Patent Publication Number: US-2023134392-A1

Title: Automated decisioning based on predicted user intent

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 63/274,689, filed Nov. 2, 2021, which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     DESCRIPTION OF THE RELATED TECHNOLOGY 
     People engage in transactions with a variety of entities throughout their day-to-day lives. For instance, people engage with merchants to purchase products, with service providers to receive services, with financial institutions such as banks and credit unions and lenders for financial services, and the like. Traditionally, information related to such transactions has remained siloed at the individual entities with which a person engages. Further, a person may at times engage in the same or similar types of transactions with the same entity or similar entities periodically in a pattern. Such patterns of transactions are typically inefficient and cumbersome for the person to maintain, and may result in problems for the person if the person forgets a transaction in the pattern. 
     SUMMARY 
     Disclosed are systems, apparatuses, methods, computer readable medium, and circuitry for automated account interaction. In some examples, a system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies at least one transaction involving the account. The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like. The system provides the historical information to one or more trained machine learning models. The system uses the one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models. The system provides the intent for the transaction to the one or more trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models. In some examples, the system uses the first trained machine learning engine to identify the intent for the transaction and uses a second trained machine learning engine to generate the recommended transaction. In some examples, the system uses a first trained machine learning engine both to identify the intent for the transaction and to generate the recommended transaction. The system outputs the recommended transaction. In some examples, the system outputs the recommended transaction by displaying the recommended transaction using a display. In some examples, the system outputs the recommended transaction by sending the recommended transaction to the user device associated with the user. In some examples, the system outputs the recommended transaction by initiating, processing, and/or completing the transaction. In some examples, the system outputs the recommended transaction by communicating with a second system to request that the second system that initiate, process, and/or complete the transaction. The system receives a confirmation regarding the recommended transaction. In some examples, the confirmation is approval from the user to initiate the recommended transaction. In response to the confirmation, the system can initiate, process, and/or complete the transaction. In response to the confirmation, the system can communicate with a second system to request that the second system initiate, process, and/or complete the transaction. In some examples, the confirmation is a confirmation received from a second system (or a component of the system) that confirms that the transaction has been initiated, processed, and/or completed. The system can output a message based on the confirmation, for instance by displaying the message using a display and/or sending the message to the user device associated with the user. 
     In one example, a method for automated account interaction is provided. The method includes: receiving historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; using one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models; using the one or more trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the one or more trained machine learning models; outputting the recommended transaction; receiving a confirmation regarding the recommended transaction; and based on the confirmation, using the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
     In another example, a system for automated account interaction is provided. The system includes a storage (e.g., a memory configured to store data, such as virtual content data, one or more images, etc.) and one or more processors (e.g., implemented in circuitry) coupled to the memory and configured to execute instructions. Execution of the instructions by the one or more processors causes the one or more processors, in conjunction with various components (e.g., a network interface, a display, an output device, etc.), to: receive historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; use one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models; use the one or more trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the one or more trained machine learning models; output the recommended transaction; receive a confirmation regarding the recommended transaction; and use, based on the confirmation, the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
     In another example, a non-transitory computer readable storage medium having embodied thereon a program is provided. The program is executable by a processor to perform a method of automated account interaction. The method includes: receiving historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; using one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models; using the one or more trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the one or more trained machine learning models; outputting the recommended transaction; receiving a confirmation regarding the recommended transaction; and based on the confirmation, using the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
     In another example, a system for automated account interaction is provided. The system includes: means for receiving historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; means for using one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models; means for using the one or more trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the one or more trained machine learning models; means for outputting the recommended transaction; means for receiving a confirmation regarding the recommended transaction; and means for using, based on the confirmation, the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Illustrative embodiments of the present application are described in detail below with reference to the following figures: 
