Automatically generating interactive sessions based on internal identification tokens

Abstract

In some embodiments, the present disclosure provides an exemplary method that may include the steps of: identifying a plurality of entities seeking to interact with each other; analyzing each entity to determine a constraint type between each entity in the plurality of entities; automatically generating an internal identification token associated with each entity based on the stored information; and performing at least one action associated with the interaction of the plurality of entities using the internal identification token.

Description

Automatically generate interactive sessions based on internal identifier tokens.

[0001] Related applications

[0002] This application claims the benefit of U.S. Application No. 63 / 494,195, filed April 4, 2023, entitled “Computer-based Systems Configured to Automatically Connect a Plurality of Data Frameworks Simultaneously and Methods of Use Thereof,” which is incorporated herein by reference in its entirety. Technical Field

[0003] This disclosure generally relates to a computer-based system configured to automatically program multiple controls to modify a communication session associated with a transaction, and methods of using the system thereon. Background Technology

[0004] Typically, a data schema defines how data is organized within a relational database. This includes logical constraints such as table names, fields, data types, and the relationships between these entities. Schemas are usually presented visually to communicate the database's architecture, forming the basis of an organization's data management discipline. This data schema design process is also known as data modeling. Summary of the Invention

[0005] In some embodiments, this disclosure provides an exemplary, technically improved computer-based method comprising at least the following steps: identifying a plurality of entities seeking to interact with each other by at least one processor; analyzing each entity by the at least one processor to determine the type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including historical entity data associated with each entity, the historical entity data being associated with at least one of: a plurality of historical entities, at least one individual, a client master module. The at least one processor generates the following: the results of the profile manager module, the results of the address manager module, and the results of the constraint manager module; and at least one training output for each training constraint pair, the at least one training output including multiple historical constraint types associated with each entity; automatically generates an internal identification token associated with each entity based on stored information and constraint types between each of the multiple entities, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and performs at least one action associated with the interaction with the multiple entities using the internal identification token. In some embodiments, the trained machine learning module is trained to represent a unique structure detailing different relationship types. In some embodiments, the trained machine learning module includes a trained machine learning model including multiple trained machine learning parameters. In some embodiments, the multiple trained machine learning parameters are trained to be based on the input-output constraint types of the entities. In some embodiments, the information includes a unique password associated with each entity, a location associated with each entity, a historical type associated with each entity, and a salt value associated with the hash algorithm of each entity. In some embodiments, the profile manager module includes an account manager module. In some embodiments, the pattern includes multiple values ​​organized in multiple feature sets. In one embodiment, a first feature set in the plurality of feature sets represents a unique sequence of numbers associated with a specific constraint type, a second feature set in the plurality of feature sets represents a location associated with a specific entity, and a third feature set in the plurality of feature sets represents a historical type associated with the specific entity. In some embodiments, the plurality of feature sets are combined in a hash algorithm to generate an internal identification token. In some embodiments, the at least one action includes generating an interaction session for the at least two entities to be interacted with based on the internal identification token. In some embodiments, this disclosure utilizes a data workstation architecture to display multiple data layers for use during the generated interaction session.In some embodiments, the data workstation architecture includes: an entity interface layer, a context passing layer, a topic management layer, a navigation layer, and a login authorization layer. In some embodiments, this disclosure utilizes the data workstation architecture to provide a site map layer with multiple recently viewed applications in response to an entity entering a generated interactive session; optimize navigation capabilities associated with the generated interactive session; generate at least one information query within the generated interactive session; and automatically display at least one result of the at least one query on at least one window of the data workstation architecture based on the site map layer and navigation capabilities.

[0006] In some embodiments, a non-transitory computer-readable storage medium tangibly encodes computer-executable instructions, which, when executed by a device, perform a method comprising: identifying by at least one processor a plurality of entities seeking to interact with each other; and analyzing each entity by the at least one processor to determine a type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including Historical entity data associated with each entity, which is associated with at least one of the following: multiple historical entities, at least one individual, results from a client manager module, results from a profile manager module, results from an address manager module, and results from a constraint manager module; and at least one training output for each training constraint pair, which includes multiple historical constraint types associated with each entity; an internal identification token automatically generated by the at least one processor based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and at least one action associated with the interaction with multiple entities performed by the at least one processor using the internal identification token.

[0007] In some embodiments, a system includes: a non-transitory computer memory storing software instructions; at least one processor of a computing device associated with a user; wherein, when the at least one processor executes the software instructions, the computing device is programmed to: identify a plurality of entities seeking to interact with each other; analyze each entity to determine a type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training... The input includes historical entity data associated with each entity, which is associated with at least one of the following: multiple historical entities, at least one individual, the results of the client manager module, the results of the profile manager module, the results of the address manager module, and the results of the constraint manager module; and at least one training output for each training constraint pair, which includes multiple historical constraint types associated with each entity; automatically generating an internal identification token associated with each entity based on the stored information, wherein the internal identification token includes a pattern that provides information related to the holder of the internal identification token; and performing at least one action associated with the interaction of multiple entities using the internal identification token. Attached Figure Description

[0008] Various embodiments of this disclosure can be further explained with reference to the accompanying drawings, in which similar structures are indicated by similar numbers throughout the several views. The drawings shown are not necessarily to scale, but rather the emphasis is generally placed on illustrating the principles of this disclosure. Therefore, the specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art to employ one or more illustrative embodiments in various ways.

[0009] Figure 1 depicts a block diagram of an exemplary computer-based system and platform for automatically generating internal identification tokens associated with entities, according to one or more embodiments of the present disclosure.

[0010] Figure 2 is a flowchart illustrating the operational steps for automatically generating an internal identification token associated with an entity according to one or more embodiments of the present disclosure.

[0011] Figures 3A and 3B are illustrations of an internal identifier token generation module associated with an exemplary computer-based system and platform according to one or more embodiments of the present disclosure.

[0012] Figure 4 depicts a block diagram of an exemplary computer-based system / platform according to one or more embodiments of the present disclosure.

[0013] Figure 5 depicts a block diagram of another exemplary computer-based system / platform according to one or more embodiments of the present disclosure.

[0014] Figures 6 and 7 are illustrations of implementations of cloud computing architectures / aspects according to one or more embodiments of the present disclosure, in which the disclosed technology can be specifically configured to operate. Detailed Implementation

[0015] This document discloses various detailed embodiments of the present disclosure in conjunction with the accompanying drawings; however, it should be understood that the disclosed embodiments are merely illustrative. Furthermore, each example given in conjunction with the various embodiments of the present disclosure is intended to be illustrative and not restrictive.

[0016] Throughout this specification, unless the context clearly indicates otherwise, the following terms have the meaning explicitly associated with this document. The phrases “in one embodiment” and “in some embodiments” as used herein do not necessarily refer to the same(s) embodiments(s), but may refer to the same(s) embodiments(s). Furthermore, the phrases “in another embodiment” and “in some other embodiments” as used herein do not necessarily refer to different embodiments, but may refer to different embodiments. Therefore, as described below, various embodiments can be readily combined without departing from the scope or spirit of this disclosure.

[0017] Additionally, the term "based on" is not exclusive and may be based on additional factors not described, unless the context clearly specifies otherwise. Additionally, throughout the specification, the meanings of "a," "an," and "the" include plural references. The meaning of "in..." includes both "in..." and "on...".

[0018] As used herein, the terms “and” and “or” are used interchangeably to refer to sets of items in conjunctive and disjunctive forms to encompass a complete description of combinations and alternatives of items. For example, a set of items can be listed using either a disjunctive “or” or a conjunctive “and”. In either case, the set should be interpreted as meaning each item as an alternative individually, and any combination of the listed items.

[0019] It should be understood that at least one aspect / function of the various embodiments described herein can be performed in real time and / or dynamically. As used herein, the term "real time" refers to an event / action that can occur instantaneously or nearly instantaneously in time while another event / action has already occurred. For example, "real-time processing," "real-time computation," and "real-time execution" all relate to the execution of computation during the actual time during which relevant physical processing (e.g., the creator interacts with an application on a mobile device) occurs, so that the results of the computation can be used to guide the physical process.

[0020] As used herein, the terms “dynamically” and “automatically”, and their logical and / or linguistic related and / or derived terms, indicate that certain events and / or actions can be triggered and / or occur without any human intervention. In some embodiments, events and / or actions according to this disclosure may be real-time and / or based on a predetermined periodicity of at least one of the following: nanoseconds, fractions of nanoseconds, milliseconds, fractions of milliseconds, seconds, fractions of seconds, minutes, minutes, hours, days, days, weeks, months, etc.

[0021] As used herein, the term "runtime" refers to any behavior that is dynamically determined during the execution of a software application or at least a portion thereof.

[0022] Each principle, method, and / or system arrangement detailed herein may be used in conjunction with one or more of the following: U.S. Patent Nos. 8,370,244; 8,271,261; 8,103,564; 8,200,567; 8,214,279; 8,793,182; 8,396,785; 8,788,318; 8,521,632; 8,606,669; 9,009,062; 9,507. 667; 10,497,062; 8,930,482; 9,319,368; 8,856,046; 9,183,531; 9,195,957; 9,967,238; 10,333,910; 9,967,238; 10,979,405; 10,556,254; 10,133,970; 10,460,218; 10,832,105; 10,554,699; 10,825,093; 11,030,695 or any combination thereof.

