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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/21/2026 has been entered.
DETAILED ACTION
This action is responsive to the Amendment filed with an RCE on 7/21/2026. Claims 1, 11 and 20 have been amended. Claims 2, 5 and 12 have been canceled. Claims 21-23 have been added. Claims 1, 3-4, 6-11, and 13-23 are pending in the case. Claims 1, 11 and 20 are independent claims.
Examiner’s Note:
The claimed “machine learning model” without much specificity as what these models are or how the machine learning model functions/trains the data and only providing the input (input data) and output (some end result of report) without explaining how the data is being trained from input data to the output data, the “machine learning model” claimed through the claims are being treated as any software module under BRI.
Response to Arguments
Applicant's arguments filed 7/21/2026 have been fully considered but they are not persuasive.
Applicant argues that the newly amended feature overcome the applied art.
Examiner respectfully disagrees.
Without explaining how the cited “machine learning” is supposed to process or work, they are being treated as any software module in the system. Here, Bedard 1 discloses dynamically selecting, by the server device, one or more first machine learning models from a plurality of first machine learning models based on the description or the recipient, wherein each of the plurality of first machine learning models has been trained to perform a respective pre-processing operation on the converted tabular data for a respective description or a respective recipient; (please see Examiner’s note above, as well as 101 abstract idea where the “machine learning models”, without description how the “machine learning models” function, under BRI, it is interpreted as a software module. [0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed, hence different result will show depending on what selection of different description which is “selected” to perform different pre-processed result as the underlying workflow/programming is showing different result based on the selection, for example, state machine or workflow build-into the program) performing, using the dynamically selected one or more first machine learning models, (please see Examiner’s note above, as well as 101 abstract idea where the “machine learning models”, without description how the “machine learning models” function, under BRI, it is interpreted as a software module) pre-processing of the converted tabular data based on the description or the recipient, wherein the pre-processing includes formatting the converted tabular data; ([0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed. Further, as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats) dynamically selecting, by the server device, one or more second machine learning models from a plurality of second machine learning models based on the description, the recipient, a context of the pre-processed tabular data, or a characteristic of the pre-processed tabular data, wherein each of the plurality of second machine learning models has been trained to perform a respective processing operation on the pre-processed tabular data for a respective description, a respective recipient, a respective context, or respective characteristic; ([0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed, hence different result will show depending on what selection of different description which is “selected” to perform different pre-processed result as the underlying workflow/programming is showing different result based on the selection, for example, state machine or workflow build-into the program) performing, using the dynamically selected one or more second machine learning models, processing of the pre-processed tabular data based on the description, the recipient, the context of the pre-processed tabular data, or the characteristic of the pre-processed tabular data, wherein the processing includes performing a calculation on or analyzing the pre-processed tabular data; (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats) and performing one or more actions based on a result of processing the pre-processed tabular data using the dynamically selected one or more second machine learning models, the one or more actions including: providing a virtual dashboard to visualize the processed tabular data or the result, or generating one or more reports for the processed tabular data or the result. (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats).
Further, Bedard 2 discloses comprises a set of variables tuned via application of training data, the training data including labeled data related to formatting tabular data ([0056]-[0060] of Bedard 2, labeling information associated with data that includes dimensions and structure, aka, formatting data)
Regarding to claims 21-23, a new reference is used and the current arguments do not apply to the newly cited reference that renders the claims obvious.
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 xxxxxxxxxxxx are rejected under 35 U.S.C. 103 as being unpatentable over Bedard et al (US 20140149836 A1), hereinafter “Bedard 1”, in view of Bedard et al (US 20240232225 A1), hereinafter “Bedard 2”.