         FIG.  1    is a block diagram illustrating a system architecture of a system for automated account interaction, in accordance with some examples; 
         FIG.  2    is a block diagram illustrating a system architecture of a system for intent-based recommendations, in accordance with some examples; 
         FIG.  3 A  is a flow diagram illustrating a process for automated account interactions, in accordance with some examples; 
         FIG.  3 B  is a flow diagram illustrating a process for automated account interactions using one or more trained machine learning models, in accordance with some examples; 
         FIG.  3 C  is a flow diagram illustrating a process for automated account interactions using multiple trained machine learning models, in accordance with some examples; 
         FIG.  4    is a block diagram illustrating using one or more machine learning models of a machine learning engine to determine a predicted intent for a first transaction based on historical information associated with a user, in accordance with some examples; 
         FIG.  5 A  is a block diagram illustrating using the one or more machine learning models of the machine learning engine to generate a recommended transaction based on an intent for the first transaction and/or based on historical information associated with the user, in accordance with some examples; 
         FIG.  5 B  is a block diagram illustrating using the one or more machine learning models of the machine learning engine to generate a recommended community based on an intent for the first community and/or based on historical information associated with the user, in accordance with some examples; 
         FIG.  6    is a block diagram illustrating a system architecture of a system for intent-based recommendations, in accordance with some examples; 
         FIG.  7    is a block diagram illustrating a process for requesting feedback from a user, in accordance with some examples; 
         FIG.  8    is a block diagram illustrating exemplary of data categories of historical data about users, in accordance with some examples; 
         FIG.  9    is a block diagram illustrating generation of recommendations for users by an analysis engine based on interactions, in accordance with some examples; 
         FIG.  10    is a block diagram illustrating generation of profiles associated with a user by one or more classifiers, in accordance with some examples; 
         FIG.  11    is a block diagram illustrating rule types associated with the rules engine, in accordance with some examples; 
         FIG.  12    is a block diagram illustrating considerations for generating recommendations, in accordance with some examples; 
         FIG.  13    is a block diagram illustrating a system architecture of a system that provides intent-based recommendations, in accordance with some examples; 
         FIG.  14    is a block diagram illustrating an example of a neural network that can be used by a machine learning engine to determine intent for transactions and/or to generate recommended transactions, in accordance with some examples. 
         FIG.  15    is a block diagram of an exemplary computing device that may be used to implement some aspects of the subject technology. 
     
    
    
     DETAILED DESCRIPTION 
     People engage in transactions with a variety of entities throughout their day-to-day lives. For instance, people engage with merchants to purchase products, with service providers to receive services, with financial institutions such as banks and credit unions and lenders for financial services, and the like. Traditionally, information related to such transactions has remained siloed at the individual entities with which a person engages. Further, a person may at times engage in the same or similar types of transactions with the same entity or similar entities periodically in a pattern. Such patterns of transactions are typically inefficient and cumbersome for the person to maintain, and may result in problems for the person if the person forgets a transaction in the pattern. 
     Systems, apparatuses, methods, computer readable media, and circuitry are disclosed for automated account interaction. In some examples, a system for automated account interaction receives historical information associated with an account corresponding to a user. The historical information identifies at least one transaction involving the account. The historical information can identify a variety of transactions involving the user and/or the account, such as transactions with different entities (e.g., transactions with merchants to purchase products, with service providers to receive services, with financial institutions such as banks and credit unions and lenders for financial services, and the like). The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like. 
     In some examples, the system includes a machine learning (ML) engine with one or more ML models, which the system may train using training data. The system provides the historical information to at least one of the one or more trained ML models. The system uses the one or more trained ML models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained ML models. In some examples, the training data for the one or more ML models can include historical information and an intent for the transaction identified in the historical information. During a validation stage of training, the system can use the one or more ML models to generate a predicted intent for the transaction based on the historical information, and the system can update and/or further train the one or more ML models based on whether or not the predicted intent for the transaction matches the intent for the transaction from the training data. In some examples, the system requests and/or receives feedback from the user regarding the intent for the transaction, and updates and/or further trains the one or more ML models based on the feedback. 
