DETAILED ACTION
This action is in response to the Amendment dated 27 July 2026. Claims 1, 3, 4, 10, 15, 19 and 20 are amended. No claims have been added or cancelled. Claims 1-20 remain pending and have been considered below.
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
Based on applicant’s amendment, the 35 U.S.C. 112(b)/second paragraph rejections are withdrawn.
Based on applicant’s amendment, the 35 U.S.C. 101 rejection of claims 1, 4, 9, 10, 11-14 is withdrawn.
Allowable Subject Matter
Claims 1-14 are allowed.
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Independent claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claim 15 is directed to the abstract idea of identifying similar spreadsheets and spreadsheet regions and generating a formula, as explained in detail below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
Claim 15 recites, in part, “identifying … a reference sheet that satisfies a similar sheet primitive relative to the target sheet … identifying … a similar region in the reference sheet that is similar to the target cell, wherein the similar region satisfies a similar region primitive relative to the target cell … identifying a reference formula in the similar region … generating … a predicted formula” These steps describe the concept identifying similar spreadsheets and spreadsheet regions and generating a formula which, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. Nothing in the claim precludes the step from practically being performed in the mind. For example, but for the “machine learning model” language, “identifying and generating” in the context of these claims encompasses the user comparing spreadsheets and creating a formula. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. As such, the description in claim 15 of identifying similar spreadsheets and spreadsheet regions and generating a formula is an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites an additional element – using a machine learning model to perform the steps. The machine learning model in the steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, these claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. The claims recite the additional limitations of “machine learning model,” and “providing output.” Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities (e.g. see references cited) amount to no more than implementing the abstract idea with a computerized system. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Thus, taken alone or in combination, the additional elements do not amount to significantly more than the above-identified judicial exception.
Further, the additional elements found in dependent claims 16, 17 and 20, considered both individually and as an ordered combination along their dependency trees with their respective independent claims, also do not appear to be drawn to a practical application or amount to significantly more than the abstract idea. Therefore, dependent claims 16, 17 and 20 are similarly rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Danna et al. (US 2025/0272352 A1) in view of Guillame-Bert et al. (US 2024/0193418 A1, hereinafter: G-B) and further in view of Gaunt et al. (US 2019/0042210 A1) and further in view of Gur et al. (US 2009/0044090 A1).
As for independent claim 15, Danna teaches a method comprising:
identifying, using a first machine learning model, a reference sheet that satisfies a similar sheet primitive relative to the target sheet [(e.g. see Danna paragraphs 0019, 0020, 0045) ”the dynamic submission automation system 220 may extract column data from the tabular data. In some embodiments, each row of the tabular data may be related to one submission for the webpage. A column of the tabular data may be associated with a field on the webpage. The dynamic submission automation system 220 needs associations between column data and the fields on the webpage to ensure that correct information is submitted for each submission for each field on the webpage. In some examples, the dynamic submission automation system 220 can receive a plurality of spreadsheets or tabular data, extract a plurality of column data from the plurality of spreadsheets, and track the tabular data or spreadsheet source for each of the plurality of column data … the dynamic submission automation system 220 may determine, using a first machine learning model, one or more suggested associations for the fields on the webpage based on data types for the fields and the extracted column data. In some embodiments, the dynamic automation submission system 220 extracts fields from the webpage, and then uses the first machine learning model to determine the one or more suggested associations for the fields on the webpage. If the dynamic submission automation system 220 determines there are one or more suggested associations for the fields on the webpage based on data types for the fields and the extracted column data, then the dynamic submission automation system 220 moves to optional block 108. Otherwise, the dynamic submission automation system 220 moves to optional block 112. The one or more suggested associations may include associations between the entries in the extracted column data and fields on the webpage. The dynamic submission automation system 220 uses the first machine model to determine the one or more suggested associations by determining a required data type for a field and then determining whether one or more columns of the extracted column data includes the required data type. A column of entries in the extracted column data may be the same data type … The dynamic submission automation system 220 may be configured to classify a dataset. Classifying a dataset may include determining whether a dataset is related to another datasets. Classifying a dataset may include clustering datasets and generating information indicating whether a dataset belongs to a cluster of datasets”].
