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 .
This Office Action is in response to the Amendment filed 12/31/2025.
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.
Claim(s) 1-6 are rejected under 35 U.S.C. 103 as being unpatentable over Garrison (US 2016/0055132) in view of Reineke (US 2023/0169138), further in view of Mohan (US 2008/0140766).
Regarding claim 1, Garrison discloses a system for automatically generating and modifying style rules, the system comprising:
one or more memories (Garrison, para [0042], at least one non-transitory storage media communicably coupled to at least one processor); and
one or more processors, communicatively coupled to the one or more memories (Garrison, para [0042], at least one non-transitory storage media communicably coupled to at least one processor), configured to:
receive, at a first time, a plurality of files associated with an entity (Garrison, para [0027-31], web portal generation system obtains webpages for processing into templates. Also discussed in para [78,81] with regards to fig 1);
apply a machine learning model to the plurality of files to determine a set of rules associated with images or text included in the plurality of files (Garrison, para [0036-38], machine learning system is applied to data set to generate a template; Garrison, para [0081], with regards to fig 1, a template, which defines the way a webpage is displayed is interpreted as a set of rules);
generate a template that indicates the set of rules (Garrison, para [0036], generates template with display rules);
transmit the template for display on an intranet associated with the entity (Garrison, para [0078], with regards to fig 1, template and data provided to user devices);
receive, at a second time subsequent to the first time, a plurality of additional files associated with the entity (Garrison, para [0039], receives changes to the colors selected for display of the files);
apply the machine learning model to the plurality of additional files to determine at least one modification to the set of rules (Garrison, para [0039], updates classifier); and
transmit an instruction to modify the template to indicate the at least one modification to the set of rules (Garrison, para [0041], end of paragraph, once a change to the classifier is made, propagates revision based on detected changes to the template).
Garrison does not explicitly disclose that the template is an HTML page.
Reineke discloses that the template is a combination of HTML and CSS (Reineke, para [0038]).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the concept of a template as taught by Garrison to have it be made up of HTML based on the teachings of Reineke. The motivation for doing so would have been to be compliant with browsers that are typically run with HTML and CSS (Reineke, para [0002]).
Garrison in view of Reineke does not, but Mohan teaches “transmit, in response to receiving a first confirmation from a user device, a document indicating the set of rules for storage in a repository”, “transmit, in response to receiving a second confirmation from the user device, an instruction to the repository to update the document to reflect the at least one modification” (Mohan, para [0043], user enters command to save edited data to web server, client 104 receives a confirmation from the web server…).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the user interaction to include a confirmation to update data to a server and provide and interface to do so. The motivation for doing so would have been to provide an enhanced WYSIWYG interface in a browser environment (Mohan, para [0002]).
Regarding claim 2, Garrison in view of Reineke and Mohan discloses the system of claim 1. Garrison additionally discloses wherein the one or more processors are configured to:
train the machine learning model using the plurality of files (Garrison, para [0034], trains classifier); and
re-train the machine learning model using the plurality of additional files (Garrison, para [0039], retrains classifier),
wherein the at least one modification is determined based on comparing output from the trained machine learning model with output from the re-trained machine learning model (Garrison, para [0039], output template generated from retrained classified incorporates the information from the originally trained model and an updated positive and negative data set which updates color selections).
Regarding claim 3, Garrison in view of Reineke and Mohan discloses the system of claim 1. Garrison additionally discloses wherein the one or more processors are configured to: input the HTML page to the machine learning model, wherein the at least one modification to the set of rules is determined based on the HTML page and the plurality of additional files (Garrison, para [0027-31 and 36], crawls website at URL directed by website provider. Parses HTML, CSS and javascript).
Regarding claim 4, Garrison in view of Reineke and Mohan discloses the system of claim 1. Garrison additionally discloses herein the one or more processors, to transmit the HTML page for display on the intranet, are configured to: transmit, to the user device, the HTML page (Garrison, para [0078], with regards to fig 1, template and data provided to user devices).
Mohan discloses receive, from the user device, a confirmation (Mohan, para [0043], user enters command to save edited data to web server); and transmit the HTML page for display on the intranet in response to the confirmation (Mohan, para [0043], transmits changed data to server).
Regarding claim 5, Garrison in view of Reineke and Mohan discloses the system of claim 1. Garrison additionally discloses wherein the one or more processors, to receive the plurality of files, are configured to: receive the plurality of files from a user device (Garrison, para [0078], website users 130 can send and receive template and webpages).
