CTNF 18/736,999 CTNF 82518 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. DETAILED ACTION 1. This action is responsive to application communication filed on 6/7/2024. 2. Claims 1-20 are pending in the case. 3. Claim 1 is an independent claim. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows: 1. Determining the scope and contents of the prior art. 2. Ascertaining the differences between the prior art and the claims at issue. 3. Resolving the level of ordinary skill in the pertinent art. 4. Considering objective evidence present in the application indicating obviousness or nonobviousness. 07-21-aia AIA Claim s 1-9 and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ekron et al. (hereinafter “Ekron”), U.S. Published Application No. 20220366003 A1, in view of Dar et al. (hereinafter “Dar”), U.S. Published Application No. 20250086210 A1 . Claim 1: Ekron teaches A system comprising at least one processor configured to: receive a user request from a user device to access a resource; (e.g., remote server handle request of user of website on a user device par. 140; A remote server may include a computer that is remotely located having a web server software, database, and other resources to handle remote requests sent by the user of the website. For example, the remote server, during the browser session, may send a local script (that may be processed on the user's computing device) to invoke a remote script on the remote server for processing information.) in response to receiving the user request, retrieve program code configured to cause display of the resource; (e.g., in response to user input indicating a request to modify a displayed website, displaying the modified website based on modified display parameters par. 140; A remote server may include a computer that is remotely located having a web server software, database, and other resources to handle remote requests sent by the user of the website. par. 141; receiving input indicative of a desired change in at least one of the first and second website display parameters; and adjusting the at least one of the first and second website display parameters based on the input to implement the desired change.) generate a first version of the resource based on the program code, the first version of the resource displayed on the user device; (e.g., in response to user input indicating a request to modify a displayed website, displaying the modified website based on modified display parameters par. 141; receiving input indicative of a desired change in at least one of the first and second website display parameters; and adjusting the at least one of the first and second website display parameters based on the input to implement the desired change.) collect interaction data based on sensed interactions of a user of the user device with the displayed first version of the resource; (e.g., collecting revising data based on user input to graphical elements of the website par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile. For example, an accessibility GUI may be in the form of a menu pane on a left side of a website. The accessibility GUI pane may include graphical elements such as an “Accessibility Adjustments” window pane, “ON” and “OFF” buttons/toggles, a “Reset Settings” button, a “Statement” button, a “Hide Interface” button, a query window “Search the online dictionary . . . ,” circular icons adjacent to each web accessibility profile to indicate a selection of one or more than one of the web accessibility profiles) input the interaction data into a first machine learning model configured to generate associations between user interaction types from the interaction data and display update actions, the first machine learning model configured to generate at least one display update action for the first version of the resource; (e.g., input interaction data with website template into a machine learning model to conform to the needs of user with disabilities in effort to update the website par. 361; Based on the obtained indication, some disclosed embodiments include implementing a predefined template to alter multiple default website display parameters to conform with needs of the user with the neurodevelopmental disorder, epilepsy, visual impairment, and/or cognitive disability. A predefined template may include a guide, arrangement, instruction, or any other model of one or more website default display parameters. A predefined template may include a set value for one or more of a plurality of website display parameters, such that using the predefined template for different websites may result in the same changes to the same website display parameters of the different websites. Par. 433; In some embodiments, machine learning algorithms (also referred to as machine learning models) may be trained using training examples. Par. 433; natural language processing algorithms par. 538; Information relating to the level of accessibility of a website may be received by a client-side computing device from a website. The client-side computing device may use machine learning models to determine what type of information may be available on a website to determine the level of accessibility. For example, a machine learning model