CTNF 18/477,804 CTNF 99772 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. Specification 06-31 AIA The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification. Priority 02-09 AIA Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows: The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc. , 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994). The disclosure of the prior-filed application, Provisional Application No. 63/491,499, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Claims 2, 10, and 18 are not supported by the disclosure of the prior-filed application, as it does not recite graphs, nodes, or edges. Claim 9 is not supported by the disclosure of the prior-filed application, as it does not recite tokens. Claim 13 is not supported by the disclosure of the prior-filed application, as it does not recite a loss function. Claim 14 is not supported by the disclosure of the prior-filed application, as it does not recite parameters, or equivalent terminology such as weights or biases. 07-30-03-h AIA Claim Interpretation Examiner finds the following paragraphs from the instant application’s specification to be relevant for interpretation of terms that are common in the claims. [0042] “As used herein, in some cases, a ‘user experience platform’ includes a set of creative, analytics, social, advertising, media optimization, targeting, Web experience management, journey orchestration and content management tools. In some cases, a user experience platform comprises one or more ANNs trained to generate content. In some cases, a user experience platform provides the user interface. In some cases, the user experience platform communicates with a database. In some cases, the user experience platform comprises the database”. [0043] “As used herein, a ‘prompt’ refers to an input to a machine learning model. In some cases, the prompt includes the context. In some cases, the prompt includes an instruction to generate a user journey based on the context. In some cases, the prompt includes text provided by a content provider. In some cases, the prompt includes an instruction to generate the user journey according to one or more of the context, the text provided by the content provider, and the instruction. In some cases, one or more of the text provided by the content provider and the instruction includes natural language. As used herein, ‘natural language’ refers to any language that has emerged through natural use.” [0047] “As used herein, in some cases, ‘content’ includes any form of media, including goods, services, physically tangible media, and the like, and digital content, including media such as text, audio, images, video, or a combination thereof. In some cases, content includes text, such as descriptions, summaries, insights, opportunities, structured queries, campaign briefs, user journey outlines, copy, subject headlines, hashtags, text for inclusion in an image, etc. In some cases, content includes instructions or code (such as a macro) that is executed by the user experience platform to retrieve and/or generate content, such as an image, a chart, a slide presentation, a video, audio, etc. In some cases, content includes instructions or code that is executed by the user experience platform to display content or a representation of the content via the user interface. As used herein, a “content element” refers to a discrete item of content or a portion of the content.” [0052] “As used herein, in some cases, a ‘user journey’ refers to a description of a plan for providing content to one or more users identified by the user journey during a content distribution campaign. As used herein, in some cases, a ‘touchpoint’ refers to an interaction with a user. In some cases, the user journey includes a set of planned touchpoints in which content is provided to the user. In some cases, a touchpoint of the set of planned touchpoints is planned according to one or more of a period of time, an interaction of the user with a content channel (such as a visit to a physical location or a digital content channel such as a social media feed), or an occurrence of a previous touchpoint. In some cases, a user journey includes a text description identifying one or more of a user segment, a content distribution channel, a content element, and a touchpoint. In some cases, a user journey includes instructions or code executable by a user experience platform (such as a macro) for generating a representation of the user journey (such as a graph). In some cases, the user journey includes instructions for displaying the representation of the user journey via a user interface.” [0053] “As used herein, a ‘content distribution campaign’, ‘communication campaign’, or ‘campaign’ refers to a coordinated distribution of content through one or more content channels in order to achieve one or more goals, such as a number of product purchases, a number of content views, a number of sign-ups, etc. As used herein, an ‘element of a content distribution campaign’ refers to any information included in or associated with a content distribution campaign (e.g., an identification of a user segment, a communication channel, a campaign objective, a content element, etc.).” Claim Rejections - 35 USC § 101 07-04 AIA 07-04-01 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a No therefor, subject to the conditions and requirements of this title. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more. Regarding claim 1, Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?” Yes, the claim is directed towards a process. Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”: The limitation of generating, using a machine learning model, a user journey based on the prompt, wherein the user journey includes at least one touchpoint for the content distribution campaign; recites a commercial interaction of organizing an advertising/marketing campaign, which is a method of organizing human activity, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”: The limitation of obtaining, by a user experience platform, a prompt describing an element of a content distribution campaign; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g). The limitation of providing, by the user experience platform, digital content to a user corresponding to the at least one touchpoint based on the user journey recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g). Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”: The limitation of receiving a planning domain including lifted action models obtaining, by a user experience platform, a prompt describing an element of a content distribution campaign; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions. The limitation of providing, by the user experience platform, digital content to a user corresponding to the at least one touchpoint based on the user journey; recites transmitting data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions. Therefore, claim 1 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 2, Claim 2 adds the additional limitation to claim 1: the user journey includes a plurality of touchpoints connected by a plurality of edges recites further detail on the user journey that is generated, without changing that generation of a user journey amounts to the abstract idea of commercial interaction via organizing a marketing campaign, regardless of if it’s performed on a generic computer. Therefore, claim 2 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 3, Claim 3 adds the additional limitation to claim 2: each of the plurality of touchpoints corresponds to a stage of the content distribution campaign recites further detail on the touchpoints in the user journey that is generated, without changing that generation of a user journey amounts to the abstract idea of commercial interaction via organizing a marketing campaign, regardless of if it’s performed on a generic computer. Therefore, claim 3 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 4, Claim 4 adds the additional limitation to claim 1: the machine learning model is trained using training data including a plurality of user journeys recites mere instructions to apply training and training data to a generic machine learning model, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 4 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 5, Claim 5 adds the additional limitations to claim 1: simulating, using the machine learning model, one or more instances of the user journey; recites an evaluation of how a user journey might perform if practically implemented, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. and generating, using the machine learning model, one or more predicted performance values based on the simulation recites a judgement of a predicted performance of a user journey based on a simulation, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. Therefore, claim 5 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 6, Claim 6 adds the additional limitation to claim 5: updating, by the machine learning model, the simulation based on the one or more predicted performance values recites an evaluation of a simulation based on predicted performance, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. Therefore, claim 6 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 7, Claim 7 adds the additional limitations to claim 5: identifying, by the machine learning model, a user journey path, recites an evaluation of a user journey to determine a user journey path, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. wherein the simulation is based on the user journey path recites further detail on the simulation of a user journey, without changing that simulation of a user journey amounts to the abstract idea of evaluating potential performance of a user journey, regardless of if it’s performed on a generic computer. Therefore, claim 7 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 8, Claim 8 adds the additional limitation to claim 5: the simulation is based on one or more user attributes recites further detail on the simulation of a user journey, without changing that simulation of a user journey amounts to the abstract idea of evaluating potential performance of a user journey, regardless of if it’s performed on a generic computer. Therefore, claim 8 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 9, Claim 9 adds the additional limitations to claim 1: generating the user journey comprises encoding the prompt to obtain a sequence of input tokens … recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites tokenization, which is well-understood, routine, and conventional, see Tekgul “Tokenization and Tokenizers for Machine Learning”: (Tekgul Pg. 3) “Tokenization is the first step in preprocessing text data for machine learning and NLP tasks” and (Tekgul Pg. 8) “Tokenization plays a crucial role in generative AI and large language models like ChatGPT. These models are trained on large amounts of text data and use tokenization to break the text into individual tokens, or words. These tokens are then embedded into vector representations, allowing the model to capture the meaning of the words in the text and generate new text that is semantically similar to the input”. generating the user journey comprises…and generating a sequence of output tokens based on the sequence of input tokens, recites the mere extra-solution activity of data outputting, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites tokenization, which is well-understood, routine, and conventional, see Tekgul “Tokenization and Tokenizers for Machine Learning”: (Tekgul Pg. 3) “Tokenization is the first step in preprocessing text data for machine learning and NLP tasks” and (Tekgul Pg. 8) “Tokenization plays a crucial role in generative AI and large language models like ChatGPT. These models are trained on large amounts of text data and use tokenization to break the text into individual tokens, or words. These tokens are then embedded into vector representations, allowing the model to capture the meaning of the words in the text and generate new text that is semantically similar to the input”. wherein the user journey is based on the sequence of output tokens recites further detail on the user journey that is generated, without changing that generation of a user journey amounts to the abstract idea of commercial interaction via organizing a marketing campaign, regardless of if it’s performed on a generic computer. Therefore, claim 9 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 10, Claim 10 adds the additional limitations to claim 1: displaying, by the user experience platform, a graph representing the user journey including a node corresponding to the at least one touchpoint recites mere instructions to apply displaying of a graph to represent a user journey, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 10 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 11, Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?” Yes, the claim is directed towards a process. Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”: The limitation of … generate a user journey based on a prompt recites a commercial interaction of organizing an advertising/marketing campaign, which is a method of organizing human activity, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”: The limitation of initializing, by a training component, a machine learning model; , in light of the specification at ([0186]) “In some cases, initializing the machine learning model comprises obtaining a pre-trained machine learning model. In some cases, the training component retrieves the pre-trained machine learning model (e.g., parameters for a pre-trained machine learning model) from a database (such as the database described with reference to FIG. 1)”, recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g). The limitation of obtaining, by the training component, training data including user journey data; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g). The limitation of training, by the training component using the training data, the machine learning model to generate … recites mere instructions to apply generic training to a generic machine learning model, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f). Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”: The limitation of initializing, by a training component, a machine learning model; , in light of the specification at ([0186]) “In some cases, initializing the machine learning model comprises obtaining a pre-trained machine learning model. In some cases, the training component retrieves the pre-trained machine learning model (e.g., parameters for a pre-trained machine learning model) from a database (such as the database described with reference to FIG. 1)”, recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions. The limitation of obtaining, by the training component, training data including user journey data; recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions. The limitation of training, by the training component using the training data, the machine learning model to generate … recites mere instructions to apply generic training to a generic machine learning model, which is not significantly more than any recited judicial exceptions, MPEP 2106.05(f). Therefore, claim 11 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 12, Claim 12 adds the additional limitations to claim 1: initializing the machine learning model comprises obtaining a pre-trained machine learning model; recites the mere extra-solution activity of data gathering, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(g), and which recites receiving data over a network, which is well-understood, routine, and conventional, MPEP 2106.05(d).II., example (i) of WURC computer functions. and training the machine learning model comprises fine-tuning the pre-trained machine learning model recites mere instructions to apply fine-tuning to a generic machine learning model, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 12 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 13, Claim 13 adds the additional limitations to claim 1: generating, by the machine learning model, a predicted touchpoint; recites an evaluation of predicting a touchpoint, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. comparing, by the training component, the predicted touchpoint to a ground-truth touchpoint of the user journey data; recites an evaluation of a predicted touchpoint and a ground-truth touchpoint, which is a mental process, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. and computing, by the training component, a loss function based on the comparison, recites a mathematical calculation of a loss function, which is a mathematical concept, which is an abstract idea. wherein the training is based on the loss function recites mere instructions to apply a generic loss function to generic training, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 13 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 14, Step 1 - “Is the claim to a process, machine, manufacture or composition of matter?” Yes, the claim is directed towards a machine. Step 2A, Prong 1 - “Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea?”: … generate a user journey based on a prompt, wherein the user journey includes at least one touchpoint for a content distribution campaign recites a commercial interaction of organizing an advertising/marketing campaign, which is a method of organizing human activity, which is an abstract idea, regardless of if it’s performed on a generic computer or using a generic machine learning model. Step 2A, Prong 2 - “Does the claim recite additional elements that integrate the judicial exception into a practical application?”: one or more processors; recites mere instructions to apply generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f). one or more memory components storing instructions executable by the one or more processors; recites mere instructions to apply generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f). and a machine learning model comprising parameters stored in the one or more memory components and trained to generate … recites mere instructions to apply generic training and a generic machine learning model, which does not integrate the exception into a practical application, MPEP 2106.05(d) and 2106.05(f). Step 2B - “Does the claim recite additional elements that amount to significantly more than the judicial exception?”: one or more processors; recites mere instructions to apply generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(f). one or more memory components storing instructions executable by the one or more processors; recites mere instructions to apply generic computer components, which does not integrate the exception into a practical application, MPEP 2106.05(f). and a machine learning model comprising parameters stored in the one or more memory components and trained to generate … recites mere instructions to apply generic training and a generic machine learning model, which is not significantly more than any recited judicial exceptions, MPEP 2106.05(f). Therefore, claim 14 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 15, Claim 15 adds the additional limitation to claim 14: a user experience platform configured to generate digital content corresponding to the at least one touchpoint based on the user journey recites mere instructions to apply a user experience platform to generate content for a user journey, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 15 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 16, Claim 16 adds the additional limitation to claim 14: the machine learning model comprises a transformer architecture recites further detail on the generic machine learning model that generates a user journey, without changing that the machine learning model is merely applied to perform an abstract idea, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 16 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 17, Claim 17 adds the additional limitation to claim 14: the machine learning model comprises a large language model recites further detail on the generic machine learning model that generates a user journey, without changing that the machine learning model is merely applied to perform an abstract idea, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 17 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 18, Claim 18 adds the additional limitation to claim 14: a user interface configured to display a graph representing the user journey including a node corresponding to the at least one touchpoint recites mere instructions to apply a user interface and displaying of a graph to represent a user journey, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 18 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 19, Claim 19 adds the additional limitation to claim 14: the machine learning model is further trained to simulate one or more instances of the user journey and to generate one or more predicted performance values based on the simulation recites an evaluation of how a user journey might perform if practically implemented, and a judgement of that performance, which are mental processes, which are abstract ideas, regardless of if they’re performed on a generic computer or using a generic machine learning model. Therefore, claim 19 is found to be ineligible subject matter under 35 U.S.C. 101. Regarding claim 20, Claim 20 adds the additional limitation to claim 14: a training component configured to train the machine learning model to generate a user journey recites mere instructions to apply a generic training component and training for generating a user journey, which does not integrate the exceptions into a practical application, and is not significantly more than any recited judicial exceptions, MPEP 2106.05(d) and 2106.05(f). Therefore, claim 20 is found to be ineligible subject matter under 