DETAILED ACTION
Priority
This action is in response to the original filing dated 15 May 2024 which claims priority to a U.S. provisional application dated 15 May 2023. A preliminary amendment was submitted on 15 May 2024. No claims have been amended. Claims 19-34, 36-51 and 53-280 have been cancelled. No claims have been added. Claims 1-18, 35 and 52 are 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
The information disclosure statement (IDS) submitted on 11 September 2024 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The replacement drawings submitted on 26 December 2024 are acceptable.
Specification
Examiner respectfully requests that applicant unambiguously identify the paragraph numbers that contain amendments as examiner cannot locate any markup within the submitted 111 page specification with the heading indicated as “substitute specification - marked-up”.
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 1-10, 12-18, 35 and 52 are rejected under 35 U.S.C. 103 as being unpatentable over Hatfield et al. (US 11,334,323 B1) in view of Saxena (US 2024/0330579, US provisional app. 63/493833 dated 03 April 2023).
As for independent claim 1, Hatfield teaches an apparatus comprising:
receive, via a template selection interface element integrated into a website building system accessed using a client computing entity associated with an editing user identifier, a template selection representative of a first template [(e.g. see Hatfield col 5 lines 22-40, col 14 lines 50-53) ”Subscribing users, such as, for example, website designers and developers, of clients 110, 112, and 114 may utilize clients 110, 112, and 114 to access and utilize the website design build management services provided by server 104 and server 106 … storage 108 may store identifiers and network addresses for a plurality of registered client devices, identifiers for a plurality of subscribing users, existing best practices web design style guideline template examples, examples of common content element patterns in website design builds, raw design files, historical responses to questionnaires by website designers and developers, website accessibility guidelines, and the like. Furthermore, storage 108 may store other types of data, such as authentication or credential data that may include user names, passwords, and biometric data associated with website designers and developers, for example … The process begins when the computer receives an input to analyze a website design build from a client device of a website designer via a network (step 502)”].
receive, via one or more model interaction (MI) interface elements integrated into the website building system accessed using the client computing entity associated with the editing user identifier and one or more content selections [(e.g. see Hatfield col 14 lines 2-13, 59-63) ”a retrieved set of website design inputs corresponding to a website design build (step 402). The retrieved set of website design inputs may include, for example, existing website design artifacts, website wireframes, website user interface designs, website builds, website copy, and the like. The computer, using the cognitive component, extracts website design and content elements from the retrieved set of website design inputs to form extracted website design and content elements corresponding to the website design build based on the analysis of the retrieved set of website design inputs corresponding to the website design build (step 404) … the computer, using the cognitive component, extracts a set of website design and content elements from the set of website design inputs corresponding to the website design build based on the analysis”].
input, to a trained website editing machine learning (ML) model, one or more content selections, and one or more website editing data objects [(e.g. see Hatfield col 12 line 65 – col 13 line 7, col 13 lines 11-20) ”Website design build manager 300 utilizes machine learning module 304 to train cognitive component 302 to identify web design style guideline patterns based on existing web design style guidelines for content elements, font usage, copy usage, and other web design style guideline patterns. In addition, website design build manager 300 utilizes machine learning module 304 to train cognitive component 302 to identify best practices of web design style guidelines based on best practice web design style guideline template examples … Upon receiving a request to analyze a given website design build from a website designer/developer, cognitive component 302 retrieves website design inputs corresponding to that website design build from data sources 306. Data sources 306 represent a plurality of different website design information sources. In this example, data sources 306 include website design 308, content elements 310, raw source files 312, questionnaire responses 314, and accessibility rules 316”].
generate, using output from the trained website editing ML model generated responsive to the one or more [prompt] data objects, a second template representing the first template modified with one or more new content objects generated based at least in part on the natural language content object, the one or more content selection, and the one or more website editing data objects [(e.g. see Hatfield col 7 line 1-14, col 11 lines 55-67, col 12 lines 65-67, col 13 lines 42-52) ”Website design build manager 218 controls the process of automatically generating web design style guidelines for a specified website design build based on identifying style guide patterns in existing web design style guideline template examples, generating a recommendation to implement those web design style guidelines in the specified website design build, determining whether any gaps or accessibility compliance issues exist in the website design build, and automatically resolving any determined gaps or accessibility compliance issues. As a result, data processing system 200 operates as a special purpose computer system in which website design build manager 218 in data processing system 200 enables automatic generation and implementation of a set of web design style guidelines in a web design build … Cognitive component 302 utilizes all of the information retrieved from data sources 306 to automatically generate user experience and website design recommendations 318 for the web design build. Cognitive component 302 incorporates user experience and website design recommendations 318 in web design style guidelines 320 for implementation in the website design build. Furthermore, cognitive component 302 may automatically implement within the website design build one or more of user experience and website design recommendations 318 of web design style guidelines 320 … Website design build manager 300 utilizes machine learning module 304 to train cognitive component 302 … The cognitive component to scan the website design, including website wireframe, website user interface design, website build, website copy (i.e., text), and the like. Assessment of the website design by the cognitive component would highlight any gaps or inconsistencies in the website design build, which may include, for example, inconsistencies in color treatments, copy tone, call to action copy, button positioning on the screen, and the like. The cognitive component would highlight these gaps and make recommendations for corrective actions. However, it should be noted that the cognitive component may automatically perform one or more of the corrective actions to implement recommended changes to the website design build”].
