interface views (e.g., cube design canvas views) from the perspective of two designers collaborating on the design of a virtual multidimensional data model.
The examiner interprets designing virtual multidimensional data models to be the claimed generation of semantic layers, wherein the virtual multidimensional data models used to analyze database statements derived from subject data (subject data sets) stored in a subject database. The examiner also interprets structure of the database specified by subject data attributes to be the claimed subject dataset metadata describing subject datasets.],
the method performed by at least one computer, the method comprising: identifying a plurality of data analysis configurations [Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. The examiner interprets the units of work objects invoked by data model designers to be the claimed plurality of identified data analysis configurations.];
determining one or more data analysis attributes associated with the plurality of data analysis configurations [Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. In some cases, the interactions among the data model designers can occur concurrently. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. The examiner interprets the units of work objects invoked by data model designers to be the claimed identified data analysis configurations. Paragraph 0037 teach the interaction attributes might trigger a design transaction comprising multiple associated operations that generate a unit of work object (e.g., unit of work object 154.sub.1, . . . , unit of work object 154.sub.N) described by certain object attributes (e.g., object attributes 155.sub.1, . . . , object attributes 155.sub.N).
To elaborate, the examiner interprets work objects to be the claimed data analysis configurations and object attributes resulting from triggers generating work objects to be the claimed data analysis attributes associated with the data analysis configurations.]; and
generating semantic model metadata that constitute a semantic layer [Paragraph 0036 interaction with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events is characterized by various sets of interaction attributes. The transaction processor can apply the interaction attributes to a local set of data model rules to determine certain actions. The data model rules and/or multidimensional data model rules 172 in part define the characteristics (e.g., syntactic rules, semantic rules, etc.) of semantically correct representations (e.g., programming objects) of various aspects of the collaboratively designed virtual multidimensional data model. Paragraph 0061 teaches the rules might specify that the dimension object are to contain a hierarchy attribute and a level attribute pointing to a valid keyed-attribute as its primary-attribute. The examiner interprets the rules used to define the dimension to be the generation of semantic model metadata wherein the rules are associated with the multidimensional data model and dimension is included in the semantic model metadata.], the semantic model metadata being generated based at least in part on the one or more data analysis attributes rather than being generated based on the subject dataset metadata [Paragraph 0031 teaches the structure of the subject database can be specified by certain subject database attributes (e.g., database definitions, schema definitions, etc.) comprising subject database metadata in a distributed data metastore. Paragraph 0038 teaches processing at the multidimensional data model design collaboration engine includes receiving, merging, and validating the object attributes. The merge processor can merge the object attributes comprising various semantically correct instances of the unit of work objects received from various respective designers to determine a set of merged object attributes. The examiner interprets the object attributes to be the data analysis attributes wherein the object attributes are used to a set of merged attributes comprising semantically correct (rule defined) instances of work objects characterized by metadata (dimensions)],
the data analysis attributes derived from the plurality of data analysis configurations [Paragraph 0031 teaches analyst 102 (e.g., business intelligence analyst) interacting with certain instances of analysis tools (e.g., Tableau, Excel, QlikView, etc.) that can generate various instances of database statements to be interpreted on associated datasets. In some cases, the analyst may want to perform analyses on a set of subject data (e.g., mobile activity, social network activity, transactions, CRM activity, etc.) that is stored in a subject database (e.g., as flat file data, multidimenional data, etc.) in a distributed data warehouse. The database statements can be configured to operate on a virtual multidimensional data model. Specifically, the virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes) of the subject database. Such virtual cubes can be presented to the analyst to facilitate a broad range of analyses of the underlying data (e.g., subject data). Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. In some cases, the interactions among the data model designers can occur concurrently. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. Paragraph 0037 teach the interaction attributes might trigger a design transaction comprising multiple associated operations that generate a unit of work object (e.g., unit of work object 154.sub.1, . . . , unit of work object 154.sub.N) described by certain object attributes (e.g., object attributes 155.sub.1, . . . , object attributes 155.sub.N). Paragraph 0053 teaches the multidimensional data model attribute selection technique determining data model attributes associated with a multidimensional data model representation of a subject database by selecting a data warehouse from available (e.g., connected) data warehouses.
