Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the RCE filed 9/09/2026 in which:
Claims 1, 4 10, 14, 20-21 were amended.
No claims were cancelled/added.
Claims 1-21 are pending.
Claim(s) 1, 10, 20 is/are independent claim(s).
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 9/9/2026 has been entered.
Note Regarding Prior Art
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Note Regarding AIA Status
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 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.
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.
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.
Claim(s) 1, 2, 4, 10, 12-15, 20, 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Daniel Gruen (US PG Pub No. 2020/0285981; Filed: 03/04/2019)(hereinafter: Gruen) in view of Saurabh Abhyankar et al. (US PG Pub No. US2020; Published: 08/06/2020; Filed: 12/31/2019)(hereinafter: Abhyankar).
Claim 1:
As per independent claim 1, Gruen discloses a computer implemented method comprising:
identifying a type of report for a report [[0053] , the type of the at least one section and/or at least one option that is selected for inclusion in the generated report can be based on known information or a prediction regarding the audience of the report].
obtaining a set of data related to entries that are associated with at least the type of report [[0070] the system can further predict information that should be included in the report to be generated. The system (e.g., via generating component 403) can determine prediction z by accessing all of the one or more data items in the training data set 402, and then using classification y to search for any documents or other types of data that contain information relevant to the report to be generated].
generating input data utilizing at least one of the type of report and the set of data related to the entries [[0071] the system (e.g., via populating component 404) can populate the report to be generated with information similar to the information contained in the similar documents. In other embodiments, the system (e.g., via populating component 404) can add or change at least one section and/or at least one option of the report to be generated based on the information contained in the similar documents.]
providing the input data to one or more artificial intelligence predictive models that are pre-trained utilizing training input and output data [[0076] the system (e.g., via populating component 404) can populate the report to be generated with information similar to the information contained in the similar documents. In other embodiments, the system (e.g., via populating component 404) can add or change at least one section and/or at least one option of the report to be generated based on the information contained in the similar documents.]
generating a serialized data structure that comprises a structured set of data configured to enable a [[0053] the first generating component 310 can generate a report with at least one section and/or at least one option. For example, a report for a medical examination can include a section and/or an option for a medical history of a patient. In some embodiments, the type of the at least one section and/or at least one option that is selected for inclusion in the generated report can be based on known information or a prediction regarding the audience of the report, a situation that preceded generation of the report, and/or compulsory input to the report. In some embodiments, the first generating component 310 can add the at least one section and/or at least one option of the generated report by accessing the known information stored directly on the AI system local memory, or, alternatively, the known information stored on the external system memory.]. Report must correspond to a serialized data structure in order to be displayed.
Gruen discloses generating a report with one or more components, however Gruen failed to specifically disclose report renderer to display, on a computer display screen, the selected one or more report sections each and the selected one or more components presenting the transaction data; processing, by the one or more artificial intelligence predictive models, the input data to (i) select, from a plurality of different components, one or more components included in at least one particular section of the report and utilized to present transaction data, wherein the plurality of different components are configured to present the transaction data in a respective different representation format.
Abhyankar, in the same field of data visualization and exploration discloses these limitations in [[0005] Data defining candidate visualizations and data indicating the property of the dataset can be provided to a trained machine learning model that has been trained to evaluate the quality of the candidate visualizations…The outputs of the trained machine learning model can then be used to select a subset of the candidate visualizations that are predicted to be most appropriate for the dataset (e.g., given the properties of the specific dataset under analysis). The selected visualizations can be provided to a user, for example, as recommended visualizations available for the user to insert into a report, dashboard, or other document the user is creating. [0014] the different visualization formats comprise different visualization types including two or more from the group consisting of a graph, a line chart, a bar chart, a pie chart, a scatterplot, a heat map, a geographical map, a word-size map, a bubble chart, a hierarchy chart, a waterfall chart, a radar chart, a statistical chart, and an area chart].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gruen’s report generation to process, by the one or more artificial intelligence predictive models, the input data to (i) select, from a plurality of different components, one or more components included in at least one particular section of the report and utilized to present transaction data, wherein the plurality of different components are configured to present the transaction data in a respective different representation format as disclosed by Abhyankar. The motivation for doing so would have been to use machine learning techniques to identify which subsets of dataset are most significant and which visualization formats would best represent the identified subsets of data (0004).
Claim 2:
As per claim 2, which depends on claim 1, it is rejected under the same rationale as claim 1 above. Gruen and Abhyankar disclose generating a report including different sections but failed to specifically disclose wherein the one or more components are selected from a group consisting of a scatter plot, a line chart, a pie chart,a bar graph, and a table. Abhyankar, [[0014] the different visualization formats comprise different visualization types including two or more from the group consisting of a graph, a line chart, a bar chart, a pie chart, a scatterplot, a heat map, a geographical map, a word-size map, a bubble chart, a hierarchy chart, a waterfall chart, a radar chart, a statistical chart, and an area chart.