and transmit the second template to the client computing entity, wherein the second template is configured for rendering via a display device of the client computing entity [(e.g. see Hatfield col 11 lines 49-51, col 12 line 1-3) ”The cognitive component presents recommendations for the website content element alternatives to the web development team for implementation … The web development team can dismiss recommendations, select recommendations, or provide updates or alternatives to the recommendations”].
Hatfield does not specifically teach a natural language content object or input, to a trained website editing machine learning (ML) model, one or more prompt data objects, the natural language content object and corresponding descriptions of the one or more website editing data objects. However, in the same field of invention, Saxena teaches:
a natural language content object [(e.g. see Saxena paragraphs 0030, 0075, prov: paragraphs 0007, 0031) ”Each prompt template can be associated with a type of webpage section. For example, a first prompt template can be associated with (and used to generate text for) a first type of webpage section (e.g., an announcement bar) while a second, different prompt template is associated with a second type of webpage section (e.g., a company description section). The available webpage section types offered by the website development system 120 can each have one or more associated prompt templates. Alternatively, each prompt template can be associated with a location on a webpage, such that a template corresponds to a section that is placed at the associated location … Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API”].
input, to a trained website editing machine learning (ML) model, one or more prompt data objects, the natural language content object and corresponding descriptions of the one or more website editing data objects [(e.g. see Saxena paragraphs 0021, 0030, 0075, prov: paragraphs 0007, 0031) ”The website development system 120 receives data associated with the website 105 and/or user input 110 and, using the website data or user input, generates a prompt 125 to a large language model (LLM) 130 … Each prompt template can be associated with a type of webpage section. For example, a first prompt template can be associated with (and used to generate text for) a first type of webpage section (e.g., an announcement bar) while a second, different prompt template is associated with a second type of webpage section (e.g., a company description section). The available webpage section types offered by the website development system 120 can each have one or more associated prompt templates. Alternatively, each prompt template can be associated with a location on a webpage, such that a template corresponds to a section that is placed at the associated location … Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API”].
Therefore, considering the teachings of Hatfield and Saxena, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add a natural language content object and input, to a trained website editing machine learning (ML) model, one or more prompt data objects, the natural language content object and corresponding descriptions of the one or more website editing data objects, as taught by Saxena, to the teachings of Hatfield because it allows the output produced by a model to be customized to the content where the text will appear which saves the user time (e.g. see Saxena paragraph 0017, prov: 0006).
As for dependent claim 2, Hatfield and Saxena teach the apparatus as described in claim 1, but Hatfield does not specifically teach the following limitation. However, Saxena teaches:
wherein the template selection is received via an application programming interface (API) and the second template is transmitted via the API [(e.g. see Saxena paragraph 0080, prov: paragraph 0036) ”A computing system, such as the computing system 600 of FIG. 6, may access a remote system (e.g., a cloud-based system) to communicate with a remote language model or LLM hosted on the remote system such as, for example, using an application programming interface (API) call. The API call may include an API key to enable the computing system to be identified by the remote system. The API call may also include an identification of the language model or LLM to be accessed and/or parameters for adjusting outputs generated by the language model or LLM”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 3, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the one or more website editing data objects comprise one or more website building components [(e.g. see Hatfield col 9 lines 51-64) ”The cognitive component of illustrative embodiments extracts all common website design and content elements, such as, for example, typography, color palette, icons, imagery, copy, graphics, white space, forms, spacing, buttons, and the like (i.e., if not already existing in aggregate form). The cognitive component also identifies common web design style guideline patterns and highlights any gaps in the website design build as questions for designers/developers to answer. A gap is an inconsistency, disparity, difference, or mismatch between an existing and a new website design build. For example, a gap may be a content element, such as a color or button, in the new website design build that is not in an existing best practices website design build template example”].