Note: The examiner interprets the semantically correct units of work objects to be the claimed one or more data analysis configurations wherein the semantically correct units of work objects are generated at each instance of the multidimensional data model. The instance of the multidimensional data model comprises virtual cubes that are then presented to the analyst using the analysis tools to analyze underlying data/subject data. The examiner also interprets data model attributes associated with a multidimensional data model representation resulting from triggers generating work objects to be the claimed data analysis attributes associated with the data analysis configurations.]
based on the data analysis attributes mapped to the model from the mapping rules, determining one or more implementation recommendations associated with the semantic layer [Paragraph 0036 teaches using the cube design canvas to view and/or interact with various representations of a selected instance (e.g., latest version) of a collaboratively designed virtual multidimensional data model characterized by an associated set of data model attributes. The data model attributes are transmitted from the multidimensional data model design collaboration engine to the multidimensional data model design application for local storage in respective sets of application data upon launch of the application and/or loading of a certain data model design project. Interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events characterized by various sets of interaction attributes. FIG. 6B depicts collaborative design interface views among collaborators selecting interaction attributes in systems for data model design collaboration using semantically correct collaborative objects. Paragraph 0037 teach the interaction attributes might indicate the designer wants to create a new dimension for the collaboratively designed virtual multidimensional data model characterized by the data model attributes 142.sub.S. In this case, the data model rules can specify the additional interaction attributes (e.g., dimension name) required from the designer, the semantically correct structure of the dimension object, the minimum attributes that may be required for a valid dimension object, and/or other characteristics defining a semantically correct unit of work object. Paragraph 0068 and Figure 6B teaches responsive to designer1 130.sub.1 interacting with the cube design canvas to invoke the creation of a new dimension (e.g., see FIG. 6A), a dimension creation window 614.sub.1 is rendered in the designer1 cube design canvas view 604.sub.1. Designer1 130.sub.1 might enter the name "Customer Dimension" and select the key column "customerkey". Note: The examiner interprets the selection of the key column “customerkey” to be a result of the application providing recommendations in the form of additional interaction attributes in the form of a drop down list and the designer selecting an interaction attribute from the set of recommendations”. The data model attributes are interpreted to be the claimed data analysis attributes and the list of the interaction attributes is interpreted to the claimed implementation recommendations, wherein the data model attributes are mapped to a data model based on mapping rules.], the implementation recommendations being determined based at least in part on at least one of, the one or more data analysis attributes, or the semantic model metadata [Paragraph 0036 teaches using the cube design canvas to view and/or interact with various representations of a selected instance (e.g., latest version) of a collaboratively designed virtual multidimensional data model characterized by an associated set of data model attributes. The data model attributes are transmitted from the multidimensional data model design collaboration engine to the multidimensional data model design application for local storage in respective sets of application data upon launch of the application and/or loading of a certain data model design project. Interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events characterized by various sets of interaction attributes. FIG. 6B depicts collaborative design interface views among collaborators selecting interaction attributes in systems for data model design collaboration using semantically correct collaborative objects. To further elaborate, the examiner interprets the drop down list provided to the designer to be a list of interaction attributes that represents one or more elements of the application data wherein application data represents data transmitted from the multidimensional model data model attributes. The data model attributes are interpreted to be the claimed data analysis attributes and the list of the interaction attributes is interpreted to the claimed implementation recommendations.]; and
detecting at least one action event associated with the one or more implementation recommendations [Paragraph 0070 and Figure 6C teach designer1 130.sub.1 has selected an interaction attribute (e.g., "VALUE COLUMN"="fullname") in the dimension creation window 614.sub.2 that can serve to complete a valid set of interaction attributes such that the "Save" button can be clicked. The examiner interprets selecting the interaction attribute and clicking save to be the claimed detected action(s) associated with the implementation recommendation wherein the interaction attribute is interpreted to be the implantation recommendation.]; and executing one or more commands to carry out the at least one action event [Paragraph 0070 teaches clicking the "Save" button can generate a semantically correct unit of work object based on the specified interaction attributes, data model rules, and/or other inputs. The generated unit of work object can then be broadcast to the designers with access to the collaborative environment for the selected virtual multidimensional data model. The examiner interprets generating a semantically correct unit of work when the designer clicks “Save” to be the claimed execution of the one or more commands to carry out the action event (selecting the interaction attribute and clicking “Save”)].
Baird discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose at least one of the one or more data analysis attributes is mapped to a respective portion of the semantic model metadata based at least in part on one or more mapping rules, the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata, and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata.