may determine that a website contains audio. The model may determine that the presence of audio indicates the website is accessible to visually impaired users. ) and generate, by a second machine learning model, a second version of the resource based on the at least one display update action for the first version of the resource, the second version of the resource displayed on the user device. (e.g., revising websites based on multiple machine models par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Ekron fails to expressly teach a large language model (LLM) as described in the instant specification. However, Dar teaches a large language model (LLM) as described in the instant specification. (par. 38; In some implementations and in response to classifying the query as being associated with a user with a disability, disability access assistance process 10 processes 304 the query using a customized large language model (LLM). For example, instead of using a conventional search engine to address queries that are classified as being associated with a user with a disability, a recent alternative is the use of a large language model (LLM) such as ChatGPT. An LLM is a highly-compressed representation of the collective knowledge present on the Internet. LLMs are a particular type of neural network.) In the same field of endeavor, namely, customizing interfaces based on users with disabilities, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the plurality of machine learning models used to customize a website as taught by Ekron to include a customized large language model as taught by Dar to provide the benefit of saving time and costs to pre-trained a model (see Dar; par. 39). Claim 2 depends on claim 1: Ekron teaches wherein the at least one processor is further configured to: input the at least one display update action for the first version of the resource and the program code into the second machine learning model configured to modify the program code based on the at least one display update action for the first version of the resource; and receive modified program code to effect the at least one display update action for the first version of the resource, wherein the second version of the resource is generated based on the modified program code. (e.g., revising websites based on multiple machine models par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Claim 3 depends on claim 1: Ekron teaches wherein, when receiving the user request to access a resource, the at least one processor is configured to: receive a URL request in a browser to render a webpage; ( e.g., submitting URLs to request webpages par. 155; At least one processor may render the content for the website on a computer display. The website may display an indicator (e.g., a button, graphic, icon) associated with opening the accessibility GUI for the website (e.g., by displaying the accessibility GUI adjacent to the website).Par. 407; Executing a highlighting actionable objects command in website code on a user session basis to highlight actionable objects depicted on the website may be desirable to make the actionable objects more accessible for users with cognitive disabilities. An actionable object may include one or more of an activatable button, hyperlink, a picklist, a field to be filled-out, links, menus, dropdowns, combination boxes, URLs, or any other website element seeking a user's input or interaction. ) and receive at least one parameter related to how the webpage is rendered. (e.g., in response to user input indicating a request to modify a displayed website, displaying the modified website based on modified display parameters par. 141; receiving input indicative of a desired change in at least one of the first and second website display parameters; and adjusting the at least one of the first and second website display parameters based on the input to implement the desired change. Par. 142; In some embodiments, after a user selects a web accessibility profile one or more display parameters such as a first website display parameter and a second website display parameter may be changed from their default values. Subsequently, input indicative of a desired change in at least one of the first change and the second change may be received. In response, the first and/or the second display parameter may be further modified based on the received input. Advantageously, this may allow users to further change and/or modify the website. ) Claim 4 depends on claim 1: Ekron teaches wherein when retrieving program code configured to cause display of the resource, the at least one processor is configured to: connect to a server hosting the resource; (e.g., remote server handle request of user of website on a user device par. 140; A remote server may include a computer that is remotely located having a web server software, database, and other resources to handle remote requests sent by the user of the website. For example, the remote server, during the browser session, may send a local script (that may be processed on the user's computing device) to invoke a remote script on the remote server for processing information.) request the program code configured to cause display of the resource, including at least