35 U.S.C. 101. Prior Art The following references are used for prior art claim rejections: Parasnis et al. (U.S. Patent No. 12,411,893) Terragni and Hassani “Analyzing Customer Journey with Process Mining: from Discovery to Recommendations” Harbich et al. “Discovering Customer Journey Maps using a Mixture of Markov Models” Tran (U.S. Patent Application Publication No. 2022/0237368) Hassani and Habets “Predicting Next Touch Point in a Customer Journey: a Use Case in Telecommunication” Claim Rejections - 35 USC § 102 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-12-aia AIA (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 07-15-03-aia AIA Claim s 1, 4, 11, 12, 14-17, and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Parasnis et al. (U.S. Patent No. 12,411,893), hereinafter Parasnis . Regarding claim 1, Parasnis teaches A method for content distribution, comprising: obtaining, by a user experience platform, ((Parasnis Col. 3, lines 27-38) “The present invention relates to a system and method for creating and optimizing highly personalized cross-channel content for marketing campaigns. The system, referred to as Feed Generator (FG) or Content Alchemist, utilizes Generative Artificial Intelligence (GAI) to proactively generate multi-modal content (text copy, images, social media posts, blog posts, emails, audio, videos, etc.) that can be used in marketing campaigns. Additionally, powerful tracking tools are provided to monitor content performance in real-time and proactively suggest optimizations to enhance revenue generation and key performance indicators such as engagement, clicks, and conversions”) a prompt describing an element of a content distribution campaign; ((Parasnis Col. 8, lines 16-18) “FIG. 5 is an example of a UI 502 for configuring the campaign goal. The UI 502 includes a field 504 for entering the campaign goal, e.g., selling new Cecilie apple juice”, Parasnis Fig. 5 shows that a prompt describing the goal of the campaign is entered) PNG media_image1.png 618 982 media_image1.png Greyscale generating, using a machine learning model, a user journey based on the prompt, ((Parasnis Col. 5, lines 7-15) “A journey defines the execution of a plan associated with a project (e.g., send an email to the user about a specific product, wait for three days, and show an advertisement of the product in the user's Instagram feed). The journey includes one or more actions, and each action defines a channel, content to be presented, and one or more rules defining when to present the content. Customer Journeys is one use case for feeds, but the content from a feed can also be used with other communication automation tools”, (Parasnis Col. 22, lines 52-63) “The following points provide a high-level overview of the operational process of Retrieval Augmented Planning and Generation, utilizing an agent framework: 1. Receive a feed creation request articulated in natural language…3. The CGS creates a plan of action to accomplish the task at hand. This is done by employing a generative AI model, into which the pertinent tool and metadata information are input”) wherein the user journey includes at least one touchpoint for the content distribution campaign; ((Parasnis Col. 5, lines 10-13) “The journey includes one or more actions, and each action defines a channel, content to be presented, and one or more rules defining when to present the content”, (Parasnis Col. 39, lines 30-32) “The channel refers to the media that will be used for the communication, such as email, texting, social media posts, advertisements”, media for communication is a touchpoint) and providing, by the user experience platform, digital content to a user corresponding to the at least one touchpoint based on the user journey ((Parasnis Col. 5, lines 4-14) “The Feed Generator (FG), also referred to herein as Alchemist, is an application for creating and managing content and feeds…The journey includes one or more actions, and each action defines a channel, content to be presented, and one or more rules defining when to present the content. Customer Journeys is one use case for feeds”) Regarding claim 4, Parasnis teaches The method of claim 1, wherein: the machine learning model is trained using training data ((Parasnis Col. 41, lines 52-57) “During training 2714, the ML program, also referred to as ML algorithm or ML tool, analyzes the training data 2712 based on identified features 2702 and configuration parameters defined for the training. The result of the training 2714 is the ML model 2716, which is capable of taking inputs to produce assessments”) including a plurality of user journeys ((Parasnis Col. 40, lines 41-42) “The training data 2712 comprises examples of values for the features 2702”, (Parasnis Col. 40, lines 60-67) “In one example, the features 2702 may be of different types. They may include data from one or more of campaign 2703, audience 2704, author 2705, content performance 2706, channel 2707, demographics 2708, brand 2709, user history 2710 (e.g., content selected by the user), etc. The layout is another feature that may be considered, e.g., for communications with the same content, which layout per formed better”, data from campaigns and associated elements of a campaign correspond to user journeys) Regarding claim 11, Parasnis teaches A method for content distribution, comprising: initializing, by a training component, a machine learning model; ((Parasnis Col. 24, lines 58-65) “Often, the companies that create the GAI models make the GAI models available to users who can apply them to generate the desired content based on a GAI prompt 2310 provided to the GAI model 2312. Users can utilize the GAI model 2312 as provided by the vendor or can optionally fine-tune 2314 the GAI model 2312 with their user data to adjust the parameters of the GAI model 2312 in order to improve performance on a specific task or domain”, a GAI model being provided by a vendor is being initialized) obtaining, by the training component, training data including user journey data; ((Parasnis Col. 41, lines 52-57) “During training 2714, the ML program, also referred to as ML algorithm or ML tool, analyzes the training data 2712 based on identified features 2702 and configuration parameters defined for the training. The result of the training 2714 is the ML model 2716, which is capable of taking inputs to produce assessments”, (Parasnis Col. 40, lines 41-42) “The training data 2712 comprises examples of values for the features 2702”, (Parasnis Col. 40, lines 60-67) “In one example, the features 2702 may be of different types. They may include data from one or more of campaign 2703, audience 2704, author 2705, content performance 2706, channel 2707, demographics 2708, brand 2709, user history 2710 (e.g., content selected by the user), etc. The layout is another feature that may be considered, e.g., for communications with the same content, which layout per formed better”, data from campaigns and associated elements of a campaign correspond to user journeys) and training, by the training component using the training data, the machine learning model ((Parasnis Col. 38, lines 63-66) “Operation 2602 is for accessing campaign information and performance data. As described above with reference to FIG. 23, there are two ways to improve the outputs: by improving the GAI prompt 2310 