[0023] At least some embodiments of this disclosure provide technical solutions(s) to at least one computer-centered technical problem associated with generating data patterns among multiple parties seeking to interact with each other. The illustrative computer-centered technical problem associated with identifying multiple parties seeking to interact is typically organized solely based on shared constraints between the parties. Shared constraints between the parties can determine the type of interaction that may be required for multi-party interaction, as shared constraints may place limitations on the extent of interaction between the parties. For example, a first party may require a specific threshold of funds for transactional interaction, while a second party may need to interact at a specific time of day, where these parties will be disallowed from interacting due to incompatible relationships based on constraints associated with each relationship. This illustrative computer-centered technical problem, based on relationships between parties, reduces the ability of a particular party to interact with more parties. As detailed in at least some embodiments herein, at least one computer-centered technical solution associated with this illustrative computer-centered technical problem may include: automatically generating an internal identification token associated with each of the multiple parties based on stored information extracted from analysis from a trained machine learning module; and utilizing the internal identification token to perform at least one action associated with the interaction of the multiple parties. In some embodiments, this disclosure may identify multiple entities seeking to interact with each other. In some embodiments, this disclosure may analyze each entity to determine the type of constraint between each of a plurality of entities. In some embodiments, this disclosure may automatically generate an internal identity token associated with each entity based on stored information. In some embodiments, this disclosure may utilize the internal identity token to perform at least one action associated with an interaction between the plurality of entities. In some embodiments, this disclosure may utilize a data workstation framework to display multiple windows on a user dashboard after initiating a generated interaction session, wherein multiple authenticated single sign-on codes may be a result of the data workstation framework and correspond to the generated internal identity token to authenticate an interaction session between at least two entities.

[0024] Figure 1 depicts a block diagram of an exemplary computer-based system and platform for automatically generating internal identification tokens associated with entities, according to one or more embodiments of the present disclosure.

[0025] In some embodiments, the illustrative computing system 100 of this disclosure may include a computing device 102 associated with at least one user and an illustrative program engine 104. In some embodiments, the illustrative program engine 104 may be stored on the computing device 102. In some embodiments, the illustrative program engine 104 may be stored on the computing device 102, which may include a processor 108, non-transitory memory 110, a communication circuitry system 112 for communicating via a communication network 114 (not shown), and input and / or output (I / O) devices 116, such as, for example, a keyboard, mouse, touchscreen, and / or display. In some embodiments, the computing device 102 may refer to at least one of a plurality of communication-enabled computing devices.

[0026] In some embodiments, the illustrative program engine 104 may be configured to instruct the processor 108 to execute one or more software modules, such as, but not limited to, the exemplary internal identifier token generator module 118, machine learning module 120, and / or data output module 122.

[0027] In some embodiments, the exemplary internal identifier token generator module 118 of this disclosure utilizes at least one machine learning algorithm described herein to identify multiple entities seeking to interact with each other. In some embodiments, the multiple entities may refer to at least one of, but not limited to: at least one individual, a client manager module, a profile manager module, an address module, a constraint manager module, and a financial institution. In some embodiments, interaction may refer to the initiation of a transaction and / or communication session between at least two entities. In some embodiments, the exemplary module 118 may identify each of the multiple entities as a prospect, client, party, spouse, child, other relative, lawyer, accountant, stakeholder, company, partnership, other legal entity, partner, officer, employer, charity, entity representing a set of accounts, any other related legal entity, and a single entity with multiple entity records, and customize subsequent interactions based on the identifier of each entity.

[0028] In some embodiments, the exemplary internal identifier token generator module 118 can analyze each entity to determine the type of constraint shared between each of a plurality of entities. In some embodiments, the exemplary module 118 can perform the analysis using a trained machine learning module 120. In some embodiments, the trained machine learning module 120 can represent a unique structure that describes different types of constraints in detail by generating a unique string specific to each entity that identifies the type of constraint between at least two entities. In some embodiments, the exemplary module 118 can generate an interaction session based on a unique string associated with the type of constraint shared between at least two entities. For example, the exemplary module 118 can generate a specific interaction session based on: between at least one person and / or entity and an account; between a person / entity and any other person / entity; between an address and an account; between an account and other accounts; and / or between groups (such as reporting families and marketing families), and any combination thereof. In some embodiments, the trained machine learning module 120 includes a trained data model based on a plurality of trained data parameters, where the parameters are the outputs of constraints and types of constraint pairs. In some embodiments, the machine learning module 120 can be trained by utilizing at least one training input for each possible training constraint pair and at least one historical entity data as an established baseline. Historical entity data can be the result of multiple external security modules capable of performing analysis on multiple entities. These external security data modules can refer to one or more data managers 126 and include: a client manager, a profile manager, an address manager, and a constraint manager. In some embodiments, the output of the trained machine learning module 120 can refer to multiple combination types that can be used for subsequent interaction sessions between multiple entities based on training input pairs and the results of each external security data module.

[0029] In some embodiments, the exemplary internal identifier token generator module 118 can automatically generate an internal identifier token associated with each entity based on stored information. In some embodiments, the internal identifier token may refer to a digital representation of a unique set of features obtained via a trained machine learning module 120. The trained machine learning module 120 may be trained to identify the feature set based on received user preferences (as input) and determine the type of constraint between each of the plurality of entities based on the identified feature set. The received user preferences may refer to multiple preferences associated with each entity, which are used to distinguish each of the plurality of entities. In some embodiments, the feature set may be fed into a subsequent trained machine learning module, which is capable of predicting the type of communication session to be generated based on the determined type of constraint between the plurality of entities. In some embodiments, the internal identifier token may include a data pattern that provides information 124 related to the holder of the internal identifier token (e.g., an entity or individual), wherein the data pattern may include multiple values ​​organized in a plurality of data features. Information 124 may refer to a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, a salt value associated with the hash algorithm of each entity, subsequent modifications to user preferences associated with the identity of each entity, and / or modifications to authentication steps required for interaction with external computing devices. In some embodiments, each data feature may represent a specific constraint type, a specific entity, a specific location, and / or a specific history type. In some embodiments, "feature" or "data feature" may refer to a value, based on a suitable data type, to represent a constraint type, entity, location, history type, etc. Values ​​may include, but are not limited to, strings, integers, booleans, floating-point numbers, fixed-point numbers, pointers, lists, arrays, trees, etc., or any combination thereof.

[0030] In some embodiments, the exemplary internal identity token generator module 118 may utilize internal identity tokens to perform at least one action associated with an interaction with multiple entities. In some embodiments, the at least one action may refer to generating an interaction session for at least two entities based on the generated internal identity token. The generation of an interaction session for at least two entities is based on the output of a trained machine learning module 120, the determination of the relationship type, and the results of an external security data module. This allows communication to be specifically tailored to the type of interaction associated with the identity of each entity. For example, the exemplary module 118 may provide each entity with an internal account having a unique identifier (“ID”), a related client unique ID, a related client branch code ID, and / or all account-specific data to optimize transactions occurring between the two entities based on internal identity tokens that can be exchanged prior to the initialization of the interaction session. In some embodiments, the at least one action may refer to the orchestration of transactions between at least two entities.

[0031] In some embodiments, the exemplary internal identity token generator module 118 may utilize a data workstation structure 128 located within computing device 102 to maintain multiple service layers that display the execution of multiple actions and subsequent actions. In some embodiments, the multiple service layers may refer to: an entity interface (hereinafter referred to as a "UI / UX") layer, a context passing layer, a theme management layer, a masthead and / or navigation layer, and a login authorization and / or permission layer. In some embodiments, the data workstation structure 128 may have a front-end framework that provides structure and control for system, page, and component-level customization.

[0032] In some embodiments, the exemplary internal identity token generator module 118 may utilize the data workstation structure 128 to allow multiple users to dynamically navigate to the platform from a menu to interact with a generated interactive session. In some embodiments, the exemplary internal identity token generator module 118 may request a digital single sign-on (hereinafter referred to as "SSO") entry for each user navigating the data workstation structure 128.

[0033] In some embodiments, the exemplary internal identifier token generator module 118 may utilize a screen container to display multiple windows on a user dashboard after startup. In some embodiments, the exemplary internal identifier token generator module 118 may display a top menu structure based on multiple user permissions within the data workstation structure 128. In some embodiments, the data workstation structure 128 may simultaneously orchestrate multiple data windows associated with an interactive session generated between at least two of a plurality of entities. For example, the data workstation structure 128 may display market data in the form of a snapshot quote box within at least one window of the user dashboard within a generated interactive session based on a specific constraint type. In some embodiments, the exemplary internal identifier token generator module 118 may dynamically print via a browser printing function using the data workstation structure, and multiple independent applications may retain their own printing functions where applicable.