     The system provides the intent for the transaction to the one or more trained ML models. The system uses the trained ML models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained ML models. In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the system can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the system can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data. In some examples, the system uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction. In some examples, the system uses a first trained ML engine both to identify the intent for the transaction and to generate the recommended transaction. 
     The system outputs the recommended transaction. In some examples, the system outputs the recommended transaction by displaying the recommended transaction using a display. In some examples, the system outputs the recommended transaction by sending the recommended transaction to the user device associated with the user. In some examples, the system outputs the recommended transaction by initiating, processing, and/or completing the transaction. In some examples, the system outputs the recommended transaction by communicating with a second system to request that the second system that initiate, process, and/or complete the transaction. 
     The system receives a confirmation regarding the recommended transaction. In some examples, the confirmation is approval from the user to initiate the recommended transaction. In response to the confirmation, the system can initiate, process, and/or complete the transaction. In response to the confirmation, the system can communicate with a second system to request that the second system initiate, process, and/or complete the transaction. In some examples, the confirmation is a confirmation received from a second system (or a component of the system) that confirms that the transaction has been initiated, processed, and/or completed. The system can output a message based on the confirmation, for instance by displaying the message using a display and/or sending the message to the user device associated with the user. 
     The system uses the intent and/or the recommended transaction to update the one or more machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. In some examples, the system requests and/or receives feedback from the user regarding the intent and/or recommended transaction, and updates and/or further trains the one or more ML models based on the feedback. For instance, if the feedback is positive, then the update to the ML models can reinforce weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction and/or to the generation of the recommended transaction. If the feedback is negative, then the update can weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction and/or to the generation of the recommended transaction. 
     The systems, apparatuses, methods, computer readable media, and circuitry for automated account interaction described herein provide a number of benefits over traditional account management technologies. For instance, the automated account interaction systems, apparatuses, methods, computer readable media, and circuitry described herein provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization. The automated account interaction systems, apparatuses, methods, computer readable media, and circuitry described herein improve efficiency of account management, point of sale (POS), and financial management technologies, for instance by initiating, processing, and/or completing recommended transactions automatically based on intelligent predictions, forecasts, and/or estimates of transactions aligning with the user&#39;s intent, which the system determines based on prior transaction(s). 
       FIG.  1    is a block diagram illustrating a system architecture for automated account interaction. The system architecture includes various types of user devices  120 . User devices  120  include telephony devices  105 , such as landline telephones, cellular phones, smartphones, smart watches, mobile handsets, wearable devices, or combinations thereof. User devices  120  include computing devices  115  such as desktop computers, laptop computers, servers, terminals, kiosks, cellular phones, smartphones, smart watches, mobile handsets, wearable devices, or any other computing system  1500  discussed with respect to  FIG.  15   . User devices  120  include mobile devices  110 , such as smartphones, cellular phones, mobile handsets, tablet devices, portable video game consoles, portable media players, head-mounted displays (HMDs), virtual reality (VR) devices, augmented reality (AR) devices, mixed reality (MR) devices, extended reality (XR) devices, smartwatches, smart glasses, smart rings, smart bracelets, wearable devices, health monitor devices, health tracking devices, fitness tracking devices, another type of computing system  1500 , or a combination thereof. In some examples, a user device  120  may be a combination of a telephony device  105 , mobile device  110 , and/or computing device  115 . 
     The system architecture includes one or more voice interface gateway servers  125 , one or more account automation servers  130 , one or more account management servers  140 , one or more account automation data structures  135 , and one or more account management data structures  145 . In some cases, user device  120  may receive an input from a user of the user device  120 . For example, the user device  120  may receive the input through an input interface, such as a physical keyboard or keypad with physical keys or buttons, a virtual keyboard or keypad with virtual keys or buttons on a touchscreen, another touchscreen interface, a microphone that records the user&#39;s voice, a touchpad, a mouse, any input device  1545  discussed with respect to  FIG.  15   , or some combination thereof. 