Danna does not specifically teach receiving a target sheet with a target cell, identifying, using a second machine learning model, a similar region in the reference sheet that is similar to the target cell, wherein the similar region satisfies a similar region primitive relative to the target cell, or providing an output in the target cell with the predicted [formula]. However, in the same field of invention, G-B teaches:
receiving a target sheet with a target cell [(e.g. see G-B paragraph 0129) ”allows a user to identify one or more cells, identified cells 406, in the interactive spreadsheet”].
identifying, using a second machine learning model, a similar region in the reference sheet that is similar to the target cell, wherein the similar region satisfies a similar region primitive relative to the target cell [(e.g. see G-B paragraph 0111) ”The system can determine which cells in the interactive spreadsheet the trained machine learning model is trained to predict. For example, if the interactive spreadsheet is a second, different interactive spreadsheet, the system can determine if the second interactive spreadsheet is organized in the same way as the first interactive spreadsheet, e.g., by the same rows or columns. For example, if the trained machine learning model was trained to predict column “H” in a first interactive spreadsheet and the second interactive spreadsheet has columns arranged in the same way as the first interactive spreadsheet, the system can determine that the trained machine learning model is trained to predict column “H” in the second interactive spreadsheet”].
and providing an output in the target cell with the predicted [formula] [(e.g. see G-B paragraphs 0140, 0141) ”After a trained machine learning model generates a predicted value for each of the identified cells in identified cells 406, the interactive spreadsheet 106 can be updated to display the predicted values 412 … the predicted values 412 can be displayed in the original identified cells themselves”].
Therefore, considering the teachings of Danna and G-B, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add receiving a target sheet with a target cell, identifying, using a second machine learning model, a similar region in the reference sheet that is similar to the target cell, wherein the similar region satisfies a similar region primitive relative to the target cell, or providing an output in the target cell with the predicted [formula], as taught by G-B, to the teachings of Danna because providing output on the interactive spreadsheet allows the predictions and confidence to be displayed directly without need to transition away (e.g. see G-B paragraph 0096).
Danna and G-B do not specifically teach identifying, using the second machine learning model, a reference formula in the similar region in response to receiving an indication a formula is being created in the target cell, modifying, using the second machine learning model, parameters of the reference formular based on a context of the target sheet, or generating, using the second machine learning model, a predicted formula with the modified parameters for the reference formula. However, in the same field of invention, Gaunt teaches:
identifying, using the second machine learning model, a reference formula in the similar region in response to receiving an indication a formula is being created in the target cell [(e.g. see Gaunt paragraphs 0027, 0030, 0045) ”an observed system comprising a spreadsheet 400 and showing a formula fill facility 404 provided to a user. A user has a dataset and wishes to perform an aggregation function on each row or column of the data. The user manually works out the aggregation for the first two rows and asks the program predictor to provide a spreadsheet formula which fits the manually provided examples … The computer program predictor is able to learn from past experience by training the program attribute predictor … The program predictor is able to predict programs in the form of spreadsheet formulae and offer these to assist a user in manipulating data in the spreadsheet and/or to fill in cells of the spreadsheet”].
modifying, using the second machine learning model, parameters of the reference formula based on a context of the target sheet and generating, using the second machine learning model, a predicted formula with the modified parameters for the reference formula [(e.g. see Gaunt paragraphs 0046, 0053) ”The program predictor is able to infer this intention and predict a program which generalizes from these examples. The first input-output pair comprises the numerical values in the first row of the spreadsheet 400 as the input data, and the numerical value in cell 410 as the output data. The second input-output pair comprises the numerical values in the second row of the spreadsheet 400 as the input data and the numerical value in cell 412 as the output data. The user selects the range of cells where the desired aggregation result is to be displayed as indicated by rectangle 406 and an option 404 to fill the range with a formula is presented to the user. The program predictor predicts a suitable formula which is entered into all cells in the range of cells 406 to fill in the remaining values … target spreadsheet formula is non-trivial. Consider the case where the user has an aggregation function “sum the smallest n values” in a record, where n is provided as an additional input. This case is sufficiently complex to expect that most spreadsheet users would have to look up the composition of functions required to achieve the result and it is thus easier for the user to just calculate manually the first few answers as examples for the program predictor. The spreadsheet 402 of FIG. 4 shows this situation. The user has manually calculated the values in cells 414 and 416 and gives a range of cells indicated by rectangle 418 to be completed … expressed as a spreadsheet formula as: [0060] {=SUMPRODUCT(SMALL([value 1:5],ROW(INDIRECT[CONCAT(“1:”,n)))))}”].
