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 .
Response to Amendment
The amendment filed on 1/22/2026 has been entered.
Claims 1, 8, and 15 have been amended and no other claims have been added and/or canceled.
Claims 1-20 are pending with claims 1, 8, and 15 as independent claims.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dickerman et al. (US 2018/0032498, published 2/1/2018, hereinafter as Dickerman) in view of Chang et al. (US 2005/0210061, published 9/22/2005, hereinafter as Chang) in view of Gao et al. (WO2020005601A1, published 1/2/2020, hereinafter as Gao).
Claim 1. A computer-implemented method, comprising:
determining a first user input in a first cell of a data table, the data table comprising a plurality of cells arranged in rows and columns, and the first user input indicating that an operation is to be performed on the data table; Dickerman teaches in [0022-0023] “An OLAP cube may store data arranged such that each of the three dimensions (i.e., axes) of the OLAP cube provide a different arrangement of the data. For example, an OLAP cube may structure sales data arranged by date, product identifier, and customer identifier, as further described herein with reference to FIGS. 3-6. Alternately, the data table 112 may be stored in another data structure, such as an OLAP hypercube (e.g., an OLAP data structure having more than three dimensions) or some other in-memory column store. Storage of the data table 112 in the OLAP cube 116 at the memory 114 (e.g., random access memory (RAM) of the computer system 100) may help in facilitating multi-dimensional data analysis and pivot table operations as described herein. Data tables are further described herein with respect to FIGS. 3 and 5.” And in [0026] “In defining measures output by the pivot table 122, the user may input a DAX 104.” And in [0038] “a DAX 410 “SUM[Amount]” may be received at the pivot table 400, indicating a desire that the cells of the pivot table 400 contain a sum aggregation of the amount column 370 of FIG. 3 for various combinations (i.e., contexts) of date, ProdID, and CustID of the sales table 300 of FIG. 3… a first illustrative cell 412 of the pivot table 400 indicates an amount received from selling “456Red” products during 2009 regardless of customer and a second illustrative cell 414 indicates an amount received from selling “789Green” products to customer “Jon200” during 2008. That is, a first context associated with the first cell 412 may be “Time[Year]=2009; Product[ProdID]=‘456Red’” and a second context associated with the second cell 414 may be “Time[Year]=2008; Product[ProdID]=‘789Green’; Customer[CustID]=‘Jon200’.”” (emphasis added) Examiner Note: the data table may be the pivot table 400 as shown in fig. 4. The first user input may be DAX input 410 and the operation to be performed may be SUM function based on a context,
Dickerman does not explicitly disclose
in response to detection of a predetermined symbol as a first character in the first user input, initiating a natural language interpreter. However, Gao, in an analogous art, discloses in [0044] “A predetermined symbol in the metadata symbol set has two functions. The function in the first aspect is using the symbol to deduce other symbols in the subsequent parsing as described above. The function in the other aspect is to generate the computer-executable query by considering the semantic and property to which the symbol is mapped.” And in [0045] “In addition to the symbols indicating the table-related information, the symbols in the predetermined dictionary may also include important words in a given natural language or additional symbols indicating these important words. Such important words may include important stop words, such as "by," "of," "with" and the like in English. Some words related to data analysis and/or data aggregation may also be considered as important words in the data query scenario, such as "group," "sort," "different," "sum," "average," "count" and so on in English.” And in [0068] “the deduction rule defining the deduction rule "C ➔ A: [ minlmaxlsumlavg]" allows the symbol A to be deduced from the symbol C, provided that the property type corresponding to the symbol C is a numerical value (namely, C.type=num). The deduced symbol A being mapped to the predicate logic may include various predicate logics associated with the numerical values, such as taking a minimum (min), taking a maximum (max), taking a sum (sum), and averaging.” (emphasis added) Examiner Note: the word “SUM” in the DAX 210 “SUM[Amount]” may be replace by a predetermined symbol as taught by Gao. Therefore, the input DAX 210 may start with a predetermined symbol as a first character in the user input as shown in Table 4,
Accordingly, 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 Dickerman with the teaching of Gao because “For the convenience of the user, it is expected that the computer supports use of flexible natural languages to initiate queries. In such case, the computer operating on the basis of the machine query language shall understand the user's questions so as to convert the natural language query into a computer-executable query.” Gao [Background].