As for dependent claim 4, Hatfield and Saxena teach the apparatus as described in claim 1, but Hatfield does not specifically teach the following limitation. However, Saxena teaches:
wherein the natural language content object is processed prior to being included with the one or more prompt data objects [(e.g. Saxena paragraph 0033, prov: paragraph 0008) ”The parameter analysis module 225 determines a value for each of the one or more parameters in the selected prompt template. The parameter analysis module 225 can use website data or previously supplied user inputs to determine some parameter values, reducing the amount of input the user needs to provide to generate the LLM prompt. For example, at the time that a user adds a new section to a webpage, the webpage (or the website containing the webpage) may already include some information relevant to the prompt template associated with the new section. Such information can appear explicitly or implicitly in the content of the webpage”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 5, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the one or more content selections comprise a business type and a business name [(e.g. see Hatfield col 1 lines 33-43) ”The web design style guidelines provide definitions for the different elements and pages of a website in accordance with brand guidelines of an entity, such as, for example, a company, business, enterprise, organization, institution, agency, or the like. Brand is what an entity stands for or the message the entity is trying to get across to users of the website (e.g., mission, values, and the like). Brand may also include logo, color palette, and other elements corresponding to a particular entity. In other words, entity brand and website design go hand in hand”].
As for dependent claim 6, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the one or more MI interface elements comprise an overlay, a frame, or a pop-up interface element within a website [(e.g. see Hatfield col 9 lines 36-48) ”cognitive analysis of existing website design artifacts, which includes website wireframe, website user interface design, website build, website content elements, and the like. A website wireframe, also known as a page schematic or screen blueprint, is a visual guide representing the framework of a website. The wireframe is created for the purpose of arranging content elements of a website to best accomplish a particular purpose. The wireframe depicts the page layout or arrangement of the website's content, including interface and navigational elements, and how they work together”].
As for dependent claim 7, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the template selection interface element comprises a plurality of templates for selection [(e.g. see Hatfield col 10 lines 66-67, col 14 lines 50-55) ”The process begins when the computer receives an input to analyze a website design build from a client device of a website designer via a network (step 502). In response to receiving the input, the computer retrieves a set of website design inputs corresponding to the website design build … automatic generation of the web design style guidelines for a particular website design build”].
As for dependent claim 8, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
is trained using a corpus of historical website editing interaction data associated with a plurality of editing user identifiers [(e.g. see Hatfield col 12 lines 1-6, 14-21) ”The web development team can dismiss recommendations, select recommendations, or provide updates or alternatives to the recommendations. The cognitive component inputs the recommendations and any updates to the recommendations into the machine learning module for ongoing machine learning … The cognitive component also captures website design history and ongoing usage of the website design. For example, the cognitive component provides historical logs of all website design actions and activities. This historical information provides a framework for forecasting maintenance and updates to the website design. The cognitive component automatically updates and maintains the website design as website changes are made over time”].
Hatfield does not specifically teach the following limitation. However, Saxena teaches:
wherein the trained website editing ML model comprises one or more of a large language model (LLM) or generative AI (GAI) model [(e.g. see Saxena paragraph 0018, prov: paragraph 0007) ”a website development system according to implementations herein enables LLM-based text generation for a website by automating the generation of a prompt using data retrieved or derived from the website”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 9, Hatfield and Saxena teach the apparatus as described in claim 1, but Hatfield does not specifically teach the following limitation. However, Saxena teaches:
wherein the one or more prompt data objects are generated based at least in part on a content outline associated with the website and comprising logical positioning of components within webpages of the website [(e.g. see Saxena paragraph 0030, prov: paragraph 0007) ”Each prompt template can be associated with a type of webpage section. For example, a first prompt template can be associated with (and used to generate text for) a first type of webpage section (e.g., an announcement bar) while a second, different prompt template is associated with a second type of webpage section (e.g., a company description section). The available webpage section types offered by the website development system 120 can each have one or more associated prompt templates. Alternatively, each prompt template can be associated with a location on a webpage, such that a template corresponds to a section that is placed at the associated location”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 10, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the one or more new content objects comprise one or more of text or images [(e.g. see Hatfield col 13 lines 27-32) ”Content elements 310 include information regarding features or attributes of the website design build, such as, for example, typography, color palette, icons, imagery, copy, graphics, white space, forms, tables, fields, rows, spacing, tiles, buttons, navigation, and the like”].