Referring to claims 1, 11 and 20, Bedard 1 discloses a computer-implemented method for customized processing and visualization of data, comprising:
receiving, by a server device, tabular data; ([0003] of Bedard 1, tabular/spreadsheet data)
converting the tabular data to a text-based language to form converted tabular data; ([0003] of Bedard 1, convert tabular/spreadsheet data to text-based data)
dynamically selecting, by the server device, one or more first machine learning models from a plurality of first machine learning models based on the description or the recipient, wherein each of the plurality of first machine learning models has been trained to perform a respective pre-processing operation on the converted tabular data for a respective description or a respective recipient (please see Examiner’s note above, as well as 101 abstract idea where the “machine learning models”, without description how the “machine learning models” function, under BRI, it is interpreted as a software module. [0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed, hence different result will show depending on what selection of different description which is “selected” to perform different pre-processed result as the underlying workflow/programming is showing different result based on the selection, for example, state machine or workflow build-into the program);
performing, using the dynamically selected one or more first machine learning models, (please see Examiner’s note above, as well as 101 abstract idea where the “machine learning models”, without description how the “machine learning models” function, under BRI, it is interpreted as a software module) pre-processing of the converted tabular data based on the description or the recipient, wherein the pre-processing includes formatting the converted tabular data; ([0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed. Further, as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats)
dynamically selecting, by the server device, one or more second machine learning models from a plurality of second machine learning models based on the description, the recipient, a context of the pre-processed tabular data, or a characteristic of the pre-processed tabular data, wherein each of the plurality of second machine learning models has been trained to perform a respective processing operation on the pre-processed tabular data for a respective description, a respective recipient, a respective context, or respective characteristic; ([0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed, hence different result will show depending on what selection of different description which is “selected” to perform different pre-processed result as the underlying workflow/programming is showing different result based on the selection, for example, state machine or workflow build-into the program)
performing, using the dynamically selected one or more second machine learning models, processing of the pre-processed tabular data based on the description, the recipient, the context of the pre-processed tabular data, or the characteristic of the pre-processed tabular data, wherein the processing includes performing a calculation on or analyzing the pre-processed tabular data; (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats) and
performing one or more actions based on a result of processing the pre-processed tabular data using the dynamically selected one or more second machine learning models, the one or more actions including: providing a virtual dashboard to visualize the processed tabular data or the result, or generating one or more reports for the processed tabular data or the result. (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats)
Bedard 1 does not specifically disclose “receiving a first user input of a description of the tabular data and a second user input of a recipient of (i) the tabular data or (2) a report to be generated based on the tabular data.” And “and comprises a set of variables tuned via application of training data, the training data including labeled data related to formatting tabular data”
However, Bedard 2 discloses receiving a first user input of a description of the tabular data and a second user input of a recipient of (i) the tabular data or (2) a report to be generated based on the tabular data (Fig. 9 and [0214] of Bedard 2, user can view or edit varies properties associated with the table such as description, type, etc.. and can include also [0060] of Bedard 2, as well as employee names, etc..)
Further, Bedard 2 discloses comprises a set of variables tuned via application of training data, the training data including labeled data related to formatting tabular data ([0056]-[0060] of Bedard 2, labeling information associated with data that includes dimensions and structure, aka, formatting data)
Bedard 1 and Bedard 2 are analogous art because both references concern process converted data and customize the converted data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Bedard 1’s dashboard data with converted table data with user input with multiple input prompts with different results that is converted as taught by Bedard 2. The motivation for doing so would have been to allow more customization to the dashboard with different input data.
Referring to claims 3 and 13, Bedard 1 and Bedard 2 disclose the method according to claim 1, wherein formatting the converted tabular data includes one or more of: modifying a column label of the converted tabular data in accordance with one or more first rules; modifying a row label of the converted tabular data in accordance with one or more second rules; combining a plurality of first columns or a plurality of first rows, of the converted tabular data; separating a plurality of second columns or a plurality of second rows, of the converted tabular data; adding a column or a row to the converted tabular data; removing a column or a row from the converted tabular data; rearranging a plurality of third columns or a plurality of third rows of the converted tabular data: removing one or more trailing values from one or more data elements of the converted tabular data: adding one or more trailing values to one or more data elements of the converted tabular data: converting number values of the converted tabular data to string values; converting string values of the converted tabular data to number values; or applying a formatting to each data element of a respective column of the converted tabular data.. ([0011] of Bedard 1, spreadsheet DOM which is numerical value can be converted to different format, such as [0063] of Bedard 1, JSON string value)
Referring to claims 4 and 14, Bedard 1 and Bedard 2 disclose the method according to claim 1, wherein performing the calculation on the pre-processed tabular data includes performing one or more of summation or subtraction; and wherein analyzing the pre-processed tabular data includes performing one or more of a trend analysis, a forecasting, or an anomaly detection. (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats, here, having a chart showing result is a type of trend analysis/visual analysis)
Referring to claims 6 and 16, Bedard 1 and Bedard 2 disclose the method according to claim 1, wherein the context of the pre-processed tabular data includes a priority of the pre-processed tabular data, and wherein the characteristic of the pre-processed tabular data includes a granularity of the pre-processed tabular data or a quality of the pre-processed tabular data.. ([0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed. Further, as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats)
Referring to claim 10, Bedard 1 and Bedard 2 disclose the method according to claim 1, further comprising: providing a user interface for display via a user device; wherein the receiving of the tabular data further comprises: receiving the tabular data as a file upload via the user interface. (as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once u ser enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats)
Referring to claim 15, Bedard 1 and Bedard 2 disclose the method according to claim 1, wherein the description represents user log data. ([0054] of Bedard 2)
Claims 7-9 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Bedard et al (US 20140149836 A1), hereinafter “Bedard 1”, in view of Bedard et al (US 20240232225 A1), hereinafter “Bedard 2” and in further view of Adamson et al (US 10510026 B1).