[0034] In some embodiments, the exemplary internal identifier token generator module 118 may utilize the data workstation structure 128 to generate multiple navigations for at least one entity within an interaction session, and has the ability to redact at least one metadata record associated with the interaction session by adjusting the configuration associated with the generated internal identifier token. For example, if the constraint type is determined to be a financial transaction, a particular entity may utilize the data workstation structure 128 to remain anonymous during the interaction session (if this is a parameter acceptable to other entities within the interaction session).

[0035] In some embodiments, the exemplary internal identity token generator module 118 may utilize a context passing module that includes screens for displaying multiple transfers between permissions participating in an interactive session and verifying relevant contexts with multiple entities, wherein the relevant contexts are associated with information for each entity. In some embodiments, the data workstation architecture 128 may utilize the context passing module to transmit account information and client information from the context of each relevant screen along with a data dashboard associated with the generated interactive session. In some embodiments, the exemplary internal identity token generator module 118 may utilize an entity interface layer that may be controlled by the data workstation architecture 128 and can be configured via management tools to allow real-time feedback from each entity participating in the generated interactive session. In some embodiments, the exemplary internal identity token generator module 118 may utilize the data workstation architecture 128 to render additional metadata for multiple applications rendered by the entity interface layer. In some embodiments, the data workstation architecture 128 may provide a responsive design rendered on multiple devices, such as screens with sizes of 1280-1920 pixels.

[0036] In other embodiments, the exemplary internal identity token generator module 118 may allow multiple users to navigate from a data dashboard to any application within the data workstation structure 128, wherein the data dashboard provides multiple adjustable tab labels to reduce any confusion that may arise when generating interactive sessions from a multi-window workstation. In some embodiments, the exemplary internal identity token generator module 118 may allow each user to quickly navigate directly to a specific application from a top-level main menu associated with the generated interactive session. Multiple adjustable tab labels may provide users with multiple navigation tools to guide the navigation hierarchy. In some embodiments, the exemplary internal identity token generator module 118 may allow multiple users to quickly navigate across views using secondary navigation tools within the data workstation structure 128. In some embodiments, the data workstation structure 128 may include a database of frequently used applications associated with each of the multiple users.

[0037] In some embodiments, the exemplary internal identifier token generator module 118 may provide a site map layer to allow quick access to multiple recently viewed applications, multiple application favorites, and / or the entire application set enhanced via the data workstation structure 128. In some embodiments, the exemplary internal identifier token generator module 118 may allow each of multiple users to navigate to recently viewed applications, multiple quick links, and multiple user favorites based on an internal identifier token.

[0038] In some embodiments, the exemplary internal identity token generator module 118 may allow multiple users to navigate to any main window or client window within the generated interactive session. For example, the data workstation structure 128 may provide a book query login page window within a dashboard, where multiple users can launch a new application from a digital menu when the determined constraint type is to bind at least two entities for interaction. In some embodiments, the exemplary internal identity token generator module 118 may utilize the data workstation structure to display multiple data sets associated with the generated interactive session, where each data set may refer to multiple investment instruments associated with at least one entity and willing to be exchanged during the generated interactive session.

[0039] In some embodiments, the exemplary internal identifier token generator module 118 may allow multiple users to create new queries and / or navigate to multiple predetermined links associated with multiple applications. In some embodiments, creating a custom query may refer to the ability to drag and drop multiple data attributes into an editor panel; and the ability for multiple users to preview the results in a preview window within the data workstation structure 128 while the query is being created.

[0040] In some embodiments, the exemplary internal identifier token generator module 118 may provide at least one result viewing window, where multiple users can view query results within the data workstation structure 128 and navigate back to the editor screen to edit the query. In some embodiments, the exemplary internal identifier token generator module 118 may provide at least one save query window within the data workstation structure 128, where multiple users can save custom queries for future rerunning.

[0041] In some embodiments, the exemplary internal identifier token generator module 118 can integrate multiple clients and permitted searches within a predetermined time period. In some embodiments, the exemplary internal identifier token generator module 118 can provide enhanced search capabilities to multiple users via the data workstation structure 128. In some embodiments, the exemplary internal identifier token generator module 118 can extend the additional information displayed within the generated interactive session associated with the internal interaction token. In some embodiments, the exemplary internal identifier token generator module 118 can use multiple pre-generated labels and colors associated with multiple actions to modify the entity interface and / or data workstation structure 128 to update each window within the data workstation structure 128.

[0042] In some embodiments, this disclosure describes a system for automatically utilizing at least one trained machine learning algorithm from a plurality of machine learning algorithms within a machine learning module 120 capable of identifying a plurality of entities seeking to interact with each other. In some embodiments, the machine learning module 120 may analyze each entity to determine the type of constraint between each of the plurality of entities. In some embodiments, the machine learning module 120 may refer to a trained machine learning module 120 capable of outputting the type of constraint between at least two entities based on a unique structure detailing different types of constraints based on a plurality of parameters. In some embodiments, the trained machine learning module 120 may be trained to identify a set of features based on received user preferences as input and to determine the type of constraint between each of the plurality of entities based on the identified feature set. The received user preferences may refer to a plurality of preferences associated with each entity to distinguish each of the plurality of entities. In some embodiments, the feature set may be fed into a subsequently trained machine learning module capable of predicting the type of communication session to be generated based on the constraint type determined between the plurality of entities. In some embodiments, the parameters associated with the trained machine learning module 120 may refer to training inputs including historical entity data associated with each entity, wherein the historical entity data may refer to multiple historical entities, at least one individual, the results of the client manager module, the results of the profile manager module, the results of the address manager module, and the results of the constraint manager module.

[0043] In some embodiments, the trained machine learning module 120 may automatically generate an internal identification token associated with each entity based on stored information 124. Information 124 may refer to a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, a salt value associated with a hash algorithm for each entity, subsequent modifications to user preferences associated with the identity of each entity, and / or modifications in authentication steps required for interaction with external computing devices. In some embodiments, the internal identification token may refer to a pattern that provides additional information associated with the holder of the internal identification token. In some embodiments, the trained machine learning module 120 may automatically generate an internal identification token, wherein the internal identification token may include multiple values ​​organized in a feature set. In some embodiments, a first feature set may refer to a unique sequence of numbers associated with a particular constraint type; a second feature set may refer to a location associated with a particular entity; and a third feature set may refer to a history type associated with a particular entity. In some embodiments, multiple features may be combined in a hash algorithm to generate an internal identification token. In some embodiments, the trained machine learning module 120 may utilize the internal identification token to perform at least one action associated with interaction with multiple entities.

[0044] In some embodiments, the trained machine learning module 120 may leverage the data workstation architecture 128 to identify multiple login authorizations and / or permissions associated with internal identity tokens to connect to external data sources to establish sessions associated with at least one of multiple entities. In some embodiments, the theme management module may control styles and branding, which may be centrally managed by the data workstation framework and allow for consistent updates throughout the data ecosystem. In some embodiments, multiple navigations may be configurable, component-based, and permission-based. In some embodiments, the trained machine learning module 120 may leverage the data workstation architecture 128 to allow multiple users to dynamically navigate from menus to the platform to interact with generated interactive sessions. In some embodiments, the trained machine learning module 120 may require a digital SSO entry for each user navigating the data workstation architecture 128.

[0045] In some embodiments, the trained machine learning module 120 may utilize the data workstation structure 128 to generate multiple navigations for at least one entity within an interaction session, and has the ability to edit at least one metadata record associated with the interaction session by adjusting the configuration associated with the generated internal identity token. For example, if the constraint type is determined to be a financial transaction, a particular entity may utilize the data workstation structure 128 to remain anonymous during the interaction session (if this is a parameter acceptable to other entities within the interaction session).

[0046] In some embodiments, the trained machine learning module 120 may utilize a context passing module that includes screens for displaying multiple transfers between permissions participating in an interactive session and verifying relevant contexts with multiple entities, wherein the relevant contexts are associated with information for each entity. In some embodiments, the data workstation architecture 128 may utilize the context passing module to transmit account information and client information from the context of each relevant screen along with a data dashboard associated with the generated interactive session. In some embodiments, the trained machine learning module 120 may utilize an entity interface layer that may be controlled by the data workstation architecture 128 and can be configured via management tools to allow real-time feedback from each entity participating in the generated interactive session. In some embodiments, the trained machine learning module 120 may utilize the data workstation architecture 128 to render additional metadata for multiple applications rendered by the entity interface layer. In some embodiments, the data workstation architecture 128 may provide a responsive design rendered on multiple devices, such as screens with dimensions of 1280-1920 pixels.

[0047] In some embodiments, the data output module 122 may generate results identifying multiple entities seeking to interact with each other. In some embodiments, the data output module 122 may generate results from analysis of each entity by a trained machine learning module 120 to determine the type of constraint between each of the multiple entities. In some embodiments, the data output module 122 may automatically generate an internal identification token associated with each entity based on stored information. In some embodiments, the data output module 122 may utilize the internal identification token to perform at least one action associated with the interaction of the multiple entities. In some embodiments, the data output module 122 may utilize the data workstation structure 128 to allow multiple entities to dynamically navigate from a menu to the platform to interact with a generated interactive session. In some embodiments, the data output module 122 may request a digital SSO entry for each user navigating the data workstation structure 128.