     If the input device used is a microphone of the user device  120 , the microphone may record a voice recording of the user&#39;s voice. In some examples, the user device  120  sends the voice recording to the voice interface gateway servers  125 . The voice interface gateway servers  125 , upon receiving the voice recording, can convert the voice recording into a string of text using a conversion algorithm, which may be referred to as parsing the voice recording, automatic speech recognition (ASR), computer speech recognition (CSR), speech to text (STT), or a combination thereof. The voice interface gateway servers  125  can convert the voice recording into a string of text dynamically and/or in real-time as the voice interface gateway servers  125  continues to receive more of the user&#39;s voice. The conversion algorithm may convert the voice recording into the string of text using, for example, hidden Markov models, dynamic time warping (DTW)-based speech recognition, deep neural networks, deep feedforward neural networks (DNNs), recurrent neural networks (RNNs), time delay neural networks (TDNNs), convolutional neural networks (CNNs), denoising autoencoders, or combinations thereof. 
     In some examples, the user device  120  converts the voice recording into the string of text using the conversion algorithm. In some examples, the voice recording is converted into the string of text by a combination of the user device  120  and the voice interface gateway servers  125 , for example with the different devices performing various operations of the conversion algorithm. For example, if the user device  120  engages in a phone call or voice-over-IP call with an automated assistant running on the voice interface gateway servers  125  and/or on the account automation servers  130 , the user&#39;s voice may be sent from the user device  120  to the voice interface gateway servers  125  over telephone, cellular network, or internet network lines, and can be recorded during the phone call and temporarily stored by the voice interface gateway servers  125  while the voice recording is converted into the string of text. In some examples, the voice recording can be a request or statement to a virtual personal assistant whose speech recognition functionality runs at least partially on the user device  120  and/or at least partially on the voice interface gateway servers  125 . Once the voice recording is converted into a string of text by the user device  120  and/or the voice interface gateway servers  125 , the string of text is sent to the account automation servers  130  from the user device  120  and/or the voice interface gateway servers  125 . The account automation servers  130  may then parse the string of text to determine the intent of the received input. 
     The one or more account automation servers  130  can obtain historical information about an account of a user from the one or more account management data structures  145  and/or from the one or more account automation data structures  135 . The historical information identifies at least one transaction involving the account. The historical information can identify a variety of transactions involving the user and/or the account, such as transactions with different entities (e.g., transactions with merchants to purchase products, with service providers to receive services, with financial institutions such as banks and credit unions and lenders for financial services, and the like). The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like. In some examples, the one or more account automation servers  130  store the historical information in the account automation data structures  135 . 
     The one or more account automation servers  130  can include a machine learning (ML) engine with one or more trained ML models. The one or more account automation servers  130  can use the ML engine to train the one or more ML models using training data. The one or more account automation servers  130  provide the historical information to at least one of the one or more trained ML models. The one or more account automation servers  130  use the one or more trained ML models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained ML models. In some examples, the one or more account automation servers  130  store the intent for the transactions in the account automation data structures  135 . 
     In some examples, the training data for the one or more ML models can include historical information and an intent for the transaction identified in the historical information. During a validation stage of training, the one or more account automation servers  130  can use the one or more ML models to generate a predicted intent for the transaction based on the historical information, and the one or more account automation servers  130  can update and/or further train the one or more ML models based on whether or not the predicted intent for the transaction matches the intent for the transaction from the training data. 
     In some examples, the one or more account automation servers  130  request and/or receive feedback from the user device  120  associated with the user regarding the intent for the transaction. The one or more account automation servers  130  can update and/or further train the one or more ML models based on the feedback from the user device  120  associated with the user. For instance, if the feedback is positive (e.g., confirming, indicating, and/or suggesting that the intent for the transaction determined by the one or more ML models is accurate), then the one or more account automation servers  130  can reinforce weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction. If the feedback is negative (e.g., indicating and/or suggesting that the intent for the transaction determined by the one or more ML models is inaccurate, and/or indicating and/or suggesting an alternate intent for the transaction that contradicts the intent for the transaction determined by the one or more ML models), then the one or more account automation servers  130  can weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction. 
