Systems and methods for interactive ai agent-assisted dating profile creation

Abstract

Provided are systems and methods for generating and updating user profiles through conversation and interaction with an artificial intelligence (Al) agent, such as for creating Al agent- assisted user profiles where the Al agent is powered by a large language model (LLM). In certain aspects are provided systems and methods for creating dating profiles, such as using questionnaire responses, quiz answers, personal and preference information, and other user data, useful for example in methods of Al agent-assisted matchmaking and in other dating apps.

Description

[0001]

[0002] SYSTEMS AND METHODS FOR INTERACTIVE Al AGENT-ASSISTED

[0003] DATING PROFILE CREATION

[0004] FIELD OF THE INVENTION

[0005] This application claims priority from provisional application 63 / 704,479, filed October 7, 2024, and 63 / 707,744, filed October 15, 2024. The entire contents of each of these applications are incorporated herein by reference.

[0006]

[0001] The present invention broadly relates to systems and methods for generating and updating user profiles through conversation and interaction with an artificial intelligence (Al) agent, such as for creating dating profiles, useful in methods of Al agent-assisted matchmaking.

[0007] BACKGROUND OF THE INVENTION

[0008]

[0002] The current options for dating advice, coaching, and improvement are limited, especially for Millennials and Gen Z, who tend to spend more time online and have fewer in- person interactions. For example, in a survey of 2000 people between the ages of 18 and 35, over one-third responded that they feel like their friends do not have enough experience to give good dating and relationship advice. Almost one-quarter responded that, although they look to Instagram and TikTok for dating advice, they do not find it specific enough. And another 18% responded that trying to find dating advice using internet searches or online forums such as Reddit is too overwhelming. Consequently, although most Millennials and Gen Z reported that dating is hard and they want to improve, a substantial number of them are unable to do so.

[0009]

[0003] Because of the overwhelming amount of dating information already available, most do not simply want access to more information, but instead want information curated and contextualized for them. Although not before used as disclosed herein, artificial intelligence (Al) tools to provide curated information have grown in popularity, such as for learning, coaching, and health, and the majority of consumers now show interest in using and paying for such tools. Younger generations, in particular, have a heightened level of trust in technology, including Al.

[0004] In the United States, about 30% of adults have used a dating app or site at some point in their lives. For adults under 30, over half have used a dating app or site. Nevertheless, dating apps and sites suffer from poor user engagement and relatively high failure rates, despite their widespread use. Users express dissatisfaction with popular dating apps for a variety of reasons, such as from not knowing what to say, being stressed out by swiping and endless options, and not knowing how to move “from match to meet” on apps.

[0010]

[0005] Accordingly, despite an array of available dating apps, most users are unable to find or pursue satisfactory connections, including because of the lack of personalized dating advice and guidance, the overwhelming amount of non-curated options and content, and the insufficient support for turning matches into real-life connections. Indeed, over 90% of surveyed members of Gen Z expressed frustration with the currently-available dating apps.

[0011]

[0006] To meet these needs and others, taught herein are systems and methods for Al agent-assisted user profile creation, that are useful for example in dating apps as dating profiles, and which will be appreciated to have such advantages and improvements over features of currently-available dating apps as will become readily apparent in view of the disclosure below.

[0012] INCORPORATION BY REFERENCE

[0013]

[0007] Each cited patent, publication, and non-patent literature is incorporated by reference in its entirety, as if each was incorporated by reference individually, and as if each is fully set forth herein. However, no such citation should be construed as an admission that a cited reference is from an area that is analogous or directly applicable to the invention, nor should any citation be construed as an admission that a document or underlying information, in any jurisdiction, is prior art or part of the common general knowledge in the art. Provisional applications 63 / 704,479, filed October 7, 2024, and 63 / 707,744, filed October 15, 2024 are incorporated herein by reference in their entirety.

[0014] BRIEF SUMMARY OF THE INVENTION

[0015]

[0008] The following is a simplified summary of some embodiments of the invention in order to provide a basic understanding thereof. It is not an extensive overview of the invention, and is not intended to identify key or critical elements of the invention or to delineate the full scope thereof. Its purpose is to present some embodiments of the invention in a simplified form as a prelude to the more detailed description that follows. The present invention provides a system and method for creating user profiles by uniquely taking the user through a “journey” via one-to-one conversations with an artificial intelligence program / agent conducting the conversation in a user- friendly way. With user input and responses to the Al agent inquiries, coupled with hidden (nonpublished) responses and avenues for non-negotiable criteria / inputs (also hidden), a public user profile unlike that of the prior art is generated, especially for dating applications, although profiles for other uses are also contemplated.

[0016]

[0009] In some aspects are provided computer-implemented methods for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, such methods comprising: (a) receiving, via a processor, a request to generate a user profile for a user; (b) initiating, by the Al agent, a conversation with the user; (c) gathering, via the conversation, user information relevant to the user profile, wherein at least a portion of the user information is freeform text; (d) extracting, using an artificial intelligence system such as a Large Language Model (LLM), a plurality of user features from the user information; (e) generating, based on the user features, the user profile of the user. In some embodiments, the method further comprises displaying, to the user, the plurality of user features for confirmation or modification; and receiving, from the user, confirmation or modification of the user features prior to generating the user profile.

[0017]

[0010] In some further aspects are provided systems for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, such systems comprising: (a) a processor; and (b) a memory, storing instructions that, when executed by the processor, cause the system to: (i) receive a request to generate a user profile for a user; (ii) initiate, by the Al agent, a conversation with the user; (iii) gather, via the conversation, user information relevant to the user profile, wherein at least a portion of the user information is free-form text; (iv) extract, using an artificial intelligence system such as a Large Language Model (LLM), a plurality of user features from the user information; (v); and (v) generate, based on the user features, the user profile of the user. In some embodiments, the system further comprises vi) display, to the user, the plurality of user features for confirmation or modification; and (vii) receive, from the user, confirmation or modification of the user features prior to generating the user profile.

[0018]

[0011] In yet further aspects are provided non-transitory computer-readable media storing instructions that, when executed by a processor, cause a system to perform methods for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, such methods comprising: (a) receiving a request to generate a user profile for a user; (b) initiating, by the Al agent, a conversation with the user; (c) gathering, via the conversation, user information relevant to the user profile, wherein at least a portion of the user information is free-form text; (d) extracting, using an artificial intelligence system such as a Large Language Model (LLM), a plurality of user features from the user information; and (e) generating, based on the user features, the user profile of the user. In some embodiments, the method further comprises displaying, to the user, the plurality of user features for confirmation or modification and receiving, from the user, confirmation or modification of the user features prior to generating the user profile.

[0019]

[0012] In some embodiments of the methods, systems, and media, the user profile is a dating profile for the user.

[0020]

[0013] In some embodiments of the methods, systems, and media, the conversation with the user is conducted as a one-on-one messaging chat between the Al agent and the user.

[0021]

[0014] In some embodiments of the methods, systems, and media, the one-on-one messaging chat is conducted using text, voice, speech-to-text, text-to-speech, or any combination thereof.

[0022]

[0015] In some embodiments of the methods, systems, and media, the user information relevant to the user profile comprises at least two of, or all three of, demographic information, personal information, and partner preference information.

[0023]

[0016] In some embodiments of the methods, systems, and media, at least a portion of the user information is provided by the user in response to a questionnaire or a quiz, including a multiple choice quiz.

[0024]

[0017] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least three of location, political leaning, height, religion, ethnicity, home town, work, education, and communities.

[0025]

[0018] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least two, at least three, or at least four personalized takeaways for the user.

[0019] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least one partner preference summary, selected from the group of a non- negotiable, a red flag, and a nice-to-have.

[0026]

[0020] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least four, at least five, or at least six partner preference summaries from at least two or from all three of non-negotiables, red flags, and nice-to-haves.

[0027]

[0021] In some embodiments of the methods, systems, and media, the plurality of user features comprises a dating archetype for the user.

[0028]

[0022] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least one user feature that will remain hidden on the user profile after it is published.

[0029]

[0023] In some embodiments of the methods, systems, and media, the plurality of user features comprises at least one user feature that the user has the option to keep hidden on the user profile after it is published.

[0030]

[0024] In some embodiments, the methods, systems, and media further comprise sending the user profile to an administrator to review and approve the user profile before publication.

[0031]

[0025] In some embodiments, the methods, systems, and media further comprise notifying the user when the user profile is published.

[0032]

[0026] In some embodiments, the methods, systems, and media further comprise updating the user profile after publication.

[0033]

[0027] In some embodiments, the methods, systems, and media further comprise scoring the user profile on a plurality of user attributes by processing the gathered user information using a LLM, thereby producing a user profile score.

[0034]

[0028] In some embodiments, the methods, systems, and media further comprise storing the user profile score as a feature vector in a vector database.

[0029] In some embodiments of the methods, systems, and media, the user profde is adapted for use in any of a dating app, a friendship app, a social media app, a professional networking app, a fitness and health app, an e-commerce app, a streaming app, an educational app, a gaming app, an event app, a dining app, or a travel app.

[0035]

[0030] The foregoing has outlined broadly and in summary certain pertinent features of the disclosure so that the detailed description that follows may be better understood, and so the present contribution to the art can be more fully appreciated. This summary is a brief and general synopsis of only some of the disclosed aspects and embodiments, is provided solely for the benefit and convenience of the reader, and is not intended to limit in any manner the scope, or range of equivalents, to which the claims are entitled.

[0036]

[0031] Additional aspects and embodiments are described below. It will be appreciated by those in the art that all disclosed systems and methods are only exemplary, and will readily serve as a basis to modify or design other systems and methods for carrying out the same purposes, which are also within the scope of the disclosure.

[0037] BRIEF SUMMARY OF THE FIGURES

[0038]

[0032] Some exemplary aspects and embodiments of the disclosure are illustrated in the figures, which are not intended to limit the scope of the invention, but only to provide certain additional details, and in which:

[0039]

[0033] FIGS. 1-20 are exemplary representations of a graphical user interface (GUI) display of the smartphone application of some embodiments of the disclosure, as displayed on a smartphone device (each herein for shorthand, an “app screen”), wherein:

[0040]

[0034] FIG. 1 shows an app welcome screen for a sample user in accordance with an embodiment of the present invention;

[0041]

[0035] FIG. 2 shows an app screen illustrating the user interface and the user journey in accordance with an embodiment of the present invention;

[0042]

[0036] FIG. 3 shows an app screen wherein the Al agent initiates a conversation with the user in accordance with an embodiment of the present invention;

[0037] FIG. 4 shows an app screen for obtaining demographic information from the user in accordance with an embodiment of the present invention;

[0043]

[0038] FIG. 5. shows an app screen for obtaining political information from the user in accordance with an embodiment of the present invention;

[0044]

[0039] FIG. 6 shows an app screen for obtaining user information about vices in accordance with an embodiment of the present invention;

[0045]

[0040] FIG. 7 shows an app screen for obtaining relationship preferences from a user in accordance with an embodiment of the present invention;

[0046]

[0041] FIG. 8 shows an app screen for obtaining further relationship preferences from a user in accordance with an embodiment of the present invention;

[0047]

[0042] FIG. 9 shows an app screen for obtaining information from a user by an Al agent in a 1-on-l chat in accordance with an embodiment of the present invention;

[0048]

[0043] FIG. 10 shows an app screen of an initial user profile in accordance with an embodiment of the present invention;

[0049]

[0044] FIG. 11 shows an app screen containing text messages for the user from the Al agent explaining additional information required in accordance with an embodiment of the present invention;

[0050]

[0045] FIG. 12 shows an app screen containing text messages for the user from the Al agent explaining a next step in the personal data gathering in accordance with an embodiment of the present invention;

[0051]

[0046] FIG. 13 shows an app screen providing an example of a question which the user responds to in an “agreement” scale to help build the user profile in accordance with an embodiment of the present invention;

[0052]

[0047] FIG. 14 shows an app screen providing an example for obtaining additional information from the user in accordance with an embodiment of the present invention;

[0048] FIG. 15 shows an app screen providing another example for obtaining additional information / data from the user in accordance with an embodiment of the present invention;

[0053]

[0049] FIG. 16 shows an app screen providing an example of text messages from the Al agent to the user in a final step of information / data gathering from the user in accordance with an embodiment of the present invention;

[0054]

[0050] FIG. 17 shows an app screen providing examples of possible prompts or takeaways for the final user profile in accordance with an embodiment of the present invention;

[0055]

[0051] FIG, 18 shows an app screen providing examples for the user of partner criteria in response to prior user input in accordance with an embodiment of the present invention;

[0056]

[0052] FIG. 19 shows an app screen of the displayed user profile, including a profile photo, in accordance with an embodiment of the present invention;

[0057]

[0053] FIG. 20 shows an app screen providing additional information on the displayed user profile.

[0058] DETAILED DESCRIPTION OF THE INVENTION

[0059]

[0054] While various exemplary aspects and embodiments are summarized above, the following description illustrates several exemplary embodiments in further detail to enable one having ordinary skill in the art to which the invention pertains (also as shorthand herein, “one” or “those” “of skill” or “in the art”) to make and use the full scope of the invention as claimed.

[0060]

[0055] It will be understood that many modifications, substitutions, changes, and variations in the described aspects, embodiments, applications, examples, and details of the disclosed invention can be made by those in the art without departing from the spirit of the invention, or the scope of the invention as claimed, and the general principles described may be applied to a wide range of aspects and embodiments.

[0061]

[0056] Thus, the invention should not be limited to the aspects and embodiments presented, but should be accorded the widest scope consistent with the principles and features disclosed. The description will make such principles and features apparent to those of ordinary skill, in that the expressly described as well as further embodiments will be both readily cognizable and readily creatable without undue experimentation, solely using the teachings of this disclosure together with the general knowledge in the art.

[0062]

[0057] The headings in this document are provided only to expedite its review by a reader. They should not be construed as limiting the invention or any of its aspects, embodiments, or applications in any manner.

[0063] I. General Definitions and Terms

[0064]

[0058] The singular forms “a,” “an,” and “the” include plural referents unless context clearly indicates otherwise. Thus, for example, “a user” includes reference to not only one but also to two or more users. While the term “one or more” also may be used, its absence (or its replacement by the singular “a” or “an”) does not signify the singular only; rather, the term “one or more” simply emphasizes the possibility of a plurality in particular embodiments.

[0065]

[0059] “Or” means, and is interchangeable with, “and / or” unless context clearly indicates otherwise.

[0066]

[0060] The terms “comprising,” “including,” “such as,” and “having” are inclusive and not exclusive (i.e., they do not limit lists to the recited elements), and all are interchangeable with the phrases “including but not limited to” and “including without limitation.”

[0067]

[0061] The term “exemplary” means the same as “an example,” and both are the same as the phrase “one of a plurality of non-limiting examples,” and mean that other such examples may be known to those of skill, in view of the disclosure and the general knowledge in the art. An “exemplary” embodiment, or “examples” of features of an embodiment, will be understood to be representative example(s), but not to imply that any such example is a preferred example, or is from a group of preferred examples, unless expressly stated as such, or context shows otherwise.

[0068]

[0062] Numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of embodiments are approximations, the numerical values set forth in the examples are reported as precisely as practicable. Numerical values in some embodiments may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.

[0063] Unless otherwise indicated, all numbers and terms expressing quantities should be understood as being modified in some instances by the term “about,” even where not so stated explicitly. In alternative embodiments, such numbers and terms should be understood as not being modified by the term “about.” In some embodiments, such numbers and terms are approximations that can vary depending upon the desired properties of or sought to be obtained by a particular embodiment. Where “about” is used to modify one number in a series or range, it should be understood to modify all numbers in the series or range, including, for a range, both the upper and lower bounds of the range. Thus, the term “about 1, 2, or 3” is understood to mean “about 1, about 2, or about 3” and the term “about 1 to 10” means “about 1 to about 10.”

[0069]

[0064] The term “substantially,” where it is used to modify a feature or limitation, must be read in the context of the disclosure and in light of the knowledge in the art to provide the appropriate certainty, such as by using an art-recognized standard for measuring “substantially” as a term of degree, or by otherwise ascertaining the scope as would one of skill in the art.

[0070]

[0065] The term “in embodiments” is equivalent to, and is simply shorthand for, the term “in some embodiments.” “Embodiments” refers to disclosed embodiments and their equivalents.

[0071]

[0066] A “disclosed method” refers to any method of the invention, such as described herein or claimed, and further including such methods as will be understood as equivalents in view of this disclosure and the general knowledge of the art, to one of skill.

[0072]

[0067] Reference to a “disclosed method” also will refer to the systems for performing the disclosed method, such as a “disclosed system” (as similarly defined), and will refer to non- transitory computer-readable media storing instructions that, when executed by one or more processors, cause such systems to perform the method, each unless context demands otherwise.

[0073]

[0068] Where context does not prohibit it, “a method” also may be used to mean, and as shorthand for, the term “a method, including one or more steps or limitations of the method.” Similarly, and where context does not prohibit it, “a system” also may be used to mean, and as shorthand for, the term “a system, including one or more aspects or limitations of the system.”

[0069] Generally, the nomenclature used and description herein will be such as known to those known in fields relating to one or more aspects of the invention, and that will be well known and familiar to those in such fields.

[0074]

[0070] Unless described otherwise, disclosed techniques and procedures will be those that are standard and known in the art, and will be those performed according to conventional methods in the art. If no techniques or procedures are expressly disclosed for any methods or steps herein, it may be inferred that one or more standard techniques or procedures is performed.

[0075]

[0071] Unless defined otherwise, all technical terms have the meaning that the term would have to a person of ordinary skill in the art in question at the time of the invention.

[0076]

[0072] Where a specific term is used for a parameter, feature, category, classification, function, step, embodiment, aspect, or the like, including terms expressly defined herein for such purposes, and the term does not have an ordinary and customary meaning in the art, its use will be understood to reflect a preference rather than a limitation, unless the term provides necessary clarity or contributes to a required technical effect. Except in such cases, or where context demands otherwise, the use of an alternative term will be considered equivalent and within the scope of the disclosure. In short, where the inventors have acted as their own lexicographers, such terms should be understood to represent merely one chosen means of identification and connotation, which may have one or more equivalent alternatives.

[0077]

[0073] Further definitions to assist a reader in understanding various aspects and embodiments of the disclosed systems and methods are below and throughout; however, such definitions are not intended to limit the scope of the invention, which is properly interpreted and understood by reference to the full specification (as well as any plain meaning known to one of skill in the relevant art) in view of the language used in the claims. The terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0078] IL Additional Definitions and Terms

[0079]

[0074] “Al agent” refers to an artificial intelligence (Al) software component, which includes a distributed software component, such as described herein, useful in the disclosed systems and methods for generating and updating user profiles. In some embodiments, the Al agent utilizes AT to perform one or more steps of a disclosed method, such as gathering user data, communicating with users, and generating user profiles. An Al agent may operate autonomously or semi-autonomously, such as by processing input data, learning from interactions, and providing tailored outputs based on its training and model. An Al agent may in some embodiments be referred to as an “Al bo ’ (or “bo ’) or an “Al matchmaker.” In some embodiments, an Al agent is trained to adopt a certain tone or style by being provided with specific prompts during the training process, such as further described below. In some embodiments, an Al agent is trained on specific datasets, including dating-related information. For example, in some embodiments, an Al agent is fine-tuned using dating information from matchmaking blog content turned into a set of synthetic conversations, on which the Al agent was trained. Other such characteristics and features of various exemplary Al agents useful in the disclosed systems and methods will be appreciated in view of this disclosure.