or by fine-tuning 2314 the GAI model with user data”, fine tuning is training) to generate a user journey based on a prompt (Parasnis Col. 22, lines 52-63) “The following points provide a high-level overview of the operational process of Retrieval Augmented Planning and Generation, utilizing an agent framework: 1. Receive a feed creation request articulated in natural language…3. The CGS creates a plan of action to accomplish the task at hand. This is done by employing a generative AI model, into which the pertinent tool and metadata information are input”, (Parasnis Col. 5, lines 13-14) “Customer Journeys is one use case for feeds”) Regarding claim 12, Parasnis teaches The method of claim 11, wherein: initializing the machine learning model comprises obtaining a pre-trained machine learning model; ((Parasnis Col. 24, lines 61-62) “Users can utilize the GAI model 2312 as provided by the vendor”, (Parasnis Col. 24, lines 52-53) “The GAI models generate items of different types, such as GAI models for creating text (e.g., GPT-4”, the ‘GPT’ in GPT-4 stands for Generative Pre-trained Transformer and is a pre-trained machine learning model) and training the machine learning model comprises fine-tuning the pre-trained machine learning model ((Parasnis Col. 24, lines 61-65) “Users can utilize the GAI model 2312 as provided by the vendor or can optionally fine-tune 2314 the GAI model 2312 with their user data to adjust the parameters of the GAI model 2312 in order to improve performance on a specific task or domain”) Regarding claim 14, Parasnis teaches An apparatus for content distribution, comprising: one or more processors; ((Parasnis Col. 43, lines 31-33) “Another general aspect is for a system that includes a memory comprising instructions and one or more computer processors”) one or more memory components storing instructions executable by the one or more processors; (Parasnis Col. 43, lines 31-35) “Another general aspect is for a system that includes a memory comprising instructions and one or more computer processors. The instructions, when executed by the one or more computer processors, cause the one or more computer processors to perform operations”) and a machine learning model comprising parameters stored in the one or more memory components and trained to generate a user journey ((Parasnis Col. 42, lines 6-8) “The ML algorithms usually explore many possible functions and parameters before finding what the ML algorithms identify to be the best correlations within the data;” (Parasnis Col. 43, lines 35-45) “operations comprising:…proactively generating, using one or more generative artificial intelligence (GAI) tools, items based on the campaign parameters, the items being multimodal, wherein the created items are included in a feed; providing a user interface (UI) with an option to select one or more items of the feed for inclusion in a journey associated with the campaign”, generating items for a feed that plans a user journey corresponds to generating a user journey) based on a prompt, ((Parasnis Col. 8, lines 16-18) “FIG. 5 is an example of a UI 502 for configuring the campaign goal. The UI 502 includes a field 504 for entering the campaign goal, e.g., selling new Cecilie apple juice”, Parasnis Fig. 5 shows that a prompt describing the goal of the campaign is entered) wherein the user journey includes at least one touchpoint for a content distribution campaign ((Parasnis Col. 5, lines 10-13) “The journey includes one or more actions, and each action defines a channel, content to be presented, and one or more rules defining when to present the content”, (Parasnis Col. 39, lines 30-32) “The channel refers to the media that will be used for the communication, such as email, texting, social media posts, advertisements”, media for communication is a touchpoint) Regarding claim 15, Parasnis teaches The apparatus of claim 14, further comprising: a user experience platform configured to generate digital content ((Parasnis Col. 3, lines 27-38) “The present invention relates to a system and method for creating and optimizing highly personalized cross-channel content for marketing campaigns. The system, referred to as Feed Generator (FG) or Content Alchemist, utilizes Generative Artificial Intelligence (GAI) to proactively generate multi-modal content (text copy, images, social media posts, blog posts, emails, audio, videos, etc.) that can be used in marketing campaigns. Additionally, powerful tracking tools are provided to monitor content performance in real-time and proactively suggest optimizations to enhance revenue generation and key performance indicators such as engagement, clicks, and conversions”) corresponding to the at least one touchpoint based on the user journey ((Parasnis Col. 5, lines 4-14) “The Feed Generator (FG), also referred to herein as Alchemist, is an application for creating and managing content and feeds…The journey includes one or more actions, and each action defines a channel, content to be presented, and one or more rules defining when to present the content. Customer Journeys is one use case for feeds”) Regarding claim 16, Parasnis teaches The apparatus of claim 14, wherein: the machine learning model comprises a transformer architecture ((Parasnis Col. 24, lines 52-53) “The GAI models generate items of different types, such as GAI models for creating text (e.g., GPT-4”, the ‘GPT’ in GPT-4 stands for Generative Pre-trained Transformer and is based on a transformer architecture) Regarding claim 17, Parasnis teaches The apparatus of claim 14, wherein: the machine learning model comprises a large language model ((Parasnis Col. 21, lines 37-45) “AI agents, also referred to as workers 1906, are used to generate and curate content. In one example, the workers 1906 are built with orchestration on SOTA LLMs, and the image generation models continually use feedback based on content insights to generate new content. The content is built with meta prompts and agentic workflows to drive content for a feed. The meta prompts include plans and guidelines to generate new content, like an email journey for a new customer onboarding”) Regarding claim 20, Parasnis teaches The apparatus of claim 14, further comprising: a training component configured to train the machine learning model (Parasnis Col. 38, lines 63-66) “Operation 2602 is for accessing campaign information and performance data. As described above with reference to FIG. 23, there are two ways to improve the outputs: by improving the GAI prompt 2310 or by fine-tuning 2314 the GAI model with user data”, fine tuning is training) to generate a user journey ((Parasnis Col. 22, lines 52-63) “The following points provide a high-level overview of the operational process of Retrieval Augmented Planning and Generation, utilizing an agent framework: 1. Receive a feed creation request articulated in natural language…3. The CGS creates a plan of action to accomplish the task at hand. This is done by employing a generative AI model, into which the pertinent tool and metadata information are input”, (Parasnis Col. 5, lines 13-14) “Customer Journeys is one use case for feeds”) 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. 