Vaschillo discloses:
at least one of the one or more data analysis attributes mapped to a respective portion of the semantic model metadata based on one or more mapping rules [Paragraph 0038 and Figure 1 teach source data model 102 and a target data model 104, which mapping occurs from the source model 102 to the target model 104 via a mapping component 106. Each data model (102 and 104) has associated therewith metadata that exposes one or more entities that can be related. That is, the source model 102 exposes source metadata 108 and the target model 104 exposes target metadata 110, which metadata (108 and 110) each comprise conceptual entities that are directly relatable via the mapping component 106. The metadata entities include the concepts (or expressions) of structure, field, and relationship. Paragraph 0039 teaches mapping relates and connects between the same, different, or a combination of the same and different mappable concepts from at least two mapped models. Mapping is directional. Paragraph 0178 teaches BasedOnMap should refer to a valid Map name. BasedOnRelationship should refer to a valid Relationship name in the source domain. A valid BasedOnMap should exist if BasedOnRelationship is specified. Copied FieldMaps are subject to the all other mapping rules as they are explicitly defined. The examiner interprets the source model metadata/entities to be to the claimed data analysis attributes and the mapping rules to be the claimed mapping rules used to map source model metadata/entities to target model metadata/entities. Explicitly defined directional mapping based on relationships is interpreted to be the claimed respective mapping between data analysis attributes and the respective portion of the semantic model metadata].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Baird, by incorporating mapping rules to map model metadata/entities (see Vaschillo Paragraph 0038, 0039, and 0178), because both applications are directed to semantic data model processing; configuring the steps used to generate semantic layers to include steps for adherence to rules for mapping metadata and attributes/entities allows for users to take advantage of the extensibility mechanism of a domain (see Vaschillo Paragraph 0065).
Baird and Vaschillo discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata, and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata.
Ayachitula discloses:
the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata [Paragraph 0003 teaches a CMDB is broad and semantically rich enough that it may apply to higher layers such as, for example, a business process or a distributed application. Paragraph 0058 teaches if all attributes have been iterated, the methodology continues to block 1346, where a new model object is constructed in memory that represents the retrieved model object in CMDB the new model object is pruned based on the template metadata and contains only the attributes that are in the template metadata definition and ignores all the attributes not defined in the template. Note: Implementing a new model (semantic model metadata) that semantically rich with template metadata as part of a configuration management database or CMDB based on instructions (recommendation) to execute a pruning process that prunes unused attributes that are not defined in template metadata reads on the claims.]
and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata [Paragraph 0003 teaches a CMDB is broad and semantically rich enough that it may apply to higher layers such as, for example, a business process or a distributed application. Paragraph 0058 teaches if all attributes have been iterated, the methodology continues to block 1346, where a new model object is constructed in memory that represents the retrieved model object in CMDB the new model object is pruned based on the template metadata and contains only the attributes that are in the template metadata definition and ignores all the attributes not defined in the template. Note: Executing a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Baird and Vaschillo, by incorporating a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes (see Ayachitula Paragraph 0003 and 0058), because both applications are directed to semantic data model processing; incorporating a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes provides manageable and flexible database operability (see Ayachitula Paragraph 0008).
As to claim 2:
Baird discloses:
The method of claim 1, further comprising:
processing one or more data statements issued by one or more data analysis applications [Paragraph 0031 teaches instances of analysis tools (e.g., Tableau, Excel, QlikView, etc.) that can generate various instances of database statements to be interpreted on associated datasets. The examiner interprets analysis tools to be the claimed analysis applications and the database statements to be the claimed data statements.], the one or more data statements being configured to operate over the semantic layer [Paragraph 0031 teaches the database statements from the analysis tools are conformed to database connectivity statements (e.g., using ODBC, JDBC, OLE-DB, etc.) by an instance of middleware. The database connectivity statements can then be delivered to a query planner to produce associated instances of subject database statements that can be issued to a distributed data query engine for operation on the subject database. In an exemplary embodiment, the database statements can be configured to operate on a virtual multidimensional data model. To elaborate, virtual multidimensional data model is interpreted by the examiner to be the semantic layer.]