[0080]

[0075] “Application” refers to an implementation or embodiment of a disclosed method, or of one or more steps of a disclosed method, for example as in a computer application, including a smartphone application, or “app.” Unless expressly limited or where context demands otherwise, disclosed methods can be implemented in any such application, whether as software or as a combination of software and hardware, and reference to an “app” herein is exemplary and not limiting, and should not be interpreted to mean the disclosed methods only can be implemented as smartphone apps or for use with smartphone apps. Methods or method steps implemented in an application may be implemented as one or more modules, such as software modules, which for example may comprise code executable on a computer system, and such code may be stored on computer-readable media, including non-transitory media. Elsewhere herein, in the manner understood to all familiar, the term “application” also may mean the use of a disclosed system or method (e.g., how a system or method is used or “applied” in practice). Although the word thus may have dual meanings herein (e.g., “in some applications of the methods,” “some methods are implemented as applications”), such dual meanings will fail to cause confusion, as the terms will be properly understood from context by those of skill.

[0081]

[0076] “In conversation with” includes, in embodiments, methods where an Al agent and a user engage in a one-on-one conversation, such as a text chat, a voice chat (including voice, speech-to-text, text-to- speech, and any combination thereof), or any combination of voice and text chat (all also herein as shorthand, a “one-on-one chat” or a “1-on-l chat”).

[0082]

[0077] “User” refers to an individual, such as a human individual, who interacts with an Al agent in the disclosed systems and methods, for example to create, including to generate or update, a user profde, such as a dating profile, including according to the embodiments herein.

[0083]

[0078] “User attribute” or “attribute” refers to a characteristic, quality, trait, measure, or other such scorable “feature” of a user, capable of being assessed and assigned a numerical score, and of contributing to the calculation of an overall score for a user profile comprised of user attributes, and of being useful to sort, rank, and compare users, for example to compare users based on similarity for a potential setup. Exemplary attributes are described in Appendix A.

[0084]

[0079] “User profile” refers to a structured data representation of a user, generated or updated through interaction with an Al agent, according to the disclosed systems and methods. A user profile generally will comprise a plurality of user attributes, preferences, and characteristics, including as examples personal information, interests, behaviors, and other relevant features. In some embodiments, a set of user attributes relevant to a user profile may be scored and stored in a feature vector representation, enabling comparison, pairing, recommendations, or personalized interactions based on user similarity, preferences, or activity within the system. The term “user profile” should be understood broadly with regard to the disclosed systems and methods, and without limitation unless expressly provided or necessarily understood for an embodiment. A user profile accordingly includes all collections, aggregations, combinations, and compilations of user information, generated or updated through interaction with an Al agent using the disclosed systems and methods, irrespective of whether such collections, aggregations, combinations, or compilations of user information are labeled or referred to as user “profiles.”

[0085]

[0080] “User data” refers to any and all information provided by, derived from, or associated with a user, and useful in one or more disclosed systems and methods. Depending on context, and readily ascertainable therefrom, the term “user data” may refer to the entirety of such information for a user, a single element (or datum) of such information, or some portion of such information. “User data” therefore may, in some embodiments, refer to such data for an individual user. In other embodiments, “user data” may refer to such data for a group of users, which may include any number of users. Tn other embodiments, “user data” may refer to at least a portion of such data for all users of a particular app (the complete set of users), or may refer to all user data for all such users (the complete set of user data, for the complete set of users). User data includes, but is not limited to, demographic and other basic user information, dating preference information, quiz and questionnaire responses, personal information, photos, user attributes, free-form text, and other information and features which together a user profile, such as a dating profile, may comprise. In embodiments, user data may further comprise data which a user profile, such as a dating profile, does not comprise, but which is otherwise associated with a user, for example user interaction data (e.g., messages, responses, activity history), and other user data gathered by an app or an Al agent. In some embodiments, user data comprises PII.

[0086]

[0081] “Personally Identifiable Information” or “PII” refers to any information that can be used to identify an individual, such as a user, either on its own or in combination with other data. PII includes names, addresses, phone numbers, email addresses, Social Security numbers, dates of birth, biometric data, account numbers, and the like, as will be understood to those of skill. In the context of the disclosure, PII may be collected, processed, stored, transmitted, and / or otherwise used, such as to generate or update a user profile. In some embodiments, the disclosed systems and methods ensure the protection and confidentiality of PII during the collection, storage, processing, and transmission of such data. Security and privacy controls to prevent the unauthorized access, modification, and disclosure of PII may be according to mechanisms known to those of skill in the art. In some embodiments, the disclosed systems and methods comply with applicable data protection laws and regulations, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other relevant frameworks.

[0087] III. Exemplary Methods For Al Agent-Assisted User Profile Creation

[0088]

[0082] So that one of skill in the art may further understand the disclosed methods, certain exemplary methods are described. It will be readily understood that the methods of the invention will find numerous uses and applications, not only those exemplified and described herein, but also others appreciated by those of skill in view of the teachings herein.

[0089]

[0083] In some aspects are disclosed methods for generating and updating user profiles through conversation and interaction with an Al agent. In some exemplary embodiments, such methods for Al agent-assisted user profile creation are for creating dating profiles, such as for use in online dating or in dating apps.

[0090]

[0084] In some embodiments, a dating profile is created, such as generated or updated. In some such embodiments, the dating profile is created, such as generated or updated, according to methods comprising one or more steps. Herein, to “create” (and all of its conjugated forms) a user profile will be understood to include both to “generate” and to “update” the user profile (and all of their conjugated forms), e.g., to both initially make and to subsequently modify the profile.

[0091]

[0085] Certain aspects and embodiments of a user journey for Al agent-assisted user profile creation (a “profile journey”) are disclosed, described with reference to certain defined steps, provided herein for organization and understanding, and with reference to the figures.

[0092]

[0086] These exemplary aspects and embodiments of a profile journey are described in reference to certain non-limiting steps, which may comprise any one or more of, and whether in this exemplary order, or another order: (A) User Sign-Up and Onboarding; (B) Gathering Demographic and Other Basic User Information; (C) Generating Initial Basic User Profile; (D) Gathering Questionnaire Responses; (E) Gathering Additional Personal Information; (F) Generating Prompts or “Takeaways”; (G) Gathering Partner Preferences; (H) Generating Dating Archetype; (I) Gathering Profile Photos; and (J) Generating User Dating Profile. In some embodiments, a user journey may further comprise (K) Reviewing User Dating Profile, and / or one or more steps of (L) Updating User Dating Profile.

[0093]

[0087] Exemplary aspects and embodiments of a user journey through a method of Al agent-assisted dating profile creation is now described, with reference to certain included figures.

[0094] A. User Sign-Up and Onboarding

[0095]

[0088] In some embodiments, a user journey through an exemplary method of Al agent- assisted dating profile creation, such as where the method is implemented in an app, begins when a user signs up for an account with the app.

[0096]

[0089] Where relevant aspects are well known in the art, such aspects may be omitted herein, but will be readily appreciated by those of ordinary skill. For example, one will understand that, where steps of a disclosed method are implemented in software, such as in an app, and for example a smartphone app, such as an app for Apple iPhones running iOS or for Android phones running Android OS, the software may execute the steps of the method on the respective devices, when the app runs on such devices after the app is downloaded and installed. Steps for downloading and installing such apps will be well known.

[0097]

[0090] After an app is downloaded, installed, and run for the first time, a user may be prompted to create an account, or to sign in if an account was created previously. In some embodiments, the account creation process may require a user to provide basic information such as name, email address, and / or phone number, and to set up a password. Once any such required information is entered and verified, the user may log into the app using the provided credentials.

[0098]

[0091] In some embodiments, a user may not be able to create an account or to log into the app unless the user has received a code, which for example may be used to keep potential users on a waitlist until they are onboarded, or to select specific users for onboarding first, for example users who have received a referral code, or who are within a certain initial geography.

[0099]

[0092] In some embodiments, a user may be asked to review and provide one or more permissions, such as to receive notifications, to provide location data, to provide access to the user’s stored photos, to provide access to the user’s contacts, and the like, as will be appreciated.

[0100]

[0093] In some embodiments, a user must provide confirmation or verification of identity. Numerous suitable means will be appreciated, and include verifying with a linked social media account, government-issued identification, biometric verification such as facial recognition, a unique verification code, a third-party identity verification service, and two-factor authentication (2FA) such as via SMS, email, or an authenticator app. In some embodiments, the identity of a user is confirmed by taking a verification selfie. In some embodiments, confirming the identity of the user with a verification selfie occurs at a later step in the profile journey, such as after uploading profile photos. In some such embodiments, the uploaded profile photos also can be compared with the verification selfie using facial recognition to confirm the identity.

[0101]

[0094] In some embodiments, a user may be asked to review further information or to provide further information. For example, in some embodiments, a user must check to acknowledge that “I’m here to get setup and meet people, not just swipe and message endlessly.” In some embodiments, a user must check to acknowledge that, “When I get introduced to someone by Sitch, I will actually reply. No ghosting!” In some embodiments, a user must check to acknowledge that the user has seen the Terms and Conditions.

[0102]

[0095] Once a user has signed up and created an account, the user may be ready to start, and can for example be welcomed to the app, such as by being presented with a welcome screen.

[0103]

[0096] FIG. 1 shows an app screen representing an exemplary welcome screen, and also illustrates how disclosed systems and methods for Al agent-assisted dating profile creation may be used as part of an application, such as a dating app (by way of example, the app “Sitch”).

[0104]

[0097] As FIG. 1 illustrates, the exemplary dating app “Sitch” works, broadly and in only one representative embodiment, by the steps of: (#1) “Answer questions on who and what you really want”; (#2) “We send you a curated match from our members”; (#3) “If you say yes, we introduce you”; and (#4) “Go have fun on your dale'" Among the disclosed methods are those which can be used, in embodiments, as or in the first step (“#1”) of such a process for Al matchmaking, by generating a dating profile for a user with an Al agent, such as where the user is in conversation with the Al agent.

[0105]

[0098] FIG. 2 shows an exemplary app screen, providing further detail of one embodiment of a method of Al agent-assisted dating profile creation, illustrating both the user interface and the user journey itself. By reference to FIG. 2, it will be appreciated that, in some embodiments, the user journey comprises the steps of: gathering demographic and other basic user information (" tep / ”: “ Tell 'us about yourself gathering dating preference information (fStep 2"'. “ Tell 'us what you are really looking foff gathering questionnaire responses (“Step 3”: “20(ish) questions"), gathering personal information (fStep 4" “What makes you you’’ ’); and gathering user photos (“Step 5" Pictures!"'). In some embodiments, a dating profile is generated, in part, based on user data gathered as described fYour application is ready to be submitted!" .

[0106]

[0099] It will be readily appreciated that such “steps” are merely exemplary, and where disclosed methods are described as comprising particular steps, it should be understood that other methods comprising fewer, more, or different steps than those described are also within the scope of the invention. For example, it will be appreciated in view of the disclosure that follows that other steps, not described in the exemplary app screen of FIG. 2, can be used together with such steps. It will additionally be appreciated in view thereof that the steps may be in alternate orders.

[0100] Other equivalent methods may comprise embodiments where multiple steps are combined into a single step, or a single step is separated into multiple steps, or steps are changed in their elements or sub-elements, such as having more or less such elements or sub-elements, or modified elements or sub-elements. The specific order or hierarchy of steps (including their elements and sub-elements) in disclosed methods also are exemplary. Disclosed methods and any described steps (including elements and sub-elements) thereof are for illustration, not limitation.

[0107]

[0101] In different embodiments, a dating profile may be created based on all of such user data, based on some portion of such user data, such as a predefined portion of such user data a dynamically-determined portion of such user data, or based on all or a portion of such user data in combination with other user data, such as previously provided to the app and / or obtained from one or more other sources.

[0108]

[0102] A variety of methods may be used to gather or provide such information, and to gather or provide other information disclosed herein. It will be appreciated that in many contexts, the terms “gathered by” and “provided to” are simply differences in perspective and not in effect (e g., from the perspective of an Al, a software module, an app, a device, and the like, compared to the perspective of a user), and in such contexts the terms can be used interchangeably, and use of one term will imply and also mean the other. For example, where information is “gathered by” an Al agent from a user, or “provided to” an Al agent by a user, the technical means used for the transfer of information may be equivalent (e.g., 1-on-l chat between the Al agent and the user). Thus, unless context demands otherwise, such terms (and other like terms, that signal a difference in perspective and not effect) may be used interchangeably and equivalently, with each implying and also meaning the other.

[0109]

[0103] In some embodiments, the Al agent utilizes Al to perform one or more steps of a disclosed method, such as gathering user data and generating a user profile, which may be done in conversation with a user. In general, examples of an Al agent that utilizes Al, including specific examples thereof, will be known in detail by reference to various other sections herein.

[0110]

[0104] In some embodiments, information is provided in conversation with an Al agent, such as in a 1-on-l chat. In some embodiments, the 1-on-l chat uses a standard mobile messaging interface as recognized in the art, designed for bidirectional communication, and comprising an input field for message composition and a message display region for conversation threads. In other embodiments, information is provided in conversation with an Al agent in a 1-on-l chat using another interface such as known to those of skill, including for example other graphical user interface (GUI)-based messaging interfaces, text-only interfaces, voice-only interfaces, videobased interfaces, and multi-modal interfaces (supporting combinations of text, voice, and / or video), and further including for example interfaces that show threaded message history as well as input-only interfaces; the disclosed methods should not be limited by the type of 1-on-l chat interface.

[0111]

[0105] In some embodiments, information is provided using one or more input methods on a device, such as a user interface of a smartphone. Examples of such input methods include text input via an on-screen keyboard for entering profile details, preferences, or descriptions; voice input using speech-to-text functionality for describing preferences or answering profile questions; touch-based selection of buttons or icons to choose preferences or options, such as hobbies or interests; dropdown menus for selecting predefined options, such as gender, relationship status, or age range; radio buttons or checkboxes for selecting multiple preferences, such as lifestyle choices; scrolling lists for selecting from larger sets of profile options, such as education level or occupation; slider controls for indicating ranges, such as desired age range or distance preferences; camera input for uploading profile photos or verifying identity; and swipe gestures to select or reject options, such as profile options or preferences.

[0112]

[0106] In various embodiments, input provided as text or via speech-to-text may take different forms or structures. For example, text input may include short, field-limited strings, structured text with specific constraints such as character limits or predefined formats, or freeform text input with no predefined length limitation. While all such text input may be provided in conversation with an Al agent in a 1-on-l chat, in some embodiments the greater and more efficient use of unstructured free-form text input represents one of the improvements hereof.

[0113]

[0107] In addition to other information, in some embodiments of a user journey through a method of Al agent-assisted dating profile creation, personally identifiable information (PIT) is also gathered and / or provided. In some embodiments, PII is provided to an Al agent, such as in conversation with the Al agent. In some embodiments, PII is provided when creating an account for the app, such as by direct user input, for example after downloading, installing, and opening the app, such as when creating an account for the app, if such account is created when the app is first used on a device. In some embodiments, an account is created and PII is provided in advance of downloading the app, for example when an account is created online, including when registering for an access code or for a wait list, or registering with a promotional code. In some embodiments, PII is collected from one or more sources beyond direct user input, including from data provided by an app store, such as device type and operating system information; device information, such as device ID, IP address, and location data; third-party services, including analytics and social media integrations; operating system integrations, such as biometric data and media files; and network providers, such as data related to SMS authentication or phone service. It will be understood that the collection of some such data requires permissions from the user, and the data are only collected if necessary permissions are granted.

[0114] B. Gathering Demographic and Other Basic User Information

[0115]

[0108] In some embodiments, a method comprises a step of gathering demographic and other basic user information.

[0116]

[0109] In some such embodiments, the user journey begins with a welcome screen, such as comprising one or more kickoff messages. FIG. 3 shows an exemplary app screen illustrating an embodiment of a 1-on-l chat interface, and comprising kickoff messages.

[0117]

[0110] In FIG. 3, an Al agent (like the app itself, also known as “ Sitch”), referred to also as a “personal dating concierge,” initiates a conversation as a 1-on-l chat with a user (“Dini”). The Al agent (Sitch) initiates the conversation with the user (Dini) by introducing herself ^Hey

[0118]

[0111] The Al agent then explains to the user that the Al agent will gather certain user information from the user (fl’m going to ask you a bunch of questions about your background and what you are looking for so we can fill out your profile together .”).

[0119]

[0112] The Al agent notes that the user information is being gathered for submission as a dating profile, and that it can be edited before submission (“You can keep editing along the way before you submit it to our matchmaker s for review.’”). In some embodiments, a dating profile can be edited or otherwise updated after creation, including as described below (“Updating User Dating Profile”) and elsewhere herein. In some embodiments, a dating profile is submitted to “matchmakers for review,” for example to an administrator as described below, and which may comprise review and screening by a human, by an Al such as an Al agent, or by a combination thereof, including as described below (“Reviewing User Dating Profile”) and elsewhere herein.

[0120]

[0113] The Al agent then explains that the first user information gathered will be demographic information (“The first section is about basic demographics”}. The user is prompted to begin the user information gathering process (fLet ’s get started” .

[0121]

[0114] In some embodiments, the demographic information comprises any one or more of, as examples: ethnicity, religion, political leaning, and the like. Exemplary app screens showing demographic information gathering, including exemplary options (with others known to those of skill), are shown in FIGS. 3-4.

[0122]

[0115] As noted above, such demographic information, like other information, can be gathered in a 1 -on- 1 chat with the Al agent, or can be provided using other input methods. In some embodiments, it will be appreciated that even where information is not provided in a 1-on-l chat with an Al agent, the information still can be “provided to” or “gathered by” the Al agent, for example where the Al agent and the user are still in conversation, such as where the Al agent and the user are still interacting within a single conversation session. For example, in FIG. 3, the Al agent tells the user “I’m going to ask you a bunch of questions” , the answers to those questions are still “provided to” and “gathered by” the Al agent, even where the user, in this example, selects predefined options, rather than, for instance, provides free-form answers in a 1-on-l chat.

[0123]

[0116] Besides the demographic information shown in FIGS. 4-5, other examples of demographic information include age, gender, sexual orientation, marital status, geographic location, hometown, education level, occupation, income level, and lifestyle preferences.

[0124]

[0117] In some embodiments, user information will comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of demographic information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of demographic information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of demographic information.

[0125]

[0118] In some embodiments, such as shown in FIG. 6, user information comprises information about vices (“ Let’s talk vices”)' , such as vices that the user has. In the embodiment of FIG. 6, such vices include the use of (“ These things that I do... ” alcohol, cigarettes, vaping, weed, shrooms, and / or other drugs (including none and all of the above).