     The one or more account automation servers  130  provides the intent for the transaction to the one or more trained ML models. The one or more account automation servers  130  uses the trained ML models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained ML models. In some examples, the one or more account automation servers  130  store the recommended transaction in the account automation data structures  135 . 
     In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the one or more account automation servers  130  can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the one or more account automation servers  130  can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data. In some examples, the one or more account automation servers  130  uses the first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction. In some examples, the one or more account automation servers  130  uses a first trained ML engine both to identify the intent for the transaction and to generate the recommended transaction. 
     The one or more account automation servers  130  output the recommended transaction. In some examples, the one or more account automation servers  130  output the recommended transaction by displaying the recommended transaction using a display. In some examples, the one or more account automation servers  130  output the recommended transaction by sending the recommended transaction to the user device  120  associated with the user over a network using a communication transceiver. In some examples, the one or more account automation servers  130  output the recommended transaction by initiating, processing, and/or completing the transaction. In some examples, the one or more account automation servers  130  output the recommended transaction by communicating with a transaction processing system over a network to request that the transaction processing system that initiate, process, and/or complete the transaction. 
     The one or more account automation servers  130  receive a confirmation regarding the recommended transaction. In some examples, the confirmation is an approval from the user device  120  associated with the user to initiate the recommended transaction. In response to the confirmation, the one or more account automation servers  130  can initiate, process, and/or complete the transaction. In response to the confirmation, the one or more account automation servers  130  can communicate with a transaction processing system to request that the transaction processing system initiate, process, and/or complete the transaction. In some examples, the confirmation is a confirmation received from a transaction processing system (or a component of the one or more account automation servers  130 ) that confirms that the transaction has been initiated, processed, and/or completed. The one or more account automation servers  130  can output a message based on the confirmation, for instance by displaying the message using a display and/or sending the message to the user device  120  associated with the user. 
     In some examples, the one or more account automation servers  130  request and/or receive feedback from the user device  120  associated with the user regarding the recommended transaction. The one or more account automation servers  130  can update and/or further train the one or more ML models based on the feedback from the user device  120  associated with the user. For instance, if the feedback is positive (e.g., confirming, indicating, and/or suggesting approval and/or authorization of the recommended transaction), then the one or more account automation servers  130  can reinforce weights and/or connections within the one or more ML models that contributed to the generation of the recommended transaction. If the feedback is negative (e.g., indicating and/or suggesting disapproval and/or lack of approval/authorization of the recommended transaction), then the one or more account automation servers  130  can weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the generation of the recommended transaction. 
     The account automation servers  130  can use the account management servers  140  and/or account management data structures  145  to automatically perform transactions and/or interactions between different accounts associated with different users. The accounts can include at least the account associated with the user and a second account. The second account can be associated with, for instance, a second user, a merchant, a service provider, a financial institution, a donor, a donation recipient, or another entity discussed herein. The transactions and/or interactions between different accounts that are performed by the account automation servers  130  and/or the account management servers  140  can correspond to the recommended transaction. The transactions and/or interactions can include an automatic transfer of one or more assets, such as an automatic donation, purchase, or transfer of funds. In some examples, the account automation servers  130  identifies (e.g., based on information received from the account management servers  140  and/or account management data structures  145 , such as the historical information, the intent for the transaction, and/or the recommended transaction) that a first user has purchased a product (a good or a service) as part of a first purchase using a first user payment account. The account automation servers  130  automatically select a second user payment account of a second user, a merchant, a service provider, a financial institution, a donor, a donation recipient, or another entity discussed herein. The second user payment account can be a user payment account, a merchant account, a donation account, and the like. The account automation servers  130  and/or the account management servers  140  can automatically trigger, initiate, process, and/or complete transfer of a quantity of the one or more assets (e.g., funds, stocks, bonds, points, store credit, in-game credit, gift card credit, cryptocurrencies, non-fungible tokens (NFTs), other digital assets, etc.) from the second user payment account to the first user payment account, and can communicate an indicator to the user device  120  indicating that the transfer has been initiated, processed, completed, and/or performed. 