Regarding claim 6, Garrison in view of Reineke and Mohan discloses the system of claim 1. Garrison additionally discloses wherein the one or more processors, to receive the plurality of files, are configured to: transmit, to a repository, a request for the plurality of files; and receive, from the repository, the plurality of files in response to the request (Garrison, para [0078], with regards to fig 1, repositories represented by web servers 160 or third party suppliers 150. Pages and data provided for web portal generation 140 and website provider 120).
Claim(s) 7-9, 11, 14-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Garrison (US 2016/0055132) in view of Mohan (US 2008/0140766).
Regarding claim 7, Garrison discloses a method of automatically generating and publishing style rules, comprising:
receiving, from a repository, a plurality of files associated with an entity (Garrison, para [0027-31], web portal generation system obtains webpages for processing into templates. Also discussed in para [78,81] with regards to fig 1; Garrison, para [0078], with regards to fig 1, repositories represented by web servers 160 or third party suppliers 150. Pages and data provided for web portal generation 140 and website provider 120);
applying, by a style system, a machine learning model to the plurality of files to determine a set of rules associated with images or text included in the plurality of files (Garrison, para [0036-38], machine learning system is applied to data set to generate a template; Garrison, para [0081], with regards to fig 1, a template, which defines the way a webpage is displayed is interpreted as a set of rules);
generating, by the style system, a document that indicates the set of rules (Garrison, para [0036], generates template with display rules); and
transmitting, to a user device, the document (Garrison, para [0078], with regards to fig 1, template and data provided to user devices).
Garrison does not explicitly teach transmitting, in response to receiving a confirmation from the user device, an instruction to the repository to store the document. Mohan teaches transmitting, in response to receiving a confirmation from the user device, an instruction to the repository to store the document (Mohan, para [0043], user enters command to save edited data to web server, client 104 receives a confirmation from the web server…).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the user interaction to include a confirmation to update data to a server and provide and interface to do so. The motivation for doing so would have been to provide an enhanced WYSIWYG interface in a browser environment (Mohan, para [0002]).
Regarding claim 8, Garrison in view of Mohan discloses the method of claim 7. Garrison additionally discloses wherein the set of rules includes one or more of: an illustration style rule; a color rule; an image size rule; a tone rule; a grammar rule; or a font rule (Garrison, para [0038], color matching).
Regarding claim 9, Garrison in view of Mohan discloses the method of claim 7.
Mohan further discloses: receiving, from the user device, a second confirmation (Mohan, para [0043], user enters command to save edited data to web server); and transmitting the document for display on an intranet, associated with the entity, in response to the second confirmation (Mohan, para [0043], transmits changed data to server).
Regarding claim 11, Garrison in view of Mohan discloses the method of claim 7. Garrison additionally discloses receiving, from the user device, an indication of one or more features to use in the machine learning model (Garrison, para [0039], receives changes to the colors selected for display of the files).
Regarding claim 14, Garrison discloses a non-transitory computer-readable medium storing a set of instructions for automatically modifying style rules, the set of instructions comprising: one or more instructions that, when executed by one or more processors of a device, cause the device to:
receive at least one document indicating a style guide associated with an entity (Garrison, para [0027-31], web portal generation system obtains webpages for preprocessing. Also discussed in para [78,81] with regards to fig 1; Garrison, para [0078], with regards to fig 1, repositories represented by web servers 160 or third party suppliers 150. Pages and data provided for web portal generation 140 and website provider 120);
receive a plurality of files associated with the entity (Garrison, para [0036], crawls and parses web pages at URL);
apply a machine learning model to the at least one document and the plurality of files to determine at least one modification to the at least one document (Garrison, para [0036-38], machine learning system is applied to data set to generate a template; Garrison, para [0081], with regards to fig 1, a template, which defines the way a webpage is displayed is interpreted as a set of rules; Garrison, para [0039], updates classifier when a modification occurs); and
transmit, to a user device, an indication of the at least one modification (Garrison, para [0041], end of paragraph, once a change to the classifier is made, propagates revision based on detected changes to the template; Garrison, para [0078], with regards to fig 1, template and data provided to user devices).
Garrison does not but Mohan teaches “update, in response to receiving a confirmation from the user device, the at least one document to reflect the at least one modification” (Mohan, para [0043], user enters command to save edited data to web server, client 104 receives a confirmation from the web server…).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the user interaction to include a confirmation to update data to a server and provide and interface to do so. The motivation for doing so would have been to provide an enhanced WYSIWYG interface in a browser environment (Mohan, para [0002]).
Regarding claim 15, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Mohan further discloses wherein the indication of the at least one modification includes tracked changes relative to the at least one document (Mohan, para [0005, 38], identify areas of the web pages that correspond to master and content pages, such that when a user edits an element within an area, the browser-based web authoring tool can track whether the edit is made to the master page or one of the content pages).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the transmitted indication to include tracked changes to be displayed in a user interface. The motivation for doing so would have been to provide an enhanced WYSIWYG interface in a browser environment (Mohan, para [0002]).