07-21-aia AIA Claim s 2, 3, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Parasnis, in view of Terragni and Hassani “Analyzing Customer Journey with Process Mining: from Discovery to Recommendations”, hereinafter Terragni . Regarding claim 2, Parasnis teaches The method of claim 1, wherein: Terragni teaches the following further limitation that Parasnis does not teach: the user journey includes a plurality of touchpoints connected by a plurality of edges ((Terragni Pg. 1) “analyzing the customer journey has become a major issue. For this reason, customer journey maps have become very popular. These diagrams represent graphically the story of the customer, showing all the touch-points (s)he comes into contact with”, Terragni Pg. 1, Fig. 1 shows that the touchpoints are connected by edges) PNG media_image2.png 527 853 media_image2.png Greyscale At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Terragni by taking the method of claim 1, including a user journey, taught by Parasnis, and including that the user journey contains touchpoints connected by edges, taught by Terragni, as Terragni teaches (Terragni Pg. 1) “Analyzing how customers navigate their journey is critical to assess and deliver their needs. Nowadays, thanks to the data driven transformation that most of the companies are facing, analyzing the customer journey has become a major issue. For this reason, customer journey maps have become very popular. These diagrams represent graphically the story of the customer, showing all the touch-points (s)he comes into contact with. They allow the companies to have an overall idea of how the customer behaves, to compare this behavior with what they expect, and to make the user experience more personalized and unique”. Such a combination would be obvious. Regarding claim 3, Parasnis and Terragni jointly teach The method of claim 2, wherein: Terragni further teaches: each of the plurality of touchpoints corresponds to a stage of the content distribution campaign (Terragni Pg. 1, Fig. 1 shows touchpoints on a website with stages of starting page, 1st interaction, and 2nd interaction) At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Parasnis and Terragni for the parent claim of claim 3, claim 2. No new embodiments are introduced, so the reason to combine is the same as for the parent claim. Regarding claim 10, Parasnis teaches The method of claim 1, further comprising: displaying, by the user experience platform, a [graph] representing the user journey … (Parasnis Fig. 6 shows a user experience platforming displaying a graphical representation of a user journey, Parasnis does not explicitly teach displaying a graph representing a user journey) PNG media_image3.png 847 1137 media_image3.png Greyscale Terragni teaches the following further limitation that Parasnis does not teach: displaying…a graph representing the user journey including a node corresponding to the at least one touchpoint (Terragni Pg. 1, Fig. 1 shows a Google Analytics user interface displaying a graph with a user journey over several touchpoints, which are nodes in the graph) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Terragni by taking the method of claim 1, including displaying a graphical representation of a user journey, taught by Parasnis, and including that the display is a graph including nodes corresponding to touchpoints, taught by Terragni, for the same reason given to combine for claim 2. Such a combination would be obvious. Regarding claim 18, Parasnis teaches The apparatus of claim 14, further comprising: a user interface configured to display a [graph] representing the user journey … (Parasnis Fig. 6 shows a user experience platforming displaying a graphical representation of a user journey, Parasnis does not explicitly teach displaying a graph representing a user journey) Terragni teaches the following further limitation that Parasnis does not teach: a user interface configured to display a graph representing the user journey including a node corresponding to the at least one touchpoint (Terragni Pg. 1, Fig. 1 shows a Google Analytics user interface displaying a graph with a user journey over several touchpoints, which are nodes in the graph) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Terragni by taking the apparatus of claim 14, including displaying a graphical representation of a user journey, taught by Parasnis, and including that the display is a graph including nodes corresponding to touchpoints, taught by Terragni, for the same reason given to combine for claim 2. Such a combination would be obvious . 07-21-aia AIA Claim s 5-8, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Parasnis, in view of Harbich et al. “Discovering Customer Journey Maps using a Mixture of Markov Models”, hereinafter Harbich, further in view of Hassani and Habets “Predicting Next Touch Point in a Customer Journey: a Use Case in Telecommunication”, hereinafter Hassani . Regarding claim 5, Parasnis teaches The method of claim 1, further comprising: Harbich teaches the following further limitations that Parasnis does not teach: simulating, using the [machine learning] model, ((Harbich Pg. 2) “Our model for event sequences is a Markov model”, a Markov model is a statistical model but not a machine learning model) one or more instances of the user journey; ((Harbich Pg. 4) “Feature 3. The next most probable event of an incomplete journey can be computed on the fly by checking the most probable transition parameter under its most likely model (see Fig. 3). Moreover, one can also use the transition matrix to generate new journeys that have never been seen but that are still likely to happen according to the mixture of models. Generating such data journeys can be useful for training or analysis purposes”) and generating, using the [machine learning] model, one or more predicted performance values based on the simulation ((Harbich Pg. 2) “Our model for event sequences is a Markov model: according to the model, each sequence is generated by choosing the first event in the sequence, X 1 , with probability P(X 1 = i) = f i . The probability of every successive event in the sequence depends only on the event preceding it…By choosing the parameters f and T to match the probability of the first event and the probability of event transitions for a group of observed sequences, the model captures our assumption that journeys belonging to the same group share the same probability of transitioning between events”, the probabilities that groups will go from one event to another event in a journey based on a simulation corresponds to predicted performance values) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Harbich by taking the method of claim 1, including a user journey, taught by Parasnis, and including simulating instances of the user journey to predict performance values using a model, taught by Harbich, as Harbich teaches (Harbich Pg. 6) “one can also use the transition matrix to generate new journeys that have never been seen but that are still likely to happen according to the mixture of models. Generating such data journeys can be useful for training or analysis purposes”, that is, that simulation of user journeys is useful for analysis, as well as potentially creating new data for training a machine learning model. Such a combination would be obvious. Hassani teaches the following limitation fragment that Harbich does not teach and more explicitly than Parasnis teaches: simulating, using the machine