     In some examples, the account automation servers  130  and/or account management servers  140  can process transactions dynamically and/or in real-time while receiving additional historical data about additional transactions, determining intents for additional transactions, and/or determining additional recommended transactions based on the additional transactions. In some examples, the account automation servers  130  and/or account management servers  140  can determine the recommended transaction based on the transaction dynamically and/or in real-time while receiving additional historical data about additional transactions and/or determining intents for additional transactions. In some examples, the account automation servers  130  and/or account management servers  140  can determine the intent for the transaction dynamically and/or in real-time while receiving additional historical data about additional transactions. In some cases, certain users, user devices  120 , and/or accounts can be associated with banks, credit card institutions, debit card institutions, financial institutions, or other companies, organizations, or institutions. For example, a financial institution or company can have one or more accounts and/or user devices  120  that interact with the voice interface gateway servers  125 , the account automation servers  130 , and/or the account management servers  140 . 
     While the voice interface gateway servers  125 , the account automation servers  130 , and the account management servers  140  are referred to herein as servers, it should be understood that these may be laptop computers, desktop computers, mobile devices, terminals, kiosks, mobile handsets, smartphones, any other type of computing system  1500  discussed herein, or a combination thereof. The account automation data structures  135  and account management data structures  145  may be any type of data structures, such as databases, tables, spreadsheets, key-value stores, dictionaries, relational models, arrays, lists, arraylists, trees, hashgraphs, distributed ledgers, blockchain ledgers, distributed acyclic graph (GAD) ledgers, other types of data structures discussed herein, or some combination thereof. In some cases, the account automation data structures  135  may be referred to as valid request databases, or with the phrase “valid request” followed by any of the other types of data structures. Similarly, the account management data structures  145  may be referred to as account management databases, or with the phrase “account management” followed by any of the other types of data structures. 
       FIG.  2    is a block diagram  200  illustrating a system architecture of a system for intent-based recommendations. The system includes a user front-end  202 , an agent back-end  210 , an automation engine  216 , historical information sources  228 , a cloud computing engine  244 , a user-agent communication engine  250 , web server(s)  208 , and a customer relationship management (CRM) engine  226 . 
     The user front-end  202  provides user devices  120  with access to the automation engine  216 . The user front-end  202  includes a software application interface  204  that connects to software applications, programs, or apps running on user devices  120 . The software application interface  204  can include one or more application programming interfaces (APIs) that the software applications, programs, or apps running on user devices  120  can call in order to trigger certain actions at, or interactions with, the automation engine  216 . The user front-end  202  includes a web interface  206  that provides data to, and/or receives data from, a website hosted by the web server(s)  208 . A user device  120  can access the website, for instance through a browser or another software application capable of receiving data from a network and presenting a page or interface at the user device  120 . The web interface  206  can include one or more application programming interfaces (APIs) that the user device  120  and/or web server(s)  208  can call in order to trigger certain actions at, or interactions with, the automation engine  216 . In some examples, a user device  120  can request information from the automation engine  216  via the software application interface  204  and/or via the web interface  206  (e.g., via the website hosted by the web server(s)  208 ). In some examples, a user device  120  can provide information to the automation engine  216  via the software application interface  204  and/or via the web interface  206  (e.g., via the website hosted by the web server(s)  208 ). In some examples, the user front-end  202  can include the voice interface gateway server(s)  125 . 