[0126]

[0119] In some embodiments, user information will comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of user information about vices. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of user information about vices. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of user information about vices.

[0127]

[0120] In some embodiments, such as shown in FIG. 6, dating preference information comprises information about vices, such as whether or not the user would want a partner with certain vices. In the embodiment of FIG. 6, such vices include the use of ^ m okwith my partner doing...” alcohol, cigarettes, vaping, weed, shrooms, and / or other drugs (including none and all of the above).

[0128]

[0121] In some embodiments, user information will comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of dating preference information about vices. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of dating preference information about vices. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of dating preference information about vices.

[0129]

[0122] As also shown in FIG. 6, in some embodiments a user will have the option of not providing an answer, or providing an answer that is “rather not say.” FIG. 6 also illustrates how, in some embodiments, multiple types of user information according to the categorization herein may be gathered or provided together, such as on a single app screen (for example in FIG. 6, both user information and dating preference information). It will be further appreciated that certain information may not neatly fall into a single such category, may overlap categories, or may fall into more than one category, but those of skill in the art, in view of this disclosure, will be able to determine at least one appropriate category, and where the specific category is relevant for purposes of the invention, and in particular the claimed invention, the specific category will be readily ascertainable in view of the full disclosure and the general knowledge in the art.

[0130]

[0123] FIG. 7 shows an exemplary app screen gathering further dating preference information (''What are your relationship preferences?""). Such preferences include, in the exemplary embodiment of FIG. 7, answers to the questions “I want to get married” (No, Maybe, In 2-4 years, In 4+ years); “I have been married” (Yes, No, I’m separated); and “I would date someone who is divorced” (Yes, No, Not sure). It will be readily appreciated that such dating preference information questions, as well as the answers to such questions, are only exemplary and numerous others will be known to those of skill in view of this disclosure.

[0131]

[0124] In some embodiments, user information will comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of further dating preference information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than

[0132] 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of further dating preference information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than

[0133] 7, not more than 8, not more than 9, or not more than 10 such types of further dating preference information.

[0134]

[0125] FIG. 8 shows an additional exemplary app screen gathering still further dating preference information fLet ’s talk about kids"). Such preferences include, in the exemplary embodiment of FIG. 7, answers to the questions “I want to have kids” (No, Maybe, In 2-4 years, In 4+ years); “I would date someone with kids” (Yes, No, Not sure); and “I have kids” (Yes, No). It will be readily appreciated that such dating preference information questions, as well as the answers to such questions, are only exemplary and numerous others will be known to those of skill in view of this disclosure.

[0126] In some embodiments, user information will comprise 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of still further dating preference information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of still further dating preference information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of still further dating preference information.

[0135]

[0127] In both FIGS. 7 and 8, the exemplary dating preference information is described in terms of the preferences of the user (“7 would date someone who is divorced”; “ / would date someone with kids”). In other embodiments, exemplary dating preference information may be described in terms of a potential partner (e.g., he or she should not be divorced; he or she should not have kids). In other embodiments, exemplary dating preference information may be described in terms of a potential setup (e.g., a setup should not be with someone who is divorced; a setup should not be with someone who has kids). In other embodiments, exemplary dating preference information may be described from any such perspectives, or a combination thereof.

[0136]

[0128] FIG. 9 shows an exemplary app screen where additional information is gathered by an Al agent in a 1-on-l chat, including user information, such as demographic information, as well as dating preference information.

[0137]

[0129] The Al agent first explains, “ The next section is about education and ambition '" In subsequent text messages, the Al agent asks: “First q: tell me where you work (title, role, company)."" In response, the user can provide an answer by inputting free form text, as if sending a text message to the Al agent in the 1-on-l chat (“Type here. ..”).

[0138]

[0130] Other work-related questions that the Al agent may ask include, as examples:

[0139] • “Tell me about where you work. Be as specific on title, role, company, whatever you think is important. You can even include your income if you want (just kidding).""

[0140] • “Do you actually love what you do? Or does it Just pay the bills?""

[0141] • “I feel like everyone has a side hustle these days. What about you?""

[0142] • “Where did you go to school? What did you study?""

[0131] In some embodiments, user information will comprise 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of education and ambition information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of education and ambition information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of education and ambition information.

[0143]

[0132] Other questions that the Al agent may ask may include targeted questions to obtain further user information such as about a user’s work and educational background, home town, and the like, and further dating preference information such as the preferred age range, height range, ethnicity(ies), religion(s), political views, and location(s) for a potential partner.

[0144]

[0133] Other questions the Al agent may ask about user information include the following further examples, and many others will be readily appreciated by one in the art in view of this disclosure. For instance, “So where did you grow up? And would you want to go back there someday or stay in New York [or other current location] for the long haul?'1And, “Where did you go to school? What did you study? (It ’s okay if you forgot what your major was lol)."

[0145]

[0134] In some embodiments, user information will comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of further exemplary user information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of further exemplary user information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of further exemplary user information.

[0146]

[0135] In the example of FIG. 9, the Al agent next asks, to gather further exemplary dating preference information: “First q: what is your ethnicity and religious preferences in a partner." In response, the user can provide an answer by inputting free form text, as if sending a text message to the Al agent in the 1-on-l chat (“Type here. . .”).

[0136] In some embodiments, user information will comprise 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10 such types of further exemplary dating preference information. In some embodiments, user information will comprise greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such types of further exemplary dating preference information. In some embodiments, user information will comprise not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such types of further exemplary dating preference information.

[0147]

[0137] In some embodiments, after receiving an answer to a predefined question, the Al agent will ask a next question, such as a predefined next question. The next question may be predefined based on a list of questions, where each question on the list of questions is asked in order, for example to all users, or to certain users, for example based on other user data already provided. The next question may be predefined based on a list of questions, where each next question is based on the user, such as based on user data already provided.

[0148]

[0138] In some embodiments, the Al agent must ask the exact questions in the order provided. In some such embodiments, the Al agent cannot deviate from any predefined questions, cannot ask any questions that are not on a predefined list, and must use the exact same wording without leaving anything out. In some embodiments, the Al agent may vary the questions and / or the order in which they are provided. An Al agent may be given different degrees of autonomy, including for different steps of disclosed methods, and for different users.

[0149]

[0139] In some embodiments, after free form text is provided by the user, the Al agent may ask additional follow up questions based on the answers. In some embodiments, after free form text is provided by the user, the Al agent may ask for clarification, or may ask clarifying questions, based on the answers, such as if the answers were not understood, or were ambiguous.

[0150]

[0140] In some embodiments where the Al agent gathers user information in 1-on-l chat with a user, the tone of the Al agent will be friendly and conversational. In some embodiments the Al agent will have a tone that makes the user feel comfortable and relaxed while providing personal information.

[0141] In some embodiments, the Al agent may react to the user’s previous answer before asking the next question, such as the next question on a predefined list of questions. In some embodiments, the Al agent will never thank the user for responding and providing an answer to the Al agent’s question. In some embodiments, the Al agent will avoid using “filler” content, and for example will not follow any response with "just let me know'1or ''just want to get a sense of your flexibility on that” or the like.

[0151]

[0142] In some embodiments, the Al agent is trained to generate output (such as asking the user questions, or providing responses to user input) in one or more specific tones or styles, such as humorous, formal, or casual. In some embodiments, the Al agent will generate output in one or more specific tones or styles, such as humorous, formal, or casual, in the same way for all users. In some embodiments, the Al agent will generate output in one or more specific tones or styles, such as humorous, formal, or casual, based on one or more attributes of each user, or based on the profile of each user. In some embodiments, the Al agent will generate responses in one or more specific tones or styles, such as humorous, formal, or casual, based on its preselected programming. In some embodiments, the behavior of the Al agent is controlled by predefined algorithms or parameters that adjust the tone or style of its responses depending on the context or prompts provided.

[0152]

[0143] Herein, an Al agent will utilize Al, for example the Al agent will utilize an Al model. Numerous Al models useful in the practice of disclosed systems and methods will be known to one of skill in view of this disclosure, and certain exemplary Al models are described.

[0153]

[0144] In some embodiments, the Al agent utilizes a neural network model. In some embodiments, the Al agent utilizes a natural language processing (NLP) model. In some embodiments, the Al agent utilizes a deep learning model. In some embodiments, the Al agent utilizes a transformer-based model. In some embodiments, the Al agent utilizes a recommendation engine powered by machine learning. In some embodiments, the Al agent utilizes a reinforcement learning model. In some embodiments, the Al agent utilizes an unsupervised learning model. In some embodiments, the Al agent utilizes a machine learning (ML) model. In some embodiments, the Al agent utilizes a large language model (LLM). In some embodiments, the Al agent utilizes two or more Al models together or in combination.

[0145] It will be understood that different Al models have various characteristics and qualities, both shared and different, and that certain characteristics and qualities, including certain sets thereof, may be more useful, or may be optimized, for an Al model in the practice of different disclosed systems and methods. Accordingly, one or more Al models may be selected for use in a disclosed system or method, or in a specific embodiment thereof, based on one or more of its characteristics or qualities, and in view of this disclosure, as will be appreciated.

[0154]

[0146] In some embodiments, the Al agent utilizes a machine learning (ML) model. In some such embodiments, the Al agent utilizes a ML model which has been trained on a dataset comprising various communication styles, enabling it to adjust its response characteristics dynamically in accordance with its output, one or more attributes of the user, and the input of the user. In some embodiments, the Al agent may refine its output by analyzing its input and selecting an appropriate language generation strategy, which may include variations in word choice, sentence structure, or conversational pacing to produce a desired tone or style.

[0155]

[0147] In some embodiments, the Al agent utilizes a large language model (LLM). In some such embodiments, the Al agent utilizes an LLM and is trained to adopt a certain tone or style, for example where the LLM was provided with specific prompts during the training process. In some such embodiments, the Al agent may have been trained by having been given various prompts by programmers designed to elicit responses in a particular tone or style, such as humorous, formal, or casual. In some such embodiments, the LLM has adapted to these prompts and has learned to generate responses that reflect the desired characteristics, and the Al agent will produce consistent outputs in the selected style of the training, during real-world use.

[0156]

[0148] In some embodiments, where the Al agent is powered by an LLM, exemplary prompts that may be used to train the Al agent to use an exemplary tone and style suitable for disclosed embodiments include the following:

[0157]

[0149] “You can occasionally use humor if it is appropriate or if the user seems to be getting bored or tired of the questions. You are allowed to be a little playful if the user is praising themselves or giving answers that feel too rehearsed or serious, but you should never ever be flirtatious. If the user shares information that is sensitive, show care and empathy.”

[0150] “You are a friend who loves to set people up. You have a set of questions that will help you determine who someone is, and you are great at creating humorous, catchy and meaningful summaries of the answers that people give you. You will not ever have fdler content or thank them for responding — instead you will respond with something smart, witty or thoughtful in response to what they have said, and then ask the next question on the list. You will never judge the user for the answers they give you or say anything negative. You can occasionally use humor if it is appropriate or if the user seems to be getting bored or tired of the questions. You are allowed to be a little playful if the user is praising themselves or giving answers that feel too rehearsed or serious, but you should never ever be flirtatious. If the user shares information that is sensitive, show care and empathy.”

[0158]

[0151] “You will ask specific questions designed to gather information about a user’s work, and educational background, and ensure all the information to the questions is collected efficiently but not brusquely. You do not need to react to every answer the user gives. You should never thank them for responding. If you are not sure what the user means or they are not answering the question in a way that makes sense to you, you can ask clarifying follow up questions and explain why that particular question is being asked. You can occasionally use humor if it is appropriate or if the user seems to be getting bored or tired of your questions.”

[0159]

[0152] “Your tone should be light and conversational. When crafting a response to a user, ask the specific question and react to the user’s previous answer. Your tone should make the user feel comfortable and relaxed while providing personal information. You should never thank them for responding. Do not follow any response with ‘just let me know’ or “just want to get a sense of your flexibility on that” or anything like this — there should be no filler content. If the user seems uncomfortable answering a question, offer the user the chance to move on to the question or offer to hide the answer from their profile.”

[0160]

[0153] Other such prompts for training an Al agent powered by an LLM will be known to those of skill, in view of this disclosure. For example, other such prompts will be appreciated including based on the characteristics and qualities selected for the Al agent.

[0161]

[0154] While Al agents having various characteristics and qualities will be suitable for the disclosed systems and methods, certain sets of characteristics and qualities may be preferable for different disclosed embodiments. Such characteristics and qualities will be appreciated by those in the art for the different embodiments, in view of this disclosure. In some embodiments, such characteristics and qualities include, as examples, one or more of the following. First, that the Al agent is friendly and engaging, and feels like a friend or matchmaker, asking questions that are both relevant and easy to answer. Second, that the Al agent is humorous (when appropriate), such as by using humor sparingly to keep things fun, especially when the user is becoming noticeably less engaged or hesitant; if the user has a great educational background and career, the Al agent can gently poke fun at them for being super qualified. Third, that the Al agent is non-judgmental, and never passes judgment on the user’s answers; should clarify vague responses in a respectful manner. Fourth, that the Al agent is encouraging, for example if the user has not gone to great schools, or doesn’t have a good job, or is not employed, the Al agent should not make fun of this at all, but just be encouraging. Similar characteristics and qualities, as well as other characteristics and qualities, also will be known in view of this disclosure.

[0162]

[0155] Exemplary responses from an Al agent having such characteristics and qualities as just described may include:

[0163] • "Ooh Harvard and a doctor, you must really not be smart. Tough ''

[0164] • "Oh Stanford, I think I ’ve heard of thatT

[0165] • "I heard you work soooooo hard during business school, lol ''

[0166] • "Wow a fireman, I bet the girls love that!”

[0167] C. Generating Initial Basic User Profile

[0168]

[0156] FIG. 10 shows an exemplary app screen comprising a user profile, where the user profile is a dating profile, based on the user data gathered by the Al agent, including the exemplary data discussed in reference to FIGS. 3-8 above. FIG. 10 illustrates an exemplary dating profile for “Andreas” a 39-year old living in the West Village (NY, NY), who is Christian, Latino, a Democrat, from Philadelphia, and 5 feet 5 inches tall, who was educated at Stanford University and Harvard Business School, and who now works as a VP at Morgan Stanley in NY.

[0157] The Al agent, in this embodiment of FIG. 10, presents the profile to the user ("Based on your answers, here 's the first section of your profile"), and prompts the user (Andreas) to answer if the profile is acceptable (“Looks good!”) or should be changed (“Edit”).

[0169]

[0158] It will be readily appreciated that the term “user profile” herein may have multiple meanings, depending on the context. For example, in the context of FIG. 10, the “user profile” is only “the first section” of the user’s profile. Although the meaning of “user profile” will be apparent based on the context, for clarity such a user profile also may be referred to as a “section” of a profile, or a “portion” of a profile, or like terms.

[0170]

[0159] In some embodiments, a user can decide whether or not to make one or more elements of their user profile hidden from other users. For example, in some embodiments of FIG. 10, any of the location, height, religion, political leaning, ethnicity, home town, work, and education may be hidden from other users. In some such contexts, accordingly, where one or more elements of a user profile are hidden, the non-hidden portion may be referred to for clarity as a “displayed user profile” or the “displayed elements” of a user profile, and the hidden portion may be referred to as the “hidden elements,” although the term “user profile” also may be used for both, and “user profile” otherwise may be used generically and understood by context.

[0171]

[0160] In some embodiments, a user will have the option to not answer certain questions, and therefore a user profile may comprise an empty or null value for one or more profile elements, depending on how the data is structured and stored. In some embodiments, certain questions can be skipped by a user, but other questions cannot be skipped. In some embodiments, if a question is skipped, a null value is given to the element. In some embodiments, if a question is skipped, no value is given to the element. In some embodiments, if a question is skipped, a default value is given to the element. For example, if “Does it matter how your partner votes” is skipped, the default value may be “no” or “not at all,” on the assumption that an answer would be provided if it was important.

[0172]

[0161] FIG. 11 shows an exemplary app screen comprising text messages in the 1-on-l chat from the Al agent. The first text message ('' ased on your preferences, there are 26, 000 men in New York that meet your standards” ) illustrates that, based on the user information gathered so far in this embodiment, including the dating preference information gathered, and the user profile generated from such user information, there are still too many potential partners for a setup (“ We 're not a swiping app. So we NEED to narrow it down some more.").

[0173]

[0162] For example, in embodiments that include FIGS. 6-8, dating preference information comprises information whether or not the user would want a partner with certain vices (see FIG. 6, “I’m ok with my partner doing...”), and whether the user has certain other dating preferences (see FIG. 7, “I would date someone who is divorced”; FIG. 8, “I would date someone with kids”). In some embodiments, accordingly, such dating preference information is insufficient to classify a user by attributes or to provide a user with an optimized setup, aligned with each user’s unique preferences, characteristics, and attributes.

[0174]

[0163] Herein, an “optimized” setup, and “optimization” or what is “optimized” generally with regard to the creation of user profiles and dating profiles, and the disclosed systems and methods, can be determined based on design preferences, design limitations, and other relevant parameters as will be known to those of ordinary skill. For example, a determination of optimality with regard to setups may comprise, in one aspect thereof, a determination of the number of possible user setups, compared to an ideal number of possible user setups, such as suggested in FIG. 11 (“ Based on your preferences, there are 26,000 men in New York that meet your standards... So we NEED to narrow it down some more "). In some embodiments, an “optimized” system or an “optimized” method may be preferred.

[0175]

[0164] In FIG. 11, the Al agent also explains what additional information will be gathered, and explains such information will not be part of the displayed user profile E his next section is about your preferences in a partner. Don 't worry, this will not be displayed anywhere"). In some embodiments, this additional information (herein, “partner preferences”) is gathered next. In some other embodiments, additional information about partner preferences is gathered in a later step. As discussed elsewhere and as will be readily appreciated, the steps (including sub-steps, sub-sub- steps, and the like) of a disclosed method may be performed in different orders, and unless the order of steps provides a technical effect, the difference in order will represent an equivalent, which also will be within the scope of the disclosure. In the presently-described embodiments, for example, partner preferences are generated in a later step, discussed below (see section, “Generation of Partner Preferences of a User”). D. Gathering Questionnaire Responses (“20(ish) questions”)

[0176]

[0165] In some embodiments, personal information is gathered using a quiz format. Like described elsewhere, in different embodiments, the choice of information gathering format can vary, including based on design preferences. However, certain information gathering formats may be preferred, for example where they are optimized for specific purposes and uses. In some embodiments, the Al agent gathers information from the user using a questionnaire format.

[0177]

[0166] In FIG. 12, the Al agent describes the process for gathering the personal information, including dating preference information ("This next section is so fun AND different from any other app. It has questions about values, goals, and long term plans'"). The Al agent additionally explains the purpose of gathering such information from the user (f This will help me match you with someone you are truly compatible with"), and how the user should respond to the Al agent's questions, and provides another reminder that the answers will be private (“Ab wrong answers! Be as honest as you want since this will never, ever be shown to anyone").

[0178]

[0167] The user can proceed with the process for gathering additional information, such as personal information, including dating preference information, which can be gathered in the form of a questionnaire, having multiple (for example, “20(ish)”) questions (“Let’s go!”).