Dickerman does not explicitly disclose
determining a first analysis operation for the data table based on semantics of the data table and output of the natural language interpreter using the first user input, the first analysis operation corresponding to the first user input. However, Chang, in an analogous art, teaches in [0026] “interpreting natural language input to drive an application and its associated actions… the schema interacts both with the application itself and semantic interpretations of natural language input by a user… The schema can be used to render a table of columns and rows or a single cell for example.” And in [0027] “A user provides natural language input to user interface module 202 in a form of a command, question or other input related to generating a table. For example, the user may provide, "Show gross profit for aircraft and destination by year.", or, "What were the total revenues for 737 in 1999?", or simply "profit". The user interface module 202 receives the natural language input and provides it to table generation module 204.” And in [0029] “The interpretation module 206 analyzes the user input, schema and database words and phrases to generate candidate semantic interpretations of what information to render to the user.” (emphasis added) Examiner Note: Chang teaches that the input may be in natural language. Therefore, a semantic module may be used to analyze the input,
Accordingly, 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 Dickerman with the teaching of Chang because “there has been an ongoing effort to provide applications with a natural language (NL) interface. The natural language interface extends the functionality of applications beyond their limited input set and opens the computer system to inputs in a natural language format. The natural language interface is responsible for performing a translation from the relatively vague and highly context based realm of natural language into the precise and rigid set of inputs required by a computer application.” Chang [Background], and
presenting a result of the first analysis operation in a first region of the data table related to the first cell; further, Dickerman teaches in [0017] “The DAX may be executed to populate the column, where the value in each cell is calculated based on a row context for that cell. Alternately, cells of the pivot table may be populated by executing the DAX, where the value in each cell of the pivot table is calculated based on a context (e.g., a filter context) associated with that cell.” And in [0026] “It should also be noted that the pivot table 400 may also be generated using a different DAX than the DAX 410.” (emphasis added) Examiner Note: the pivot table may be generated based executing DAX input and data related to the sales table 300,
storing dependency information in a data structure, the dependency information defining a directional dependency relationship between the first analysis operation and at least one downstream analysis operation, wherein the at least one downstream analysis operation is determined based on a second user input in a second cell and is configured to utilize the result of the first analysis operation as input data; Dickerman teaches in [0004-0005 and 0019-0020] “Thus, DAXs may empower people familiar with existing spreadsheet applications to perform multi-dimensional data analysis and data analysis with respect to relational data models (e.g., within existing spreadsheet applications). Unlike conventional spreadsheet formulae, a DAX beneficially is independent of particular cell ranges of the spreadsheet… a DAX may refer to a first data table and a second data table, and executing the DAX may include traversing a relationship between the first data table and the second data table (e.g., following a relationship that may exist between a column in a first table and a column in a second table). DAXs may also support dynamic re-execution. For example, a DAX may be automatically re-executed with respect to a set of rows of a data table in response to a user modification to data stored in the set of rows… The system includes a memory and a data interface configured to receive data, to create one or more data tables based on the received data, and to store the data tables in a column-based in-memory store (e.g., a structure that maps to an online analytical processing (OLAP) cube structure). And in [0023] “The spreadsheet pivot table module 118 includes logic 120 to generate a pivot table 122 based on the data table 112 referenced by the OLAP cube 116. The pivot table 122 may support “pivot” operations, where row headers, column headers, filters, or slicers of the pivot table 122 are changed and data values in the pivot table 122 are automatically updated to reflect the changes… updating the pivot table 122 in response to a pivot operation includes re-executing a query of the in-memory OLAP cube 116, so that data from the OLAP cube is arranged and viewed along different dimensions of the OLAP cube.” And in [0031] “the logic 204 may receive the query 201 in response to a user changing a setting at the pivot table 122 of FIG. 1. In response to receiving the query 201, the logic 204 may automatically generate a command 208 to recalculate a DAX associated with the pivot table and send the command 208 to the analysis module 220.” And in [0032] “the logic 206 may be configured to detect changes at a data table such as the pivot table 112 of FIG. 1. The changes may be detected based on user input 202. For example, the user input 202 may include a new value for a cell of the data table. In response to detecting changes in the data table(s), the logic 206 may automatically generate the command 208 to recalculate a DAX (e.g., a column definition DAX) associated with the changed data table(s) and send the command 208 to the analysis module 220.” And in [0033] “In response to receiving the command 208, the analysis module 220 may automatically recalculate one or more DAXs at the spreadsheet application. For example, the analysis module 220 may automatically recalculate column definition DAXs at data tables of the spreadsheet application 210, DAXs at a pivot table of the spreadsheet application 210”. (emphasis added) Examiner Note: the second user input, input value for a cell of the first data table, may cause the generation of command 208 to calculate a DAX, the logic relating the first data table with the second data table because the data analysis expression or DAX may refer to traversing a relationship between the first data table and the second data table in spreadsheet application 210, wherein the logic relating the data tables may be stored in the system memory. In other words, changes made by a user in the first data table may cause changes in a second data table through the use of the DAX in spreadsheet application 210. Here, the DAX may represent directional dependency relationship between the first data table, as the first analysis operation, and the second data table, as the downstream analysis operation, and