As for dependent claim 12, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the at least one non-transitory computer-readable storage medium comprises instruction that, when executed by the one or more processors, further cause the apparatus to: generate an input map data structure of the first template [(e.g. see Hatfield col 9 lines 37-47) ”perform cognitive analysis of existing website design artifacts, which includes website wireframe, website user interface design, website build, website content elements, and the like. A website wireframe, also known as a page schematic or screen blueprint, is a visual guide representing the framework of a website. The wireframe is created for the purpose of arranging content elements of a website to best accomplish a particular purpose. The wireframe depicts the page layout or arrangement of the website's content, including interface and navigational elements, and how they work together”].
and input the input map data structure to the trained website editing ML model [(e.g. see Hatfield col 12 line 65 – col 13 line 10, col 13 lines 25-28) ”Website design build manager 300 utilizes machine learning module 304 to train cognitive component 302 to identify web design style guideline patterns based on existing web design style guidelines for content elements, font usage, copy usage, and other web design style guideline patterns. In addition, website design build manager 300 utilizes machine learning module 304 to train cognitive component 302 to identify best practices of web design style guidelines based on best practice web design style guideline template examples. Web site design build manager 300 further utilizes machine learning module 304 to train cognitive component 302 to identify whether website content elements are compliant with accessibility guidelines … Website design 308 includes information regarding an overall design of a website design build, such as, for example, website wireframe, website user interface design, and the like”].
As for dependent claim 13, Hatfield and Saxena teach the apparatus as described in claim 1, but Hatfield does not specifically teach the following limitation. However, Saxena teaches:
wherein the one or more prompt data objects comprise a plurality of sections and a corresponding natural language description of sections of the plurality of sections [(e.g. see Saxena paragraphs 0030, 0075, prov: paragraphs 0007, 0031) ”Each prompt template can be associated with a type of webpage section. For example, a first prompt template can be associated with (and used to generate text for) a first type of webpage section (e.g., an announcement bar) while a second, different prompt template is associated with a second type of webpage section (e.g., a company description section). The available webpage section types offered by the website development system 120 can each have one or more associated prompt templates. Alternatively, each prompt template can be associated with a location on a webpage, such that a template corresponds to a section that is placed at the associated location … Inputs to an LLM may be referred to as a prompt, which is a natural language input that includes instructions to the LLM to generate a desired output. A computing system may generate a prompt that is provided as input to the LLM via its API”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 14, Hatfield and Saxena teach the apparatus as described in claim 1 and Hatfield further teaches:
wherein the at least one non-transitory computer-readable storage medium comprises instructions that, when executed by the one or more processors, further cause the apparatus to: perform quality assurance operations prior to transmitting the second template to the client computing entity [(e.g. see Hatfield col 11 lines 15-31, 65-67) ”the machine learning module to train the cognitive component to differentiate between best practices and worst practices of web design style guidelines based on analyzing one or more best practice web design style guideline template examples. Illustrative embodiments utilize best practice examples for machine learning purposes to demonstrate preferred approaches for output of recommended web design style guidelines. In addition, illustrative embodiments utilize worst (e.g., poor or failed) web design style guideline template examples to identify what not to emulate for the output of the web design style guideline recommendations. The cognitive component scans existing web design style guideline template examples of a preferred or specific type based on technology used for a particular website design build … it should be noted that the cognitive component may automatically perform one or more of the corrective actions to implement recommended changes to the website design build”].
As for dependent claim 15, Hatfield and Saxena teach the apparatus as described in claim 14 and Hatfield further teaches:
wherein the quality assurance operations comprise confirming output from the trained website editing ML model conforms to a format in accordance with the first template [(e.g. see Hatfield col 7 lines 1-10, col 11 lines 15-31, col 11 line 65 6 col 12 line 3) ”Website design build manager 218 controls the process of automatically generating web design style guidelines for a specified website design build based on identifying style guide patterns in existing web design style guideline template examples, generating a recommendation to implement those web design style guidelines in the specified website design build, determining whether any gaps or accessibility compliance issues exist in the website design build, and automatically resolving any determined gaps or accessibility compliance issues … the machine learning module to train the cognitive component to differentiate between best practices and worst practices of web design style guidelines based on analyzing one or more best practice web design style guideline template examples. Illustrative embodiments utilize best practice examples for machine learning purposes to demonstrate preferred approaches for output of recommended web design style guidelines. In addition, illustrative embodiments utilize worst (e.g., poor or failed) web design style guideline template examples to identify what not to emulate for the output of the web design style guideline recommendations. The cognitive component scans existing web design style guideline template examples of a preferred or specific type based on technology used for a particular website design build … it should be noted that the cognitive component may automatically perform one or more of the corrective actions to implement recommended changes to the website design build. The web development team can dismiss recommendations, select recommendations, or provide updates or alternatives to the recommendations”].
