DETAILED ACTION
Claims 1, 3-9, and 11-20 are pending. Claims 1, 9, and 17 have been amended. Claims 1, 3-9, and 11-20 are rejected.
The instant application has PRO application No. 63/243,012 filed on 09/10/2021.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 03/11/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-9, and 11-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (All Claims)
According to the first part of the analysis, in the instant case, claims 1 and 3-8 are directed to a system which includes a processor, claims 9 and 11-16 are directed to a method, and claims 17-20 are directed to a non-transitory computer readable storage medium. Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2A, Prong 1 (Claims 1, 9, and 17)
Regarding claim 1, the following limitations are abstract ideas:
a set of statistics are automatically calculated for each of the plurality of columns of the data set; is a step that can be performed as a mental process, with the aid of pen and paper.
wherein, based on each set of statistics calculated for each of the plurality of columns, a score is generated for each column of the plurality of columns, wherein the score generated for each column of the plurality of columns utilizes a configurable set of rules that operate on each set of statistics for each of the plurality of columns of the data set, the configurable set of rules comprising at least one rule selected from a set of column scoring rules, the set of column scoring rules comprising a column selection rule, a user interest flag rule, a null percentage rule, a cardinality rule, a null penalty rule, an integer rule, a key word rule and a text length rule; is a step that can be performed as a mental process, with the aid of pen and paper.
wherein a set of the plurality of columns is selected, the selection being based upon the score for each column; is a step that can be performed as a mental process, with the aid of pen and paper.
wherein, based upon the selected set of the plurality of columns, a plurality of data visualizations are determined to be generated, and are generated, for the selected set of the plurality of columns, the determination of the plurality of data visualizations being based at least upon another set of rules, said another set of rules comprising at least a data visualization type selection rule, said data visualization type selection rule utilizing at least one computation of statistics from the selected set of the plurality of columns, wherein the plurality of data visualizations are generated at least based upon data received in response to structured queries to the data set; is a step that can be performed as a mental process, with the aid of pen and paper.
wherein each of the plurality of determined and generated data visualizations are scored, respectively, based upon at least each of the scores of the selected set of the plurality of columns; is a step that can be performed as a mental process, with the aid of pen and paper.
The above analysis applies to each independent claim as they all contain similar limitations. The different additional limitations will be analyzed below.
Step 2A, Prong 2 (Claims 1, 9, and 17)
Regarding claim 1, the following limitations are additional elements:
a computer including one or more processors, (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
wherein the computer receives, at the analytic applications environment, a data set comprising a plurality of columns; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
wherein, upon receiving the data set comprising the plurality of columns, is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
wherein, based upon a set of rules, a set of the plurality of data visualizations is selected for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
Regarding claim 9, the following limitations are additional elements:
providing a computer including one or more processors, that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
receiving, at the analytic applications environment, a data set comprising a plurality of columns; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
upon receiving the data set comprising the plurality of data columns, is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
selecting, based upon a set of rules, a set of the plurality of data visualizations for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
Regarding claim 17, the following limitations are additional elements:
A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising: (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
providing a computer including one or more processors, (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
receiving, at the analytic applications environment, a data set comprising a plurality of columns; is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
upon receiving the data set comprising the plurality of data columns, is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
selecting, based upon a set of rules, a set of the plurality of data visualizations for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. is directed to the insignificant extra-solution activity of mere data gathering and/or selecting a particular data source or type of data to be manipulated as identified in MPEP 2106.05(g).