one parameter related to how the resource is rendered; (e.g., in response to user input indicating a request to modify a displayed website, displaying the modified website based on modified display parameters par. 141; receiving input indicative of a desired change in at least one of the first and second website display parameters; and adjusting the at least one of the first and second website display parameters based on the input to implement the desired change. Par. 142; In some embodiments, after a user selects a web accessibility profile one or more display parameters such as a first website display parameter and a second website display parameter may be changed from their default values. Subsequently, input indicative of a desired change in at least one of the first change and the second change may be received. In response, the first and/or the second display parameter may be further modified based on the received input. Advantageously, this may allow users to further change and/or modify the website. ) and receive a response comprising the program code from the server hosting the resource, wherein the first version of the resource is generated based on the response. (e.g., remote server handle request of user of website on a user device par. 140; A remote server may include a computer that is remotely located having a web server software, database, and other resources to handle remote requests sent by the user of the website. For example, the remote server, during the browser session, may send a local script (that may be processed on the user's computing device) to invoke a remote script on the remote server for processing information.) Claim 5 depends on claim 1: Ekron teaches wherein when collecting interaction data, the at least one processor is configured to: monitor the sensed interactions of the user of the user device for a feedback event, wherein the feedback event is explicit or implicit. (e.g., monitoring explicit feedback on graphical elements par. 141; The accessibility GUI pane may include graphical elements such as an “Accessibility Adjustments” window pane, “ON” and “OFF” buttons/toggles, a “Reset Settings” button, a “Statement” button, a “Hide Interface” button, a query window “Search the online dictionary . . . ,” circular icons adjacent to each web accessibility profile to indicate a selection of one or more than one of the web accessibility profiles, a “Content Adjustments” window pane, a “Content Scaling” window pane with a button to increase or decrease content scaling of the website, an “Adjust Font Sizing” window pane with a button to increase or decrease the font size of the website, and one or more buttons labeled “Highlight Titles,” “Highlight Links,” “Readable Font,” “Text Magnifier,” and “Align Center.” Although some exemplary graphical elements have been described above, it is contemplated that the accessibility GUI may include one or more additional or alternative graphical elements that may allow a user to adjust, change, or modify one or more website display parameters. The one or more graphical elements may allow a user to customize the one or more selected web accessibility profiles.) Claim 6 depends on claim 5: Ekron teaches wherein the monitoring for a feedback event comprises continuously and/or continually monitoring for any feedback events based on the sensed interactions of the user of the user device in real-time. (e.g., dynamic real-time changes to website in response to monitoring explicit feedback on graphical elements par. 141; The accessibility GUI pane may include graphical elements such as an “Accessibility Adjustments” window pane, “ON” and “OFF” buttons/toggles, a “Reset Settings” button, a “Statement” button, a “Hide Interface” button, a query window “Search the online dictionary . . . ,” circular icons adjacent to each web accessibility profile to indicate a selection of one or more than one of the web accessibility profiles, a “Content Adjustments” window pane, a “Content Scaling” window pane with a button to increase or decrease content scaling of the website, an “Adjust Font Sizing” window pane with a button to increase or decrease the font size of the website, and one or more buttons labeled “Highlight Titles,” “Highlight Links,” “Readable Font,” “Text Magnifier,” and “Align Center.” Although some exemplary graphical elements have been described above, it is contemplated that the accessibility GUI may include one or more additional or alternative graphical elements that may allow a user to adjust, change, or modify one or more website display parameters. The one or more graphical elements may allow a user to customize the one or more selected web accessibility profiles.) Claim 7 depends on claim 5: Ekron teaches wherein the feedback event is based on a user interaction pattern. (e.g., touch input for each selection for the graphical elements is a consistent interaction pattern par. 80; An accessibility GUI generally refers to an interface which allows users to customize website display parameters through graphical elements (e.g., icons, menus, scroll bars, windows, transitional animations, dialogue boxes, and more). In one embodiment, the disclosed methods may involve receiving input via the accessibility GUI indicative