Claim 4:
As per claim 4, which depends on claim 1 Gruen and Abhyankar disclose wherein the one or more components have one or more associated display options. Gruen [[0079] the computer-implemented method can comprise identifying (e.g., via the generating component 110) at least one of one or more relevant sections or one or more relevant options of an automatically generated report, wherein the identifying is based on a defined factor and employs artificial intelligence.]
Claim 21:
As per claim 21, which depends on claim 1, it is rejected under the same rationale as claim 1 above. Additionally, Gruen and Abhyankar disclose wherein the one or more components include a plurality of components and the plurality of components enable visual comparison of the same model output data. Abhyankar, [[0069] The visualization 210a is a bar chart that shows the category labels along the horizontal axis, with item counts indicated along the vertical axis. The visualization 210b has the same axes but shows the data in a line graph. The visualization 210c is a bar chart that has the axes reversed compared to visualization 210a. The visualization 210d is a pie chart that shows the item count data in percentages rather than in counts. Accordingly, each of the visualizations 210a-210d is derived from the same portion of the data set (e.g., a column showing category labels for different records) but the visualization type, formatting, or level of aggregation or abstraction of the data is different for each visualization 210a-210d..].
Claim 10:
As per independent claim 10, it recites a system comprising: an application executing on a processor of a device, the application including a report generator, the report generator configured to perform the method of claim 1, therefore it is rejected under the same rationale as claim 1 above.
Claim 12:
As per claim 12, which depends on claim 10, Gruen and Abhyankar disclose wherein the device is a server. Gruen [[0032] System 100 can optionally include a server device, one or more networks and one or more devices (not shown). The system 100 can also include or otherwise be associated with at least one processor 102 that executes computer executable components stored in memory 104.]
Claim 13:
As per claim 13, it is rejected under the same rationale as claim 2 above.
Claim 14:
As per claim 14, it is rejected under the same rationale as claim 4 above.
Claim 15:
As per claim 15, which depends on claim 10, Gruen and Abhyankar disclose wherein the set of data is stored within the serialized data structure. Gruen, [[0053] the first generating component 310 can generate a report with at least one section and/or at least one option. For example, a report for a medical examination can include a section and/or an option for a medical history of a patient. In some embodiments, the type of the at least one section and/or at least one option that is selected for inclusion in the generated report can be based on known information or a prediction regarding the audience of the report, a situation that preceded generation of the report, and/or compulsory input to the report. In some embodiments, the first generating component 310 can add the at least one section and/or at least one option of the generated report by accessing the known information stored directly on the AI system local memory, or, alternatively, the known information stored on the external system memory.]. Report must correspond to a serialized data structure and at least temporarily stored in order to be displayed.
Claim 20:
As per independent claim 20, it recites a non-transitory computer readable medium having software encoded thereon, the software when executed by or more computing devices operable to perform the method of claim 1, therefore it is rejected under the same rationale as claim 1 above.
Claim(s) 3, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gruen and Abhyankar in view of Adam Rodriguez et al (US PG Pub. 20180367483; Published: 12/20/2018)(hereinafter: Rodriguez).
Claim 3:
As per claim 3, which depends on claim 1, Gruen and Abhyankar failed to specifically disclose further comprising: providing, as a suggestion in a chat session window, the one or more report sections that are to be included in the report and the one or more components that are to be included in at least one particular section of the report.
Rodriguez, in the same field of document editing discloses this limitation in [[0354] the first embedded application can be a shared document application causing display of a shared content document (e.g., list of items) in the embedded interface that is displayed by a subset of the user devices participating in the chat conversation, where the embedded interface is configured to receive user input that changes one or more items in the shared document, and the suggested response items include one or more suggested commands operative to modify the shared document. [0259] A suggestion event can be a change of an item or element in a shared content document based on local (first user device) or remote (other member/chat device) user input, or other shared data being edited in an embedded application of the embedded session]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gruen’s report generation to provide as a suggestion in a chat session window, the one or more report sections that are to be included in the report and the one or more components that are to be included in at least one particular section of the report as disclosed by Rodriguez. The motivation for doing so would have been to implement a chat-based interface in order to foment collaboration thus increasing efficiency.
Claim 11:
As per claim 11, it is rejected under the same rationale as claim 3 above.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gruen and Abhyankar in view of Seymour Duncker et al (US PG Pub. 2017/0206684; Published: 07/20/2017)(hereinafter: Duncker).
Claim 5:
As per claim 5, which depends on claim 4 Gruen and Abhyankar failed to specifically disclose wherein the display options include one or more of sparkline visual.
Duncker, in the same field of report generation discloses this limitation in [[0048] The analytic visualization 110 may be any type of visualization useful in analyzing data. For example, the analytic visualization 110 may be line graph, a bar chart, a pie chart, an area graph, a scatter plot, a volume graph, a surface graph, a doughnut chart, a bubble chart, a box plot, a radar chart, a sparkline chart, a cone chart, a pyramid chart, a stock chart, a histogram, a Gantt chart, a waterfall chart, a binary chart (e.g., win/loss), a pictograph, an organizational chart, a flow chart, a map, a gauge, a table, or another type of chart, graph, or indicator].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gruen’s report generation to include one or more of sparkline visual as disclosed by Duncker. The motivation for doing so would have been to include some visual information for ease of report consumption by the end user.