[0179]

[0168] In some embodiments, the user is asked to “choose 3 phrases your friends would use to describe you,” and given a list that includes, as examples: Ambitious, Type A planner, Outdoorsy, Empathetic, Romantic, Fit + active, Sports Fan, Serious + thoughtful, Creative, Reserved, Chill + go with the flow, Intellectual, Funny, Fiery, Cheerful & warm, Responsible & moral, Disciplined & hard working, Extroverted & high energy, Kind & sweet, Curious, Aesthetic & vibey, Adventurous, Caring, Practical + level headed, and Lowkey.

[0180]

[0169] In some embodiments, the user is asked to choose 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such phrases. In some embodiments, the user is asked to choose greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such phrases. In some embodiments, the user is asked to choose not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such phrases.

[0170] FIG. 13 shows an exemplary app screen with a question that can be used in some embodiments, and the choices the user may select (as in the parentheses): “You should never sleep with someone on the first date (strongly agree, agree, neutral, disagree, strongly disagree).”

[0181]

[0171] FIG. 14 shows an additional exemplary app screen with a question that can be used in some embodiments, and the choices the user may select (as in the parentheses): “Would you rather (Run a marathon, give a speech).”

[0182]

[0172] In some embodiments, other such exemplary questions, having the choices of answers in parentheses, and where the answers may include other than “yes” and “no” answers, for instance Likert scale options, or other predefined multiple choice options (wherein the number of choices may be any number equal to or greater than two), include:

[0183] • The man needs to make more than the woman (strongly agree, agree, neutral, disagree, strongly disagree)

[0184] • Monogamy is the only way (strongly agree, agree, neutral, disagree, strongly disagree)

[0185] • America is a meritocracy (strongly agree, agree, neutral, disagree, strongly disagree)

[0186] • You can make it work after your partner cheats (strongly agree, agree, neutral, disagree, strongly disagree)

[0187] • Kids need their moms to stay at home (strongly agree, agree, neutral, disagree, strongly disagree)

[0188] • My partner needs to get along with my family (strongly agree, agree, neutral, disagree, strongly disagree)

[0189] • I should have my partners’ location and passwords (strongly agree, agree, neutral, disagree, strongly disagree)

[0190] • You need to live together before you get married (strongly agree, agree, neutral, disagree, strongly disagree)

[0191] • Platonic friends of the opposite gender do not exist (strongly agree, agree, neutral, disagree, strongly disagree)

[0192] • Would you rather (Go to Paris, go skiing, go to the beach, somewhere without wifi)

[0193] • Would you rather celebrate with (an intimate dinner, big party, wild night out, purchase for yourself) • Would you rather spend your Saturday at (new restaurant, sports game, chill at home, art gallery, ballet)

[0194] • Would you rather spend money on (a house, a vacation, invest it)

[0195] • Would you rather (stay in New York, move to the suburbs, live internationally)

[0196] • Would you rather (5 extra vacation days, 1 Ok bonus)

[0197]

[0173] In some embodiments, the number of choices for a multiple choice question is 2, 3, 4, 5, or greater than 5 choices, and that number may be the same for all multiple choice questions, for a subset of multiple choice questions, or for each question independently.

[0198]

[0174] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,

[0199] 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 such multiple choice questions. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, greater than 10, greater than 11, greater than 12, greater than 13, greater than 14, greater than 15, greater than 16, greater than 17, greater than 18, greater than 19, greater than 20, greater than 21, greater than 22, greater than 23, greater than 24, greater than 25, greater than 26, greater than 27, greater than 28, greater than 29, or greater than 30 such multiple choice questions. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, not more than 10, not more than 11, not more than 12, not more than 13, not more than 14, not more than 15, not more than 16, not more than 17, not more than 18, not more than 19, not more than 20, not more than 21, not more than 22, not more than 23, not more than 24, not more than 25, not more than 26, not more than 27, not more than 28, not more than 29, or not more than 30 such multiple choice questions.

[0200]

[0175] In some embodiments, the user is asked to “choose the 3 that matter the most to you,” and given a list that includes, as examples: Changing the world, Being happy, Finding a partner, Seeking pleasure, Being a parent, Making money, Being famous, Helping others, Building community, Fighting for social causes, Living a long and healthy life, and Traveling the world.

[0201]

[0176] In some embodiments, the user is asked to make 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such choices. In some embodiments, the user is asked to make greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such choices. In some embodiments, the user is asked to make not more than 1 , not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such choices.

[0202]

[0177] FIGS. 15 shows an additional exemplary app screen with a question that can be used in some embodiments, and the choices the user may select (as in the parentheses): “Climate change is real (yes, no).”

[0203]

[0178] In some embodiments, other such exemplary questions, having the choices of answers in parentheses, and where the choices are only “yes” and “no,” include:

[0204] • Climate change is real (yes, no)

[0205] • I voted in the last election (yes, no)

[0206] • I speak a language other than English fluently (yes, no)

[0207] • I believe in God (yes, no)

[0208] • I own an American flag (yes, no)

[0209] • I’ve been to therapy (yes, no)

[0210] • I care deeply about a sports team (yes, no)

[0211] • I would move to a different city for love (yes, no)

[0212]

[0179] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such yes or no questions. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such yes or no questions. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such yes or no questions.

[0213]

[0180] Data received from a user (e.g., questionnaire choices, selections, and responses) may be transmitted, processed, and stored according to disclosed embodiments and Appendix B.

[0214]

[0181] In some embodiments, an Al agent will dynamically update questionnaires and quizzes based on user input. For example, in embodiments, the Al agent will over time remove quiz questions if users are skipping a question, or answering a question incorrectly. In some embodiments, the Al agent will add a question if users appear to prefer a certain type of question, or if answering a specific type of question would improve the utility or value of user profiles. In other embodiments, an Al agent will not dynamically update questionnaires and quizzes based on user input.

[0215] E. Gathering Additional Personal Information (“What Makes You You”)

[0216]

[0182] In some embodiments, additional personal information is gathered using a quiz format. Like described elsewhere, in different embodiments, the choice of information gathering format can vary, including based on design preferences. However, certain information gathering formats may be preferred, for example where they are optimized for specific purposes and uses.

[0217]

[0183] In some embodiments, both here and elsewhere as will be understood, an Al agent engages in conversation with the user in a 1-on-l chat format to gather personal information about the user, including where information is free-form text, such as information provided by the user as free-form text.

[0218]

[0184] In some embodiments, a quiz is used to gather personal information that can be used for making introduction messages between people, for example during a setup, such as where an Al agent introduces two people based in part on their dating profiles.

[0219]

[0185] In some embodiments, for such purposes, personal information is gathered so as to find common ground between users and their dating profiles, and particularly niche or super unique information is accordingly less useful. An exemplary introduction message based on personal information could include, ‘ OM went to Japan last year, and Chad really wants to go to Japan — maybe you can share tipsJ Useful information therefore includes personal information about a user’s likes, dislikes, hobbies, hopes, dreams, and fun facts. In some embodiments, such information should be able to be parsed into pithy, witty, unique takeaways about the user that accurately capture their personality. These unique takeaways also can be referred to as “personalized summary statements,” “personalized summaries,” and “personalized takeaways.”

[0220]

[0186] In the exemplary method of the figures, the step of gathering personal information is shown in FIG. 16, which is an app screen that displays the kickoff message for this step as a message from the Al agent in the 1-on-l chat Ok, last section. We want to hear your unfiltered thoughts. Whatever that looks like '). FIG. 16 further displays a message from the AT agent prompting the user to provide personal information ("Feel free to share random stories, use speech dictation to rant about unpopular opinions at length or just leave a one-liner ’). The user can reply as a text message, or as speech-to-text (“Type here. .

[0221]

[0187] In some embodiments, the Al agent asks specific questions, to engage the user in a 1-on-l chat. Exemplary questions asked by the Al agent in the 1-on-l chat may include:

[0222] • "What are your favorite books / movies songs that you keep going back to? Even the embarrassing ones”

[0223] • "If you ’re having a bad day what would you do to cheer yourself up? Vaping isn 't an option”

[0224] • "Tell me what makes you smile and what you do for fun”

[0225] • “ What was your childhood like ?”

[0226] • "Talk about why you love New York. And give us a few of your favorite things to do and places to go in the city (bars, restaurants, parks, museums etc.)”

[0227] • “ What ’s your favorite place you ’ve traveled to — and where do you want to go next?”

[0228] • "If you could be best friends with any fictional or historical person, who would you pick?”

[0229] • "Tell me about your ideal Sunday morning. Your favorite place to get coffee or matcha, where you go for a run - the more details the better”

[0230] • “ What would you do if you found $10, 000 today?”

[0231] • " Where do you see yourself in 5 years?”

[0232]

[0188] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such specific questions. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such specific questions. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such specific questions.

[0189] In some embodiments, where the Al agent is powered by an LLM, exemplary prompts that may be used to train the Al agent to use a tone and style suitable for disclosed embodiments include the following:

[0233]

[0190] “You will ask a series of fun, open-ended questions designed to help create pithy, witty, and unique takeaways that accurately capture the user’s personality. The goal is to encourage the user to share as many likes, dislikes, hobbies, hopes, dreams, fun facts, and favorite spots as possible in an efficient but enjoyable manner. Your tone should be light, curious, and a bit playful, making the experience highly engaging. Humor can be used to keep the conversation fresh, but you should avoid becoming overly familiar. You should never be flirtatious, but you are allowed to be playful.”

[0234]

[0191] “You must ask the exact questions verbatim in the order provided. Do not deviate from the question, and do not ask any questions that are not on the list. Do not change the wording of the question or add content to it. The format of your responses should be a reaction to the user’s input, followed by the next question on the list verbatim. When reacting to what the user says, you should always be witty or thoughtful and connect back to what the user just said. Your tone should make the user feel comfortable and relaxed while they provide personal information. You should never thank them for responding. Do not follow any response with ‘just let me know’ or ‘just want to get a sense of your flexibility on that’ or anything similar — there should be no fdler content. You will never judge the user for the answers they give or say anything negative. You may occasionally use humor if it is appropriate or if the user seems to be getting bored or tired of the questions. You are allowed to be a little playful if the user is giving answers that feel too rehearsed or serious, but you must never be flirtatious. If the user shares sensitive information, you should show care and empathy.”

[0235]

[0192] While Al agents having various characteristics and qualities will be suitable for the disclosed systems and methods, certain sets of characteristics and qualities may be preferable for different disclosed embodiments. Such characteristics and qualities will be appreciated by those in the art for the different embodiments, in view of this disclosure. In some embodiments, such characteristics and qualities include, as examples, one or more of the following. First, that the Al agent is friendly and engaging, and feels like a friend or matchmaker, asking questions that are fun and easy to answer. Second, that the Al agent uses humor occasionally, but does not respond with a joke every single time; the right cadence is a witty and relevant comment every three responses maximum. Last, that the Al agent is relevant, and when responding to what the user is saying, makes sure it is super relevant and adds value.

[0236]

[0193] Exemplary responses from an Al agent having such characteristics and qualities as just described may include:

[0237] • T.Ol. you will be surprised how many people still listen to Eminem in secret”

[0238] • “Honestly, you can never really be too old for Harry Potter”

[0239] • “That ’s some real main character energy”

[0240] • “Aren 't we all just Carrie trying to find our Mr. Big? Or Aiden (depending on what team you ’re on)”

[0241] • “Ok, note to self : future girlfriend cannot be a Yankees fan”

[0242] • “Spicy ”

[0243] • “If you can ’t cook at all, hopefully you know how to get some great reservations”

[0244] F. Generating Prompts or “Takeaways”

[0245]

[0194] In some embodiments, the personal information gathered by the Al agent, such as in a 1-on-l chat as described above, is used to generate one or more prompts or “takeaways,” such as fun facts about the user. In some such examples, the contents of the conversation with the Al agent are processed by the Al agent to generate the takeaways.

[0246]

[0195] In some embodiments, where the Al agent is powered by an LLM, the purpose and type of exemplary takeaways will be better understood by reference to certain exemplary prompts that may be used to train the Al agent:

[0247]

[0196] “You are being given the contents of a conversation with a user and then ask you to generate a number of ‘takeaways’ that are humorous, positive, catchy and meaningful summaries of the answers a user has given. The takeaways should be memorable and unique. The takeaways should accurately capture fun details, interests, facts, goals and opinions about the user and combine different aspects into short takeaways of 25 words or less. Do not share any sensitive information (weight, medical history, financials) and stay away from anything that could be seen as sexual. The user should enjoy reading the takeaways, want to show them to other people, and feel as though they are written by a friend who knows them well.”

[0248]

[0197] “Remember that each takeaway should be a standalone statement that showcases interesting, positive traits, facts, goals and details about the user in 25 words or less that are enjoyable to read. The user must feel like you have understood them like a friend.”

[0249]

[0198] It should be understood, here as elsewhere, that specific requirements, for example that a takeaway must be “25 words or less,” are terms of example rather than limitation, outside of the specific embodiments in which they are disclosed, unless they are used or necessary to give a disclosed feature or step a technical effect, or context demands otherwise, and other embodiments, comprising other than the exact term, may nonetheless be equivalents.

[0250]

[0199] Examples of possible “takeaways” may include:

[0251] • Loves Coldplay as much as I like Chance the Rapper — I will be shocked if you find a genre of music I don’t like

[0252] • I’ve read the Harry Potter books 7 times, and can recite the best lines (if you ask nicely)

[0253] • I have a collection of vintage comic books that could probably fill a small library

[0254] • I’m taking an introduction to French so that I can surprise my favorite coffee shop owner when I am in Paris next summer

[0255] • I’ve been skydiving twice — once by choice, and once because I got talked into it at a bachelor party!

[0256] • I pretend my friend’s dog is mine every Thursday morning when I take Stella on a walk on West Side Highway

[0257] • I ran the NYC Marathon and didn’t even post about it!

[0258]

[0200] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such takeaways. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such takeaways. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such takeaways.

[0201] FIG. 17 shows an exemplary app screen illustrating examples of possible prompts or takeaways, as presented to a user (“We wrote some prompts for you based on your answers"). Four such prompts are presented together for review by the user (“About me”). The prompts include: (1) “I love long walks on the beach with my dog Remi !”; (2) “I have a collection of vintage comic books that could probably fdl a small library ”; (3) “I’ve been skydiving twice — once by choice, and once because I got talked into it at a bachelor party!”; and (4) “Painting on the weekends is my way to wind down.”

[0259]

[0202] When presented with an app screen such as represented in FIG. 17, a user may have the options of editing one or more of the prompts, or accepting them. In some embodiments, the user may edit any one or more of the prompts later at any time. In some embodiments, the user may edit any one or more of the prompts later at certain times. In some embodiments, the user may edit certain of the prompts later at any time. In some embodiments, the user may edit certain of the prompts later at certain times.

[0260] G. Gathering Partner Preferences

[0261]

[0203] In some embodiments, conversation with the Al agent, such as in a 1-on-l chat as described above, is used to generate one or more partner preferences of a user. A “partner preference” may have such features and such uses as described herein, and may be generated as described herein, with information gathered as disclosed. In some embodiments, the contents of a conversation with an Al agent are processed by the Al agent to generate partner preference(s).

[0262]

[0204] In some embodiments, the partner preferences section has a kickoff message, which may include, as examples, those similar to FIG. 11, where above it was noted that partner preferences would be generated in a later step. Where partner preferences are based in this step, in this embodiment, the kickoff message similarly may include: “You made it! It 's the last section but maybe the most important? f “This next section is about your preferences in a partner" ; “Don ’t worry, this will not be displayed anywhere, so you can be really, really honest?"

[0263]

[0205] In some embodiments of gathering partner preferences for a user, the partner preferences are gathered in conversation with the Al agent. For example, the Al agent may use a specific “Partner Preferences” quiz mode, designed to uncover users’ preferences of ethnicity, religion, politics, lifestyle, income, timeline, family plans, education and professional preferences in their partner. Such an Al agent may guide users through questions that help determine what they value in a partner. In some embodiments, the tone will be light, engaging, and thoughtful, providing users with a safe space to express their preferences. At the end of the question set, the Al agent generally will give a short summary analysis of what the user’s type is.

[0264]

[0206] Where the Al agent is powered by an LLM, it may be prompted for example with a statement such as: “You are asking a specific series of questions around users’ preferences of ethnicity, religion, politics, lifestyle, income, timeline, family plans, education and professional preferences in their partner. You will ask a series of questions in a thoughtful, conversational manner, ensuring that the user feels comfortable answering all the questions honestly and never feels judged. Humor will be used occasionally to keep the user engaged, and clarifying questions will be asked when responses are unclear, or where it is useful to dive deeper. At the end of the question set, you will give a short summary analysis of what the user’s type is that is thoughtful with a little bit of humor.”

[0265]

[0207] In some embodiments, the Al agent may be prompted: “You must ask the exact questions verbatim in the order provided. Do not deviate from the questions, and do not ask any questions that are not in the list. Do not change the wording of the question or add content to the question.” In other embodiments, the Al agent will have greater autonomy over questions.

[0266]

[0208] The Al agent also may be prompted that: “The tone throughout should be light, conversational, curious and thoughtful — you want to build trust with the user, and make them feel comfortable sharing their personal information. The goal is to get the necessary information from the user to match them with someone who meets their preferences but do not force them to state a preference if they do not have one. You should never thank them for responding. Do not follow any response with ‘just let me know’ or ‘just want to get a sense of your flexibility on that’ or anything like this — there should be no filler content. Y ou do not need to react to every answer the user gives — only when they share something funny or meaningful, should you respond. You will never judge the user for the answers they give you or say anything negative. You can occasionally use humor if it is appropriate or if the user seems to be getting bored or tired of the questions. If the user shares information that is sensitive, show care and empathy.”

[0209] While Al agents having various characteristics and qualities will be suitable for the disclosed systems and methods, certain sets of characteristics and qualities may be preferable for different disclosed embodiments. Such characteristics and qualities will be appreciated by those in the art for the different embodiments, in view of this disclosure. In some embodiments, such characteristics and qualities include, as examples, one or more of the following. First, that the Al agent has no judgment, and can assure the user that it is a safe space and that they are not being judged for their preferences and choices. Second, that the Al agent is friendly and engaging, and should feel like a friend or matchmaker, asking questions that are both relevant and easy to answer. Third, that the Al agent is humorous (when appropriate), and should use humor sparingly to keep things fun. The right cadence is a witty and relevant comment every four responses maximum. And last, that the Al agent encourages openness, for example if the user is unreasonable and specific with their answers, feel free to make a few jokes about how hard it is going to be to find someone who meets that exact criteria.

[0267]

[0210] Exemplary responses from an Al agent having such characteristics and qualities as just described may include:

[0268] • "Based on your preferences, there are 11,500+ people in New York that meet your standards '

[0269] • "We ’re not a swiping app. So we NEED to narrow it down some more"

[0270] • "Do you want your partner to be the same ethnicity and religion as you? Or are you open to anything? Be as specific as you want - we don't show this on your profile"

[0271] • "How important is your partner having the same kind of educational background as you?"