[0048] In some embodiments, the illustrative program engine 104 may identify multiple entities seeking to interact with each other. In some embodiments, the illustrative program engine 104 may analyze each entity to determine the type of constraint between each of the multiple entities based on multiple trained machine learning parameters, and output the constraint type based on the entity's input. In some embodiments, the illustrative program engine 104 may determine the type of constraint between the multiple entities based on at least one input and at least one output of each trained constraint pair. In some embodiments, the illustrative program engine 104 may automatically generate an internal identification token associated with each entity based on stored information. In some embodiments, the illustrative program engine 104 may utilize the internal identification token to perform at least one action associated with the interaction of the multiple entities. In one embodiment, the illustrative program engine 104 may utilize the data workstation structure 128 to allow multiple entities to dynamically navigate from a menu to the platform to interact with a generated interactive session. In some embodiments, the illustrative program engine 104 may request an SSO entry for each user navigating the data workstation structure 128.

[0049] In some embodiments, non-transitory memory 110 may store identifiers of multiple entities seeking to interact with each other. In some embodiments, non-transitory memory 110 may store analysis of each entity to determine the type of relationship between each of the multiple entities. In some embodiments, non-transitory memory 110 may store multiple training inputs and multiple training outputs for each training constraint pair. In some embodiments, non-transitory memory 110 may store internal identifier tokens associated with each entity based on stored information and patterns including multiple feature sets. In some embodiments, non-transitory memory 110 may store multiple instructions to perform at least one action associated with the interaction of the multiple entities. In some embodiments, non-transitory memory 110 may store multiple instructions associated with data workstation structure 128 that allow multiple entities to dynamically navigate from menus to the platform to interact with generated interactive sessions. In some embodiments, non-transitory memory 110 may store digital SSO entries for each user navigating data workstation structure 128 for later use.

[0050] Figure 2 is a flowchart 200 illustrating operational steps for automatically generating an internal identification token associated with an entity according to one or more embodiments of the present disclosure.

[0051] In step 202, the illustrative program engine 104 of computing device 102 identifies a plurality of entities seeking to interact with each other. In some embodiments, the illustrative program engine 104 may identify a plurality of entities seeking to interact with each other. In some embodiments, each of the plurality of entities may refer to at least one of, but is not limited to: an individual, computing device 102 associated with an individual, a financial institution, and / or server computing device 106 associated with a company. In some embodiments, an exemplary internal identity token generator module 118 may identify a plurality of entities seeking to interact with each other.

[0052] In step 204, the illustrative program engine 104 analyzes each entity. In some embodiments, the illustrative program engine 104 may analyze each entity to determine the type of constraint between each of a plurality of entities. In some embodiments, the illustrative program engine 104 may utilize a trained machine learning module 120 to analyze each entity to determine the type of constraint between each of a plurality of entities. In some embodiments, the analysis of each entity may refer to the execution of the trained machine learning module 120, which is trained based on a plurality of trained machine learning parameters to represent a unique structure detailing different types of constraints. In some embodiments, the trained machine learning module 120 may be trained to output the type of constraint based on inputs associated with each entity. In some embodiments, the inputs associated with each entity may refer to at least one training input for each trained constraint pair, wherein the at least one training input may refer to historical entity data associated with each entity. In some embodiments, the historical entity data associated with each entity may refer to at least one of, but not limited to: a plurality of historical entities, at least one individual, the results of a client manager module, the results of a profile manager module, the results of an address manager module, and the results of a constraint manager module. In some embodiments, the output of each training constraint pair may refer to multiple historical constraint types associated with each entity. In some embodiments, the exemplary internal identifier token generator module 118 may utilize the trained machine learning module 120 to analyze each entity to determine the constraint type between each of the multiple entities.

[0053] In step 206, the illustrative program engine 104 automatically generates internal identification tokens. In some embodiments, the illustrative program engine 104 may automatically generate internal identification tokens associated with each entity based on stored information. In some embodiments, the internal identification token may refer to a data pattern that provides information related to the holder of the internal identification token. In some embodiments, the data pattern of the internal identification token may refer to multiple values ​​organized into multiple feature sets. In some embodiments, the multiple feature sets associated with the internal identification token may refer to the result of a hash algorithm having a salted character unique to each holder (i.e., individual). In some embodiments, the multiple feature sets may include: a first feature set representing a unique sequence of numbers associated with a particular constraint type; a second feature set representing a location associated with a particular entity; and a third feature set representing a historical type associated with a particular entity. In some embodiments, the exemplary internal identification token generator module 118 may automatically generate internal identification tokens associated with each entity based on stored information.

[0054] In step 208, the illustrative program engine 104 performs at least one action. In some embodiments, the illustrative program engine 104 may utilize an internal identification token to perform at least one action associated with an interaction with multiple entities. The program engine 104 may select, configure, or otherwise determine the action based on features encoded in the internal identification token. For example, a constraint type may be encoded as a feature in the internal identification token, such that the program engine 104 can extract features representing the constraint type and determine at least one action associated with the constraint type. In some embodiments, the illustrative program engine 104 may exchange the internal identification token with a second token associated with another entity; verify the second token associated with the other entity; establish a connection between the two entities after verifying the authenticity of the second token; and allow communication between the two entities after establishing the connection. In some embodiments, at least one action may refer to generating an interaction session for at least two of the multiple entities based on the internal identification token. In some embodiments, an exemplary internal identification token generator module 118 may utilize the internal identification token to perform at least one action associated with an interaction with multiple entities. In some embodiments, the exemplary internal identifier token generator module 118 can establish a communication session by using an internal identifier token to initiate a set of predetermined protocols or handshake procedures depending on the type of communication being established between at least two entities. For example, a communication session can refer to a TCP / IP socket connection, a WebSocket connection, and / or any other application-specific protocol.

[0055] In some embodiments, the exemplary internal identity token generator module 118 may utilize a data workstation structure 128 located within the computing device 102 to maintain multiple service layers that display multiple actions and the execution of subsequent actions. In some embodiments, the multiple service layers may refer to: an entity interface (hereinafter referred to as a "UI / UX") layer, a context passing layer, a theme management layer, a top and / or navigation layer, and a login authorization and / or permission layer. In some embodiments, the data workstation structure 128 may have a front-end framework that provides structure and control for system, page, and component-level customization.

[0056] In some embodiments, the exemplary internal identity token generator module 118 may utilize the data workstation structure 128 to allow multiple users to dynamically navigate from menus to the platform to interact with generated interactive sessions. In some embodiments, the exemplary internal identity token generator module 118 may require a digital SSO entry for each user navigating to the data workstation structure 128.

[0057] In some embodiments, the exemplary internal identity token generator module 118 may utilize a screen container to display multiple windows on a user dashboard after startup. In some embodiments, the exemplary internal identity token generator module 118 may display a top menu structure based on multiple user permissions within the data workstation structure 128. In some embodiments, the data workstation structure 128 may simultaneously orchestrate multiple data windows associated with an interactive session generated between at least two of a plurality of entities. For example, the data workstation structure 128 may display market data in the form of a snapshot reference box within at least one window of the user dashboard within a generated interactive session based on a specific constraint type. In some embodiments, the exemplary internal identity token generator module 118 may dynamically print via a browser printing function using the data workstation structure, and multiple independent applications may retain their own printing functions where applicable.

[0058] Figure 3A depicts an exemplary enterprise framework 300 associated with an exemplary internal identity token generator module 118. In some embodiments, the exemplary framework 300 may refer to a constraint supervisor framework that can provide a supervisory data structure / schema for data associated with one or more of the following: client supervisor 305, constraint supervisor 304, account supervisor 306, and address supervisor 307. In some embodiments, client supervisor 305 may store data associated with one or more of the following: potential candidates, clients, parties, spouses, children, other relatives, lawyers, accountants, stakeholders, corporations, partnerships, other legal entities, partners, officials, employers, charities, entities representing a set of accounts, and any other related legal entities, as well as a single entity with multiple entity records. In some embodiments, client supervisor 305 may refer to an external security data module capable of performing analysis to determine information associated with each of the multiple entities. In some embodiments, the exemplary internal identity token generator module 118 may utilize client supervisor 305 to train machine learning module 120 and generate internal identity tokens. In some embodiments, the constraint supervisor module 304 may be a single central repository of all data defining constraints associated with multiple relationships between at least one person and / or entity and an account; constraints associated with multiple relationships between an individual / entity and any other individual / entity; constraints associated with multiple relationships between an address and an account; constraints associated with multiple relationships between an account and other accounts; and / or constraints associated with groupings such as reporting families and marketing families. In some embodiments, the trained machine learning module 120 may use the data from the constraint supervisor 304 as input for training constraint pairs. In some embodiments, the account supervisor 306 may provide a unique identifier (“ID”) for internal accounts, a unique ID of the associated client, a unique client branch code ID, and / or all account-specific data. In some embodiments, the account supervisor 306 may include held “external” accounts, held at least one mutual fund, and / or held at least one eligible plan. In some embodiments, the account supervisor 306 may include at least one non-customer account that may be associated with a company, enterprise, or treasury; and / or there may be a one-to-many account relationship. In some embodiments, the account supervisor 306 may refer to a profile supervisor. In some embodiments, Address Manager 307 may provide a centralized address management system associated with the outputs of Account Manager 306, Constraint Manager 304, and Client Manager 305. In some embodiments, the address management system may refer to a single source for all client address “types” (e.g., residential, primary, work, vacation home, etc.), which ensures data consistency across multiple address types, allowing any address to be used across multiple client and account contexts.In some embodiments, the address manager module 307 can provide simplified address management by providing links to the results of the client manager 305 and the account manager 306, so that a change propagates to all contexts.