of a selection for adjusting one or more website display parameters. The received input may include any type of data inputted by a user using an input device, such as a keyboard, a mouse, a touch pad, a touch screen, a joystick, a microphone, an image sensor, and/or any other device connectable to the computing device.) Claim 8 depends on claim 5: Ekron teaches wherein the feedback event is an explicit request from the user. (e.g., dynamic real-time changes to website in response to monitoring explicit feedback on graphical elements par. 141; The accessibility GUI pane may include graphical elements such as an “Accessibility Adjustments” window pane, “ON” and “OFF” buttons/toggles, a “Reset Settings” button, a “Statement” button, a “Hide Interface” button, a query window “Search the online dictionary . . . ,” circular icons adjacent to each web accessibility profile to indicate a selection of one or more than one of the web accessibility profiles, a “Content Adjustments” window pane, a “Content Scaling” window pane with a button to increase or decrease content scaling of the website, an “Adjust Font Sizing” window pane with a button to increase or decrease the font size of the website, and one or more buttons labeled “Highlight Titles,” “Highlight Links,” “Readable Font,” “Text Magnifier,” and “Align Center.” Although some exemplary graphical elements have been described above, it is contemplated that the accessibility GUI may include one or more additional or alternative graphical elements that may allow a user to adjust, change, or modify one or more website display parameters. The one or more graphical elements may allow a user to customize the one or more selected web accessibility profiles.) Claim 9 depends on claim 5: Ekron teaches wherein the feedback event is an implicit request from the user. (e.g., saving profile as implicit feedback par. 144; Advantageously, a user's selection of a web accessibility profile may be saved and/or stored for future retrieval during future browsing sessions so that the user does not need to select his or her web accessibility profile every browsing session.) Claim 14 depends on claim 1: Ekron teaches wherein the resource comprises at least one of a webpage, a mobile app, and/or a gaming environment. (e.g., web page resources par. 78; Embodiments disclosed herein may include displaying a website exhibiting various website display parameters. The website display parameters or simply display parameters) may include properties whose values provide constraints on a presentation of a website or a webpage on a screen associated with a computing device of the user.) Claim 15 depends on claim 1: Ekron teaches wherein generating the first version and/or the second version of the resource comprises generating and rendering the first version and/or the second version of the resource. (e.g., revising websites based on multiple machine models par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Claim 16 depends on claim 1: Ekron teaches wherein the at least one processor is further configured to: input the interaction data into a first machine learning model configured to generate associations between user interaction types from the interaction data and display update actions, the first machine learning model configured to generate at least one display update action for the second version of the resource; and generate, by a second machine learning model, a third version of the resource based on the at least one display update action for the second version of the resource, the third version of the resource displayed on the user device. (e.g., revising websites based on multiple machine models par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Claim 17 depends on claim 16: Ekron teaches the at least one processor further configured to: input the at least one display update action for the second version of the resource and the program code into the second machine learning model configured to modify the program code based on the at least one display update action for the second version of the resource; and receive modified program code to effect the at least one display update action for the second version of the resource, wherein the third version of the resource is generated based on the modified program code. (e.g., revising websites based on multiple machine models par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) 07-21-aia AIA Claim s 10-13 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ekron/Dar as cited above, in view of D'Agostino; Dino Paul, U.S. Published Application No. 20250307222 A1 . Claim 10 depends on claim 1: Ekron/Dar teaches wherein when inputting the interaction data into the first machine learning model, the at least one processor is configured to: (e.g., revising websites based on multiple machine models Ekron; par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Ekron/Dar fails to expressly teach parse the collected interaction data into first text data; tokenize the first text data to produce a first plurality of tokens; and encode the first plurality of tokens into a first plurality of vectors, the first plurality of vectors being input to the first machine learning model. However, D’Agostino teaches