As to claim 3:
Baird discloses:
The method of claim 2, wherein the one or more data statements are configured to operate over at least one of, at least one virtual data model, or one or more virtual cubes, that is associated with the semantic layer [Paragraph 0031 teaches the database statements from the analysis tools are conformed to database connectivity statements (e.g., using ODBC, JDBC, OLE-DB, etc.) by an instance of middleware. The database connectivity statements can then be delivered to a query planner to produce associated instances of subject database statements that can be issued to a distributed data query engine for operation on the subject database. In an exemplary embodiment, the database statements can be configured to operate on a virtual multidimensional data model. The virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes 126) of the subject database. To elaborate, the examiner interprets the database statements to be configured on both the virtual multidimensional data model (virtual model) and the virtual cubes].
As to claim 4:
Baird discloses:
The method of claim 2, wherein the one or more data statements are processed to formulate one or more data operations that are performed over at least one subject dataset [Paragraph 0031 teaches database statements to be interpreted on associated datasets. In some cases, the analyst may want to perform analyses on a set of subject data (e.g., mobile activity, social network activity, transactions, CRM activity, etc.) that is stored in a subject database (e.g., as flat file data, multidimenional data, etc.) in a distributed data warehouse].
As to claim 5:
Baird discloses:
The method of claim 1, further comprising: creating at least one virtual data model [Paragraph 0060 teaches at least one designer from a group of collaborating designers concurrently designing a virtual multidimensional data model. The examiner interprets the designing to be the claimed creation of a virtual data model.], the at least one virtual data model being created based at least in part on the semantic model metadata [Paragraph 0060 teaches a designer might want to create, modify, and/or delete some aspect (e.g., dimension, measure, relationship, etc.) of the virtual multidimensional data model. The examiner interprets the dimension, measure, and relationship to be the semantic model metadata].
As to claim 7:
Baird discloses:
The method of claim 5, wherein the at least one virtual data model is presented to at least one user in at least one user interface [Paragraph 0047 and Figure 3:300 teach client devices that can represent one of a variety of other computing devices having software (e.g., multidimensional data model design application 132.sub.1, multidimensional data model design application 132.sub.N, etc.) and hardware (e.g., a graphics processing unit, display, monitor, etc.) capable of processing and displaying information (e.g., web page, graphical user interface, etc.) on a display. Paragraph 0048 teaches the multidimensional data model design application can be launched at the client device. Launching the application might cause certain sets of application data and/or project data (e.g., comprising a selected instance of data model attributes characterizing a collaboratively designed virtual multidimensional data model) to be loaded from the application server to the plurality of client devices to enable the data model designers to perform various design operations. FIG. 6A, FIG. 6B, FIG. 6C, and FIG. 6D depict a series of multidimensional data model design application user interface views (e.g., cube design canvas views) from the perspective of two designers collaborating on the design of a virtual multidimensional data model. The examiner interprets certain sets of application data and project data to be the claimed virtual data model and the data model designers using the client/computing devices to perform design operations on the virtual multidimensional data model to be the claimed virtual data model presented to at least one user in at least one user interface with the additional user interaction of performing various design operations].
As to claim 10:
Baird discloses:
The method of claim 1, wherein the one or more implementation recommendations are presented to at least one user in at least one user interface [Paragraph 0047, Figure 2, and Figure 3 teach client devices (e.g., client device 204.sub.1, . . . , client device 204.sub.N) that can represent one of a variety of other computing devices (e.g., a smart phone 204.sub.3, a tablet 204.sub.4, a WiFi phone 204.sub.5, a laptop 204.sub.6, a workstation 204.sub.7, etc.) having software (e.g., multidimensional data model design application 132.sub.1, multidimensional data model design application 132.sub.N, etc.) and hardware (e.g., a graphics processing unit, display, monitor, etc.) capable of processing and displaying information (e.g., web page, graphical user interface, etc.) on a display. Paragraph 0068, Figure 6A, and Figure 6B teach responsive to designer1 130.sub.1 interacting with the cube design canvas to invoke the creation of a new dimension (e.g., see FIG. 6A), a dimension creation window 614.sub.1 is rendered in the designer1 cube design canvas view 604.sub.1. Designer1 130.sub.1 might enter the name "Customer Dimension" and select the key column "customerkey". To elaborate, the examiner interprets the selection of the key column “customerkey” to be a result of the application providing recommendations and the designer selecting an item from the set of recommendations based on the designer entering “Customer Dimension”. Recommendations are provided via a drop-down and is interpreted to be associated with an action event displayed via the user interface].