[0059] Figure 3B depicts another schematic representation 302 of the constraint supervisor framework 310. In some embodiments, all client system integration can be performed through the Enterprise Integration Service Layer (“EISL”) 301 to integrate internal and external applications while providing access to multiple data services without modifying formats or protocols for multiple different applications. In some embodiments, the client and constraint database 308 can be used for data transformation within the constraint supervisor framework 310, and an exemplary internal identifier token generator module 118 can backbridge data in real time to the computing device 102 associated with the client via the client legacy database 309. In some embodiments, the exemplary internal identifier token generator module 118 can utilize the constraint supervisor framework 310 to transmit the end of a file in real time to the computing device 102 associated with the client. In some embodiments, the constraint supervisor framework 310 can deliver daily feeds for display by multiple user interfaces 312, such as a corporate action interface 312a, a cost basis interface 312b, a profit interface 312c, a mutual fund interface 312d, a regulatory transaction interface 312e, a securities transaction interface 312f, and a service platform interface 312g. In some embodiments, the constraint master framework 310 may splice or split files based on downstream needs and may deliver a base API to downstream components of a subsequent framework 313 associated with the exemplary internal identity token generator module 118. For example, the constraint master framework 310 may splice files based on an insurance framework 313a, a regulatory account framework 313b, a non-regulatory account framework 313c, a securities module framework 313d, an account registration framework 313e, and a software application framework 313f. In some embodiments, the exemplary internal identity token generator module 118 may construct a custom API 314 using the constraint master framework 310 and / or EISL 301 in response to generating internal identity tokens. In some embodiments, the custom API 314 may refer to a specific pattern that can utilize internal identity tokens based on constraint pairs to verify downstream frameworks between multiple entities. For example, the custom API 314 may include a relationship grouping layer 314a, a tax reporting layer 314b, a trading layer 314c, a workstation layer 314d, an aggregation layer 314e, and a security console layer 314f.

[0060] In another embodiment, the exemplary internal identity token generator module 118 may allow multiple users to navigate from a data dashboard to any application within the data workstation structure 128, wherein the data dashboard provides multiple adjustable tab labels to reduce any confusion that may arise in interactive sessions generated from a multi-window workstation. In some embodiments, the exemplary internal identity token generator module 118 may allow each user to quickly navigate directly to a specific application from a top-level main menu associated with the generated interactive session. In some embodiments, the exemplary internal identity token generator module 118 may allow each user to quickly navigate directly to a specific application from a top-level main menu associated with the generated interactive session. Multiple adjustable tab labels may provide users with multiple navigation tools to guide them through a navigation hierarchy. In some embodiments, the exemplary internal identity token generator module 118 may allow multiple users to quickly navigate across views using secondary navigation tools within the data workstation structure 128. In some embodiments, the data workstation structure 128 may include a database of frequently used applications associated with each of the multiple users. For example, the data workstation structure 128 may display multiple adjustable tab labels to display at least one of the following, but not limited to: Consultation Portal tab 311a, Reverse Bridging Data tab 311b, Billing tab 311c, Cash Management tab 311d, Address Management System tab 311e, Client Communication tab 311f, and Performance Report tab 311g.

[0061] In some embodiments, the exemplary internal identifier token generator module 118 may provide a site map layer to allow quick access to multiple recently viewed applications, multiple application favorites, and / or the entire application set enhanced via the data workstation structure 128. In some embodiments, the exemplary internal identifier token generator module 118 may allow each of multiple users to navigate to recently viewed applications, multiple quick links, and multiple user favorites based on an internal identifier token.

[0062] In some embodiments, the exemplary internal identifier token generator module 118 may allow multiple users to navigate to any main window or client window within a generated interactive session. For example, the data workstation architecture 128 may provide a book query login page window within a dashboard, where multiple users can launch a new application from a digital menu when the determined constraint type is to bind at least two entities for interaction.

[0063] In some embodiments, the exemplary internal identifier token generator module 118 may allow multiple users to create new queries and / or navigate to multiple predetermined links associated with multiple applications. In some embodiments, creating a custom query may refer to the ability to drag and drop multiple data attributes into an editor panel; and the ability for multiple users to preview the results in a preview window within the data workstation structure 128 while the query is being created.

[0064] In some embodiments, the exemplary internal identifier token generator module 118 can integrate multiple clients and permitted searches within a predetermined time period. In some embodiments, the exemplary internal identifier token generator module 118 can provide enhanced search capabilities to multiple users via the data workstation structure 128. In some embodiments, the exemplary internal identifier token generator module 118 can extend the additional information displayed within the generated interactive session associated with the internal interaction token. In some embodiments, the exemplary internal identifier token generator module 118 can use multiple pre-generated labels and colors associated with multiple actions to modify the entity interface and / or data workstation structure 128 to update each window within the data workstation structure 128.

[0065] Figure 4 depicts a block diagram of an exemplary computer-based system / platform 400 according to one or more embodiments of the present disclosure. However, practicing one or more embodiments may not require all of these components, and the arrangement and type of components may be varied without departing from the spirit or scope of the various embodiments of the present disclosure. In some embodiments, the exemplary inventive computing device and / or exemplary inventive computing component of the exemplary computer-based system / platform 400 may be configured to automatically generate an internal identification token associated with each entity based on stored information, and to use the internal identification token to perform at least one action associated with the interaction of multiple entities, as detailed herein.

[0066] In some embodiments, the exemplary computer-based system / platform 400 may be based on a scalable computer and / or network architecture that incorporates various strategies for evaluating data, caching, searching, and / or database connection pooling. An example of a scalable architecture is one capable of operating multiple servers. In some embodiments, the exemplary inventive computing devices and / or exemplary inventive computing components of the exemplary computer-based system / platform 400 may be configured to automatically utilize at least one machine learning model described herein to remotely execute instructions associated with the exemplary processing module 118 of this disclosure.

[0067] In some embodiments, referring to FIG4, members 402-404 (e.g., clients) of the exemplary computer-based system / platform 400 may include virtually any computing device capable of automatically generating an internal identity token associated with each entity based on stored information using an exemplary internal identity token generator module 118, and using the internal identity token to perform at least one action associated with multiple entities traveling to and from each other via a network (e.g., cloud network 109) (such as network 405) and from another computing device (such as servers 406 and 407). In some embodiments, member devices 402-404 may be smartphones, personal computers, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCs, etc. In some embodiments, one or more member devices within member devices 402-404 may include computing devices connected using wireless communication media (such as cellular phones, smartphones, pagers, walkie-talkies, radio frequency (RF) devices, infrared (IR) devices, CBs, integrated devices combining one or more of the aforementioned devices, or virtually any mobile computing device, etc.). In some embodiments, one or more member devices within member devices 402-404 may be devices capable of connecting using wired or wireless communication media (such as PDAs, POCKET PCs, wearable computers, laptops, tablets, desktop computers, netbooks, video game devices, pagers, smartphones, ultra-mobile personal computers (UMPCs), and / or any other device equipped to communicate via wired and / or wireless communication media (e.g., NFC, RFID, NB-IoT, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments, one or more member devices within member devices 402-404 may include devices capable of launching one or more applications, such as internet browsers, mobile applications, voice calls, video games, video conferencing, and email. In some embodiments, one or more member devices within member devices 402-404 may be configured to receive and send web pages, etc. In some embodiments, the exemplary processing module 118 of this disclosure may be configured to collect market input to modify transformed uniform data state input data based on an established baseline to match market output data, and employ virtually any web-based language, including but not limited to Standard Generalized Markup Language (SMGL), such as Hypertext Markup Language (HTML), Wireless Application Protocol (WAP), and Handheld Device Markup Language (HDML), such as Wireless Markup Language (WML), WMLScript, XML, JavaScript, etc. In some embodiments, member devices within member devices 402-404 may be specifically programmed in Java, .Net, QT, C, C++, and / or other suitable programming languages.In some embodiments, one or more member devices within member devices 402-404 may be specifically programmed to include or execute applications to perform various possible tasks, such as, but not limited to, messaging functions, browsing, searching, playing, streaming, or displaying content in various forms, including locally stored or uploaded messages, images and / or videos and / or games.