As for dependent claim 16, Hatfield and Saxena teach the apparatus as described in claim 15 and Hatfield further teaches:
wherein the quality assurance operations further comprise verifying content in sections of the first template meet one or more length thresholds [(e.g. see Hatfield col 11 lines 58-62, col 13 lines 25-32) ”Assessment of the website design by the cognitive component would highlight any gaps or inconsistencies in the website design build, which may include, for example, inconsistencies in color treatments, copy tone, call to action copy, button positioning on the screen, and the like … an overall design of a website design build, such as, for example, website wireframe, website user interface design, and the like. Content elements 310 include information regarding features or attributes of the website design build, such as, for example, typography, color palette, icons, imagery, copy, graphics, white space, forms, tables, fields, rows, spacing, tiles, buttons, navigation, and the like”].
As for dependent claim 17, Hatfield and Saxena teach the apparatus as described in claim 15 and Hatfield further teaches:
wherein the quality assurance operations comprise generating one or more second prompt data objects to obtain corrected output from the trained website editing ML model [(e.g. see Hatfield col 11 line 63 – col 12 line 1) ”The cognitive component would highlight these gaps and make recommendations for corrective actions. However, it should be noted that the cognitive component may automatically perform one or more of the corrective actions to implement recommended changes to the website design build”].
As for independent claim 18, Hatfield and Saxena teach a non-transitory computer-readable medium. Claim 18 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for independent claim 35, Hatfield and Saxena teach a method. Claim 35 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for independent claim 52, Hatfield and Saxena teach an apparatus. Claim 52 discloses substantially the same limitations as claims 1, 12 and 14. Therefore, it is rejected with the same rational as claims 1, 12 and 14. Further, Hatfield teaches output map data structure [(e.g. see Hatfield col 11 line 55 – col 12 line 1) ”illustrative embodiments utilize the cognitive component to scan the website design, including website wireframe, website user interface design, website build, website copy (i.e., text), and the like. Assessment of the website design by the cognitive component would highlight any gaps or inconsistencies in the website design build, which may include, for example, inconsistencies in color treatments, copy tone, call to action copy, button positioning on the screen, and the like. The cognitive component would highlight these gaps and make recommendations for corrective actions. However, it should be noted that the cognitive component may automatically perform one or more of the corrective actions to implement recommended changes to the website design build”].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Hatfield et al. (US 11,334,323 B1) in view of Saxena (US 2024/0330579, US provisional app. 63/493833 dated 03 April 2023), as applied to claim 10 above, and further in view of Chrysanthou (US 12,008,064 B1).
As for dependent claim 11, Hatfield and Saxena teach the apparatus as described in claim 10, but do not specifically teach wherein the images are received from an external image generation entity. However, in the same field of invention, Chrysanthou teaches:
wherein the images are received from an external image generation entity [(e.g. see Chrysanthou col 3 lines 44-48, col 14 lines 10-16) ”As used herein, the term “content” means any form of electronic or digital content, media, compilations, files, streaming data, and the like. This includes, but is not limited to, video, images, animations, audio, slideshows, text, web- or hyper-links, and any combination thereof, and the like … the website generator platform 102 is coupled to at least one third-party content generation service 106-2 using at least one content generation API 240. In this embodiment, the functions described here with regards to the content engine 214 can be wholly or partially performed by the at least one third-party content generation service 106-2”].
Therefore, considering the teachings of Hatfield, Saxena and Chrysanthou, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the images are received from an external image generation entity, as taught by Chrysanthou, to the teachings of Hatfield and Saxena because relying on third-party content allows future website templates and websites to be generated more efficiently, quickly, and accurately (e.g. see Chrysanthou col 7 lines 21-24).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent 11,699,019 B2 issued to Souche et al. on 11 July 2023. The subject matter disclosed therein is pertinent to that of claims 1-18, 35 and 52 (e.g. AI-based designing of website layouts).
U.S. PGPub 2023/0245580 A1 issued to Alioto et al. on 03 August 2023. The subject matter disclosed therein is pertinent to that of claims 1-18, 35 and 52 (e.g. designing a website framework that is editable with machine learning).
Contact Information
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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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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