Regarding claim 16, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Mohan further discloses wherein the one or more instructions, when executed by the one or more processors, cause the device to: receive, from the user device, a second confirmation(Mohan, para [0043], user enters command to save edited data to web server); and
transmit the at least one modification for display on an intranet, associated with the entity, in response to the confirmation (Mohan, para [0043], transmits changed data to server).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the user interaction to include a confirmation to update data to a server and provide and interface to do so. The motivation for doing so would have been to provide an enhanced WYSIWYG interface in a browser environment (Mohan, para [0002]).
Regarding claim 18, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Garrison additionally discloses wherein the one or more instructions, that cause the device to apply the machine learning model, cause the device to: train the machine learning model using the at least one document; and apply the trained machine learning model to the plurality of files to determine the at least one modification (Garrison, para [0039], output template generated from retrained classified incorporates the information from the originally trained model and an updated positive and negative data set which updates color selections).
Regarding claim 20, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Garrison additionally discloses wherein the plurality of files includes an image file, a video file, a hypertext markup language (HTML) file, or a portable document format file (Garrison, para [0027-31 and 36], crawls website at URL directed by website provider. Parses HTML, CSS and javascript).
Claim(s) 10 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Garrison (US 2016/0055132) in view of Mohan (US 2008/0140766) further in view of Tseng US (2022/0143825).
Regarding claim 10, Garrison in view of Mohan discloses the method of claim 7. Garrison does not but Tseng discloses receiving, from the user device, a second confirmation; and
outputting the document in a portable document format (Tseng, para [0044], user selects to save page as a PDF, interpreted as a confirmation, and outputs the current page as a PDF).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method to include outputting a PDF. The motivation for doing so would have been to increase efficiency of general administrative work, such as outputting documents (Tseng, para [0004]).
Regarding claim 17, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Garrison does not but Tseng discloses wherein the one or more instructions, when executed by the one or more processors, cause the device to: receive, from the user device, a confirmation; and output the document in a portable document format (Tseng, para [0044], user selects to save page as a PDF, interpreted as a confirmation, and outputs the current page as a PDF).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the method to include outputting a PDF. The motivation for doing so would have been to increase efficiency of general administrative work, such as outputting documents (Tseng, para [0004]).
Claim(s) 12-13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable Garrison (US 2016/0055132) in view of Mohan (US 2008/0140766) further in view of Corwin (US 2021/0150263).
Regarding claim 12, Garrison in view of Mohan discloses the method of claim 7. Garrison does not but Corwin discloses wherein the machine learning model uses deep learning (Corwin, para [0215], deep learning network).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the learning to be deep learning of an ensemble neural network. The motivation to automatically recognize features within images that correspond to training inputs (Corwin, para [0215]).
Regarding claim 13, Garrison in view of Mohan discloses the method of claim 7. Garrison does not but Corwin discloses wherein applying the machine learning model comprises: applying a first machine learning model to determine a first portion of the set of rules associated with a first style category; and applying a second machine learning model to determine a second portion of the set of rules associated with a second style category (Corwin, para [0234-235], ensemble of machine learning models where each model of the ensemble represents its own classifier. Applying a classifier represents applying a set of rules associated with a category).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the application of a machine learning model with a classifier to include an ensemble with multiple classifiers. The motivation to automatically recognize features within images that correspond to training inputs (Corwin, para [0215]).
Regarding claim 19, Garrison in view of Mohan discloses the non-transitory computer-readable medium of claim 14. Garrison does not but Corwin discloses wherein the one or more instructions, that cause the device to apply the machine learning model, cause the device to: input the at least one document to a first set of input nodes associated with the machine learning model; and input the plurality of files to a second set of input nodes associated with the machine learning model (Corwin, para [0234-235], ensemble of machine learning models where each model of the ensemble represents its own classifier for input data. The ensemble model can include individually trained neural networks, representing at least a first set and second set of nodes).
Before the time of the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the application of a machine learning model with a classifier to include an ensemble with multiple classifiers. The motivation to automatically recognize features within images that correspond to training inputs (Corwin, para [0215]).
Response to Arguments
Applicant’s arguments with respect to claims 1-3, 5-8, 11, 14, 18, and 20 have been considered but are moot because the new ground of rejection.
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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kieu Vu whose telephone number is 571-272-4057. The examiner can normally be reached Monday-Friday, 9:00 am-5:00pm PT.
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/KIEU D VU/Supervisory Patent Examiner, Art Unit 2171
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