[0068] In some embodiments, exemplary network 405 may provide network access, data transmission, and / or other services to any computing device coupled thereto. In some embodiments, exemplary network 405 may include and implement at least one dedicated network architecture, which may be at least partially based on one or more standards defined by, for example, but not limited to, the Global System for Mobile Communications (GSM) Association, the Internet Engineering Task Force (IETF), and the Global Microwave Access Interoperability (WiMAX) Forum. In some embodiments, exemplary network 405 may implement one or more of the GSM architecture, the General Packet Radio Service (GPRS) architecture, the Universal Mobile Telecommunications System (UMTS) architecture, and UMTS evolution known as Long Term Evolution (LTE). In some embodiments, exemplary network 405 may include and implement the WiMAX architecture defined by the WiMAX Forum as an alternative or in combination with one or more of the foregoing. In some embodiments, and optionally, in conjunction with any embodiments described above or below, exemplary network 405 may also include at least one of, for example, a Local Area Network (LAN), a Wide Area Network (WAN), the Internet, a Virtual LAN (VLAN), an Enterprise LAN, a Layer 3 Virtual Private Network (VPN), an Enterprise IP Network, or any combination thereof. In some embodiments, and optionally, in conjunction with any of the embodiments described above or below, at least one computer network communication via exemplary network 405 may be transmitted at least partially based on one of a plurality of communication modes, such as, but not limited to: NFC, RFID, Narrowband Internet of Things (NB-IoT), ZigBee, 3G, 4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, and any combination thereof. In some embodiments, exemplary network 405 may also include mass storage devices, such as network-attached storage (NAS), storage area network (SAN), content delivery network (CDN), or other forms of computer or machine-readable media.

[0069] In some embodiments, exemplary server 406 or exemplary server 407 may be a web server (or a series of servers) running a network operating system, examples of which may include, but are not limited to, Microsoft Windows Server, Novell NetWare, or Linux. In some embodiments, exemplary server 406 or exemplary server 407 may be used for and / or provide cloud and / or network computing. Although not shown in Figure 4, in some embodiments, exemplary server 406 or exemplary server 407 may have connections to external systems such as email, SMS messaging, text messaging, advertising content providers, etc. Any feature of exemplary server 406 may also be implemented in exemplary server 407, and vice versa.

[0070] In some embodiments, one or more of the exemplary servers 406 and 407 may be specifically programmed to perform, in non-limiting examples, as an authentication server, search server, email server, social networking service server, SMS server, IM server, MMS server, exchange server, photo sharing service server, advertising delivery server, financial / banking related service server, travel service server, or any similar suitable service-based server for users of member computing devices 401-404.

[0071] In some embodiments, optionally, in combination with any of the embodiments described above or below, such as one or more illustrative computing member devices 402-404, exemplary server 406 and / or exemplary server 407, may include specially programmed software modules that can be configured to automatically generate internal identification tokens associated with each entity based on stored information, and to use the internal identification tokens to perform at least one action associated with the interaction of the multiple entities.

[0072] Figure 5 depicts a block diagram of another exemplary computer-based system / platform 500 according to one or more embodiments of the present disclosure. However, practicing one or more embodiments may not require all of these components, and the arrangement and type of components may be varied without departing from the spirit or scope of the various embodiments of the present disclosure. In some embodiments, each of the illustrated member computing devices 502a, 502b to 502n includes at least a computer-readable medium, such as random access memory (RAM) 508 coupled to processor 510 or flash memory. In some embodiments, processor 510 may execute computer-executable program instructions stored in memory 508. In some embodiments, processor 510 may include a microprocessor, ASIC, and / or state machine. In some embodiments, processor 510 may include or be able to communicate with a medium, such as a computer-readable medium storing instructions that, when executed by processor 510, may cause processor 510 to perform one or more steps described herein. In some embodiments, examples of computer-readable media may include, but are not limited to, electronic, optical, magnetic, or other storage or transmission devices capable of providing computer-readable instructions to a processor such as processor 510 of client 502a. In some embodiments, other examples of suitable media may include, but are not limited to, floppy disks, CD-ROMs, DVDs, magnetic disks, memory chips, ROMs, RAMs, ASICs, configured processors, all optical media, all magnetic tapes or other magnetic media, or any other media from which a computer processor can read instructions. Furthermore, various other forms of computer-readable media may transmit or carry instructions wired and wirelessly to or from computers (including routers), private or public networks, or other transmission devices or channels. In some embodiments, instructions may include code from any computer programming language, including, for example, C, C++, Visual Basic, Java, Python, Perl, JavaScript, etc.

[0073] In some embodiments, member computing devices 502a to 502n may further include multiple external or internal devices, such as a mouse, CD-ROM, DVD, physical or virtual keyboard, display, speaker, or other input or output devices. In some embodiments, examples of member computing devices 502a to 502n (e.g., clients) may be any type of processor-based platform connected to network 506, such as, but not limited to, personal computers, digital assistants, personal digital assistants, smartphones, pagers, digital tablets, laptops, internet-connected appliances, and other processor-based devices. In some embodiments, member computing devices 502a to 502n may be specifically programmed with one or more applications according to one or more principles / methods detailed herein. In some embodiments, member computing devices 502a to 502n may run on any operating system capable of supporting browsers or browser-enabled applications (such as Microsoft...). TM Windows TM Running on Linux. In some embodiments, the illustrated member computing devices 502a to 502n may include, for example, executing browser applications (such as Microsoft's Internet Explorer). TM Apple Computer's Safari TM Personal computers running Mozilla Firefox and / or Opera. In some embodiments, users 512a to 512n can communicate with each other and / or with other systems and / or devices coupled to network 506 via member computing client devices 502a to 502n. As shown in FIG5, exemplary server devices 504 and 513 may also be coupled to network 506. Exemplary server device 504 may include a processor 505 of memory coupled to storage network engine 517. Exemplary server device 513 may include a processor 514 of memory 516 coupled to storage network engine. In some embodiments, one or more member computing devices 502a to 502n may be mobile clients. As shown in FIG5, network 506 may be coupled to one or more cloud computing / architectures 525. One or more cloud computing / architectures 525 may include cloud services coupled to cloud infrastructure and cloud platforms, wherein the cloud platform may be coupled to cloud storage.

[0074] In some embodiments, at least one of the exemplary databases 507 and 515 can be any type of database, including databases managed by a database management system (DBMS). In some embodiments, the database managed by the exemplary DBMS can be specifically programmed to control the organization, storage, management, and / or retrieval of data in the respective database. In some embodiments, the database managed by the exemplary DBMS can be specifically programmed to provide the ability to query, back up and replicate, enforce rules, provide security, perform calculations, perform change and access logging, and / or automate optimizations. In some embodiments, the database managed by the exemplary DBMS can be selected from Oracle Database, IBM DB2, Adaptive Server Enterprise, FileMaker, Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and NoSQL implementations. In some embodiments, the database managed by the exemplary DBMS can be specifically programmed to define each respective schema of each database in the exemplary DBMS according to a specific database model of this disclosure, which may include a hierarchical model, a network model, a relational model, an object model, or some other suitable organization that can produce one or more applicable data structures, which may include fields, records, files, and / or objects. In some embodiments, the database managed by the exemplary DBMS can be specifically programmed to include metadata about the stored data.

[0075] Figures 6 and 7 illustrate schematic diagrams of exemplary implementations of one or more cloud computing / architectures in which the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure can be specifically configured to operate. Figure 6 illustrates an extended view of one or more cloud computing / architectures 525 found in Figure 5. Figure 7 illustrates that exemplary inventive computer-based components of this disclosure can be specifically configured to operate as a source database 704 in cloud computing / architecture 525, wherein the source database 704 may be a web browser, mobile application, thin client, and terminal emulator. In Figure 7, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure can be specifically configured to operate in cloud computing / architectures such as, but not limited to, Infrastructure as a Service (IaaS) 710, Platform as a Service (PaaS) 708, and / or Software as a Service (SaaS) 706.

[0076] In some embodiments, optionally, in conjunction with any of the embodiments described above or below, the exemplary trained neural network model may specify the neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of the neural network may include the configuration of the nodes of the neural network and the connections between these nodes. In some embodiments, optionally, in conjunction with any of the embodiments described above or below, the exemplary trained neural network model may also be specified to include other parameters, including but not limited to bias values / functions and / or aggregation functions. For example, the activation function of a node may be a step function, a sine function, a continuous or piecewise linear function, a sigmoid function, a hyperbolic tangent function, or other types of mathematical functions representing a threshold at which a node is activated. In some embodiments, optionally, in conjunction with any of the embodiments described above or below, the exemplary aggregation function may be a mathematical function that combines an input signal with nodes (e.g., summation, product, etc.). In some embodiments, optionally, in conjunction with any of the embodiments described above or below, the output of the exemplary aggregation function may be used as input to the exemplary activation function. In some embodiments, optionally, in conjunction with any of the embodiments described above or below, bias can be a constant value or function that can be used by aggregation functions and / or activation functions to make a node more or less activated.