As to claim 11:
Baird discloses:
The method of claim 1, wherein the plurality of data analysis configurations are accessed by one or more data analysis applications to perform one or more data operations over at least one subject dataset [Paragraph 0031 teaches analyst 102 (e.g., business intelligence analyst) interacting with certain instances of analysis tools (e.g., Tableau, Excel, QlikView, etc.) that can generate various instances of database statements to be interpreted on associated datasets. In some cases, the analyst may want to perform analyses on a set of subject data (e.g., mobile activity, social network activity, transactions, CRM activity, etc.) that is stored in a subject database (e.g., as flat file data, multidimenional data, etc.) in a distributed data warehouse. The database statements can be configured to operate on a virtual multidimensional data model. Specifically, the virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes) of the subject database. Such virtual cubes can be presented to the analyst to facilitate a broad range of analyses of the underlying data (e.g., subject data). Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. Paragraph 0053 teaches the multidimensional data model attribute selection technique determining data model attributes associated with a multidimensional data model representation of a subject database by selecting a data warehouse from available (e.g., connected) data warehouses.
To further elaborate, the examiner interprets the semantically correct units of work objects to be the claimed one or more data analysis configurations wherein the semantically correct units of work objects are generated at each instance of the multidimensional data model. The instance of the multidimensional data model comprises virtual cubes that are then presented to the analyst using the analysis tools to analyze underlying data/subject data].
As to claim 13:
Baird discloses:
A computer readable medium, embodied in a non- transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors to perform a set of acts for generation of semantic layers used to perform data analyses over subject datasets that are described at least in part by subject dataset metadata [Paragraph 0079, 0080, and Figure 8A teach a "computer readable medium" or "computer usable medium" as any medium that participates in providing instructions to processor 807 for execution. Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, or any other magnetic medium; CD-ROM or any other optical medium; punch cards, paper tape, or any other physical medium with patterns of holes; RAM, PROM, EPROM, FLASH-EPROM, or any other memory chip or cartridge, or any other non-transitory medium. ], the acts comprising:
identifying a plurality of data analysis configurations [Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. The examiner interprets the units of work objects invoked by data model designers to be the claimed plurality of identified data analysis configurations.];
determining one or more data analysis attributes associated with the plurality of data analysis configurations [Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. In some cases, the interactions among the data model designers can occur concurrently. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. The examiner interprets the units of work objects invoked by data model designers to be the claimed identified data analysis configurations. Paragraph 0037 teach the interaction attributes might trigger a design transaction comprising multiple associated operations that generate a unit of work object (e.g., unit of work object 154.sub.1, . . . , unit of work object 154.sub.N) described by certain object attributes (e.g., object attributes 155.sub.1, . . . , object attributes 155.sub.N).
To elaborate, the examiner interprets work objects to be the claimed data analysis configurations and object attributes resulting from triggers generating work objects to be the claimed data analysis attributes associated with the data analysis configurations.]; and
generating semantic model metadata that constitute a semantic layer [Paragraph 0036 interaction with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events is characterized by various sets of interaction attributes. The transaction processor can apply the interaction attributes to a local set of data model rules to determine certain actions. The data model rules and/or multidimensional data model rules 172 in part define the characteristics (e.g., syntactic rules, semantic rules, etc.) of semantically correct representations (e.g., programming objects) of various aspects of the collaboratively designed virtual multidimensional data model. Paragraph 0061 teaches the rules might specify that the dimension object are to contain a hierarchy attribute and a level attribute pointing to a valid keyed-attribute as its primary-attribute. The examiner interprets the rules used to define the dimension to be the generation of semantic model metadata wherein the rules are associated with the multidimensional data model and dimension is included in the semantic model metadata.], the semantic model metadata being generated based at least in part on the one or more data analysis attributes rather than being generated based on the subject dataset metadata [Paragraph 0031 teaches the structure of the subject database can be specified by certain subject database attributes (e.g., database definitions, schema definitions, etc.) comprising subject database metadata in a distributed data metastore. Paragraph 0038 teaches processing at the multidimensional data model design collaboration engine includes receiving, merging, and validating the object attributes. The merge processor can merge the object attributes comprising various semantically correct instances of the unit of work objects received from various respective designers to determine a set of merged object attributes. The examiner interprets the object attributes to be the data analysis attributes wherein the object attributes are used to a set of merged attributes comprising semantically correct (rule defined) instances of work objects characterized by metadata (dimensions)],