Step 2B (Claims 1, 9, and 17)
Regarding claim 1, the following limitations are additional elements:
a computer including one or more processors, (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
wherein the computer receives, at the analytic applications environment, a data set comprising a plurality of columns; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
wherein, upon receiving the data set comprising the plurality of columns, when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
wherein, based upon a set of rules, a set of the plurality of data visualizations is selected for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. when re-evaluated under step 2B is further directed to the insignificant extra-solution activity of selecting information based on types of information and availability of information for collection, analysis and display as identified in MPEP 2106.05(g) “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”
Regarding claim 9, the following limitations are additional elements:
providing a computer including one or more processors, that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
receiving, at the analytic applications environment, a data set comprising a plurality of columns; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
upon receiving the data set comprising the plurality of data columns, when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
selecting, based upon a set of rules, a set of the plurality of data visualizations for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. when re-evaluated under step 2B is further directed to the insignificant extra-solution activity of selecting information based on types of information and availability of information for collection, analysis and display as identified in MPEP 2106.05(g) “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”
Regarding claim 17, the following limitations are additional elements:
A non-transitory computer readable storage medium having instructions thereon, which when read and executed by a computer including one or more processors cause the computer to perform a method comprising (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s)):
providing a computer including one or more processors, (recited at a high‐level of generality (i.e., generic computer components performing generic computer functions) such that they amount to no more than components comprising mere instructions to apply the exception. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s))
that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
receiving, at the analytic applications environment, a data set comprising a plurality of columns; when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
upon receiving the data set comprising the plurality of data columns, when re-evaluated under step 2B is further directed to the well-understood, routine, and conventional activity of receiving or transmitting data as identified in MPEP 2106.05(d)II “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added));”
selecting, based upon a set of rules, a set of the plurality of data visualizations for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules. when re-evaluated under step 2B is further directed to the insignificant extra-solution activity of selecting information based on types of information and availability of information for collection, analysis and display as identified in MPEP 2106.05(g) “iii. Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);”
The dependent claims 3-8, 11-16, and 18-20 mental steps thus is not eligible under 101.
Claims 3-8, 11-16, and 18-20 further clarify certain aspects of the independent claims. These claims further clarify the scores, statistics, data visualizations, and/or columns. These clarifications do not overcome the rejection under 35 U.S.C. 101. Therefore, claims 3-8, 11-16, and 18-20 fall under the same analysis as the independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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, 4-9, 12-17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oberbreckling et al., Patent Application Publication No. 2018/0075104 (hereinafter Oberbreckling) in view of Kazem et al., Patent Application Publication No. 2021/0271709 (hereinafter Kazem) and Luo et al., Patent Application Publication No. 2020/0272651 (hereinafter Luo).
Regarding claim 1, Oberbreckling teaches:
A system for generating automatic insights of analytics data (Oberbreckling Paragraph [0004], presents an enormous opportunity for businesses to gain valuable insights that can help them make improved and timely decisions that will win, serve, and retain customers. A key part of preparing these data sources for analysis is the ability to merge or join), comprising:
a computer including one or more processors (Oberbreckling Paragraph [0010], The memory may store instructions that are executable by the one or more processors to perform methods and operations disclosed herein), that provides access by an analytic applications environment to a data warehouse for storage of data by a tenant (Oberbreckling Paragraph [0040], prior to loading data into a data warehouse (or other data target) the data is processed through a pipeline (also referred to herein as a semantic pipeline) which includes various processing stages);
wherein the computer receives, at the analytic applications environment, a data set comprising a plurality of columns (Oberbreckling Paragraph [0012], A method may include for each of the one or more column pairs: based on a type of join specified via a graphical interface, computing a plurality of scores for the column pair, each of the plurality of scores computed based on a different one of a plurality of scoring functions, the score indicating a measure for joining columns in the column pair; computing a plurality of weighted scores);
wherein a set of the plurality of columns is selected, the selection being based upon the score for each column (Oberbreckling Paragraph [0150], If a column pair is selected based on a lower rank, then weights may be adjusted to favor one or more scoring functions to improve ranking of the column pairs closer to the selection);