[0077] The material disclosed herein may be implemented as software, firmware, or a combination of both, or as instructions stored on a machine-readable medium that can be read and executed by one or more processors. A machine-readable medium may include any medium and / or mechanism for storing or transmitting information in a machine-readable form (e.g., a computing device). For example, a machine-readable medium may include read-only memory (ROM), random access memory (RAM); disk storage media; optical storage media; knowledge corpora; stored audio recordings; flash memory devices; electrical, optical, acoustic, or other forms of propagation signals (e.g., carrier waves, infrared signals, digital signals, etc.) and others.

[0078] As used herein, the terms “computer engine” and “engine” identify at least one software component and / or a combination of at least one software component and at least one hardware component that is designed / programmed / configured to manage / control other software and / or hardware components (such as libraries, software development kits (SDKs), objects, etc.).

[0079] Examples of hardware components may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, etc.), integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), logic gates, registers, semiconductor devices, chips, microchips, chipsets, etc. In some embodiments, one or more processors may be implemented as complex instruction set computer (CISC) or reduced instruction set computer (RISC) processors; x86 instruction set compatible processors, multi-core processors, or any other microprocessor or central processing unit (CPU). In various implementations, one or more processors may be one or more dual-core processors, one or more dual-core mobile processors, etc.

[0080] As used herein, computer-related systems, computer systems, and systems include any combination of hardware and software. Examples of software may include software components, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, procedures, software interfaces, application programming interfaces (APIs), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. Determining whether an embodiment is implemented using hardware and / or software components can vary depending on a variety of factors, such as desired computing speed, power levels, thermal tolerance, processing cycle budget, input data rate, output data rate, memory resources, data bus speed, and other design or performance constraints.

[0081] One or more aspects of at least one embodiment can be implemented by representative instructions stored on a machine-readable medium, representing various logic within a processor, which, when read by a machine, cause machine-manufacturing logic to perform the techniques described herein. Such a representation (referred to as an "IP core") can be stored on a tangible machine-readable medium and supplied to various customers or manufacturing plants for loading into manufacturing machines that produce logic or processors. It is worth noting that the various embodiments described herein can, of course, be implemented using any suitable hardware and / or computational software language (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.).

[0082] In some embodiments, one or more of the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure may be partially or wholly included or incorporated into at least one personal computer (PC), laptop computer, ultra-laptop computer, tablet computer, touchpad, portable computer, handheld computer, PDA, personal digital assistant (PDA), cellular phone, cellular phone / PDA combination, television, smart device (e.g., smartphone, smart tablet, or smart TV), mobile internet device (MID), messaging device, data communication device, etc.

[0083] As used herein, the term "server" should be understood to refer to a point of service that provides processing, database, and communication facilities. For example, but not limited to, the term "server" can refer to a single physical processor associated with communication, data storage, and database facilities, or it can refer to a networked or clustered complex of processors and associated network and storage devices, as well as the operating software and one or more database systems and application software that support the services provided by the server. In some embodiments, a server may store transactions and dynamically trained machine learning models. A cloud server is an example.

[0084] In some embodiments, as detailed herein, one or more of the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure may acquire, manipulate, transmit, store, transform, generate, and / or output (e.g., from within and / or outside a particular application) any digital object and / or data unit, which may be in any suitable form, such as, but not limited to, files, contacts, tasks, emails, social media posts, maps, entire applications (e.g., calculators), etc. In some embodiments, as detailed herein, one or more of the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure may be implemented across one or more of various computer platforms, such as, but not limited to: (1) FreeBSD™, NetBSD™, OpenBSD™; (2) Linux™; (3) Microsoft Windows™; (4) OSX (MacOS)™; (5) MacOS 11™; (6) Solaris™; (7) Android™; (8) iOS™; (9) Embedded Linux™; (10) Tizen™; (11) WebOS™; (12) IBM i™; (13) IBM AIX™; (14) Wireless Binary Runtime Environment (BREW)™; (15) Cocoa (API)™; (16) Cocoa Touch™; (17) Java Platform™; (18) JavaFX™; (19) JavaFX Mobile;™; (20) Microsoft DirectX™; (21) .NET Framework™; (22) Silverlight™; (23) Open Web Platform™; (24) Oracle Database™; (25) Qt™; (26) Eclipse Rich Client Platform™; (27) SAP NetWeaver™; (28) Smartface™; and / or (29) Windows Runtime™.

[0085] In some embodiments, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure may be configured to utilize hardwired circuitry systems that may be used in place of or in combination with software instructions to achieve features consistent with the principles of this disclosure. Therefore, implementations consistent with the principles of this disclosure are not limited to any particular combination of hardware circuitry systems and software. For example, various embodiments may be implemented as software components in a variety of different ways, such as, but not limited to, standalone software packages, combinations of software packages, or they may be software packages incorporated as “tools” into a larger software product.

[0086] For example, exemplary software specifically programmed according to one or more principles of this disclosure may be downloaded from a network (e.g., a website), as a standalone product, or as a plug-in package installed in an existing software application. For example, exemplary software specifically programmed according to one or more principles of this disclosure may also be used as a client-server software application or a web-enabled software application. For example, exemplary software specifically programmed according to one or more principles of this disclosure may also be implemented as a software package installed on a hardware device. In at least one embodiment, the exemplary spam prevention module 118 of this disclosure utilizing at least one machine learning model described herein may be referred to as exemplary software.

[0087] In some embodiments, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure can be configured to handle a large number of concurrent tests against a software agent, which may be, but are not limited to, at least 100 (e.g., but not limited to 100-999), at least 1,000 (e.g., but not limited to 1,000-9,999), at least 10,000 (e.g., but not limited to 10,000-99,999), or at least 100,000 (e.g., but not limited to 100,000-99,999). 000-999,999), at least 1,000,000 (e.g., but not limited to 1,000,000-9,999,999), at least 10,000,000 (e.g., but not limited to 10,000,000-99,999,999), at least 100,000,000 (e.g., but not limited to 100,000,000-999,999,999), at least 1,000,000,000 (e.g., but not limited to 1,000,000,000-999,999,999,999), etc.

[0088] In some embodiments, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure can be configured to output to different, specially programmed graphical user interface implementations of this disclosure (e.g., desktop, web applications, etc.). In various implementations of this disclosure, the final output can be displayed on a display screen, which can be, but is not limited to, a computer screen, a mobile device screen, etc. In various implementations, the display can be a holographic display. In various implementations, the display can be a transparent surface capable of receiving visual projections. Such projections can convey various forms of information, images, and / or objects. For example, such projections can be visual overlays in mobile augmented reality (MAR) applications.

[0089] In some embodiments, the exemplary inventive computer-based systems / platforms, exemplary inventive computer-based devices, and / or exemplary inventive computer-based components of this disclosure may be configured for use in various applications, including but not limited to the exemplary spam prevention module 118 of this disclosure utilizing at least one machine learning model described herein, games, mobile games, video chat, video conferencing, real-time video streaming, video streaming and / or augmented reality applications, mobile communication applications, and other similar suitable computer device applications.

[0090] As used herein, the term "mobile electronic device" can refer to any portable electronic device that may or may not have location tracking capabilities (e.g., MAC address, Internet Protocol (IP) address, etc.). For example, a mobile electronic device can include, but is not limited to, a mobile phone, a personal digital assistant (PDA), a Blackberry™, a pager, a smartphone, or any other reasonable mobile electronic device.

[0091] Of course, the above examples are illustrative and not restrictive.

[0092] At least some aspects of this disclosure will now be described with reference to the following numbered clauses.

[0093] Clause 1. A method may include: identifying a plurality of entities seeking to interact with each other by at least one processor; analyzing each entity by the at least one processor to determine the type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including historical entity data associated with each entity, the historical entity data being associated with at least one of: a plurality of historical entities, at least one individual, results of a client manager module, results of a profile manager module, results of an address manager module, and results of a constraint manager module; and at least one training output for each training constraint pair, the at least one training output including a plurality of historical constraint types associated with each entity; automatically generating an internal identification token associated with each entity by the at least one processor based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and performing at least one action associated with the interaction of the plurality of entities by the at least one processor using the internal identification token.

[0094] Clause 2. The method described in Clause 1, wherein the trained machine learning module is trained to represent a unique structure detailing different relation types.

[0095] Clause 3. The method according to Clause 1 or 2, wherein the trained machine learning module includes a trained machine learning model, the trained machine learning model including a plurality of trained machine learning parameters.

[0096] Clause 4. The method described in accordance with Clause 1, 2 or 3, wherein the plurality of trained machine learning parameters are trained to an entity-based input-output constraint type.

[0097] Clause 5. The method described in accordance with Clauses 1, 2, 3 or 4, wherein the information includes a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, and a salt value associated with the hash algorithm of each entity.

[0098] Clause 6. The method described in accordance with Clauses 1, 2, 3, 4 or 5, wherein the profile manager module includes an account manager module.

[0099] Clause 7. The method described in accordance with Clauses 1, 2, 3, 4, 5 or 6, wherein the pattern comprises multiple values ​​organized in multiple sets of features.