learning model … ((Hassani Pg. 3) “We want to predict the activity a of event e i+1 given that we are at event e i belonging to the same customer journey c j and having the timestamp t i < t i+1 ”, Hassani Pg. 7, Fig. 3 shows that the various models used by Hassani experimentally are Logistic, Random Forest, XGBoost, and LSTM, which are all machine learning models) PNG media_image4.png 408 652 media_image4.png Greyscale At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis, Harbich, and Hassani by taking the method of claim 1, including a user journey, and simulating instances of the user journey to predict performance values using a model, jointly taught by Parasnis and Harbich, and using the machine learning model of Hassani substitute for the Markov model of Harbich, as the machine learning model of Hassani simulates a customer moving from one touchpoint to another touchpoint, and the Markov model of Harbich simulates a customer moving from one touchpoint to another repeatedly for an entire user journey, so the machine learning model of Hassani could be simply substituted for the Markov model of Harbich in the method of Harbich and would predictably simulate an entire user journey in the same fashion. Such a combination would be obvious. Regarding claim 6, Parasnis, Harbich, and Hassani jointly teach The method of claim 5, further comprising: Harbich further teaches: updating, by the [machine learning] model, the simulation based on the one or more predicted performance values ((Harbich Pg. 4) “Feature 4. One of the interesting features of our probabilistic-approach is the way it handles shifting customers’ behavior. For example, an incomplete journey is assigned to a Markov model with a given probability. The most probable Markov Model might change when new events are observed. Hence, it allows us to dynamically cope with shifting behaviors”, a Markov model is a statistical model, but Hassani teaches machine learning models that could perform simulation equivalently) At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Parasnis, Harbich, and Hassani for the parent claim of claim 6, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim. Regarding claim 7, Parasnis, Harbich, and Hassani jointly teach The method of claim 5, further comprising: Harbich further teaches: identifying, by the [machine learning] model, a user journey path, ((Harbich Pg. 1) “Customer Journey Maps (CJMs) respond by proposing an intelligible visualization that makes it possible to understand, discuss, or improve the main paths (i.e., journeys) in the usage of a service”, Hassani but not Harbich teaches a specifically machine learning model) wherein the simulation is based on the user journey path ((Harbich Pg. 2) “We contribute by proposing the first probabilistic approach that converts event logs in a CJM”, (Harbich Pg. 2) “Our model for event sequences is a Markov model”) At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Parasnis, Harbich, and Hassani for the parent claim of claim 7, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim. Regarding claim 8, Parasnis, Harbich, and Hassani jointly teach The method of claim 5, wherein: Harbich further teaches: the simulation is based on one or more user attributes ((Harbich Pg. 2) “The starting point for our approach is to assume that the customers belong to a small number of groups, each having a similar behavior in the sense of having the same probability of transitioning from one event to the next”, clustering users with similar behaviors into groups for the simulation corresponds to the simulation being based on user attributes) At the time of filing, one of ordinary skill in the art would have motivation to combine the method jointly taught by Parasnis, Harbich, and Hassani for the parent claim of claim 8, claim 5. No new embodiments are introduced, so the reason to combine is the same as for the parent claim. Regarding claim 19, Parasnis teaches The apparatus of claim 14, wherein: Harbich teaches the following further limitation that Parasnis does not teach: the [machine learning] model is further trained to simulate one or more instances of the user journey ((Harbich Pg. 2) “Our model for event sequences is a Markov model”, a Markov model is a statistical model but not a machine learning model, (Harbich Pg. 4) “Feature 3. The next most probable event of an incomplete journey can be computed on the fly by checking the most probable transition parameter under its most likely model (see Fig. 3). Moreover, one can also use the transition matrix to generate new journeys that have never been seen but that are still likely to happen according to the mixture of models. Generating such data journeys can be useful for training or analysis purposes”) and to generate one or more predicted performance values based on the simulation ((Harbich Pg. 2) “Our model for event sequences is a Markov model: according to the model, each sequence is generated by choosing the first event in the sequence, X 1 , with probability P(X 1 = i) = f i . The probability of every successive event in the sequence depends only on the event preceding it…By choosing the parameters f and T to match the probability of the first event and the probability of event transitions for a group of observed sequences, the model captures our assumption that journeys belonging to the same group share the same probability of transitioning between events”, the probabilities that groups will go from one event to another event in a journey based on a simulation corresponds to predicted performance values) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Harbich by taking the apparatus of claim 14, including a user journey, taught by Parasnis, and including simulating instances of the user journey using a model to predict performance values, taught by Harbich, for the same reason to combine given for claim 5. Such a combination would be obvious. Hassani teaches the following limitation fragment that Harbich does not teach and more explicitly than Parasnis teaches: the [machine learning] model is further trained to simulate … ((Hassani Pg. 3) “We want to predict the activity a of event e i+1 given that we are at event e i belonging to the same customer journey c j and having the timestamp t i < t i+1 ”, Hassani Pg. 7, Fig. 3 shows that the various models used by Hassani experimentally are Logistic, Random Forest, XGBoost, and LSTM, which are all machine learning models that must be trained) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis, Harbich, and Hassani by taking the apparatus of claim 14, including a user journey, and simulating instances of the user journey using a model to predict performance values, jointly taught by Parasnis and Harbich, and including performing the simulation using a machine learning model specifically, taught by Hassani, for the same reason to combine given for claim 5. Such a combination would be obvious . 