parse the collected interaction data into first text data; tokenize the first text data to produce a first plurality of tokens; and encode the first plurality of tokens into a first plurality of vectors, the first plurality of vectors being input to the first machine learning model. (e.g., the transformer model includes an encoder 710 and a decoder 720. par. 156; Referring to FIG. 7A, the transformer model includes an encoder 710 and a decoder 720. For example, the encoder 710 may convert input data (such as a block of text) into a format that is easier for the rest of the model to understand, such as a vector, number, etc. Here, each word in the input sequence is broken up into units (tokens) through tokenization. The tokens are transformed into vectors. The encoding process is referred to as input embedding. Meanwhile, the decoder 720 can convert the data generated by the rest of the model back into a format that is understandable to a human, such as a text-based description, sentence, or the like.) It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify the machine learning models as taught by Ekron/Dar to analyze input via transformer model as taught by D’Agostino to provide the benefit of improving the accuracy and relevancy of the model results when determining the contextual meaning of content in effort to better determine user intent. . Claim 11 depends on claim 10: Ekron/Dar teaches the at least one processor configured to: input content from the first version of the resource into the first machine learning model by: (e.g., revising websites based on multiple machine models Ekron; par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Ekron/Dar fails to expressly teach parsing the content of the first version of the resource into second text data; tokenizing the second text data to produce a second plurality of tokens; and encoding the second plurality of tokens into a second plurality of vectors, the second plurality of vectors being input to the first machine learning model. However, D’Agostino teaches parsing the content of the first version of the resource into second text data; tokenizing the second text data to produce a second plurality of tokens; and encoding the second plurality of tokens into a second plurality of vectors, the second plurality of vectors being input to the first machine learning model. (e.g., the transformer model includes an encoder 710 and a decoder 720. D’Agostino; par. 156; Referring to FIG. 7A, the transformer model includes an encoder 710 and a decoder 720. For example, the encoder 710 may convert input data (such as a block of text) into a format that is easier for the rest of the model to understand, such as a vector, number, etc. Here, each word in the input sequence is broken up into units (tokens) through tokenization. The tokens are transformed into vectors. The encoding process is referred to as input embedding. Meanwhile, the decoder 720 can convert the data generated by the rest of the model back into a format that is understandable to a human, such as a text-based description, sentence, or the like.) It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify the machine learning models as taught by Ekron/Dar to analyze input via transformer model as taught by D’Agostino to provide the benefit of improving the accuracy and relevancy of the model results when determining the contextual meaning of content in effort to better determine user intent. Claim 12 depends on claim 11: Ekron/Dar / D’Agostino teaches wherein the first machine learning model is configured to combine the first plurality of vectors and the second plurality of vectors to produce a third plurality of vectors, the third plurality of vectors corresponding to input used to generate the second version of the resource. (e.g., combining word associated vectors to create a sentence associated vector D’Agostino; par. 138; The text values may be converted into a vector via execution of an LLM 650 or the like which embeds the text values into a vector 652. For example, the LLM 650 may be a transformer neural network with an encoder/decoder framework which can embed a block of text into a single vector. Here, the LLM 650 may convert a block of text, such as a sentence, phrase, combination of words, word, or the like, into a multi-dimensional vector. ) Claim 13 depends on claim 12: Ekron/Dar/D’Agostino teaches the at least one processor configured to: input the third plurality of vectors into the second machine learning model, the second machine learning model configured to: decode the third plurality of vectors into a third plurality of tokens; detokenize the third plurality of tokens into third text data; and convert the third text data into modified program code for generating the second version of the resource. (e.g., the transformer model includes an encoder 710 and a decoder 720. D’Agostino; par. 156; Referring to FIG. 7A, the transformer model includes an encoder 710 and a decoder 720. For example, the encoder 710 may convert input data (such as a block of text) into a format that is easier for the rest of the model to understand, such as a vector, number, etc. Here, each word in the input sequence is broken up into units (tokens) through tokenization. The tokens