Therefore, considering the teachings of Danna, G-B and Gaunt, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add identifying, using the second machine learning model, a reference formula in the similar region in response to receiving an indication a formula is being created in the target cell, modifying, using the second machine learning model, parameters of the reference formular based on a context of the target sheet, and generating, using the second machine learning model, a predicted formula with the modified parameters for the reference formula, as taught by Gaunt, to the teachings of Danna and G-B because it assists a user in manipulating data in the spreadsheet (e.g. see Gaunt paragraph 0030).
Danna, G-B and Gaunt do not specifically teach based on a context of the target sheet by changed parameter cells of the reference formula with parameters in the similar region of the target sheet. However, in the same field of invention, Gur teaches:
based on a context of the target sheet by changed parameter cells of the reference formula with parameters in the similar region of the target sheet [(e.g. see Gur paragraphs 0049-0052 and Fig. 2C) ”when entering a reference in a formula using a syntax such as that provided in Table 1, auto-completion of the name being typed is available and/or provided. In the cases in which multiple potential matches exist for the name being typed, an interface that includes the potential matches is presented from which a desired value can be selected by the user … if multiple matches exist, a drop down list of matching auto-completion options is provided. In some embodiments, an option to turn the auto-completion feature on or off is provided. In some embodiments, in addition to appending the completion of the name being typed, the auto-completion feature prepends extra information or identifiers such as a document name, sheet name, and/or table name, for example, to provide further disambiguation when the same (row, column, and/or table) name is used multiple times … In addition to appending completions, the auto-completion feature prepends additional parameters, such as the sheet name and/or table name in the example of FIG. 2C to distinguish the same column name ("Sales") used in different tables and/or sheets. The auto-completion feature provides the user with easy access to the Sales column in the same table (i.e. "Sales" in list 216), in other tables on the same sheet (i.e. "February::Sales" in list 216), and in other tables on different sheets (i.e. "2006::January::Sales", "2006::February::Sales", and "March::Sales" in list 216) … the sheet name is omitted if a table name appears only once in a document. Thus, in the given example, the sheet name "2006" is not included with "March::Sales" in list 216. In some embodiments, if a table name appears multiple times on different sheets of a document, the sheet name is included for a table that is not on the current sheet. In some embodiments, the sheet name is included for any table not on the current sheet. In such a case, "March::Sales" in list 216 would be "2006::March::Sales" … in the cases in which multiple potential matches exist such as in FIG. 2C, the most likely match is by default selected (i.e. highlighted) in an associated list, and the associated completion is appended to the entered characters inline”].
Therefore, considering the teachings of Danna, G-B, Gaunt and Gur, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add based on a context of the target sheet by changed parameter cells of the reference formula with parameters in the similar region of the target sheet, as taught by Gur, to the teachings of Danna, G-B and Gaunt because it allows appropriate syntax to be used to refer to or specify specific cells (e.g. see Gur paragraph 0042).
As for dependent claim 16, Danna, G-B, Gaunt and Gur teach the method as described in claim 15, but Danna and G-B do not specifically teach the following limitation. However, Gaunt teaches:
wherein the output automatically presents the predicted formula in the target cell [(e.g. see Gaunt paragraphs 0045, 0046) ”asks the program predictor to provide a spreadsheet formula which fits the manually provided examples. This formula is then automatically applied to the remaining rows (or columns) … The user selects the range of cells where the desired aggregation result is to be displayed as indicated by rectangle 406 and an option 404 to fill the range with a formula is presented to the user. The program predictor predicts a suitable formula which is entered into all cells in the range of cells 406”].
The motivation to combine is the same as that used for claim 15.
As for dependent claim 17, Danna, G-B, Gaunt and Gur teach the method as described in claim 15, but Danna and G-B do not specifically teach the following limitation. However, Gaunt teaches:
wherein the user accepts the predicted formula [(e.g. see Gaunt paragraph 0045) ”The result is both completion of the aggregation task and expose of a spreadsheet formula which can be adapted or debugged by the user”].