07-21-aia AIA Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Parasnis, in view of Tran (U.S. Patent Application Publication No. 2022/0237368), hereinafter Tran . Regarding claim 9, Parasnis teaches The method of claim 1, wherein: wherein the user journey is based on the [sequence of] output [tokens] (Parasnis Fig. 2 shows a content item, which is a touchpoint, which is a component of a user journey, being based off of the output of a generative AI model, Parasnis does not teach that the generative AI model outputs a sequence of tokens specifically) PNG media_image5.png 762 1157 media_image5.png Greyscale Tran teaches the following further limitation that Parasnis does not teach: generating the [user journey] comprises encoding the prompt to obtain a sequence of input tokens ((Tran [0188]) “One embodiment uses a simple decoder-only transformer that receives both the text prompt and the drawings as a single stream of 1280 tokens”, Parasnis but not Tran teaches a user journey) and generating a sequence of output tokens based on the sequence of input tokens, ((Tran [0189]) “a transformer language model receives both the text and the inventive concept as a single stream of data containing up to 1280 tokens, and is trained using maximum likelihood to generate all of the tokens”, (Tran [0329]) “GPT2, like traditional language models, outputs one token at a time, the model to predict the next token in a sequence”) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Tran by taking the method of claim 1, including a prompt and a user journey based off of the output of a generative AI model, taught by Parasnis, and including that prompt is encoded into a sequence of tokens and in response a sequence of tokens is output, taught by Tran, as generative AI models such as large language models process input and output text as tokens in order to render text data in a numerical format suitable for processing by a machine learning model, as well as to compress the data, as is well known in the art. Such a combination would be obvious . 07-21-aia AIA Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Parasnis, in view of Hassani . Regarding claim 13, Parasnis teaches The method of claim 11, further comprising: Hassani teaches the following further limitations that Parasnis does not teach: generating, by the machine learning model, a predicted touchpoint; ((Hassani Abstract) “We propose a model to predict the customer’s next contact type (touch point)”) comparing, by the training component, the predicted touchpoint to a ground-truth touchpoint of the user journey data; (Hassani Pg. 7, Fig. 3 shows comparisons of each tested model’s F1 score, a metric for measuring the ratio of correctly determined labels to incorrectly determined labels, with respect to ground truth touchpoints) and computing, by the training component, a loss function based on the comparison, ((Hassani Pg. 6) “As our problem consists of predicting one of multiple outcomes, we need a multi-class classification model. Therefore we have to set the multi class parameter in our logistic regression to multinomial, then it will use cross-entropy loss to find the best model”, cross-entropy loss is a loss function, because the model predicts touchpoint labels, the loss is based on comparison of predicted touchpoint labels to ground truth touchpoint labels) wherein the training is based on the loss function ((Hassani Pg. 6) “We will now shortly discuss the different hyperparameters we will tune. • Gradient descent optimization algorithm: This optimization technique tries to minimize the loss function after each iteration by tweaking the weights”, tweaking weights after each iteration is part of training) At the time of filing, one of ordinary skill in the art would have motivation to combine Parasnis and Hassani by taking the method of claim 11, including a machine learning model that generates user journeys (which consists of touchpoints), taught by Parasnis, and including predicting a touchpoint with the machine learning model, taught by Hassani, as Hassani teaches: (Hassani Pg. 2) “Knowing what a customer will do next, gives the company the ability to proactively provide support to the customer. Helping the customer proactively saves time and therefore also costs, as well as increases the customer satisfaction as he/she is helped faster”, and predicting a next touchpoint accomplishes this. Additionally, training a loss function based on comparisons between predicted labels and ground truth labels is very well-known in the art, as it allows a machine learning model to gradually improve as it is presented with more data. Such a combination would be obvious . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yan et al. (U.S. Patent Application Publication No. 2019/0278378) discloses systems and methods for determining performance for touchpoints in digital content campaigns via use of neural networks. Balakrishnan et al. (U.S. Patent No. 12,346,347) discloses systems and methods for performing actions in response to events that occur as part of a user undergoing a user journey. Any inquiry concerning this communication or earlier communications from the examiner should be directed to VICTOR A NAULT whose telephone number is (703) 756-5745. The examiner can normally be reached M - F, 12 - 8. 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, Miranda Huang can be reached at (571) 270-7092. 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. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /V.A.N./Examiner, Art Unit 2124 /Kevin W Figueroa/Primary Examiner, Art Unit 2124 Application/Control Number: 18/477,804 Page 2 Art Unit: 2124 Application/Control Number: 18/477,804 Page 3 Art Unit: 2124 Application/Control Number: 18/477,804 Page 4 Art Unit: 2124 Application/Control Number: 18/477,804 Page 5 Art Unit: 2124 Application/Control Number: 18/477,804 Page 6 Art Unit: 2124 Application/Control Number: 18/477,804 Page 7 Art Unit: 2124 Application/Control Number: 18/477,804 Page 8 Art Unit: 2124 Application/Control Number: 18/477,804 Page 9 Art Unit: 2124 Application/Control Number: 18/477,804 Page 10 Art Unit: 2124 Application/Control Number: 18/477,804 Page 11 Art Unit: 2124 Application/Control Number: 18/477,804 Page 12 Art Unit: 2124 Application/Control Number: 18/477,804 Page 13 Art Unit: 2124 Application/Control Number: 18/477,804 Page 14 Art Unit: 2124 Application/Control Number: 18/477,804 Page 15 Art Unit: 2124 Application/Control Number: 18/477,804 Page 16 Art Unit: 2124 Application/Control Number: 18/477,804 Page 17 Art Unit: 2124 Application/Control Number: 18/477,804 Page 18 Art Unit: 2124 Application/Control Number: 18/477,804 Page 19 Art Unit: 2124 Application/Control Number: 18/477,804 Page 20 Art Unit: 2124 Application/Control Number: 18/477,804 Page 21 Art Unit: 2124 Application/Control Number: 18/477,804 Page 22 Art Unit: 2124 Application/Control Number: 18/477,804 Page 23 Art Unit: 2124 Application/Control Number: 18/477,804 Page 24 Art Unit: 2124 Application/Control Number: 18/477,804 Page 25 Art Unit: 2124 Application/Control Number: 18/477,804 Page 26 Art Unit: 2124 Application/Control Number: 18/477,804 Page 27 Art Unit: 2124 Application/Control Number: 18/477,804 Page 28 Art Unit: 2124 Application/Control Number: 18/477,804 Page 29 Art Unit: 2124 Application/Control Number: 18/477,804 Page 30 Art Unit: 2124 Application/Control Number: 18/477,804 Page 31 Art Unit: 2124 Application/Control Number: 18/477,804 Page 32 Art Unit: 2124 Application/Control Number: 18/477,804 Page 33 Art Unit: 2124 Application/Control Number: 18/477,804 Page 34 Art Unit: 2124 Application/Control Number: 18/477,804 Page 35 Art Unit: 2124 Application/Control Number: 18/477,804 Page 36 Art Unit: 2124