in response to an update to the first user input or to any data item in the data table that feeds the first analysis operation, automatically re-executing the first analysis operation to generate an updated result, and based on the stored dependency information, sequentially re-executing the at least one downstream analysis operation using the updated result from the first analysis operation, so that updated results are presented in respective regions of the data table without further user action. Dickerman teaches in [0023 and 0031-0033] “The pivot table 122 may support “pivot” operations, where row headers, column headers, filters, or slicers of the pivot table 122 are changed and data values in the pivot table 122 are automatically updated to reflect the changes. In a particular embodiment, updating the pivot table 122 in response to a pivot operation includes re-executing a query of the in-memory OLAP cube 116, so that data from the OLAP cube is arranged and viewed along different dimensions of the OLAP cube.” (emphasis added) Examiner Note: changes to the data table 112, which feeds pivot table 122, may be detected such that the changes may be reflected on the pivot table 122 via the DAX that relates the first data table and the second data table.
Claims 2, 9, and 16. The rejection of the method of claim 1is incorporated, wherein determining the first analysis operation comprises:
determining in the data table at least one correlated column matching semantics of the first user input and at least one adjacent column adjacent to the first cell, based on semantics of the data table; Dickerman teaches in [0038-0039] “a second illustrative cell 414 indicates an amount received from selling “789Green” products to customer “Jon200” during 2008…a second context associated with the second cell 414 may be “Time[Year]=2008; Product[ProdID]=‘789Green’; Customer[CustID]=‘Jon200’.”” And in [0040] “if the amount column 370 of FIG. 3 did not exist, the pivot table 400 may be generated by incorporating the “=Qty*Price” formula of the amount column 370 into the DAX 410. For example, the DAX 410 may be “SUM[Qty*Price].”” (emphasis added) Examiner Note: despite that the DAX input for SUM for columns Qty and Price, adjacent columns of CustID and ProdID are also considered in the generated pivot table as shown in fig. 400. As can be seen here, Dickerman only uses context and semantics to identify other adjacent columns. However, Chang using natural language input would extend Dickerman’s beyond his limited DAX input set, and
determining the first analysis operation based on the at least one correlated column matching the semantics of the first user input and the at least one adjacent column adjacent to the first cell; although, the DAX input indicates SUM for columns Qty and Price, adjacent columns of CustID and ProdID are also considered based on context analysis in the generated pivot table and the sales table 300 as shown in fig. 400. As can be seen here, Dickerman only uses context and semantics to identify other adjacent columns. However, Chang using natural language input would extend Dickerman’s beyond his limited DAX input set such as NL command to “hide class” in the generated table.
Claims 3, 10, and 17. The rejection of the method of claim 2 is incorporated, wherein determining the first analysis operation comprises:
determining a first act related to the at least one correlated column based on data items filled in the at least one correlated column; Dickerman teaches in [0038] “For example, a DAX 410 “SUM[Amount]” may be received at the pivot table 400, indicating a desire that the cells of the pivot table 400 contain a sum aggregation of the amount column 370 of FIG. 3 for various combinations (i.e., contexts) of date, ProdID, and CustID of the sales table 300 of FIG. 3. Accordingly, a first illustrative cell 412 of the pivot table 400 indicates an amount received from selling “456Red” products during 2009 regardless of customer and a second illustrative cell 414 indicates an amount received from selling “789Green” products to customer “Jon200” during 2008. That is, a first context associated with the first cell 412 may be “Time[Year]=2009; Product[ProdID]=‘456Red’” and a second context associated with the second cell 414 may be “Time[Year]=2008; Product[ProdID]=‘789Green’; Customer[CustID]=‘Jon200’.”” (emphasis added) Examiner Note: the first act may be to identify columns associated with the first context and the second context for cells 412 and 414, respectively,
determining a second act related to the at least one adjacent column based on the first act and data items filled in the at least one adjacent column; Dickerman teaches in [0038] the second act may be similar products in the ProdID column may be represented once for corresponding year such “123Blue” product corresponding to year 2008 and “123Blue” product corresponding to year 2009 as shown in generated pivot table in fig. 4, and
determining a representation of the first analysis operation based on the second act; Dickerman teaches in [0038] the processing of the first act, by summing the amounts corresponding to each ProdID column and CustID column with corresponding Date column, has been represented by presenting each product and customer values once as shown in the pivot table in fig. 4.