the data analysis attributes derived from the plurality of data analysis configurations [Paragraph 0031 teaches analyst 102 (e.g., business intelligence analyst) interacting with certain instances of analysis tools (e.g., Tableau, Excel, QlikView, etc.) that can generate various instances of database statements to be interpreted on associated datasets. In some cases, the analyst may want to perform analyses on a set of subject data (e.g., mobile activity, social network activity, transactions, CRM activity, etc.) that is stored in a subject database (e.g., as flat file data, multidimenional data, etc.) in a distributed data warehouse. The database statements can be configured to operate on a virtual multidimensional data model. Specifically, the virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes) of the subject database. Such virtual cubes can be presented to the analyst to facilitate a broad range of analyses of the underlying data (e.g., subject data). Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. In some cases, the interactions among the data model designers can occur concurrently. The unit of work objects invoked by the data model designers can then be merged, validated, and the committed changes broadcast to all the designers by the multidimensional data model design collaboration engine. Paragraph 0037 teach the interaction attributes might trigger a design transaction comprising multiple associated operations that generate a unit of work object (e.g., unit of work object 154.sub.1, . . . , unit of work object 154.sub.N) described by certain object attributes (e.g., object attributes 155.sub.1, . . . , object attributes 155.sub.N). Paragraph 0053 teaches the multidimensional data model attribute selection technique determining data model attributes associated with a multidimensional data model representation of a subject database by selecting a data warehouse from available (e.g., connected) data warehouses.
Note: The examiner interprets the semantically correct units of work objects to be the claimed one or more data analysis configurations wherein the semantically correct units of work objects are generated at each instance of the multidimensional data model. The instance of the multidimensional data model comprises virtual cubes that are then presented to the analyst using the analysis tools to analyze underlying data/subject data. The examiner also interprets data model attributes associated with a multidimensional data model representation resulting from triggers generating work objects to be the claimed data analysis attributes associated with the data analysis configurations.]
based on the data analysis attributes, determining one or more implementation recommendations associated with the semantic layer [Paragraph 0036 teaches using the cube design canvas to view and/or interact with various representations of a selected instance (e.g., latest version) of a collaboratively designed virtual multidimensional data model characterized by an associated set of data model attributes. The data model attributes are transmitted from the multidimensional data model design collaboration engine to the multidimensional data model design application for local storage in respective sets of application data upon launch of the application and/or loading of a certain data model design project. Interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events characterized by various sets of interaction attributes. FIG. 6B depicts collaborative design interface views among collaborators selecting interaction attributes in systems for data model design collaboration using semantically correct collaborative objects. Paragraph 0037 teach the interaction attributes might indicate the designer wants to create a new dimension for the collaboratively designed virtual multidimensional data model characterized by the data model attributes 142.sub.S. In this case, the data model rules can specify the additional interaction attributes (e.g., dimension name) required from the designer, the semantically correct structure of the dimension object, the minimum attributes that may be required for a valid dimension object, and/or other characteristics defining a semantically correct unit of work object. Paragraph 0068 and Figure 6B teaches responsive to designer1 130.sub.1 interacting with the cube design canvas to invoke the creation of a new dimension (e.g., see FIG. 6A), a dimension creation window 614.sub.1 is rendered in the designer1 cube design canvas view 604.sub.1. Designer1 130.sub.1 might enter the name "Customer Dimension" and select the key column "customerkey". To elaborate, the examiner interprets the selection of the key column “customerkey” to be a result of the application providing recommendations in the form of additional interaction attributes in the form of a drop down list and the designer selecting an interaction attribute from the set of recommendations”. The data model attributes are interpreted to be the claimed data analysis attributes and the list of the interaction attributes is interpreted to the claimed implementation recommendations.], the implementation recommendations being determined based at least in part on at least one of, the one or more data analysis attributes, or the semantic model metadata [Paragraph 0036 teaches using the cube design canvas to view and/or interact with various representations of a selected instance (e.g., latest version) of a collaboratively designed virtual multidimensional data model characterized by an associated set of data model attributes. The data model attributes are transmitted from the multidimensional data model design collaboration engine to the multidimensional data model design application for local storage in respective sets of application data upon launch of the application and/or loading of a certain data model design project. Interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events characterized by various sets of interaction attributes. FIG. 6B depicts collaborative design interface views among collaborators selecting interaction attributes in systems for data model design collaboration using semantically correct collaborative objects. To further elaborate, the examiner interprets the drop down list provided to the designer to be a list of interaction attributes that represents one or more elements of the application data wherein application data represents data transmitted from the multidimensional model data model attributes. The data model attributes is interpreted to be the claimed data analysis attributes and the list of the interaction attributes is interpreted to the claimed implementation recommendations.]; and