are transformed into vectors. The encoding process is referred to as input embedding. Meanwhile, the decoder 720 can convert the data generated by the rest of the model back into a format that is understandable to a human, such as a text-based description, sentence, or the like.) Claim 18 depends on claim 16: Ekron/Dar teaches wherein when inputting the interaction data into the first machine learning model, the at least one processor is configured to: (e.g., revising websites based on multiple machine models Ekron; par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Ekron/Dar fails to expressly teach parse the collected interaction data into fourth text data; tokenize the fourth text data to produce a fourth plurality of tokens; and encode the fourth plurality of tokens into a fourth plurality of vectors, the fourth plurality of vectors being input to the first machine learning model. However, D’Agostino teaches parse the collected interaction data into fourth text data; tokenize the fourth text data to produce a fourth plurality of tokens; and encode the fourth plurality of tokens into a fourth plurality of vectors, the fourth plurality of vectors being input to the first machine learning model. (e.g., the transformer model includes an encoder 710 and a decoder 720. par. 156; Referring to FIG. 7A, the transformer model includes an encoder 710 and a decoder 720. For example, the encoder 710 may convert input data (such as a block of text) into a format that is easier for the rest of the model to understand, such as a vector, number, etc. Here, each word in the input sequence is broken up into units (tokens) through tokenization. The tokens are transformed into vectors. The encoding process is referred to as input embedding. Meanwhile, the decoder 720 can convert the data generated by the rest of the model back into a format that is understandable to a human, such as a text-based description, sentence, or the like.) It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify the machine learning models as taught by Ekron/Dar to analyze input via transformer model as taught by D’Agostino to provide the benefit of improving the accuracy and relevancy of the model results when determining the contextual meaning of content in effort to better determine user intent. Claim 19 depends on claim 18: Ekron/Dar teaches the at least one processor configured to: input the content from the second version of the resource into the first machine learning model by: (e.g., revising websites based on multiple machine models Ekron; par. 141; Customizing a selected web accessibility profile may include changing, modifying, adjusting, correcting, reshaping, revising, reworking, tweaking, varying, or altering a web accessibility profile par. 538; Alternatively, or additionally, a separate computing device may contain a machine learning model or algorithm to determine the level of accessibility of a website and the separate computing device may transmit the information to a client-side computing device. par. 500; According to some embodiments, the operations further include using artificial intelligence (AI) to identify in the website code the plurality of elements and applying a plurality of rules for updating the website code. The term “artificial intelligence” may include one or more machine (e.g., computer) processes based on rationality or reasoning. The processes may include reasoned decision making, knowledge representation, planning, learning, natural language processing,) Ekron/Dar fails to expressly teach parsing the content of the second version of the resource back into third text data; tokenizing the third text data to produce the third plurality of tokens; and encoding the third plurality of tokens into the third plurality of vectors, the third plurality of vectors being input to the first machine learning model. However, D’Agostino teaches parsing the content of the second version of the resource back into third text data; tokenizing the third text data to produce the third plurality of tokens; and encoding the third plurality of tokens into the third plurality of vectors, the third plurality of vectors being input to the first machine learning model. It would have been obvious to one of ordinary skill in the art before effective filing date of the claimed invention to modify the machine learning models as taught by Ekron/Dar to analyze input via transformer model as taught by D’Agostino to provide the benefit of improving the accuracy and relevancy of the model results when determining the contextual meaning of content in effort to better determine user intent. Claim 20 depends on claim 19: Ekron/Dar/D’Agostino teaches wherein the first machine learning model is configured to combine the fourth plurality of vectors and the third plurality of vectors to produce a fifth plurality of vectors, the fifth plurality of vectors corresponding to input used to generate the third version of the resource. (e.g., combining word associated vectors to create a sentence associated vector D’Agostino; par. 138; The text values may be converted into a vector via execution of an LLM 650 or the like which embeds the text values into a vector 652. For example, the LLM 650 may be a transformer neural network