Referring to claims 7 and 17, Bedard 1 and Bedard 2 disclose the method according to claim 1. Bedard 1 and Bedard 2 do not specifically disclose wherein the one or more actions further comprise: sending a message that includes the one or more generated reports, or storing the one or more generated reports.
However, Adamson discloses disclose wherein the one or more actions further comprise: sending a message that includes the one or more generated reports, or storing the one or more generated reports (Col. 6, lines 13-22 and col. 15, lines 42-61 of Adamson).
Bedard 1 and Bedard 2 and Adamson are analogous art because both references concern process converted data and customize the converted data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Bedard 1’s dashboard data with converted table data with user input with multiple input prompts with different results that is converted as taught by Bedard 2 and sending notification and invite people for a calendar meeting as taught by Adamson. The motivation for doing so would have been to allow more customization to the dashboard with different input data as well as sharing the customized data with other users.
Referring to claims 8 and 18, Bedard 1 and Bedard 2 disclose the method according to claim 1. Bedard 1 and Bedard 2 do not specifically disclose wherein the one or more actions further comprise: determining one or more individuals to attend a meeting based on the one or more generated reports or a result of the processing using the one or more second machine learning models.
However, Adamson discloses wherein the one or more actions further comprise: determining one or more individuals to attend a meeting based on the one or more generated reports or a result of the processing using the one or more second machine learning models. (Col. 6, lines 13-22 and col. 15, lines 42-61 of Adamson).
Bedard 1 and Bedard 2 and Adamson are analogous art because both references concern process converted data and customize the converted data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Bedard 1’s dashboard data with converted table data with user input with multiple input prompts with different results that is converted as taught by Bedard 2 and sending notification and invite people for a calendar meeting as taught by Adamson. The motivation for doing so would have been to allow more customization to the dashboard with different input data as well as sharing the customized data with other users.
Referring to claims 9 and 19, Bedard 1 and Bedard 2 and Adamson disclose the method according to claim 8, further comprising: scheduling the meeting for the one or more individuals; generating a calendar invite for the meeting; and sending the calendar invite to user devices associated with the one or more individuals. (Col. 6, lines 13-22 and col. 15, lines 42-61 of Adamson).
Claims 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Bedard et al (US 20140149836 A1), hereinafter “Bedard 1”, in view of Bedard et al (US 20240232225 A1), hereinafter “Bedard 2” and in further view of King et al (US 20230274051 A1).
Referring to claims 21-23, Bedard 1 in view of Bedard 2 disclose the method according to claim 1, wherein performing the pre-processing using the dynamically selected one or more first machine learning models (Even though [0094] of Bedard 1, when the user enter a value for sales for the first quarter (quarter being the description of the data that is being pre-processed and of course depending on the different values the user enters into the sales for the first quarter or the 2nd quarter, or the third quarter, different data can be pre-processed. Further, as shown in Fig. 4 and also described in [0026]-[0030] and [0094]-[0099] of Bedard 1, once user enters data into the spreadsheet, it is converted into JSON text format and based on other user’s selection, the data is being filtered/processed further to produce pie chart or bar chart formats)
Bedard 1 in view of Bedard 2 do not specifically disclose selecting the right software module to use “comprises applying one or more of a weight, bias, or classification on the converted tabular data.”
However, King discloses selecting the right software module to use “comprises applying one or more of a weight, bias, or classification on the converted tabular data.” ([0084] of King).
Bedard 1 and Bedard 2 and King are analogous art because both references concern process converted data and customize the converted data. Accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Bedard 1’s dashboard data with converted table data with user input with multiple input prompts with different results that is converted as taught by Bedard 2 and using classification to sort data to be used as taught by King. The motivation for doing so would have been to allow more efficient data sorting to different specific software modules/models.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAIMEI JIANG whose telephone number is (571)270-1590. The examiner can normally be reached M-F 9-5pm.
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/HAIMEI JIANG/Primary Examiner, Art Unit 2142