Claims 4, 11, and 18. The rejection of the method of claim 1is incorporated, further comprising:
determining a second user input in a second cell of the data table, the second cell adjacent to the first cell, and the second user input indicating that an operation is to be performed on the data table; Dickerman teaches in [0045] “The pivot table 600 may receive a DAX 610 “SUM[Amount]” similar to the DAX 410 of FIG. 4, and the value of the DAX 610 may be recursively calculated to populate cells of the pivot table 600.” (emphasis added) Examiner Note: DAX input 610 may be a second user input and the operation to be performed may involve one or more columns from Inventory Table 500,
determining a second analysis operation for the data table based on the second user input, semantics of the data table and the result of the first analysis operation, the second analysis operation corresponding to the second user input; Dickerman teaches in [0042-0045] “The pivot table 600 may receive a DAX 610 “SUM[Amount]” similar to the DAX 410 of FIG. 4, and the value of the DAX 610 may be recursively calculated to populate cells of the pivot table 600. Populating the pivot table 600 may include identifying a relationship between the sales table 300 of FIG. 3 and the inventory table 500 of FIG. 5 and retrieving data from both the sales table 300 of FIG. 3 and the inventory table 500 of FIG. 5… a row header 612 “Blue Bike” of the pivot table 600 may be populated based on the identified relationship between the sales table 300 of FIG. 3 and the inventory table 500 of FIG. 5. A first illustrative cell 614 of the pivot table 600 may be associated with a first context “Time[Year]=2009; Product[Description]=‘Red Bike’ and a second illustrative cell 616 of the pivot table 600 may be associated with a second context “Time[Year]=2008; Product[Description]=‘Green Trike’; Customer[CustID]=‘Jon200’.”” (emphasis added) Examiner Note: the second analysis may be based on the first analysis that generated pivot table shown in fig. 4. The second analysis may include at least new column (description) in the inventory table that the system has identified as related to the sales table. The second analysis may calculate DAX input 610 based new first and second contexts involving Product Description column as shown in fig. 6, and
presenting a result of the second analysis operation in a second region of the data table related to the second cell; Dickerman teaches in [0042-0045] the second region may be pivot table 600, which resembles pivot table 400. However, information from cell values from the description column provides different view indicates relationship between Sales Table 300 and Inventory Table 500 as shown in fig. 6.
Claims 5, 12, and 19. The rejection of the method of claim 4 is incorporated, further comprising: Dickerman does not explicitly disclose
in accordance with a determination that the first user input is updated, updating the result of the first analysis operation presented in the first region; and updating the result of the second analysis operation presented in the second region based on the updated result of the first analysis operation. However, Chang, in an analogous art, teaches in [0034] “table generation module 204 can define a schema based on actions available for building and modifying a PivotTable.” And in [0042] “Upon user selection of this interpretation, the current description 306 and table display 302 are updated to show the selected table and associated description. The user is then allowed to enter further natural language commands in field 310 pertaining to the table in display 302 or pertaining to a new table. For example, the user can provide "Hide Australia", "show only 747", "highlight revenues over $10,000", etc. In these examples, the application will hide the Australia column, render a table only with data associated with the 747 Type of Aircraft and highlight Total Revenue values greater than $10,000, respectively.” (emphasis added) Examiner Note: using natural language, a user can update the pivot table by addition command such as “show only 747”, “hide Australia”, etc.
Accordingly, 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 Dickerman with the teaching of Chang because “there has been an ongoing effort to provide applications with a natural language (NL) interface. The natural language interface extends the functionality of applications beyond their limited input set and opens the computer system to inputs in a natural language format. The natural language interface is responsible for performing a translation from the relatively vague and highly context based realm of natural language into the precise and rigid set of inputs required by a computer application.” Chang [Background].