The motivation to combine is the same as that used for claim 15.
As for dependent claim 18, Danna, G-B, Gaunt and Gur teach the method as described in claim 15, but Danna and G-B do not specifically teach the following limitation. However, Gaunt teaches:
further comprising: receiving additional information for the formula being created in the target cell; and providing an updated predicted formula in the target cell in response to receiving the additional information [(e.g. see Gaunt paragraph 0045) ”The result is both completion of the aggregation task and expose of a spreadsheet formula which can be adapted or debugged by the user”].
The motivation to combine is the same as that used for claim 15.
As for dependent claim 20, Danna, G-B, Gaunt and Gur teach the method as described in claim 15, but Danna does not specifically teach the following limitations. However, G-B teaches:
wherein the second machine learning model is a fully connected network trained to identify whether regions of spreadsheets are similar or dis-similar [(e.g. see G-B paragraphs 0072, 0111) ”The system 100 can use a training system 110 to train a machine learning model on the values in the cells of the interactive spreadsheet 106 to predict values for the one or more identified cells 108, resulting in a trained model 112 … types of machine learning models can include … a neural network model … The system can determine which cells in the interactive spreadsheet the trained machine learning model is trained to predict. For example, if the interactive spreadsheet is a second, different interactive spreadsheet, the system can determine if the second interactive spreadsheet is organized in the same way as the first interactive spreadsheet, e.g., by the same rows or columns. For example, if the trained machine learning model was trained to predict column “H” in a first interactive spreadsheet and the second interactive spreadsheet has columns arranged in the same way as the first interactive spreadsheet, the system can determine that the trained machine learning model is trained to predict column “H” in the second interactive spreadsheet”].
Danna and G-B do not specifically teach and trained to identify formulas. However, Gaunt teaches:
and trained to identify formulas [(e.g. see Gaunt paragraph 0027, 0030) ”The computer program predictor is able to learn from past experience by training the program attribute predictor … The program predictor is able to predict programs in the form of spreadsheet formulae and offer these to assist a user in manipulating data in the spreadsheet and/or to fill in cells of the spreadsheet”].
The motivation to combine is the same as that used for claim 15.
Danna, G-B and Gaunt do not specifically teach the following limitation. However, Gur teaches:
wherein similar spreadsheet regions include similar formula expressions in a same location in a pair of spreadsheets [(e.g. see Gur paragraph 0048 and Figs. 2A-B) ”FIGS. 2A and 2B illustrate embodiments of two sheets, associated with tabs 202 and 204, respectively, of a document of a spreadsheet application. The sheet associated with tab 202, the contents of which are shown in FIG. 2A, is named "2007" and includes three tables: January 206, February 207, and Specials 208. The sheet associated with tab 204, the contents of which are shown in FIG. 2B, is named "2006" and also includes three tables: January 210, February 212, and March 214. In the given examples, each table in each sheet is uniquely named (January, February, Specials, March), but all tables except the Specials table 208 comprise the same table structure and include the same header cell values (Bags, Shoes, and Sales). Both sheets include tables named January and February. The rows, columns, and cells of any of the tables can be uniquely identified or referred to using a syntax such as the syntax of Table 1”].
The motivation to combine is the same as that used for claim 15.
Response to Arguments
Applicant's arguments, filed 27 July 2026, have been fully considered but they are not persuasive.
Applicant argues that [“Applicant submits that the combination of the cited references does not disclose or suggest at least the features recited in the amended independent claims.” (Page 12).].
The argument described above, in paragraph number 12, with respect to the newly added limitations to the independent claims has been considered, but is moot in view of the new grounds of rejection.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPub 2022/0121844 A1 issued to Lee et al. on 21 April 2022. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g. matching sheet titles with a trained machine learning model).
U.S. PGPub 2016/0055139 A1 issued to Creason et al. on 25 February 2016. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g. updating reference ranges in matching formulas).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 CHRISTOPHER J FIBBI whose telephone number is (571)-270-3358. The examiner can normally be reached Monday - Thursday (8am-6pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Bashore can be reached at (571)-272-4088. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