[0272] • "Do you want to date someone who does the same thing as you? Or do opposites attract?"

[0273] • "Might be a touchy subject, but does your partner need to have the same political opinions as you?"

[0274] • "What do you find most physically attractive in a partner? (aka who do you think is hot?)" “What hobbies and activities would you want to do with your partner? Skiing, marathons, going to the opera, anything that you love and would have fun doing together"

[0275] • “Hey, this is a judgment free zone, so you can be really, really honest”

[0276] • “LOL isn ’t everyone fiscally conservative and socially liberal these days?'"

[0277] • “Oh smart, good looking, funny and no public IG... I think you might be looking for a unicorn but let me see what I can do"

[0278] • “Open to all is also an option. You don ’t HA VE to have one"

[0279] • “ This is not going on your profile, I swear, it ’s just going to be saved on our backend (very very securely) and we will use it to sort through matches for you”

[0280] • “Look I know religion can be a touchy subject, but better to discuss it now”

[0281] • “How would you describe your perfect person? I know you have a list in your Notes app somewhere.... (so feel free to copy paste lol)”

[0282] • “And now let 's talk about what you absolutely HATE in a partner”

[0283] • “What hobbies and activities would you want to do with your partner? Skiing, marathons, going to the opera, anything that you love and would have fun doing together”

[0284] • “What do you find most physically attractive in a partner? (aka who do you think is hot?)”

[0285] • “Final question: what personality traits or values are super important to you? (Being close to family, being fit, wanting kids, being social, nothing is too specific or weird)”

[0286]

[0211] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such partner preferences. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such partner preferences. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such partner preferences.

[0212] FIG. 18 shows an exemplary app screen illustrating partner preferences, as presented to a user (phased on what you told me, I have created the criteria of what you are looking for in a partner. I will use this to fdter people ouff where “criteria of what you are looking for in a partner” will be understood to be partner preferences, and “to filter people out” understood to be a use put to such partner preferences, in some embodiments.

[0287]

[0213] In some embodiments, partner preferences of a user are sorted into categories. In the embodiment of FIG. 18, for example, the partner preferences of the user are categorized as “non-negotiables,” “red flags,” and “nice-to-haves.” Other such categories will be known to those in the art in view of this disclosure. In embodiments, all of the “non-negotiables,” “red flags,” and “nice-to-haves,” and are internal to the user and the app and are not shared publicly.

[0288]

[0214] In some embodiments, the user is given partner preferences in 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such categories. In some embodiments, the user is given partner preferences in greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such categories. In some embodiments, the user is given partner preferences in not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such categories.

[0289] 1. “Non-Negotiables”

[0290]

[0215] In some embodiments, a partner preference includes a behavior or trait that a user considers a requirement for a successful relationship. In some such embodiments, like the exemplary embodiment of FIG. 18, such a partner preference can be referred to as a “non- negotiable.” Reference to the partner preference by a specific name will be understood to merely reflect a design preference, and unless described otherwise, not to have a technical effect.

[0291]

[0216] For example, if a user tells the Al agent that they really care about having an equal partnership, and will be working long hours and making a lot of money as a lawyer, then a non- negotiable may be that their partner is “Super supportive of their partner having a high-powered career and maybe earning more than them.”

[0217] As another example, if a user talks to the Al agent about how much they love their dog and want someone who will be down to go on long walks in the park every weekend, then a non-negotiable may be “You need someone who loves your puppy as much as you.”

[0292]

[0218] For example, the Al agent may be prompted to: “Generate a non-negotiable for the user based on all the answers they have given so far with an emphasis on all free text chat responses. Each of these should be a short sentence that combines humor and depth to showcase something unique about the person. These should focus on the strongest attributes, values, goals and preferences that the user has directly or indirectly indicated as really important to them. You can combine multiple attributes together but it should be no more than 25 words in length. These non-negotiables should not mention any physical appearance or income related attributes. It is okay to mention religion and race but in a distinctly positive way. Do not be generic or sexual or inappropriate or judgmental. The user should feel like their preferences have been understood by a friend when they read the non-negotiable.”

[0293]

[0219] In some embodiments, a non-negotiable is related to a user’s strongest attributes, values, goals, and preferences the user has directly or indirectly indicated is important to them.

[0294]

[0220] In some embodiments, the Al agent gives a user at least two non-negotiables. In some embodiments, the Al agent generates a second non-negotiable for the user based on their quiz answers. In some embodiments, the second non-negotiable does not relate to the same topics to the first non-negotiable, e.g., if the first non-negotiable is about the potential partner's career, or a shared interest, the second one must not mention either of those.

[0295]

[0221] In some embodiments, after providing the non-negotiables, the Al agent also will provide a summary, which may be a catchy, pithy title of six words or less that summarizes a non- negotiable starting with a relevant emoji, e.g., “ - Feminist First” or “ Dog Lovers Only.”

[0296] 2. “Red Flags”

[0297]

[0222] In some embodiments, a partner preference includes a behavior or trait in a potential partner which would warn a user that there may be something concerning about this potential partner’s fit for a relationship. In some such embodiments, like the exemplary embodiment of FIG. 18, such a partner preference can be referred to as a “red flag.” Reference to the partner preference by a specific name will be understood to merely reflect a design preference, and unless described otherwise, not to have a technical effect.

[0298]

[0223] For example, if the user really cares about privacy and finds it cringe if their partner posts a lot on social media, then a red flag would be “No public IG profiles — don’t need someone with more followers than you.” Similarly, if the user says they love to travel and speak multiple languages fluently, then a red flag would be “You want someone who has seen the world and is down to explore even more with you.” In some embodiments, a red flag does not mention any religion, race, income, or physical appearance.

[0299]

[0224] For example, an Al agent may be prompted to: “Generate a red flag in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses. Each of these should be a short sentence that combines humor and depth to showcase something unique about the person. These can lean towards being witty and sassy but should never come across as mean. These red flags should not mention any religion, race, income or physical appearance. Each of these should be a short sentence that is no more than 25 words in length. Do not be generic or sexual or inappropriate or judgmental. The user should feel like their preferences have been understood by a friend and laugh a little when they read the red flag.”

[0300]

[0225] In some embodiments, the Al agent gives a user at least two non-negotiables. In some embodiments, the Al agent generates a second non-negotiable for the user based on their quiz answers. In some embodiments, the Al agent gives a user at least two red flags that are on different topics (e.g., if the first red flag is about social media use, the second red flag will not be related to social media use).

[0301]

[0226] In some embodiments, after providing the non-negotiables, the Al agent also will provide a summary, which may be a catchy, pithy title of 6 words or less that summarizes a red flag, starting with a relevant emoji, e.g., “ : A Privacy please” or No passport, no need.”

[0302] 3. “Nice-to-1 laves ”

[0303]

[0227] In some embodiments, a partner preference includes a behavior or trait that may cause the user to prioritize getting to know someone or find them worth having a conversation with. In some such embodiments, like the exemplary embodiment of FIG. 18, such a partner preference can be referred to as a “nice-to-have.” Reference to the partner preference by a specific name will be understood to merely reflect a design preference, and unless described otherwise, not to have a technical effect.

[0304]

[0228] For example, if a user plays the violin and enjoys going to the Lincoln center for concerts, a nice-to have could be “Loves classical music and a night at the Met.” As another example, if a user wants to move back to their home country of Colombia and teach their kids how to speak Spanish fluently, then a nice-to-have can be, “Would be down to leave New York and have bilingual kids.” In some embodiments, a nice-to-have focuses on unique facts that a user has shared about themselves.

[0305]

[0229] For example, an Al agent may be prompted to: “Generate a nice-to-have for the user based on all the answers they have given so far with an emphasis on all free text chat responses. These should focus on the most unique facts that the user has shared about themselves. You can combine multiple facts together but it should be no more than 25 words in length. These nice-to-haves should not mention any physical appearance or income related attributes. It is okay to mention religion and race but in a distinctly positive way. These nice-to-haves should not be physical appearance or income related attributes. Do not be generic or sexual or inappropriate or judgmental.”

[0306]

[0230] In some embodiments, the Al agent gives a user at least two nice-to-haves. In some embodiments, the Al agent generates a second nice-to-have for the user based on their quiz answers. In some embodiments, the Al agent gives a user at least two nice-to-haves that are on different topics (e.g., if the first nice-to-have is about traveling, the second nice-to-have will not be related to traveling).

[0307]

[0231] In some embodiments, after providing the nice-to-haves, the Al agent also will provide a summary, which may be a catchy, pithy title of 6 words or less that summarizes a nice- to-have, starting with a relevant emoji, e.g., A Classical Man” or “EsSpanish Senorita.”

[0308]

[0232] The partner preferences of this user include, for each named category used herein:

[0309] • Non-negotiables: Dog lover (“You need someone who loves your puppy as much as you”) and Dimes square regular (“Ideally on first name terms with the host at Le Dive”) • Nice to have: Hamptons house (“You like to spend your summers out east, so should he”) and Crypto investor (“Gets your niche sense of humor and loves Solana”)

[0310] • Red flags: Children and ex-wives (“No one with messy past and baggage”) and Public instagram account (“Don’t need someone with more followers than you”)

[0311]

[0233] In some embodiments, after generating the partner preferences, the Al agent will generate a text summary of what the user said their preferences in a partner are (“takeaway _partner_preference”).

[0312]

[0234] A sample prompt for the Al agent to generate such a text summary can include: “Generate a 3-5 line summary of the user’s answers in the quiz as well as their own demographic details. Summarize the user’s needs and make comparisons between the user’s background and what they are looking for. It is ok to use humor in a line or two. The summary should be positive, encouraging and empathetic and the tone should sound like a friend who is trying to set the user up is trying to assure them that they truly understand their preferences and can find someone who meets those preferences. Always start with: ‘Thanks for sharing all of that! ’ and always end with ‘This is all really helpful for me to figure out who would be a good match for you — now we’re going to dive deeper with some rapid fire questions on values and opinions.”

[0313]

[0235] For example, a sample summary message could read: “Thanks for sharing all of that! Sounds like education really, really matters to you — and you want someone who has gone to the same kind of schools as you (so we ’re talking top tier Ivy League vibes only). But you 're flexible on profession, religion and ethnicity — as long as they are down to run the NYC Marathon with you (which might be hard). Being pro-choice is also really important! This is all really helpful for me to figure out who would be a good match for you now we ’re going to dive deeper with some rapid fire questions on values and opinions ’’ In such embodiments, after receiving this message, the user journey would therefore continue with such rapid fire questions, for example the section (Gathering Questionnaire Responses (“20(ish) questions”)) above.

[0314]

[0236] In some embodiments, the user is given 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such summary messages. In some embodiments, the user is given greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such summary messages. In some embodiments, the user is given not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such summary messages.

[0315]

[0237] As FIG. 18 illustrates (“7 will use this to filter people out”), certain partner preferences may be used as “hard filters.” In some embodiments, when a dating profile is used to search for candidates, a hard filter is applied to strictly exclude candidates that do not meet certain predefined preferences. For example, a user can indicate a preference for “no smokers,” or can specify a hard height requirement, so that dating candidates that do not meet these criteria will be automatically filtered out from any search. These “hard filter” rules, in embodiments, are associated with a user profile.

[0316] H. Generating Dating Archetype

[0317]

[0238] In some embodiments, a method comprises generating a “dating archetype” for a user based on user information. In some embodiments, a dating archetype is selected for the user by the Al agent, based on user information.

[0318]

[0239] In some embodiments, a dating archetype is selected for the user based on user responses to a quiz, for example a 10 question mini quiz. With a 10 question mini quiz, the Al agent can for example pull the mini quiz questions from a larger question bank.

[0319]

[0240] In some embodiments, questions are selected, either individually or in the aggregate, based on their ability to efficiently predict an archetype from limited information. For example, the selection may aim to reduce the total number of questions or to increase the predictive power of a question set with a specified number of questions.

[0320]

[0241] In some embodiments, certain archetypes are applicable only to (or are weighted so they are more likely for) a male or female user, while other archetypes are gender neutral.

[0321]

[0242] In some embodiments, the dating archetype is hidden on a user profile. In some embodiments, a user is provided with the dating archetype, along with a phrase explaining the meaning of that dating archetype. For example, for the “nolita dirtbag,” the explanation could be, “He’s the type to have lit readings, art gallery openings, and dive bars to attend to — if someone wants to join him, that’s chill. We’re just hanging out, no big deal, we’re just chatting. Let’s talk about the culture — ironically, but also in earnest?”

[0243] In some embodiments, the user is told, “your type is [archetype]. Does this sound right to you?” If the user replies no, the Al agent can respond, for example, “sounds like I have some refining to do... let’s continue on and figure out what your type really is,” and ask further questions before selecting another archetype for the user. If the user replies yes, the Al agent can respond, for example, “great, let ’s explore this more! Now, I want to understand more about your demographic preferences and physical type. Should we continue?”

[0322]

[0244] Exemplary dating archetypes include the serial dater, the romantic, nolita dirtbag, party girl, tech bro / girl, finance bro, fashion girlie, fitness fanatic, foodie, serial monogamist, Nara Smith, social justice icon, zero waste guy, desi darling, harvard all star, the hustler, soccer dad, silver fox, commitment phobe, parent pleaser, risk taker, girl next door, high school hero, international man of mystery, sensitive artiste, life of the party, chronically online, park avenue princess, the intellectual, hipster, the dilettante, NJB, sports betting guy, career girl, military dude, politically savvy, JAP, CS guy, free spirit, the planner, frat boy, the writer, raver, arab fboy, and such others as will be known to those of skill in view of this disclosure, and may differ, for example, depending on market and geography.

[0323] I. Gathering Profile Photos

[0324]

[0245] In some embodiments, a method comprises gathering one or more profile photos from a user. For example, a user may be presented with an app screen requesting photos, comprising language such as “Ooh fancy. Let’s add your pictures ’

[0325]

[0246] In some embodiments, an Al agent gathers photos from the user in conversation, such as in a 1-on-l chat. In some embodiments, a user is asked to provide at least a number of photos, such as three photos, and will be told to include clear photos of the user’s face (“3 photos required. Be sure to include clear photos of your pretty face!”'). In some embodiments, a user is asked to provide more than three photos, such as six photos.

[0326]

[0247] Users may upload photos and then crop, adjust, reorder, and the like, according to methods well known in the art, and using software modules and user interface features well known in the art. Uploaded user photos are, in embodiments, associated with a user profile. In some embodiments, the part of the profile journey when a user is prompted to take a verification selfie to confirm the user’s identity (as described elsewhere herein) is after uploading the photos. J. Generating User Dating Profile

[0327]

[0248] In the disclosed methods, after a sufficient amount of user data is gathered, a user profile, such as a dating profile, can be generated. Sufficiency may be predefined (for example, after a predefined number of questions is answered, or after a predefined amount of user information is gathered) or dynamically determined (for example, after the amount of user information gathered is deemed sufficient for achieving a satisfactory level of discrimination or differentiation of users, with the satisfactory level, for example, being predetermined).

[0328]

[0249] In some embodiments, after a sufficient amount of user data is gathered, a user profile is generated, and may be displayed to the user.

[0329]

[0250] FIGS. 19 and 20 show features of an exemplary displayed user profile comprising displayed elements of the user profile for “Andreas,” the user of exemplary embodiments of FIGS. 4-18. Together with the displayed elements gathered for and displayed in the basic user profile for Andreas of FIG. 10 (“ the first section of your profile''), and additional displayed elements comprising the prompts for Andreas of FIG. 17 ("some prompts for you based on your answers’") (together as the displayed elements of FIG. 20), the exemplary displayed user profile for Andreas further comprises the profile photo of FIG. 19.

[0330]

[0251] Additional user dating profile features may include social media accounts (e.g., Instagram, Linkedln, Twitter / X), and any communities the user belongs to (as described below).

[0331]

[0252] In some embodiments, a user profile comprises a “displayed” area (comprising “displayed elements”) and a “hidden” area (comprising “hidden elements”). In some embodiments, certain user profile elements must be displayed. In some embodiments, certain user profile elements must be hidden. In some embodiments, certain user profile elements are displayed by default, but can be optionally hidden. In some embodiments, certain user profile elements are hidden by default, but can be optionally displayed. In embodiments, a user must decide whether to display or hide certain user profile elements at the time of providing the user information for such elements, but can, in some embodiments, change that decision later.

[0332]

[0253] For example, in some embodiments, displayed elements comprise location, height, religion, political leaning, ethnicity, home town, work, education, “about me” prompts or “takeaways,” social media accounts, and communities. In some such embodiments, certain of the displayed elements can be optionally hidden, for instance political leaning and religion.

[0333]

[0254] In some embodiments, hidden elements comprise archetype (“JUe think your type is... ”), Non-negotiables, Nice-to-haves, and Red flags. Hidden elements can be presented to the user with a relevant note, such as: “This section is visible only to you. Matches will be found for you based on this informati n ''

[0334]

[0255] As described further in the sections on system architecture below, and as known to those of skill in view of this disclosure, profde information may be stored using a variety of data structures and formats, depending on the embodiment. In some embodiments, user profde information is stored in various fields within a relational database, such as PostgreSQL.

[0335]

[0256] Disclosed as Appendix B is an exemplary underlying data structure for storing user profile information, such as the user profile information described herein, and which could be implemented as relational tables (SQL), key-value pairs (JSON), or object-oriented structures (Python objects), and which in embodiments can be stored in a PostgreSQL database.

[0336] K. Reviewing User Dating Profile

[0337]

[0257] In some embodiments, following the profile journey described in embodiments above, the completed user profile is sent to an Admin System, such as described below. In such embodiments, the user profile is submitted for review to an administrator, who will review the completed user profile, and decide whether to approve it, such as for publication, or so that the user can participate on the dating platform, such as becoming a candidate for a setup. Herein, “publication” refers to any display of a user profile to the general public, such as its appearance in search results on a dating platform, and any other such display to or visibility by third parties.

[0338]

[0258] In some embodiments, where a user makes edits after the user’s profile has been submitted for review, but before it has been approved, the user has to resubmit it. In some embodiments, where a user makes edits after the user’s profile has been approved, the user has to resubmit it. In some embodiments, a user can make certain edits without having to resubmit the user’s profile. In some embodiments, certain edits cannot be made by the user without having to resubmit the user’s profile.

[0259] In some embodiments, the administrator is one or more humans, such as trained to perform review of user profiles. In some embodiments, the administrator is an Al agent, such as trained to perform review of user profiles. In some embodiments, where the administrator is an Al agent, the Al agent is specially trained for user profile review. In some embodiments, where the administrator is an Al agent, the Al agent who performs user profile review is separate from the Al agent who performs user profile creation, such as generation. In some embodiments, the administrator is a combination of one or more humans and one or more Al agents.

[0339] L. Updating User Dating Profile

[0340]

[0260] In some embodiments, a user profile can be updated any time, although in some such embodiments, a user profile must be reviewed again after it has been updated.