Claims 6, 13, and 20. The rejection of the method of claim 1 is incorporated, Dickerman does not explicitly disclose wherein the first user input is a natural language input starting with a predetermined symbol. However, Chang, in an analogous art, teaches in [0037] “It is worth noting that the command need not be explicitly expressed in the natural language input, but can be implied from the input. For example, the input "apples and bananas" can be implied to be used with a "show" command.” (emphasis added) Examiner Note: the term “show” may be a predetermined symbol, which can be explicitly written in the input or implied if it is not explicitly written.
Accordingly, 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 Dickerman with the teaching of Chang because “there has been an ongoing effort to provide applications with a natural language (NL) interface. The natural language interface extends the functionality of applications beyond their limited input set and opens the computer system to inputs in a natural language format. The natural language interface is responsible for performing a translation from the relatively vague and highly context based realm of natural language into the precise and rigid set of inputs required by a computer application.” Chang [Background].
Claims 7 and 14. The rejection of the method of claim 1 is incorporated, wherein presenting the result of the first analysis operation in the first region comprises:
determining the result of the first analysis operation based on at least one portion of a plurality of data items in the plurality of cells; Dickerman teaches in [0038] “a first illustrative cell 412 of the pivot table 400 indicates an amount received from selling “456Red” products during 2009 regardless of customer and a second illustrative cell 414 indicates an amount received from selling “789Green” products to customer “Jon200” during 2008. That is, a first context associated with the first cell 412 may be “Time[Year]=2009; Product[ProdID]=‘456Red’” and a second context associated with the second cell 414 may be “Time[Year]=2008; Product[ProdID]=‘789Green’; Customer[CustID]=‘Jon200’.”” (emphasis added) Examiner Note: as can be seen, the amount paid for product “456Red”, during year 2009 to customer “Doe100” in Sales Table 300, is summed in cell 412 in the pivot table 400. Similarly, the amount received for product “789Green”, sold to customer “Jon200” during 2008, has been presented in cell 414, and
presenting the result of the first analysis operation in a set of cells in the same column as the first cell; Dickerman teaches in [000038] the first analysis involving first context and second context is presented in in generated pivot table 400 as shown in fig. 4.
Claim 8. The claim is directed toward an electronic device to implement the method of claim 1. Therefore, the claim is similarly rejected as claim 1. Furthermore, Dickerman teaches a processing unit; and a memory coupled to the processing unit and comprising instructions stored thereon; in [0061] “The computing device 1010 includes at least one processor 1020 and system memory 1030.” And in [0067] “a software module executed by a processor, or in a combination of the two. A software module may reside in computer readable media, such as random access memory (RAM), flash memory, read only memory (ROM), registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.” (emphasis added).
Claim 15. The claim is directed toward a computer program product, comprising machine-executable instructions which, when executed by a device, cause the device to perform the method of claim 1. Therefore, the claim is similarly rejected as claim 1.
Response to Arguments
Applicant's arguments filed 1/22/2026 have been fully considered but they are not persuasive.
Argument: Applicant argues “Even in combination, these references would at most yield an NL-driven table system that calculates measures in context and accepts further user commands to refresh views; they do not teach or suggest the claimed recording of a directional dependency graph among analysis operations and automatic, sequential re-execution of downstream operations upon upstream updates without additional user input.”
Response: Dickerman teaches in [0004-0005 and 0056-0057] “The input includes a DAX based on at least one column of the first spreadsheet table and based on at least one column of a second spreadsheet table. For example, referring to FIG. 5, the DAX “SUM[Qty]” 560 may be received as a column definition for the column 550, where the DAX 560 refers to both the sales table 300 of FIG. 3 and the inventory table 500 of FIG. 5… a relationship between the sales table 300 of FIG. 3 and the inventory table 500 of FIG. 5 may be identified, such as identifying the ProdID columns 340 of FIG. 3 and 520 of FIG. 5 as related columns.” (emphasis added). Because the DAX 560 relates two columns in two different tables, e.g. Sales table 300 and Inventory table 500, when user input to change value in the Sales table, let’s called upstream update, the Inventory table would automatically be updated, let’s called downstream operation, based on the use of the data analysis expression. In other words, a product content item that has been sold by a salesman using the Sales table. The change of selling the product content item would automatically be reflected in the Inventory table such as by reducing one product content item from the total in the Inventory table.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AHAMED I NAZAR whose telephone number is (571)270-3174. The examiner can normally be reached 10 am to 7 pm Mon-Fri.
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/AHAMED I NAZAR/Examiner, Art Unit 2178 5/12/2026
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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