accessing at least one additional data set based on the semantic model metadata as a result of the implementation recommendations [Paragraph 0036 teaches using the cube design canvas to view and/or interact with various representations of a selected instance (e.g., latest version) of a collaboratively designed virtual multidimensional data model characterized by an associated set of data model attributes. The data model attributes are transmitted from the multidimensional data model design collaboration engine to the multidimensional data model design application for local storage in respective sets of application data upon launch of the application and/or loading of a certain data model design project. Interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events characterized by various sets of interaction attributes. FIG. 6B depicts collaborative design interface views among collaborators selecting interaction attributes in systems for data model design collaboration using semantically correct collaborative objects. Paragraph 0061 teaches when a valid set of interaction attributes are received, one or more semantically correct unit of work objects can be generated (see step 508). In some cases, the unit of work object can be semantically correct when conformed to certain aspects of the multidimensional data model rules 172. For example, the shown instance of a unit of work object 154.sub.2 for a created dimension (e.g., “customer Dimension”) can be structured according to the multidimensional data model rules 172. Specifically, the rules might specify the unit of work object are to have an XML-based structure having the shown tag taxonomy (e.g., <dimension> . . . </dimension>, <dataset> . . . </dataset>, etc.).
Note: Paragraph 0079 of the instant application specification is directed to action buttons selected by the user to invoke action events that will facilitate implementation of the recommendation which the examiner interprets to be the claimed accessing at least one additional data set based on the semantic model metadata as a result of the implementation recommendations. Therefore, the cited prior art’s recitation of interacting with certain elements (e.g., links, buttons, drag-and-drop items, drop-down selectors, etc.) in the cube design canvas to produce interaction events is also interpreted to read on the claimed accessing at least one additional data set based on the semantic model metadata as a result of the implementation recommendations. The examiner interprets the drop down list provided to the designer to be a list of interaction attributes that represents one or more elements of the application data wherein application data represents data transmitted from the multidimensional model data model attributes. The data model attributes is interpreted to be the claimed data analysis attributes and the list of the interaction attributes is interpreted to the claimed implementation recommendations. To further elaborate, valid interaction attributes associated with the designer interacting with certain elements is interpreted to be the designer interacting with the claimed implementation recommendations. When a valid set of interaction attributes are received, one or more semantically correct unit of work objects can be generated, wherein the semantically correct unit of work objects are interpreted to be the claimed additional data set based on the semantic model metadata and the one or more unit of work objects is interpreted to include the claimed additional (more) data set. Semantically correct is interpreted to read on the claimed based on the semantic model metadata and the claimed data additional data set is interpreted to be associated with or in the same scope of the claimed data analysis attributes associated with the data analysis configurations.]
Baird discloses most of the limitations as set forth in claim 1 but does not appear to expressly disclose at least one of the one or more data analysis attributes is mapped to a respective portion of the semantic model metadata based at least in part on one or more mapping rules, the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata, and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata.
Vaschillo discloses:
at least one of the one or more data analysis attributes mapped to a respective portion of the semantic model metadata based on one or more mapping rules [Paragraph 0038 and Figure 1 teach source data model 102 and a target data model 104, which mapping occurs from the source model 102 to the target model 104 via a mapping component 106. Each data model (102 and 104) has associated therewith metadata that exposes one or more entities that can be related. That is, the source model 102 exposes source metadata 108 and the target model 104 exposes target metadata 110, which metadata (108 and 110) each comprise conceptual entities that are directly relatable via the mapping component 106. The metadata entities include the concepts (or expressions) of structure, field, and relationship. Paragraph 0039 teaches mapping relates and connects between the same, different, or a combination of the same and different mappable concepts from at least two mapped models. Mapping is directional. Paragraph 0178 teaches BasedOnMap should refer to a valid Map name. BasedOnRelationship should refer to a valid Relationship name in the source domain. A valid BasedOnMap should exist if BasedOnRelationship is specified. Copied FieldMaps are subject to the all other mapping rules as they are explicitly defined. The examiner interprets the source model metadata/entities to be to the claimed data analysis attributes and the mapping rules to be the claimed mapping rules used to map source model metadata/entities to target model metadata/entities. Explicitly defined directional mapping based on relationships is interpreted to be the claimed respective mapping between data analysis attributes and the respective portion of the semantic model metadata].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Baird, by incorporating mapping rules to map model metadata/entities (see Vaschillo Paragraph 0038, 0039, and 0178), because both applications are directed to semantic data model processing; configuring the steps used to generate semantic layers to include steps for adherence to rules for mapping metadata and attributes/entities allows for users to take advantage of the extensibility mechanism of a domain (see Vaschillo Paragraph 0065).