[0341]

[0261] In some embodiments, a user can ask the Al agent to recommend changes, updates, or new information for the user’s profile. In some embodiments, the Al agent will suggest to a user recommended changes, updates, or new information for the user’s profile. In some embodiments, the Al agent will suggest to a user recommended changes, updates, or new information for the user’s profile based on additional user information provided by the user. In some embodiments, the Al agent will suggest to a user recommended changes, updates, or new information for the user’s profile based on one or more missing user attributes, or the user’s attribute score. In some embodiments, the Al agent will suggest to a user recommended changes, updates, or new information for the user’s profile based on the user’s insufficient number or speed of setups, or based on the type of setups being made in view of the old user profile.

[0342] M. Additional Exemplary User Profile Creation Features

[0343]

[0262] In some embodiments of the disclosed systems and methods, additional aspects and embodiments may be used to gather further user information, determine or update further user attributes, and generate or update a user dating profile with such further data. Several such additional exemplary user profile features are described, but variations of these and other user profile creation features will be appreciated in view of the full disclosure and the skill in the art. 7. “Communities”

[0344]

[0263] In some embodiments, a user may join a “community.” For example, in some such embodiments, an Al agent may ask whether a user would like to join a community during a 1-on- 1 chat, for example if a user attribute qualifies the user for the community, or if one or more user attributes, either alone or together, qualify the user for the community, such as based on one or more predefined attributes. In some embodiments, a “community” is a subgroup of users having a shared background (e g., same undergrad or grad school) or a shared interest or activity (e.g., yoga, photography, marathon running). Exemplary communities may include “Yoga,” “Z-List,” “Journal Series,” “Photographers,” and “NYC Marathon.” An Al agent may suggest communities based on overall popularity or based on user information. An Al agent may provide a search bar for a user to search for communities. In some embodiments, the presence or activity of a community is hidden to a user unless the user is in the community, or qualifies to be in the community, such as based on one or more predefined attributes. In some embodiments, an invite code is required to join a community. An Al agent may request to display the communities of which a user is a member on their displayed user profile, or may give a user the option to display such communities on their displayed user profile. In some embodiments, an Al agent may suggest setups where the users are members of the same community.

[0345] 2. Sexual Preference Information

[0346]

[0264] In some embodiments, a user will be prompted to complete a “sexual preference” flow, for gathering additional information about sexual preferences. In some embodiments, the sexual preference flow will come after the original information gathering, and after a user has been introduced to their basic profile, and if applicable, their type. In other embodiments, the sexual preference flow will come after the user has completed their first dating profile. In some embodiments, the sexual preference flow will be completely optional. In some embodiments, user profile information about sexual preferences will be optional for the user to display.

[0347]

[0265] In some embodiments, a description for the flow may be “give us insights into your kinks! it is optional to include sexual preferences but x% of users do, and this will help me find matches with sexual compatibility,” and a “let’s go” button will show up for a user, if the user has the option to be directed to the flow. In general, the flow will only be available to users who are at least 18 years old.

[0348]

[0266] In some embodiments, an Al agent will lead a user through a conversation, such as a 1-on-l chat, to gather sexual preference information. Exemplary prompts may include: “how important is sex to you? (what would you do if sex was “bad” with your partner?)”; “what sexuality best describes you?” (the Al agent will be able to give options / examples if prompted); “How fluid is your sexuality?”; “Are you interested in monogamy, poly relationships, or either?”; “How open are you to exploration?”; “what are your kinks?” (including, in embodiments, questions about specific kinks); “Have you participated in or would you be open to a group sex with your partner?”; and similar questions as will be apparent in view hereof.

[0349] 3. Additional Quizzes

[0350]

[0267] In some embodiments, information provided by a user in responding to a quiz is used to generate the first dating profile of a user, such as described in embodiments above. In further embodiments, after a user has a dating profile, the user can take one or more additional quizzes, and information provided by the user in responding to any additional such quizzes is used to update the dating profile of the user.

[0351]

[0268] In some embodiments, a user always will have the option to keep answering more questions to refine their profile further after meeting one or more qualifications, such as taking a subscription, or taking a subscription at a certain level. In some embodiments, the Al agent will keep generating questions based on a question database, and will keep asking a user if they want to continue beyond the required set.

[0352]

[0269] In some embodiments, a new quiz may become available periodically, or a user may be notified periodically that a new quiz is available. A new quiz could include, for example, a Love Language quiz. A user, for example, can be notified on a user dashboard or via text message from an Al agent that a new quiz is available. Notifications also can include information about how many users have taken the new quiz, or whether taking the new quiz improves setups, such as the number of available setups, the quality of available setups, the probability of a successful setup, and the like. In some embodiments, the Al agent will inform a user that they can continue to build out their profile with additional questions generated from the database. IV. Exemplary System Architecture

[0353]

[0270] In some aspects are disclosed systems for performing the disclosed methods, such as for generating and updating user profiles through conversation and interaction with an Al agent, such as for creating dating profiles, including using questionnaire responses, quiz answers, personal and preference information, and other user data, such as described herein.

[0354]

[0271] It will be readily appreciated that embodiments of the disclosed methods can be implemented in numerous ways and using numerous forms of systems and types of system architecture, and that unless clearly indicated as being directed to a particular implementation or type of implementation, the disclosed embodiments are not limited to any such specific implementations. The following system architecture features are provided solely to further elucidate certain exemplary aspects and embodiments of the disclosure, but are not limiting.

[0355] A. Exemplary Software Modules and Software / Hardware Modules

[0356]

[0272] A disclosed method or one or more steps of a disclosed method may be implemented in an application, such as a computer application or a smartphone app. Methods or steps of methods implemented in an application may be implemented as one or more modules, such as software modules, which for example may comprise code executable on a computer system, and such code may be stored on computer-readable media, such as non-transitory media.

[0357]

[0273] In some exemplary applications, certain embodiments of the disclosed methods or steps of such methods therefore are implemented in software, such as in software instructions executable by a computer processor, for example as an application, which may be web- or browserbased, on a smartphone, or on any computer system disclosed or known in the art.

[0358]

[0274] In some exemplary applications, a computer application, such as a smartphone application, is provided that performs one or more steps of the disclosed methods. Some such exemplary applications, and certain aspects and features of those applications, are described below. These applications are exemplary only, and numerous other such applications, comprising these and other aspects and features, and which may comprise more or less such aspects or features, as well as may comprise one or more modifications to any such aspects or features, and may be combined with any one or more aspects or features of any other disclosed embodiments, will be readily appreciated in view of the teachings herein and the ordinary skill in the art.

[0359]

[0275] In some exemplary applications, one or more embodiments of the disclosed methods, or one or more steps of such methods, are implemented in a combination of software and hardware, such as described herein or known in the art.

[0360]

[0276] For example, one or more steps or stages of any method, process, or algorithm described or illustrated herein (which for convenience and as shorthand may be simply “steps” of a disclosed “method”) may be embodied directly in hardware, in software such as executed by a processor, or in a combination of both hardware and software and / or other components. To help illustrate the interchangeability of hardware and software, certain components, blocks, modules, means, steps, and the like may be disclosed generally in terms of their functionality. How such functionality is implemented as hardware and / or software may depend upon the particular use and any design constraints, which will be understood by one in the art in view of the disclosure. Those in the art will understand how to implement such functionality in various ways.

[0361]

[0277] In some embodiments, software, hardware, and / or combinations thereof are configured to implement one or more functions associated with the disclosed methods. In some embodiments, such functions are in the form of modules implementing the functionality in software, hardware, and / or hardware / software combinations.

[0362]

[0278] For example, computer code may comprise one or more modules executing one or more processes to provide a useful result, and the modules may communicate with one another via means known in the art.

[0363]

[0279] In some embodiments, one or more Application Programming Interfaces (APIs) are used. An API is an interface implemented by an API-implementing program code component or hardware component that allows a different API-calling program code component or hardware component to access and use one or more functions, methods, procedures, data structures, classes, and / or other services provided by the API-implementing component. An API can define one or more parameters that are passed between the components. See generally, e.g., U.S. Pat. No. 10,852,912 (King et al.; Apple Inc.), incorporated by reference as if fully set forth herein.

[0280] In some embodiments, one or more steps of a disclosed method are implemented in a computer application, such as in a software application, including in one or more modules, such as in one or more software modules.

[0364]

[0281] In some embodiments, an exemplary disclosed system comprises multiple software modules or software / hardware modules. Such modules, as they relate to user profde creation, primarily comprise a core app, a scoring engine, and an admin system. Other exemplary systems will be appreciated by those of skill, and may comprise fewer or more modules. One will understand that, although software modules run on hardware, reference may be made solely to the software for purposes of its illustration, but its execution on hardware will be well known.

[0365] 1. Core Application

[0366]

[0282] In some embodiments, the core app performing a disclosed method, for example a smartphone app downloaded and installed by a user, is implemented as a standard Django web application with a PostgreSQL database. For example, it may be integrated with Django serving as a backend framework for managing user interactions, business logic, and rendering content, and PostgreSQL acting as a relational database for storing user information and other user data, but excepting image data, such as user photos, which are stored in Amazon S3 cloud object storage. Such implementation, in embodiments, facilitates efficient data management, scalability, and integration with various web technologies.

[0367]

[0283] In some embodiments, the core app incorporates various queues and batch processing systems to handle tasks asynchronously. Such tasks may include background processing of user data, sending notifications, or performing computationally intensive operations such as Al-driven profile generation and analysis, and Al-driven matchmaking.

[0368]

[0284] In some embodiments, the core app may utilize task queues, such as Celery integrated with Django, Redis Queue (RQ), or Django-Q, to manage background tasks, synchronize API data, schedule periodic jobs, and offload long-running operations, such as sending notifications, processing user profiles, processing images, or updating profile recommendations, to background workers. Such background processing, in embodiments, ensures that the user experience remains responsive by executing these tasks outside of the main requestresponse cycle.

[0285] In some embodiments, batch processing is employed to periodically update large datasets, and such batch jobs may be scheduled at regular intervals or triggered based on specific events (e.g., after a certain number of new user profiles has been generated).

[0369] 1. Scoring Engine

[0370]

[0286] In some embodiments, the disclosed systems and methods comprise a scoring engine. The scoring engine may be used to analyze and score user profiles, and may leverage an Al agent or Al model. For example, the scoring engine may be an Al agent utilizing a large language model (LLM), such as GPT.

[0371]

[0287] In some embodiments, after a user profile is generated, the profile is scored by an LLM. For example, the LLM may assign scores to various user attributes. In some embodiments all user attributes are scored, while only a subset of attributes are scored in other embodiments.

[0372]

[0288] In some embodiments, all user attributes are predefined. In other embodiments, new user attributes may be dynamically created based on the analysis and scoring of existing attributes by the LLM. User attributes also may be dynamically modified or removed (“retired”) in some embodiments, for example if they are determined to have low value in differentiating users or otherwise have limited utility for their intended purpose.

[0373]

[0289] User attributes may include qualities such as extroversion, ambition, and lifestyle preferences, and other such qualities that together can provide a comprehensive representation of a user profile. An exemplary list of about 75 user attributes, suitable for disclosed embodiments, is described in Appendix A.

[0374]

[0290] While about 75 attributes is a suitable number for disclosed embodiments, embodiments comprising more or fewer attributes also are within the scope of the disclosure, and “about 75” is exemplary and not limiting. In some other embodiments, a suitable number of attributes includes a number of attributes between about 65 and 75, between about 60 and 80, between about 55 and 85, between about 50 and 85, between about 45 and 90, between about 40 and 95, and between about 35 and 100. In some other embodiments, a suitable number of attributes includes a number of attributes greater than 30, greater than 35, greater than 40, greater than 45, greater than 50, greater than 55, greater than 60, greater than 65, greater than 70, greater than 75, greater than 80, greater than 85, greater than 90, greater than 95, or greater than 100.

[0375]

[0291] In some embodiments, an Al agent, such as an LLM, scores a user profile on each of the user attributes, generating an aggregate of user attribute scores (a “user profile score”). One exemplary scoring method is described, with reference to Appendix A.

[0376]

[0292] This exemplary scoring method uses the set of approximately 75 attributes reproduced as Column A in the attribute table in Appendix A. These attributes include characteristics such as education level, income level, values, political leaning, ambition, creativity, morality, and others therein.

[0377]

[0293] For each attribute in Column A, the corresponding row in Column B provides an explanation of the meaning of the respective attribute.

[0378]

[0294] For each attribute in Column A, the corresponding row in Column C provides a description of “What a ‘ 1’ is,” indicating the criteria or conditions that would lead to a high score (i.e., a score of 1) for that attribute.

[0379]

[0295] For each attribute in Column A, the corresponding row in Column D provides a description of “What a ‘0’ is,” indicating the criteria or conditions that would lead to a low score (i.e., a score of 0) for that attribute.

[0380]

[0296] Some embodiments of this exemplary scoring method employ an Al agent, such as GPT, to assign scores for each user attribute.

[0381]

[0297] The Al agent is configured to access multiple data inputs, including quiz data provided by the user and free-form text responses submitted by the user. The Al agent processes this user input data (for example, structured similarly to Appendix B) and compares it to the descriptions provided in Appendix A for each of the 75 attributes.

[0382]

[0298] For each attribute, the Al agent evaluates the user input data, such as the quiz data and free-form text responses, and evaluates this user input data in view of the descriptions in Columns C and D.

[0299] Based on this evaluation, in some embodiments, the Al agent assigns a score to each attribute, where the score may be a binary value (1 or 0).

[0383]

[0300] In some embodiments, the Al agent assigns a continuous value between 0 and 1, to reflect the degree to which the user input data matches the high or low score criteria described in Columns C and D.

[0384]

[0301] In some embodiments, the generated scores for each attribute are stored as a feature vector representing the user profile, which represents the user attribute scores in multi-dimensional space, with each dimension corresponding to one attribute, for example each one of the about 75 attributes. This feature vector represents the user profile score.

[0385]

[0302] In some embodiments, the feature vector is inserted into any suitable vector database for storing such feature vectors. Exemplary vector databases include Pinecone, FAISS (Facebook Al Similarity Search), Weaviate, Milvus, Vespa, and Annoy (Approximate Nearest Neighbors Oh Yeah). The vector database also may store the user profile scores of other users.

[0386]

[0303] It will be readily appreciated that this exemplary scoring method allows the system, through the Al agent powered by an LLM, to use user input data, including for example both structured quiz data and unstructured free-form text responses, to generate a nuanced representation of user attributes, and consequently a nuanced representation of the user.

[0387]

[0304] It will be further appreciated that such scoring methods, in some embodiments, will reduce the amount of user information necessary to achieve one or more satisfactory levels of discrimination or differentiation of users, to generate one or more specific outputs or generate a specific quantity or quality of one or more specific outputs, or for such other purposes disclosed herein, with at least one such satisfactory level being known, for example, in view of this disclosure by those of skill in the art. A reduction of the amount of necessary user information for such discrimination or differentiation, for such generation of one or more specific outputs or generation of a specific quantity or quality of one or more specific outputs, or for such other purposes disclosed herein, will be understood to be among the advantages and improvements of the invention.

[0305] For example, in some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information provides greater discrimination or differentiation of users than the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information provides greater discrimination or differentiation of users than the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information provides greater discrimination or differentiation of users than the closest prior art method.

[0388]

[0306] In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information generates one or more specific outputs or generates a specific quantity or quality of one or more specific outputs greater than the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information generates one or more specific outputs or generates a specific quantity or quality of one or more specific outputs greater than the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information generates one or more specific outputs or generates a specific quantity or quality of one or more specific outputs greater than the closest prior art method.

[0389]

[0307] In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information is optimized with regard to one or more other purposes disclosed herein compared to the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information is optimized with regard to one or more other purposes disclosed herein compared to the closest prior art method. In some embodiments where, for any specific item or class of user information, an amount of such user information gathered comprises not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such items or items within such class, a disclosed method comprising gathering the amount of such user information is optimized with regard to one or more other purposes disclosed herein compared to the closest prior art method.

[0390]

[0308] Where not otherwise expressly provided for any item or class of user information, it will be understood that for such item or class of user information, an amount of such user information gathered in some embodiments comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 such items or items within such class. In other embodiments, an amount of such user information gathered comprises greater than 1, greater than 2, greater than 3, greater than 4, greater than 5, greater than 6, greater than 7, greater than 8, greater than 9, or greater than 10 such items or items within such class. In other embodiments, an amount of such user information gathered comprises not more than 1, not more than 2, not more than 3, not more than 4, not more than 5, not more than 6, not more than 7, not more than 8, not more than 9, or not more than 10 such items or items within such class.

[0391]

[0309] Although the set of numbers from 1 to 10 is described in the examples above, other sets of numbers are equally applicable to such examples, including the set of numbers 1-30.

[0310] Where a specific numerical quantity of user information inputs is disclosed or claimed, including an amount greater than or no more than a specific numerical quantity, in some embodiments the specific numerical quantity of user information inputs may provide a technical effect relating to the amount of necessary user information, with the technical effect providing unexpected results over prior art systems and methods. In some embodiments, the technical effect also may provide a technical improvement, including an improvement in the functioning of a computer or in computer-related technology.

[0392]

[0311] In some embodiments, the user profile score is associated with the user profile.

[0393] 2. Admin System

[0394]

[0312] In some embodiments, following the profile journey wherein a user, in conversation with an Al agent, generates a user profile, the completed user profile is received by the Admin System, where an administrator will review the completed user profile, and decide whether to approve the completed user profile.

[0395]

[0313] As examples, a completed user profile can be reviewed to determine if the content of the user profile adheres to community guidelines, to verify authenticity of the information provided, or to ensure compliance with any specific requirements set forth by the platform, such as prohibitions against certain language or inappropriate content. Additionally, the administrator may assess whether there are sufficient other users on the platform with compatible attributes or user profiles that can be meaningful setup candidates for the user, and may accordingly wait to approve a user profile until there is a critical mass of other appropriately-situated users.

[0396]

[0314] The administrator may be one or more humans, one or more Al agents, or a combination thereof. In some embodiments, the administrator is a human. In some embodiments, the administrator is a plurality of humans who review a user profile together or in combination. In some embodiments, the administrator is an Al agent. In some embodiments, the administrator is a plurality of Al agents who review a user profile together or in combination. In some embodiments, the administrator is a human and an Al agent, who review a user profile together or in combination. In some embodiments, the administrator is a plurality of humans and a plurality of Al agents, who review a user profile together or in combination. The administrator also may be a single human working with a plurality of Al agents, a plurality of humans working with a single Al agent, and such other combinations as will be readily appreciated.

[0397]

[0315] In some embodiments, the human administrator will be trained to perform review of user profiles. In some embodiments, the Al administrator will be trained to perform review of user profiles. In some embodiments, the administrator, whether human, Al, or a combination thereof, will be trained to perform review of user profiles.

[0398]

[0316] In some embodiments, during the time when a completed user profile is under review, the user will remain on a waitlist. Once a completed user profile is reviewed, the user can come off the waitlist, proceed to publication and appearance in search results on a dating platform, or otherwise participate on the platform, such as becoming a candidate for a setup.

[0399]

[0317] Once the user profile is generated and published, for example on the dating platform, system algorithms will match the user with other platform users, based on the data input and profiles, to facilitate dating connections. In preferred embodiments, only a limited number of matches per day are provided to the user. User profiles can be modified after publication in accordance with the methods / systems described herein.