wherein, based upon the selected set of the plurality of columns (Oberbreckling Paragraph [0150], If a column pair is selected based on a lower rank, then weights may be adjusted to favor one or more scoring functions to improve ranking of the column pairs closer to the selection), a plurality of data visualizations are determined to be generated, and are generated (Oberbreckling Paragraph [0112], user interface 306 can generate one or more graphical visualizations based on metadata provided by profile engine 326. As explained above, the data provided by profile engine 326 may include statistical information indicating metrics about data that has been processed by profile engine 326), for the selected set of the plurality of columns (Oberbreckling Paragraph [0123], As described herein, profiled data 380 may be used to present data visualizations indicating statistics about large sets of data. At step 366, profiled data may be modified for enrichment to impute labels, such as column names. The labels may be imputed on the basis of the statistics of the profiled data), the determination of the plurality of data visualizations being based at least upon another set of rules (Oberbreckling Paragraph [0070], This gives the user immediate feedback that can be used to visualize and verify the effects of the transform engine 322 configuration. In some embodiments, the transform engine 322 can receive pattern and/or metadata information (e.g., column names and types) from profile engine 326 and recommendation engine 308), said another set of rules comprising at least a data visualization type selection rule (Oberbreckling Paragraph [0070], This gives the user immediate feedback that can be used to visualize and verify the effects of the transform engine 322 configuration. In some embodiments, the transform engine 322 can receive pattern and/or metadata information (e.g., column names and types) from profile engine 326 and recommendation engine 308), said data visualization type selection rule utilizing at least one computation of statistics from the selected set of the plurality of columns (Oberbreckling Paragraph [0123], As described herein, profiled data 380 may be used to present data visualizations indicating statistics about large sets of data. At step 366, profiled data may be modified for enrichment to impute labels, such as column names. The labels may be imputed on the basis of the statistics of the profiled data), wherein the plurality of data visualizations are generated at least based upon data received in response to structured queries to the data set (Oberbreckling Paragraph [0068], Recommendation engine 308 can request (e.g., query) knowledge service 310 for data that can be recommended to a user for the data obtained for a source);
Oberbreckling does not expressly disclose:
wherein, upon receiving the data set comprising the plurality of columns, a set of statistics are automatically calculated for each of the plurality of columns of the data set;
wherein each of the plurality of determined and generated data visualizations are scored, respectively, based upon at least each of the scores of the selected set of the plurality of columns;
wherein, based upon a set of rules, a set of the plurality of data visualizations is selected for display in a selectable manner via a user interface, wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules.
However, Kazem teaches:
wherein, upon receiving the data set comprising the plurality of columns (Kazem Paragraph [0020], “Column-to-visualization mappings,” as used herein, refer to how the concepts and statistics of the columns of the dataset are mapped to the visualizations depicted in the dashboard), a set of statistics are automatically calculated for each of the plurality of columns of the data set (Kazem Paragraph [0020], “Column-to-visualization mappings,” as used herein, refer to how the concepts and statistics of the columns of the dataset are mapped to the visualizations depicted in the dashboard);
wherein each of the plurality of determined and generated data visualizations are scored (Kazem Paragraph [0020], Dashboard template(s) or dataset(s) are then recommended based on the generated scores for the candidate targets, such as by presenting those dashboard template(s) or dataset(s) to the user's computing device with a score that exceeds a threshold value), respectively, based upon at least each of the scores of the selected set of the plurality of columns (Kazem Paragraph [0081], template/dataset recommender 105 scores the compatibility of column combinations of data in the dataset against the visualizations of the dashboard template);
wherein, based upon a set of rules, a set of the plurality of data visualizations is selected for display in a selectable manner via a user interface (Kazem Paragraph [0060], A “domain model,” as used herein, refers to a conceptual model of a domain in a graphical hierarchy (“concept hierarchy”) that incorporates behavior and data. In particular, a domain model is a formal representation of the domain with concepts (e.g., real-world concepts), where the concepts include data, such as roles, datatypes, individuals and rules), wherein the selected set of the plurality of data visualizations is scored higher than a non-selected set of the plurality of data visualizations based upon the set of rules (Kazem Paragraph [0020], Dashboard template(s) or dataset(s) are then recommended based on the generated scores for the candidate targets, such as by presenting those dashboard template(s) or dataset(s) to the user's computing device with a score that exceeds a threshold value).
The claimed invention and Kazem are from the analogous art of column statistics. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Oberbreckling in view of Kazem to have combined Oberbreckling in view of Kazem. Kazem teaches improves in the technology or technical field involving business analytics applications by improving dashboards and the visualization of datasets (Paragraph 93).
Oberbreckling in view of Kazem does not expressly disclose:
wherein, based on each set of statistics calculated for each of the plurality of columns, a score is generated for each column of the plurality of columns, wherein the score generated for each column of the plurality of columns utilizes a configurable set of rules that operate on each set of statistics for each of the plurality of columns of the data set, the configurable set of rules comprising at least one rule selected from a set of column scoring rules, the set of column scoring rules comprising a column selection rule, a user interest flag rule, a null percentage rule, a cardinality rule, a null penalty rule, an integer rule, a key word rule and a text length rule.