Claim(s) 6-8, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gruen and Abhyankar in view of Kristin E. Coppen et al (US PG Pub No. US2018/0152404; Published: 05/31/2018)(hereinafter: Coppen).
Claim 6:
As per claim 6, which depends on claim 1, Gruen and Abhyankar failed to specifically disclose further comprising ordering the one or more report sections by ranking each of the one or more report sections using each of the one or more particular components included in each of the one or more report sections.
Coppen, in the same field of document generation discloses this limitation in [[0026] At block 380, the method may determine the relative importance of each section of the conversation document. For example, block 380 may rank the sections of the document in order of their corresponding importance value, as determined in block 360. Alternatively, block 380 may assign a relative importance level (e.g., very important, fairly important, neither important/unimportant, fairly unimportant and very unimportant) or relative importance score (e.g., between 0 and 5, where 5 is very important) to each section of the document based on its corresponding importance value, as determined at block 360.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gruen’s report generation to order the one or more report sections by ranking each of the one or more report sections using each of the one or more particular components included in each of the one or more report sections as disclosed by Coppen. The motivation for doing so would have been to determine a measure of relative importance for the section based on user interactions observed by the monitoring thus assuring most important sections are predominantly displayed.
Claim 16:
As per claim 16, it is rejected under the same rationale as claim 6 above.
Claim 7:
As per claim 7, which depends on claim 6, it is rejected under the same rationale as claim 6 above. Additionally, Gruen, Abhyankar and Coppen discloses wherein ordering each of the one or more report sections further comprises assigning a rank value to each of the one or more report sections based on a selected component included in each of the one or more report sections. Coppen [[0026] At block 380, the method may determine the relative importance of each section of the conversation document. For example, block 380 may rank the sections of the document in order of their corresponding importance value, as determined in block 360. Alternatively, block 380 may assign a relative importance level (e.g., very important, fairly important, neither important/unimportant, fairly unimportant and very unimportant) or relative importance score (e.g., between 0 and 5, where 5 is very important) to each section of the document based on its corresponding importance value, as determined at block 360.]
Claim 17:
As per claim 17, it is rejected under the same rationale as claim 7 above.
Claim 8:
As per claim 8, which depends on claim 7, it is rejected under the same rationale as claim 6 above. Additionally, Gruen, Abhyankar and Coppen discloses wherein the rank value is based on a comparison of the selected component with other components included in each of the one or more report sections. Coppen, [[claim 8] determining an importance value for each section of the conversation document, based on observed user interactions therewith; comparing the importance value of each section with the importance value of one or more of the other sections of the plurality of sections; and determining a relative importance of each sections based on the comparison.].
Claim 18:
As per claim 18, it is rejected under the same rationale as claim 8 above.
Claim(s) 9, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gruen and Abhyankar in view of Hiroaki Nakata (US PG Pub No 2010/019531; Published: 08/05/2010)(hereinafter: Nakata).
Claim 9:
As per claim 9, which depends on claim 1, Gruen and Abhyankar failed to specifically disclose further comprising: receiving user input to modify a position of a selected report section on the computer display screen; and in response to receiving the user input, automatically adjusting a width of a different report section on the computer display screen.
Nakata, in the same field of document editing discloses this limitation in [[0045] copy 35 of the selected part can be resized, by the user grabbing and dragging a frame 351 (displayed with a dotted line in FIG. 5) of the copy 35 of the selected part with a mouse cursor, for example. In the case where text is included in the selected part 34, and the text does not fit on one line and is returned in accordance with the width of the frame, the return position shifts in accordance with an increase in the width of the copy 35 of the selected part. Layout can thereby be performed so as to display a large number of characters on one line].
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Gruen’s report generation receive user input to modify a position of a selected report section on the computer display screen and in response to receiving the user input, automatically adjusting a width of a different report section on the computer display screen as disclosed by Nakata. The motivation for doing so would have been to allow a user make edits on the report, thus increasing productivity and customizability.
Claim 19:
As per claim 19, it is rejected under the same rationale as claim 9 above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Cited references in the attached 892 not being relied upon in this office action are relevant to individual claim limitations.
Response to Arguments
Applicant’s arguments with respect to the 35 USC 103 rejection to claims 1, 4, 10,12, 14, 15 and 20 have been considered and are found to be persuasive. However, the arguments are directed towards newly added limitations that required new grounds of rejection. Therefore the arguments are moot.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HOWARD CORTES whose telephone number is (571)270-1383. The examiner can normally be reached on M-F, 8:00 am - 5:00 pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott T Baderman can be reached on (571)272-3644. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HOWARD CORTES/ Primary Examiner, Art Unit 2118
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