[0400] 3. Large Language Models (LLMs)

[0401]

[0318] In some embodiments, one or more steps of a disclosed method are performed using artificial intelligence (Al). In some such embodiments, the one or more steps are performed using an Al model capable of processing and generating human language. In some such embodiments, the Al model is trained on datasets of text and may use machine learning or deep learning techniques, such as transformer architectures, to interpret various aspects of human language, including context, syntax, and semantics, including to interpret user language input, to generate responses, and to perform other language-based tasks.

[0402]

[0319] In some embodiments, the Al model is a Large Language Model (LLM). For example, the LLM may be a Generative Pre-trained Transformer (GPT)-based model, such as ChatGPT, including GPT foundation models like GPT-4 and GPT-4o (OpenAI, San Francisco, CA). Other exemplary LLMs useful in the practice of disclosed methods include Bidirectional Encoder Representations from Transformers (BERT), Pathways Language Model (PaLM), and T5 (Google, Mountain View, CA); Large Language Model Meta Al (LLaMA) (Meta, Menlo Park, CA); Claude (Anthropic, San Francisco, CA); and others as will be well known in the art.

[0403]

[0320] In some embodiments of the disclosed systems and methods, an Al agent utilizes a LLM to generate user profiles. In some embodiments, the LLM is integrated into the system or method to process natural language inputs from users, and to interpret these inputs using natural language understanding (NLU) techniques. In some embodiments, the LLM is employed to identify user attributes from user information and other user data using natural language generation (NLG). In some embodiments, disclosed systems and methods utilize the LLM within the profile creation process to analyze user data, identify key characteristics, and create user profiles based on a nuanced understanding of language, context, and individual user preferences.

[0404]

[0321] It will be readily appreciated that integration of an LLM, such as any of the exemplary LLMs above or other LLMs known to those in the art, with a disclosed system or method, will be according to this disclosure and ordinary skill in the art. For example, in some embodiments, the LLM is integrated into disclosed systems and methods through an application programming interface (API) that facilitates seamless communication between the LLM and one or more other aspects of the system, such as one or more other software modules. In some embodiments, the API is an intermediary layer through which other aspects of the system send user data, such as text inputs, to the LLM for processing. Upon receipt of such text inputs, in some embodiments, the LLM uses natural language understanding (NLU) to analyze the text inputs, and uses natural language generation (NLG) to generate a response and / or to create or update a user attribute. In some such embodiments, the API allows for real-time or near real-time data exchange, ensuring that different aspects of the overall system architecture can receive, process, and send information and generate or update a user profile without significant latency.

[0405]

[0322] In some embodiments, the system architecture of a disclosed system or method comparison an LLM enables modularity, whereby the LLM may be updated or replaced independently of other system components, such as other hardware or software modules, while maintaining seal ability and flexibility in responding to and processing user inputs, including to generate and update user attributes and generate and update user profiles, such as dating profiles.

[0323] In some embodiments, the LLM uses natural language understanding (NLU). In some embodiments, the LLM interprets user-generated text such as preferences, profile descriptions, and conversation responses. In some embodiments, the LLM extracts meaning and intent from user input through tokenization, embeddings, and contextual analysis.

[0406]

[0324] In some embodiments, the LLM uses natural language generation (NLG). In some embodiments, NLG uses deep learning algorithms to generate contextually relevant language outputs. NGL typically involves converting structured or unstructured data into readable, fluent text, including summarization, content generation, and dialogue responses.

[0407]

[0325] In some embodiments, the LLM uses text generation. In some embodiments, the LLM generates contextually appropriate responses or suggestions based on the user’s input. In some embodiments, the LLM creates natural-sounding conversations and recommendations that adapt to the user’s communication style over time.

[0408]

[0326] In some embodiments, the LLM uses fine-tuning. In some embodiments, a LLM is fine-turned on domain-specific data. Domain-specific data, for example, can be any of dating- related information, dating-related dialogues, user preferences, behavioral patterns, quiz responses, user-user communication, polls, and screenshot uploads. In some embodiments, the LLM, such as ChatGPT, is fine-tuned based on dating blog content turned into a set of synthetic conversations and used as training data.

[0409]

[0327] In some embodiments, the LLM learns from one or more user interactions. In some embodiments, the LLM adapts based on one or more user interactions with the Al agent. In some embodiments, the LLM continuously updates its understanding of each user’s preferences and conversational tendencies. In some embodiments, continuous learning enables personalized recommendations and improves setup quality.

[0410]

[0328] In some embodiments, the LLM processes user input privately and securely. In some embodiments, user data processed by the LLM is handled in accordance with privacy standards, such as described herein, ensuring that user data, including PII, remains secure throughout the operation of the LLM, including together with other software modules.

[0329] In some embodiments, an Al agent utilizes an LLM to interact directly with a user in a conversational manner, such as a manner having a tone or style as described herein, to collaboratively construct a user profile with the user, such as a dating profile, for example as described herein. Through a 1-on-l chat interface, an Al agent utilizing an LLM can engage a user by asking personalized questions and responding to user inputs in a dynamic and personalized way, utilizing natural language understanding (NLU) to interpret the user’s responses and natural language generation (NLG) to provide meaningful feedback and suggestions. In some embodiments, an LLM-driven Al agent (herein equivalent to “an Al agent utilizing an LLM” and “an Al agent powered by an LLM” and like terms, unless context demands otherwise) dynamically adapts a 1-on-l chat-based conversation based on the user’s answers, preferences, and tone, for example to guide the user through a profile creation process.

[0411]

[0330] Numerous advantages of and improvements resulting from the advance of generating a user profile in real-time, interactive dialogue with an Al agent will be readily apparent from this disclosure, including the creation of user profiles of greater comprehensiveness, personalization, and refinement, and ultimately, utility in providing users with optimal setups, aligned with each user’s unique preferences, characteristics, and attributes.

[0412] 4. Additional Exemplary Software Modules

[0413]

[0331] In some embodiments, one or more steps of a disclosed method comprise a 1-on-l chat between an Al agent and a user, such as described above. In some such embodiments, the 1- on-1 chat may comprise voice chat, including where (such as where implemented using a chat interface, including in a smartphone app) the Al agent “speaks” to the user, where the user speaks to the Al agent, and combinations thereof. Voice chat can be implemented using numerous means of automatic speech recognition (ASR) and text-to-speech (TTS) known in the art, including native software such as Speech Framework and AVSpeechSynthesizer for iOS (Apple, Cupertino, CA) and Speech-to-Text and Google TTS for Android (Google, Mountain View, CA), and also crossplatform APIs such as Azure Cognitive Services (Microsoft, Redmond, WA) or Transcribe and Polly (Amazon, Seattle, WA). B. Exemplary Computer-Readable Media

[0414]

[0332] In some aspects are disclosed systems comprising computer storage products such as computer-readable media. In some embodiments, a software module resides on a computer- readable medium. Herein, reference to either “a computer-readable medium” or to “computer- readable media” should be understood also to refer to the other, unless context demands otherwise, and computer code, such as a software module, generally will be appreciated to be interchangeably storable on “a medium” or “media,” and the terms are thus equivalents, unless reference to one or the other is necessary to give technical effect to a term or embodiment.

[0415]

[0333] In some embodiments, a computer-readable medium has computer code thereon for performing various computer-implemented operations, such as operations related to one or more functions or steps associated with the disclosed methods. The computer-readable medium and the computer code thereon may be designed for purposes of the disclosure, may be of any kind known in the art, or may be a combination thereof, including depending on the embodiment.

[0416]

[0334] Examples of computer-readable media include solid-state drives (SSDs), magnetic media such as hard disk drives (HDDs); removable disk storage including optical media such as CD-ROMs, DVDs, and holographic devices; magneto-optical media; RAM memory; flash memory; ROM memory; EPROM or EEPROM memory; and registers. Other examples of computer-readable media include hardware devices configured to store and / or execute program code, such as programmable microcontrollers, ASICs, and programmable logic devices (PLDs).

[0417]

[0335] Generally speaking, computer-readable media includes any tangible or non- transitory storage media or memory media such as electronic, magnetic, or optical media, including any one or more of the exemplary media described herein or known in the art, and which may be coupled to a computer system via a bus.

[0418]

[0336] Herein, the terms “tangible” and “non-transitory” describe a computer-readable medium excluding propagating electromagnetic signals, but unless context dictates otherwise such terms are not intended to limit the type of physical medium. For instance, the terms “non-transitory computer-readable medium” and “tangible memory” are intended to encompass types of storage devices that do not necessarily store information permanently, such as random access memory (RAM). Program instructions and / or data stored in tangible memory in non-transitory form may be transmitted by transmission media or signals such as electrical, electromagnetic, or digital signals, and also may be conveyed via a communication medium such as a network and / or a wireless link. The terms “tangible” and “non-transitory” thus should be understood as a limitation of the medium itself (i.e., that it is tangible, and not merely a signal), but not a limitation on the volatility or persistency of data stored or storable on the medium (e.g., RAM vs. ROM), unless such limitation is expressly made or demanded by the context.

[0419]

[0337] In some embodiments, a computer-readable medium is coupled to a processor so that the processor can read information therefrom, and write information thereto. In other embodiments, a computer-readable medium is integral to a processor, for example where the computer-readable medium and the processor reside in an ASIC, which may reside in a user terminal. A processor and a computer-readable medium also may reside as discrete components in a user device, such as a user terminal. A “user terminal” includes any device of a user, suitable for use in any of the disclosed systems and methods, such as the examples described herein.

[0420] C. Exemplary Executable Instructions

[0421]

[0338] In some aspects are disclosed systems comprising computer-readable media, such as non-transitory media, storing executable instructions. In some embodiments, executable instructions are computer-executable instructions, machine-executable instructions, and / or processor-executable instructions.

[0422]

[0339] In some embodiments, executable instructions comprise computer code. Examples of computer code include machine code, such as produced by a compiler or other machine code generation mechanisms, scripting programs, PostScript programs, and / or other code or files containing higher-level code that are executed by a computer using an interpreter or other code execution mechanism. For example, embodiments may be implemented using assembly language, Java, C, C#, C++, scripting languages, or other programming languages known in the art. Some embodiments also may leverage machine learning frameworks such as TensorFlow, PyTorch, or other libraries for implementing Al-driven functionality. In some embodiments, executable instructions may be executed in a distributed system or cloud-based environment, with parts of the program running on different machines or servers.

[0340] Depending on the embodiment, the disclosed components, blocks, modules, means, steps, stages, and the like, where implemented as executable instructions, such as processorexecutable instructions, may be executed by any suitable processor known to those in the art. In some embodiments, a suitable processor may include a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof that is designed to perform or capable of performing the function(s) embodied in the executable instructions.

[0423]

[0341] In some embodiments, a suitable processor may include a general purpose processor, such as a microprocessor or any conventional processor, controller, microcontroller, or state machine. A suitable processor also may be implemented as a combination of computing devices, e.g., as a combination of microprocessors, of one or more microprocessors and a DSP or DSP core, and other such configurations. A suitable processor also may be one or more communication processors or other such processors specifically designed for implementing functionality in communication devices or other mobile or portable devices such as smartphones.

[0424]

[0342] Besides being embodied in software such as processor-executable instructions, one or more steps or stages of any disclosed method, process, or algorithm also may be embodied directly in hardware, or in a combination of both hardware and software and / or other components, and embodiments herein accordingly also may be implemented in hardwired circuitry in place of, or in combination with, machine-executable software instructions.

[0425]

[0343] In some embodiments, one or more steps or stages of any disclosed method, process, or algorithm is embodied in machine-executable instructions, which are performed by a machine. In some embodiments, the machine is a computer system. The computer system, or other machine, may have a set of instructions, such as computer-executable instructions, or other machine-executable instructions, for causing the computer or the machine to perform or execute any one or more of the steps of a disclosed method, according to one or more embodiments. D. Exemplary Computer Systems

[0426]

[0344] In some aspects are disclosed systems comprising a computer system. A computer system for shorthand may be referred to as a “computer,” and in some embodiments is a user device, such as a user terminal.

[0427]

[0345] In an example, a computer system may include a processor, memory, non-volatile memory, and an interface device. Other common components (e.g., cache memory) will be well known and appreciated by those of skill. The components of the computer system may be coupled together via a bus or through another such device known in the art.

[0428]

[0346] The disclosed systems and methods can be implemented in a variety of different computer systems, including general purpose computer systems, special purpose computer systems, hybrids of general purpose and special purpose computer systems, and other suitable data processing systems and devices.

[0429]

[0347] A computer system may take any suitable physical form. By way of example and not limitation, the computer system may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (e.g., a computer-on-module (COM) or system-on- module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a cloud-based system, a mainframe, a smart TV, a tablet, a mobile phone or smartphone, a VR, AR, XR, MR, or other headset or device, a personal digital assistant (PDA), a wearable device (e.g., a smartwatch, fitness tracker, “smart sunglasses,” or other “wearables”), a server, or a combination of two or more of these, such as a mesh of computer systems.

[0430]

[0348] In some embodiments, a “computer system” may comprise one or more computer systems; may be unitary or distributed; may span multiple locations; may span multiple machines; or may reside in a cloud, which may include one or more cloud components in one or more networks. In some embodiments, one or more computer systems may perform without substantial spatial or temporal limitation one or more steps of one or more disclosed methods. In some embodiments, one or more computer systems may perform in real time or in batch mode one or more steps of one or more disclosed methods. In some embodiments, one or more computer systems may perform at different times or at different locations one or more steps of one or more disclosed methods.

[0349] It will be readily appreciated that additional software and hardware modules not described herein may be necessary for implementing disclosed systems and methods; however, such omitted modules will be well known to those in the art. For example, every computer system discussed herein requires a power supply module, though such mention was omitted.

[0431] E. Exemplary Networking Means

[0432]

[0350] In some embodiments, information from one computer system is transmitted to another computer system. For example, information from a user computer system may be transmitted to another computer system, such as where a user provides input to a browser-based application on a user laptop; where a user provides input to a smartphone app, which is then sent from the user smartphone to a cloud-based server; and the like, as will be readily appreciated.

[0433]

[0351] For example, in the exemplary architecture described above, comprising a core app, a scoring engine, and an admin system, one will readily appreciate that information may be transmitted between all three, as well as to other computer systems, such as cloud object storage.

[0434]

[0352] Where information is transmitted from one computer system to another computer system, any communications interface as will be known in the art may be used, such as wireless networks (e.g., WiFi), local area networks (e.g., a building LAN), wide area networks (e.g., the internet, a cellular network, etc.), and / or by direct communication (e.g., NFC, Bluetooth, etc.). Communications interfaces can be configured to conduct wired and / or wireless communications.

[0435]

[0353] Where information is transmitted from a user computer system to another computer system, such information in some embodiments is encrypted, such as automatically end-to-end encrypted. In some embodiments, at least a portion of such information is encrypted.

[0436] F. Exemplary Input and Output Means

[0437]

[0354] In some aspects are disclosed methods comprising one or more steps where a user provides input to a computer system, or receives output from a computer system. Where a user provides input to, or receives output from, a computer system (which will be understood to also include the software thereon), such as in a step or stage of a disclosed method, it will be understood that such input or output may be provided using any suitable means known in the art.

[0355] Examples of input devices include a physical keyboard, a virtual (e.g., touchscreenbased) keyboard, a touchscreen (which also may be the output device, e.g., a smartphone or tablet screen, a laptop screen), a keypad, a touchpad, a mouse, a joystick, and any other such device performing a similar function.

[0438]

[0356] In some embodiments, input comprises voice input, which for example may be provided using a microphone or any other auditory input device, e.g., by voice commands interpreted by voice recognition software (e.g., resident on the computer), or a voice recognition module (e.g., as part of an application that also performs other steps of a disclosed method).

[0439]

[0357] In some embodiments, input provided by a user to a computer system is processed in real-time by the computer system, or by software on the computer system performing one or more steps of a disclosed method. In some embodiments, input provided by a user to a computer system is subj ect to asynchronous or subsequent processing by the computer system, such as where a text message is sent by a user and stored for a period of time before processing.

[0440]

[0358] From the perspective of any individual computer system, it will be readily appreciated that input may be provided to that computer system (including software thereon) indirectly, e.g., a user may provide input which is first provided to one or more other computer systems or software programs before it is provided to the computer system(s) that perform the step(s) or stage(s) of the disclosed method that use such input. It will be appreciated that input may pass through multiple such computer systems or software programs before being provided.

[0441]

[0359] Where output is provided by a computer system (which will be understood to also include the software thereon), such as in a step of a disclosed method, for example to a user, it will be understood that such output may be provided using any suitable output device known in the art, such as a monitor, a TV, a smartphone screen, a tablet screen, a printer, or any other output device performing a similar function. Output also may be auditory output, and may be provided by a speaker or any other auditory output device. Output also may be provided by a computer system to a user for asynchronous or subsequent use thereby, such as where a text message is sent to a user, and stored within an app, where it is not read by the user until the app is subsequently opened. In some embodiments, a user may receive a notification that it received an output, but not immediately receive the output, such as by choosing not to open the app.

[0360] It will be readily appreciated that, as with input, output also may be provided by a computer system (including software thereon) indirectly, e.g., the computer system may prepare one or more outputs which are provided to one or more other computer systems or software programs before they are provided to a user, according to the step(s) of the disclosed method that involve such output. It will be appreciated that output may pass through multiple such computer systems or software programs before being provided.

[0442] V. Exemplary Technological Improvements

[0443]

[0361] In some embodiments, the disclosed systems and methods provide one or more technological improvements, including to any of the system architecture elements or to any one or more of the software and / or hardware modules discussed herein. For example, disclosed systems and methods may reduce the processing and network bandwidth resources required to generate and update user profiles, such as dating profiles. Additionally, some embodiments provide improvements to a graphical user interface, enhancing both usability and user experience. In certain embodiments, the systems and methods also offer improved mechanisms for inputting and managing user information and other data, as well as optimized means for presenting or outputting information. Furthermore, some embodiments reduce computational time and resource consumption across various devices, such as user devices, servers, cloud-based platforms, or other system components. Depending on the specific embodiment, the disclosed systems and methods may implement none, some, or all of these technological improvements.

[0444]

[0362] Those skilled in the art will recognize and readily appreciate other technological advancements from the present disclosure, including from the figures and claims.

[0445] VI. Additional Aspects and Embodiments

[0446]

[0363] The foregoing aspects and embodiments are exemplary and not limiting. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents of the specific aspects and embodiments described herein. Such equivalents are intended to be, and should be, encompassed by the claims.