However, Luo teaches:
wherein, based on each set of statistics calculated for each of the plurality of columns (Luo Paragraph [0060], semantic information respective to the column labels, data statistics respective to applicable columns, or a search index for each column), a score is generated for each column of the plurality of columns (Luo Paragraph [0080], the dimension reduction engine 130 individually scores the columns for the same semantic entity category and for the same semantic attribute), wherein the score generated for each column of the plurality of columns utilizes a configurable set of rules that operate on each set of statistics for each of the plurality of columns of the data set (Luo Paragraph [0080], the dimension reduction engine 130 individually scores the columns for the same semantic entity category and for the same semantic attribute, Paragraph [0060], semantic information respective to the column labels, data statistics respective to applicable columns, or a search index for each column), the configurable set of rules comprising at least one rule selected from a set of column scoring rules, the set of column scoring rules comprising a column selection rule, a user interest flag rule, a null percentage rule, a cardinality rule (Luo Paragraph [0021], The system 100 further includes heuristic rules with respect to referential cardinality in data from the data source 101, and a distinctive set of heuristic rules with respect to semantics on join column identification), a null penalty rule, an integer rule, a key word rule and a text length rule (Luo Paragraph [0055], indexes entries of the metadata 103 according to respective keywords of the anticipated queries);
The claimed invention and Luo are from the analogous art of using column statistics. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Oberbreckling in view of Kazem and Luo to have combined Oberbreckling in view of Kazem and Luo. Luo teaches systems for improving computational efficiency in dimension reduction of semantic data models by heuristics (Paragraph 1).
Regarding claim 4, Oberbreckling in view of Kazem and Luo further teaches:
The system of claim 1, wherein each of the selected set of the plurality of columns comprises one of a measure and a metric (Oberbreckling Paragraph [0066], profile engine 326 can output a number of metrics and pattern information about each identified column, and can identify schema information in the form of names and types of the columns to match the data).
Regarding claim 5, Oberbreckling in view of Kazem and Luo further teaches:
The system of claim 1, wherein a cardinality of each of the plurality of columns is utilized in generating the score for each of the plurality of columns (Oberbreckling Paragraph [0186], This favors unique potential join keys over lower cardinality columns containing something like gender which is not very unique over the population).
Regarding claim 6, Oberbreckling in view of Kazem and Luo further teaches:
The system of claim 5, wherein a null percentage is further utilized in generating the score for each of the plurality of columns (Oberbreckling Paragraph [0186], The first BASE level includes very basic types (integer, decimal, date, text, etc). The second TYPE level is more specific (int, long, char, etc.). The third SUBTYPE level is more specific still (bool, gender, zero). Further, the subtypes may be augmented in several ways based on specific data distribution (important if the data is all unique for discovery to recommend as a join key useful join key)).
Regarding claim 7, Oberbreckling in view of Kazem and Luo further teaches:
The system of claim 1, wherein the generation of the plurality of data visualizations comprises a plurality of data visualization types (Oberbreckling Paragraph [0112], user interface 306 can generate one or more graphical visualizations based on metadata provided by profile engine 326. As explained above, the data provided by profile engine 326 may include statistical information indicating metrics about data that has been processed by profile engine 326).
Regarding claim 8, Oberbreckling in view of Kazem and Luo further teaches:
The system of claim 7, wherein at least one of the generated plurality of data visualizations comprises a data visualization type selected based upon the set of statistics calculated for a column represented in the at least one of the generated plurality of data visualizations (Oberbreckling Paragraph [0123], As described herein, profiled data 380 may be used to present data visualizations indicating statistics about large sets of data. At step 366, profiled data may be modified for enrichment to impute labels, such as column names. The labels may be imputed on the basis of the statistics of the profiled data).
Claims 9, 12-17, 19, and 20 are rejected in the same manner as claims 1 and 4-8 but are merely directed to different embodiments of the same invention (system, method, and computer readable storage medium). Oberbreckling teaches processors and memory (Paragraph 10).
Claim(s) 3, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oberbreckling in view of Kazem, Luo, and Gupta, Patent Application Publication No. 2019/0134506 (hereinafter Gupta).