with an encoder/decoder framework which can embed a block of text into a single vector. Here, the LLM 650 may convert a block of text, such as a sentence, phrase, combination of words, word, or the like, into a multi-dimensional vector. ) Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gutierrez; Andres Eduardo US 20240419706 A1 Par. 711; The large language models and machine-learning models of the PRE may be managed via the SKL Library of Verbs, Nouns, User Interfaces, and/or other types of schemas and data (e.g., prompts, data sets, models, etc.). The large language model can be trained by the PRE to use certain Verbs/Nouns/User Interfaces/etc. known in the SKL Library, and/or to process the user's input to search for potentially relevant Verbs from the library in real-time and then choose what Verb or Verbs from the SKL Library to utilize to retrieve relevant data to present in a personalized response to the user. BOYD, C B et al. CN 119895456 A The examples of the present disclosure provide a variety of technical effects, benefits and/or improvements in computing techniques and artificial intelligence techniques involving the use of machine learning algorithms to generate new data, such as images, audio, text, video, or other types of media. The techniques described herein improve the use of a generated model by improving the quality of the generated content. The quality of the generated content is specifically customized for the entity by using data extracted from web resources of the entity (e.g., a company, a user). For example, by using more content-related data, the system improves the performance of the generated model. In addition, the system utilizes better training techniques by developing more efficient and more efficient training techniques for the entity (e.g., based on data extracted from the web resources of the entity) to reduce the time and resources required to train the model. In addition, the system may combine user feedback and provide feedback to the generated model via enhanced learning or active learning, which may help the model to learn from user preferences and improve over time. In addition, the present disclosure may reduce processing by reducing the number of manual inputs provided by the user and by reducing the number of interface screens that must be obtained, loaded, interacted, and updated therewith. For example, the user may only need to enter the web site of the website, and the system may automatically extract content from the website and automatically generate content items for the user. Saraee; Elham et al. 20240282079 A1 Par. 957; The machine learning model of the search engine 1918 can be or include a search engine machine learning model. The machine learning model can be configured to analyze the one or more keywords and/or images received in a search from a computing device to determine an intent of the search. In doing so, the machine learning model can tokenize the one or more keywords and/or images, stem the words or tokenize, and/or perform other linguistic techniques. The machine learning model can convert the web pages (e.g., the documents of the web pages) stored in the page database 1929 as well as the keywords and/or images or words or tokens (e.g., stemmed words or tokens) generated from the keywords and/or images into numerical feature vectors. Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY ORR whose telephone number is (571)270-1308. The examiner can normally be reached 9AM-5PM EST M-F. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Adam Queler can be reached at (571)272-4140. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HENRY ORR/Primary Examiner, Art Unit 2172 Application/Control Number: 18/736,999 Page 2 Art Unit: 2172 Application/Control Number: 18/736,999 Page 3 Art Unit: 2172 Application/Control Number: 18/736,999 Page 4 Art Unit: 2172 Application/Control Number: 18/736,999 Page 5 Art Unit: 2172 Application/Control Number: 18/736,999 Page 6 Art Unit: 2172 Application/Control Number: 18/736,999 Page 7 Art Unit: 2172 Application/Control Number: 18/736,999 Page 8 Art Unit: 2172 Application/Control Number: 18/736,999 Page 9 Art Unit: 2172 Application/Control Number: 18/736,999 Page 10 Art Unit: 2172 Application/Control Number: 18/736,999 Page 11 Art Unit: 2172 Application/Control Number: 18/736,999 Page 12 Art Unit: 2172 Application/Control Number: 18/736,999 Page 13 Art Unit: 2172 Application/Control Number: 18/736,999 Page 14 Art Unit: 2172 Application/Control Number: 18/736,999 Page 15 Art Unit: 2172 Application/Control Number: 18/736,999 Page 16 Art Unit: 2172 Application/Control Number: 18/736,999 Page 17 Art Unit: 2172 Application/Control Number: 18/736,999 Page 18 Art Unit: 2172 Application/Control Number: 18/736,999 Page 19 Art Unit: 2172 Application/Control Number: 18/736,999 Page 20 Art Unit: 2172 Application/Control Number: 18/736,999 Page 21 Art Unit: 2172 Application/Control Number: 18/736,999 Page 22 Art Unit: 2172 Application/Control Number: 18/736,999 Page 23 Art Unit: 2172 Application/Control Number: 18/736,999 Page 24 Art Unit: 2172 Application/Control Number: 18/736,999 Page 25 Art Unit: 2172 Application/Control Number: 18/736,999 Page 26 Art Unit: 2172
Prosecution Insights