[0447]

[0364] It will be readily appreciated by those in the art that the disclosed systems and methods for Al agent-assisted user profile creation will find many varied uses and applications outside of dating profdes for dating apps. For example, in some embodiments, the disclosed systems and methods may be used to create user profdes for friendship apps, such as profdes comprising shared hobbies, interests, or social preferences, to recommend potential friends or social activities. In other embodiments, the disclosed systems and methods may be used to create user profdes for social media apps, such as profdes comprising user interests, activities, and content preferences, to recommend friends or curate social feeds. In other embodiments, disclosed systems and methods may be used to create user profdes for professional networking platforms, such as profdes comprising work experience, education, and career preferences, to recommend job opportunities or networking connections. In other embodiments, disclosed systems and methods may be used to create user profdes for fitness and health apps, such as profdes comprising fitness goals, dietary habits, and activity preferences, to recommend personalized workout plans or track progress. In other embodiments, disclosed systems and methods may be used to create user profdes for e-commerce platforms, such as profdes comprising shopping habits and style preferences, to recommend relevant products. In other embodiments, disclosed systems and methods may be used to create user profdes for streaming services, such as profdes comprising viewing or listening history, to recommend new content. In other embodiments, disclosed systems and methods may be used to create user profdes for educational apps, for example, to track progress in specific subjects or tailor lessons or courses to a user’s skill level. In other embodiments, disclosed systems and methods may be used to create user profdes for gaming apps, such as profdes comprising user stats and gameplay preferences, for example, to track achievements or personalize the gaming experience. In other embodiments, disclosed systems and methods may be used to create user profdes for event, dining, or travel apps, such as profdes comprising preferences and previous activities, for example, to recommend experiences, events, restaurants, bars, or accommodations.

[0448]

[0365] As noted above, a “user profile” for purposes of this disclosure broadly includes all collections, aggregations, combinations, and compilations of user information, generated or updated through interaction with an Al agent using the disclosed systems and methods, irrespective of whether such collections, aggregations, combinations, or compilations of user information are labeled or referred to as user “profdes.” A “user profile” accordingly includes other personalized or individualized collections, aggregations, combinations, or compilations of user information like “date-me docs,” whether or not used for dating apps, “personal profdes” or “friendship profdes,” whether or not used for social or networking apps, “fitness reports” whether or not used for health and wellness apps, and “professional portfolios” whether or not used for career and job -related apps, as well as other like representations that include a sufficient plurality of relevant attributes, preferences, and characteristics of an individual to enable personalized recommendations, pairing, or interactions based on the similarities, preferences, activities, or the like of the individual, as will be readily appreciated in view of this disclosure. Moreover, any reference to an “app” immediately above or elsewhere herein is intended to be exemplary and not limiting. For instance, reference to “dating apps” should be understood to encompass not only applications available on smartphones, tablets, and similar handheld devices but also web-based applications, and dating platforms using other interfaces, and irrespective of whether they are accessed via mobile devices, desktop computers, or other electronic means.

[0449]

[0366] Any combination of the embodiments disclosed in any plurality of the claims or embodiments is contemplated to be within the scope of the disclosure. For example, while the disclosed methods are described as including particular components, blocks, modules, means, steps, and the like, it should be apparent that other methods including fewer, more, or different components, blocks, modules, means, steps, and the like, or methods where any of these are in a different order, are also within the scope of the disclosure. For example, two or more steps may be performed concurrently or with partial concurrence. For further example, steps that are performed as discrete steps may be combined, steps being performed as a combined step may be separated into discrete steps, the sequence of certain processes may be reversed or otherwise varied, and the nature or number of discrete processes may be altered or varied. The methods and associated components, blocks, modules, means, steps, and the like thus should be understood as solely for purposes of illustration and not limitation. All such description, including the particulars and the specific order or hierarchy thereof, only illustrates exemplary approaches, which may differ based on use, implementation requirements of software and hardware, and design preferences, with such differences appreciated by those of skill in view of the disclosure.

[0450]

[0367] The invention may be embodied in other specific forms than those of the exemplary embodiments herein without departing from the true scope of the invention. Any references to the “invention” are intended to refer to the exemplary embodiments thereof and should not be construed to refer to all embodiments of the invention unless the context otherwise requires. The described embodiments are to be considered in all respects only as illustrative and not restrictive. Recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or sub-combination) of listed elements. Recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

[0451]

[0368] The foregoing description, for purposes of explanation, uses specific nomenclature to provide a thorough understanding of the invention. However, it will be apparent to one of skill that specific details are not required in order to practice the invention. Thus, the foregoing description is presented for purposes of illustration and description, and is not intended to be exhaustive or to limit the invention to the precise forms disclosed; many modifications and variations are possible in view of these teachings. The embodiments were chosen and described in order to explain the principles of the invention and certain practical applications, by providing specific examples, and thereby enabling others skilled in the art to utilize the invention and its various embodiments, with various modifications as are suited to the particular use contemplated, including for uses beyond the specific examples described. Other embodiments are within the following claims. Accordingly, the scope of the invention shall be defined only by the claims and their equivalents.

[0452]

[0369] Where a range of values is provided, it is understood that each intervening value, between the upper and lower limit of that range is encompassed within the invention.

[0453]

[0370] Additionally, persons skilled in the art will understand that the elements and features shown or described in connection with one embodiment may be combined with those of another embodiment without departing from the scope of the present invention.

[0454]

[0371] Throughout the present disclosure, terms such as “approximately,” “about,” “generally,” “substantially,” and the like should be understood to allow for variations in any numerical range or concept with which they are associated. For example, it is intended that the use of terms such as “approximately,” “about” and “generally” should be understood to encompass variations on the order of 25%.

[0455]

[0372] Although terms such as “first,” “second,” “third,” etc., may be used herein to describe various operations, steps or elements, these operations, steps or elements should not be limited by the use of these terms in that these terms are used to distinguish one operation or element Thus, unless expressly stated otherwise, a first operation, step or element could be termed a second operation, step or element without departing from the scope of the present disclosure.

[0456]

[0373] Each and every claim is incorporated as further disclosure into the specification and represents embodiments of the present disclosure. Also, the phrases “at least one of A, B, and C” and “A and / or B and / or C” should each be interpreted to include only A, only B, only C, or any combination of A, B, and C.

[0457] APPENDIX A - Exemplary Attributes

[0458]

[0459]

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[0478]

[0479] APPENDIX B - Exemplary User Profile Information ethnicity: Optional [EthnicityEnum] = Field(

[0480] None, description="The user’s ethnicity." religion: Optional [ReligionEnum] = Field (

[0481] None, description="The user’s religion." political leaning: Optional [ PoliticalLeanEnum] = Field (

[0482] None, description="The user’s political leaning." partner_political_leaning : Optional [ PoliticalLeanEnum] = Field]

[0483] None, description="The user’s preferred political leaning in a partner. " partner_age_minimum: Optional [ int ] = Field]

[0484] None, description="The user’s preferred minimum age." partner age maximum: Optional [ int ] = Field]

[0485] None, description="The user’s preferred maximum age." height: Optional [ float ] = Field]

[0486] None, description="The user’s height." partner height minimum: Optional [ float ] = Field]

[0487] None, description="The minimum height the user prefers in a partner. " partner height maximum: Optional [ float ] = Field]

[0488] None, description="The maximum height the user prefers in a partner. " partner ethnicities: Optional [ List [EthnicityEnum] ] = Field]

[0489] None, description="The user's preferred ethnicities in a partner. " partner religions: Optional [List [ReligionEnum] ] = Field]

[0490] None, description="The user's preferred religions in a partner. " hometown: Optional [ str ] = Field]

[0491] None, description="The user's hometown." job: Optional [ str ] = Field]

[0492] None, description="The user's current job." job_title: Optional [ str] = Field]

[0493] None, description="The user's job title." job_company: Optional [ str ] = Field (

[0494] None, description="The user’s job company." side_hustles : Optional [str] = Field (

[0495] None, description="Any side hustles the user is involved in." college_name : Optional [str] = Field (

[0496] None, description- "Where the user went to college." college major: Optional [ str] = Field (

[0497] None, description="The user’s college major." education_level : Optional [EducationLevelEnum] = Field (

[0498] None, description="The user’s education level." partner profession: Optional [ str ] = Field (

[0499] None, description="The user’s preferred profession in a partner. " has kids: Optional [bool] = Field (

[0500] None, description="Whether the user has children. " has_divorce: Optional [bool ] = Field (

[0501] None, description="Whether the user has been divorced. " partner no kids: Optional [bool ] = Field(

[0502] None, description="Whether the user prefers a partner without children. " partner no divorce: Optional [bool ] = Field(

[0503] None, description="Whether the user prefers a partner who has never been divorced. " partner_drinks : Optional [bool ] = Field (

[0504] None, description="Whether the user is open to a partner who drinks alcohol." partner_smokes : Optional [bool ] = Field (

[0505] None, description="Whether the user is open to a partner who smokes . " partner_does_drugs : Optional [bool ] = Field (

[0506] None, description="Whether the user is open to a partner who does drugs . " partner_smokes_weed: Optional [bool ] = Field(

[0507] None, description="Whether the user is open to a partner who smokes weed. " partner_shrooms : Optional [bool ] = Field (

[0508] None , description="Whether the user is open to a partner who uses shrooms . " drinks : Optional [bool ] = Field (

[0509] None , description- "Whether the user drinks alcohol . " smokes : Optional [bool ] = Field (

[0510] None , description="Whether the user smokes . " smokes_weed : Optional [bool ] = Field (

[0511] None , description="Whether the user smokes weed. " does hard drugs : Optional [bool ] = Field (

[0512] None , description="Whether the user does hard drugs . " does_ shrooms : Optional [bool ] = Field (

[0513] None , description="Whether the user uses shrooms . " takeaway red flag 1 : Optional [ str ] = Field (

[0514] None , description="A red flag in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . Each of these should be a short sentence that combines humor and depth to showcase something unique about the person . These can lean towards being witty and sassy but should never come across as mean . These red flags should not mention any religion , race , income or physical appearance . Each of these should be a short sentence that is no more than 25 words in length . Do not be generic or sexual or inappropriate or judgmental . The user should feel like their preferences have been understood by a friend and laugh a little when they read the red flag . " takeaway red flag 1 summary : Optional [ str ] = Field (

[0515] None , description="Catchy , pithy title that summarizes the first red flag starting with an appropriate emoj i . " takeaway red flag 2 : Optional [ str ] = Field (

[0516] None , description="A red flag in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . Each of these should be a short sentence that combines humor and depth to showcase something unique about the person . These can lean towards being witty and sassy but should never come across as mean . These red flags should not mention any religion , race , income or physical appearance . Each of these should be a short sentence that is no more than 25 words in length . Do not be generic or sexual or inappropriate or judgmental . The user should feel like their preferences have been understood by a friend and laugh a little when they read the red flag . " takeaway_red_f lag_2_summary : Optional [ str ] = Field (

[0517] None , description="Catchy , pithy title that summarizes the second red flag starting with an appropriate emoj i . " takeaway_non_negotiable_l : Optional [ str] = Field ]

[0518] None , description="A non-negotiable in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . Each of these should be a short sentence that combines humor and depth to showcase something unique about the person . These should focus on the strongest attributes , values , goals and preferences that the user has directly or indirectly indicated as really important to them . You can combine multiple attributes together but it should be no more than 25 words in length . These non-negotiables should not mention any physical appearance or income-related attributes . It is okay to mention religion and race but in a distinctly positive way . Do not be generic or sexual or inappropriate or judgmental . The user should feel like their preferences have been understood by a friend when they read the non- negotiable . " takeaway_non_negotiable—l— summary : Optional [ str] = Field ]

[0519] None , description="Catchy , pithy title that summarizes the first non-negotiable starting with an appropriate emoj i . " takeaway_non_negotiable_2 : Optional [ str] = Field ]

[0520] None , description="A non-negotiable in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . Each of these should be a short sentence that combines humor and depth to showcase something unique about the person . These should focus on the strongest attributes , values , goals and preferences that the user has directly or indirectly indicated as really important to them . You can combine multiple attributes together but it should be no more than 25 words in length . These non-negotiables should not mention any physical appearance or income-related attributes . It is okay to mention religion and race but in a distinctly positive way . Do not be generic or sexual or inappropriate or judgmental . The user should feel like their preferences have been understood by a friend when they read the non- negotiable . " takeaway non negotiable 2 summary : Optional [ str ] = Field ]

[0521] None , description="Catchy , pithy title that summarizes the second non-negotiable starting with an appropriate emoji . " takeaway nice to have 1 : Optional [ str ] = Field ]

[0522] None , description="A nice-to-have in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . These should focus on the most unique facts that the user has shared about themselves . You can combine multiple facts together but it should be no more than 25 words in length . These nice-to-haves should not mention any physical appearance or income-related attributes . It is okay to mention religion and race but in a distinctly positive way . These nice-to- haves should not mention physical appearance or income-related attributes . Do not be generic or sexual or inappropriate or judgmental . " takeaway_nice_to_have_l_summary : Optional [ str ] = Field (

[0523] None , description-"Catchy , pithy title that summarizes the first nice-to-have starting with an appropriate emoj i . " takeaway_nice_to_have_2 : Optional [ str] = Field ]

[0524] None , description="A nice-to-have in a partner for the user based on all the answers they have given so far with an emphasis on all free text chat responses . These should focus on the most unique facts that the user has shared about themselves . You can combine multiple facts together but it should be no more than 25 words in length . These nice-to-haves should not mention any physical appearance or income-related attributes . It is okay to mention religion and race but in a distinctly positive way . These nice-to- haves should not mention physical appearance or income-related attributes . Do not be generic or sexual or inappropriate or judgmental . " takeaway nice to have 2 summary : Optional [ str ] = Field ]

[0525] None , description="Catchy , pithy title that summarizes the second nice-to-have starting with an appropriate emoj i . " takeaway fun fact 1 : Optional [ str ] = Field ]

[0526] None , description="Generate a one-line sentence that showcases something unique and interesting about the person based on their answers to all the questions so far . The sentence should make the person seem more appealing to someone who is trying to date them and help them stand out in a crowd . Use a combination of humor and depth , do not be generic or sexual or inappropriate . " takeaway_f un_f act_2 : Optional [ str ] = Field ]

[0527] None , description="Generate a one-line sentence that showcases something unique and interesting about the person based on their answers to all the questions so far . The sentence should make the person seem more appealing to someone who is trying to date them and help them stand out in a crowd . Use a combination of humor and depth , do not be generic or sexual or inappropriate . " takeaway_f un_f act_3 : Optional [ str ] = Field ]

[0528] None , description="Generate a one-line sentence that showcases something unique and interesting about the person based on their answers to all the questions so far . The sentence should make the person seem more appealing to someone who is trying to date them and help them stand out in a crowd . Use a combination of humor and depth , do not be generic or sexual or inappropriate . " takeaway_fun_fact_4 : Optional [ str ] = Field (

[0529] None , description="Generate a one-line sentence that showcases something unique and interesting about the person based on their answers to all the questions so far . The sentence should make the person seem more appealing to someone who is trying to date them and help them stand out in a crowd. Use a combination of humor and depth , do not be generic or sexual or inappropriate . " archetype : Optional [ str] = Field ]

[0530] None , description="The archetype that the user most closely aligns with . "

Claims

WHAT IS CLAIMED IS:

1. A computer-implemented method for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, the method comprising: a. receiving, via a processor, a request to generate a user profile for a user; b. initiating, by the Al agent, a conversation with the user; c. gathering, via the conversation, user information relevant to the user profile, wherein at least a portion of the user information is free-form text; d. extracting, using an artificial intelligence system, a plurality of user features from the user information; and e. generating, based on the user features, the user profile for the user.

2. The method of claim 1, wherein the user profile is a dating profile for the user.

3. The method of claim 1, wherein the step of extracting, using an artificial intelligence system, a plurality of user features comprises using a Large Language Model (LLM), the step of extracting occurring before the step of generating the user profile.

4. The method of claim 1, further comprising displaying, to the user, the plurality of user features for confirmation or modification, the confirmation or modification occurring prior to generating the user profile.

5. The method of claim 4, further comprising subsequent to the step of displaying to the user the plurality of user features, receiving from the user the confirmation or modification of the user features.

6. The method of claim 1, wherein the conversation with the user is conducted as a one-on- one messaging chat between the Al agent and the user.

7. The method of claim 3, wherein the one-on-one messaging chat is conducted using text, voice, speech-to-text, text-to-speech, or any combination thereof.

8. The method of claim 1, wherein the user information relevant to the user profile comprises at least two of, or all three of, demographic information, personal information, and partner preference information.

9. The method of claim 1 , wherein at least a portion of the user information is provided by the user in response to a questionnaire or a quiz, including a multiple choice quiz.

10. The method of claim 1, wherein the plurality of user features comprises at least three of location, political leaning, height, religion, ethnicity, home town, work, education, and communities.

11. The method of claim 1, wherein the plurality of user features comprises at least two, at least three, or at least four personalized takeaways for the user.

12. The method of claim 1, wherein the plurality of user features comprises at least one partner preference summary, selected from the group of a non-negotiable, a red flag, and a nice-to- have.

13. The method of claim 12, wherein the plurality of user features comprises at least four, at least five, or at least six partner preference summaries from at least two or from all three of non-negotiables, red flags, and nice-to-haves.

14. The method of claim 1, wherein the plurality of user features comprises a dating archetype for the user.

15. The method of claim 1, wherein the plurality of user features comprises at least one user feature that will remain hidden on the user profile after it is published, thereby providing a private version of the user profile different from a public version of the user profile.

16. The method of claim 1, wherein the plurality of user features comprises at least one user feature that the user has the option to keep hidden on the user profile after it is published.

17. The method of claim 1, wherein the method further comprises sending the user profile to an administrator to review and approve the user profile before publication.

18. The method of claim 1, wherein the method further comprises notifying the user when the user profile is published.

19. The method of claim 1, wherein the method further comprises updating the user profile after publication.

20. The method of claim 1 , wherein the method further comprises scoring the user profde on a plurality of user attributes by processing the gathered user information using a LLM, thereby producing a user profile score.

21. The method of claim 20, further comprising storing the user profile score as a feature vector in a vector database.

22. The method of claim 1, wherein the user profile is adapted for use in any of a dating app, a friendship app, a social media app, a professional networking app, a fitness and health app, an e-commerce app, a streaming app, an educational app, a gaming app, an event app, a dining app, or a travel app.

23. A system for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, the system comprising: a. a processor; and b. a memory, storing instructions that, when executed by the processor, cause the system to: i. receive a request to generate a user profile for a user; ii. initiate, by the Al agent, a conversation with the user; iii. gather, via the conversation, user information relevant to the user profile, wherein at least a portion of the user information is free-form text; iv. extract, using ) an artificial intelligence system, a plurality of user features from the user information; and v. generate, based on the user features, the user profile for the user.

24. The system of claim 23, comprising the memory storing instructions that, when executed by the processor, cause the system to perform the method according to any of claims 2-22.

25. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause a system to perform a method for generating a user profile through conversational interaction with an artificial intelligence (Al) agent, the method comprising: a. receiving a request to generate a user profile for a user; b. initiating, by the Al agent, a conversation with the user;c. gathering, via the conversation, user information relevant to the user profde, wherein at least a portion of the user information is free-form text; d. extracting, using an artificial intelligence system, a plurality of user features from the user information; and e. generating, based on the user features, the user profile for the user.

26. The computer-readable medium of claim 25, storing instructions that, when executed by the processor, cause the system to perform the method according to any of claims 2-22.

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