Regarding claim 3, Oberbreckling in view of Kazem and Luo teaches parent claim 1.
Oberbreckling in view of Kazem and Luo does not expressly disclose:
wherein the set of rules utilized to select the plurality of data visualizations for display via the user interface comprises rules that score data visualizations with high visual contrast higher than data visualizations with low visual contrast.
However, Gupta teaches:
wherein the set of rules utilized to select the plurality of data visualizations for display via the user interface comprises rules that score data visualizations with high visual contrast higher than data visualizations with low visual contrast (Gupta Paragraph [0168], the purpose of the visual aids is to provide a high contrast, easily visible, Paragraph [0287], extensive database of individual player's history and statistics).
The claimed invention and Gupta are from the analogous art of systems using visual contrast. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention having the teachings of Oberbreckling in view of Kazem, Luo, and Gupta to have combined Oberbreckling in view of Kazem, Luo, and Gupta. Gupta teaches using stored feedback to improve the presentation of anamorphic images (Paragraph 117).
Claims 11 and 18 are rejected in the same manner as claim 3 but are merely directed to different embodiments of the same invention (system, method, and computer readable storage medium).
Response to Arguments
Applicant's arguments filed 01/07/2026 have been fully considered but they are not persuasive. A detailed explanation is provided below.
On pages 7-8, Applicant states that the amendments overcome the rejection under 35 U.S.C. 101, the Examiner disagrees. Applicant’s amendment added the limitation of specifying that the data visualizations are at least based upon data received from queries. It is not clear how merely specifying where the data is received from or part of how the data visualizations are generated would overcome the rejection under 35 U.S.C. 101. The rejection has been updated to reflect this amendment. Therefore, the claims are still rejected under 35 U.S.C. 101.
On pages 8-10, Applicant argues against the rejection under 35 U.S.C. 103.
Applicant argues that the prior art of record does not disclose the amendment, the Examiner disagrees. The amendment states “wherein the plurality of data visualizations are generated at least based upon data received in response to structured queries to the data set”, Oberbreckling teaches a recommendation engine can request (e.g., query) knowledge service for data that can be recommended to a user for the data obtained for a source (Paragraph 68). This shows that Oberbreckling teaches a query that is used to receive data that is recommended (displayed) to a user. The claim is not specific on how the data visualizations are generated based upon the received data or how they use the structured queries. Therefore, Oberbreckling does disclose the amendment.
Applicant argues that Oberbreckling does not disclose the column scores, the Examiner disagrees. Oberbreckling teaches a column pair is selected based on a lower rank, then weights may be adjusted to favor one or more scoring functions to improve ranking of the column pairs closer to the selection (Paragraph 150). This shows that a plurality of columns (pair) is selected based on a score. While Oberbreckling teaches a score for a column pair rather than a column, the claimed invention selects a plurality of columns based off of a score and Oberbreckling teaches selecting a plurality of columns. Therefore, Oberbreckling does disclose this limitation.
Applicant argues that the graphical visualization of Oberbreckling are different from the claimed data visualizations, the Examiner disagrees. Applicant further argues that the claimed data sets are generated at least based upon data received in response to structured queries to the data set and that the graphical visualizations of Oberbreckling relate to the recommended joins between different data sets and are based upon metadata. The Examiner has shown how Oberbreckling teaches the amendment which relates to the data visualizations and the structured queries. Oberbreckling teaches graphical visualizations based on metadata and data provided by a profile engine may include statistical information including metrics about data (Paragraph 112). This shows that Oberbreckling teaches graphical visualizations (data visualizations) based upon metadata combined with the previous teaching to show that data can be obtained from a query. Therefore, Oberbreckling does disclose this limitation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Budlong, Patent Application Publication No. 2021/0342962 (hereinafter Budlong). Budlong teaches outputting data and a visual display of data (Paragraph 859). Budlong further teaches applying rule logic to run queries on a structured database (Paragraph 859). This shows that Budlong is analogous art as Budlong teaches data visualization and using queries.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/DUSTIN D EYERS/ Examiner, Art Unit 2164
/AMY NG/ Supervisory Patent Examiner, Art Unit 2164