Baird and Vaschillo discloses most of the limitations as set forth in claim 13 but does not appear to expressly disclose the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata, and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata.
Ayachitula discloses:
the implementation recommendations indicative of one or more data analysis attributes unused in computing the semantic model metadata [Paragraph 0003 teaches a CMDB is broad and semantically rich enough that it may apply to higher layers such as, for example, a business process or a distributed application. Paragraph 0058 teaches if all attributes have been iterated, the methodology continues to block 1346, where a new model object is constructed in memory that represents the retrieved model object in CMDB the new model object is pruned based on the template metadata and contains only the attributes that are in the template metadata definition and ignores all the attributes not defined in the template. Note: Implementing a new model (semantic model metadata) that semantically rich with template metadata as part of a configuration management database or CMDB based on instructions (recommendation) to execute a pruning process that prunes unused attributes that are not defined in template metadata reads on the claims.]
and executing one or more commands to carry out the at least one action event, the commands for reducing or redirecting the attributes defined by the semantic model metadata [Paragraph 0003 teaches a CMDB is broad and semantically rich enough that it may apply to higher layers such as, for example, a business process or a distributed application. Paragraph 0058 teaches if all attributes have been iterated, the methodology continues to block 1346, where a new model object is constructed in memory that represents the retrieved model object in CMDB the new model object is pruned based on the template metadata and contains only the attributes that are in the template metadata definition and ignores all the attributes not defined in the template. Note: Executing a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Baird and Vaschillo, by incorporating a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes (see Ayachitula Paragraph 0003 and 0058), because both applications are directed to semantic data model processing; incorporating a pruning process that prunes unused attributes that are not defined in a template metadata, wherein pruning is interpreted to be a reduction in the amount of attributes provides manageable and flexible database operability (see Ayachitula Paragraph 0008).
Claim 19 recites similar limitations as in claim 13. Therefore claim 19 is rejected for the same reasons as set forth above. See claim 13 for analysis.
As to claim 20:
Baird discloses:
The system of claim 19, wherein the plurality of data analysis configurations are accessed by one or more data analysis applications to perform one or more data operations over at least one subject dataset [Paragraph 0031 teaches analyst 102 (e.g., business intelligence analyst) interacting with certain instances of analysis tools (e.g., Tableau, Excel, QlikView, etc.) that can generate various instances of database statements to be interpreted on associated datasets. In some cases, the analyst may want to perform analyses on a set of subject data (e.g., mobile activity, social network activity, transactions, CRM activity, etc.) that is stored in a subject database (e.g., as flat file data, multidimenional data, etc.) in a distributed data warehouse. The database statements can be configured to operate on a virtual multidimensional data model. Specifically, the virtual multidimensional data model can comprise various data model attributes that can be used to form one or more logical representations (e.g., virtual cubes) of the subject database. Such virtual cubes can be presented to the analyst to facilitate a broad range of analyses of the underlying data (e.g., subject data). Paragraph 0032 teaches various semantically correct unit of work objects can be generated at each instance of the multidimensional data model design application responsive to certain interactions (e.g., design operations) from the data model designers. Paragraph 0053 teaches the multidimensional data model attribute selection technique determining data model attributes associated with a multidimensional data model representation of a subject database by selecting a data warehouse from available (e.g., connected) data warehouses.
To further elaborate, the examiner interprets the semantically correct units of work objects to be the claimed one or more data analysis configurations wherein the semantically correct units of work objects are generated at each instance of the multidimensional data model. The instance of the multidimensional data model comprises virtual cubes that are then presented to the analyst using the analysis tools to analyze underlying data/subject data].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EARL LEVI ELIAS whose telephone number is (571)272-9762. The examiner can normally be reached Monday - Friday (IFP).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169