[0100] Clause 8. The method according to Clauses 1, 2, 3, 4, 5, 6 or 7, wherein a first feature set in the plurality of feature sets represents a unique sequence of numbers associated with a particular constraint type, a second feature set in the plurality of feature sets represents a location associated with a particular entity, and a third feature set in the plurality of feature sets represents a historical type associated with a particular entity.

[0101] Clause 9. The method according to Clauses 1, 2, 3, 4, 5, 6, 7 or 8, wherein the plurality of feature sets are combined in a hash algorithm to produce an internal identifier token.

[0102] Clause 10. The method according to Clauses 1, 2, 3, 4, 5, 6, 7, 8 or 9, wherein the at least one action includes generating an interaction session for the at least two entities to interact based on an internal identification token.

[0103] Clause 11. The method described in accordance with Clauses 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 further includes utilizing a data workstation structure to display multiple data layers for use during a generated interactive session.

[0104] Clause 12. The method described in accordance with Clauses 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11, wherein the data workstation structure includes: an entity interface layer, a context passing layer, a topic management layer, a navigation layer and a login authorization layer.

[0105] Clause 13. The method described in accordance with Clauses 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 further includes utilizing a data workstation structure to: provide a site map layer with multiple recently viewed applications in response to an entity entering a generated interactive session; optimize navigation capabilities associated with the generated interactive session; generate at least one information query within the generated interactive session; and automatically display at least one result of the at least one query on at least one window of the data workstation structure based on the site map layer and navigation capabilities.

[0106] Clause 14. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, which, when executed by a device, perform a method comprising: identifying by at least one processor a plurality of entities seeking to interact with each other; analyzing by the at least one processor each entity to determine a type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including information related to each entity. The entity is associated with historical entity data, which is associated with at least one of the following: multiple historical entities, at least one individual, the results of a client manager module, the results of a profile manager module, the results of an address manager module, and the results of a constraint manager module; and at least one training output for each training constraint pair, the at least one training output including multiple historical constraint types associated with each entity; the at least one processor automatically generates an internal identification token associated with each entity based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and the at least one processor performs at least one action associated with the interaction with the multiple entities using the internal identification token.

[0107] Clause 15. The non-transitory computer-readable storage medium as described in Clause 14, wherein a trained machine learning module is trained to represent a unique structure detailing different relation types.

[0108] Clause 16. The non-transitory computer-readable storage medium as described in Clause 14 or 15, wherein the trained machine learning module includes a trained machine learning model that includes a plurality of trained machine learning parameters.

[0109] Clause 17. A non-transitory computer-readable storage medium as described in Clauses 14, 15 or 16, wherein the plurality of trained machine learning parameters are trained to an entity-based input-output constraint type.

[0110] Clause 18. A non-transitory computer-readable storage medium as described in Clauses 14, 15, 16 or 17, wherein the information includes a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, and a salt value associated with the hash algorithm of each entity.

[0111] Clause 19. A nontransitory computer-readable storage medium as described in Clauses 14, 15, 16, 17 or 18, wherein the mode comprises multiple values ​​organized in multiple feature sets.

[0112] Clause 20. A system comprising: a non-transitory computer memory storing software instructions; at least one processor of a computing device associated with a user; wherein, when the at least one processor executes the software instructions, the computing device is programmed to: identify a plurality of entities seeking to interact with each other; analyze each entity to determine a type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including Historical entity data associated with each entity, which is associated with at least one of the following: multiple historical entities, at least one individual, results from the client manager module, results from the profile manager module, results from the address manager module, and results from the constraint manager module; and at least one training output for each training constraint pair, which includes multiple historical constraint types associated with each entity; automatically generating an internal identification token associated with each entity based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and performing at least one action associated with the interaction with the multiple entities using the internal identification token.

[0113] While one or more embodiments of this disclosure have been described, it should be understood that these embodiments are illustrative only and not restrictive, and many modifications will be apparent to those skilled in the art. Various embodiments of the inventive methods, inventive systems / platforms, and inventive devices described herein can be used in any combination thereof. Furthermore, the steps can be performed in any desired order (and any desired steps can be added and / or eliminated).

Claims

1. A computer-implemented method, comprising: Multiple entities seeking to interact with each other are identified by at least one processor; Each entity is analyzed by the at least one processor to determine the type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including historical entity data associated with each entity, the historical entity data being associated with at least one of: a plurality of historical entities, at least one individual, the results of a client manager module, the results of a profile manager module, the results of an address manager module, and the results of a constraint manager module; and at least one training output for each training constraint pair, the at least one training output including multiple historical constraint types associated with each entity; an internal identification token associated with each entity is automatically generated by the at least one processor based on the stored information and the constraint types between each of the multiple entities; wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and at least one action associated with the interaction of the multiple entities is performed by the at least one processor using the internal identification token.

2. The computer-implemented method of claim 1, wherein the trained machine learning module is trained to represent a unique structure detailing different relation types.

3. The computer-implemented method of claim 1, wherein the trained machine learning module includes a trained machine learning model, and the trained machine learning model includes a plurality of trained machine learning parameters.

4. The computer-implemented method of claim 3, wherein the plurality of trained machine learning parameters are trained to an entity-based input-output constraint type.

5. The computer-implemented method of claim 1, wherein the information includes a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, and a salt value associated with the hash algorithm of each entity.

6. The computer-implemented method of claim 5, wherein the profile manager module includes an account manager module.

7. The computer-implemented method of claim 1, wherein the pattern comprises multiple values ​​organized in a plurality of feature sets.

8. The computer-implemented method of claim 7, wherein a first feature set in the plurality of feature sets represents a unique sequence of numbers associated with a particular constraint type, a second feature set in the plurality of feature sets represents a location associated with a particular entity, and a third feature set in the plurality of feature sets represents a historical type associated with a particular entity.

9. The computer-implemented method of claim 1, wherein the plurality of feature sets are combined in a hash algorithm to generate the internal identifier token.

10. The computer-implemented method of claim 1, wherein the at least one action includes generating an interaction session for the at least two entities to interact based on the internal identification token.

11. The computer-implemented method of claim 1, further comprising utilizing a data workstation architecture to display multiple data layers for use during a generated interactive session.

12. The computer-implemented method of claim 11, wherein the data workstation structure comprises: The system consists of an entity interface layer, a context passing layer, a topic management layer, a navigation layer, and a login authorization layer.

13. The computer-implemented method of claim 1, further comprising utilizing a data workstation architecture to: provide a site map layer with multiple recently viewed applications in response to an entity entering a generated interactive session; Optimize navigation capabilities associated with the generated interactive sessions; Generate at least one information query in the generated interactive session; And based on the site map layer and navigation capabilities, automatically display at least one result of the at least one query on at least one window of the data workstation structure.

14. A non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium being tangibly encoded with computer-executable instructions, the computer-executable instructions, when executed by a device, performing a method comprising: Multiple entities seeking to interact with each other are identified by at least one processor; Each entity is analyzed by the at least one processor to determine the type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including historical entity data associated with each entity, the historical entity data being associated with at least one of: a plurality of historical entities, at least one individual, the results of a client manager module, the results of a profile manager module, the results of an address manager module, and the results of a constraint manager module; and at least one training output for each training constraint pair, the at least one training output including multiple historical constraint types associated with each entity; an internal identification token automatically generated by the at least one processor based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and the at least one processor using the internal identification token to perform at least one action associated with the interaction of the multiple entities.

15. The non-transitory computer-readable storage medium of claim 14, wherein the trained machine learning module is trained to represent a unique structure detailing different relation types.

16. The non-transitory computer-readable storage medium of claim 14, wherein the trained machine learning module includes a trained machine learning model, the trained machine learning model including a plurality of trained machine learning parameters.

17. The non-transitory computer-readable storage medium of claim 16, wherein the plurality of trained machine learning parameters are trained to an entity-based input-output constraint type.

18. The non-transitory computer-readable storage medium of claim 14, wherein the information includes a unique password associated with each entity, a location associated with each entity, a history type associated with each entity, and a salt value associated with the hash algorithm of each entity.

19. The non-transitory computer-readable storage medium of claim 14, wherein the mode comprises a plurality of values ​​organized in a plurality of feature sets.

20. A system comprising: Non-transitory computer memory that stores software instructions; At least one processor of a computing device associated with a user; wherein, when the at least one processor executes the software instructions, the computing device is programmed to: identify a plurality of entities seeking to interact with each other; analyze each entity to determine a type of constraint between each of the plurality of entities, wherein the analysis is performed using a trained machine learning module, wherein the trained machine learning module includes a plurality of trained machine learning parameters, wherein the plurality of trained machine learning parameters are trained on training constraint pairs, each training constraint pair including: at least one training input for each training constraint pair, the at least one training input including historical entity data associated with each entity, the historical entity data being associated with at least one of: a plurality of historical entities, at least one individual, results of a client manager module, results of a profile manager module, results of an address manager module, and results of a constraint manager module; and at least one training output for each training constraint pair, the at least one training output including a plurality of historical constraint types associated with each entity; automatically generate an internal identification token associated with each entity based on stored information, wherein the internal identification token includes a pattern providing information related to the holder of the internal identification token; and perform at least one action associated with the interaction with the plurality of entities using the internal identification token.

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