     The agent back-end  210  provides agent devices  260  with access to the automation engine  216 . The agent back-end  210  includes an agent interface  212 . In some examples, the agent interface  212  connects to software applications, programs, or apps running on agent devices  260 . The agent interface  212  can include one or more application programming interfaces (APIs) that the software applications, programs, or apps running on agent devices  260  can call in order to trigger certain actions at, or interactions with, the automation engine  216 . The agent back-end  210  can include a web interface (similar to web interface  206 ) that provides data to, and/or receives data from, a website hosted by the web server(s)  208 . An agent device  102  can access the website, for instance through a browser or another software application capable of receiving data from a network and presenting a page or interface at the agent device  102 . The agent interface  212  can include one or more application programming interfaces (APIs) that the agent device  260  and/or web server(s)  208  can call in order to trigger certain actions at, or interactions with, the automation engine  216 . In some examples, an agent device  102  can request information from the automation engine  216  via the agent interface  212 . In some examples, an agent device  102  can provide information to the automation engine  216  via the agent interface  212 . In some examples, the agent back-end  210  can include the voice interface gateway server(s)  125 . 
     In some examples, an agent device  260  may communicate with a user device  120 , either directly or through the user front-end  202 , the agent back-end  210 , the automation engine  216 , the web server(s)  208 , or a combination thereof. For example, an agent using the agent device  260  may communicate with a user using the user device  120 . The agent may be a human operator or an artificial intelligence (AI) assistant. In some examples, a human operator and an AI assistant may both act as the agent at the agent device  260 , for instance at different times or for different portions of a conversation with the user at the user device  120 . In some examples, the user, via the user device  120 , may ask a question or make a request, which may be sent to the agent device  260  for the agent to answer or respond to. In some examples, if the agent does not answer or respond within a threshold period of time, an AI assistant at the agent device  260  and/or at the agent back-end  210  may automatically prepare a response with a preliminary answer/response and/or with a message requesting that the user at the user device  120  please wait for the agent to respond. In some examples, the AI assistant may supply an answer and/or response if the AI assistant is capable of doing so on its own, for instance if the user&#39;s question or request matches a question or request in a frequently asked questions data structure or frequent requests data structure, respectfully. Such data structures may identify responses (or types of responses) that the AI assistant can respond to the user with, and/or tasks that the AI assistant can perform or initiate (e.g., for the automation engine  216  to perform), in response to the user&#39;s questions or requests. 
     The agent back-end  210  includes an analytics engine  214  that is coupled to the CRM engine  226 . The CRM engine  226  can receive information from the historical data sources  228 , either directly or through the automation engine  216 . The CRM engine  226  can receive and organize the information from the historical data sources  228 . The analytics engine  214  can parse and/or normalize the information from the CRM engine  226 , and can generate analytics based on the information from the CRM engine  226 . The analytics can include, for example, trends, projections, predictions as to when a user may make a request or purchase a product or service, based on patterns in the user&#39;s requests and/or transactions that the analytics engine  214  identifies based on the information from the CRM engine  226  and/or the historical information sources  228 . The analytics engine  214  can provide these analytics to the agent devices  260 , for instance through the agent interface  212 . This way, an agent communicating through their agent device  260  with a user at a user device  120  can have any information that might be useful for the agent to help answer the user&#39;s questions, fulfill the user&#39;s request, assist the user with a transaction, request feedback from the user on recommended transactions and/or intent determinations, or combinations thereof. 
     The automation engine  216  automates various tasks requested by users via user devices  120  and/or the user front-end  202 , requested by agents using agent devices  260  and/or the agent back-end  210 , or a combination thereof. The automation engine  216  receives information from, and provides information to, user devices  120  via the user front-end  202 . The automation engine  216  receives information from, and provides information to, agent devices  260  via the agent back-end  210 . The automation engine  216  includes a rules engine  218 , a transaction classifier  220 , a request engine  222 , and a security engine  224 . In some examples, the automation engine  216  can include the account automation server(s)  130 , the account automation data structure(s)  135 , the account management server(s)  140 , the account management data structure(s)  145 , the voice interface gateway server(s)  125 , or a combination thereof. 
     The automation engine  216  is coupled to the historical information sources  228 , the CRM engine  226 , and/or the analytics engine  214 . The automation engine  216  can request and/or receive information from the historical information sources  228 , the CRM engine  226 , and/or the analytics engine  214 . The historical information sources  228  include card processors  230 , card producers  238 , identity verification engines  232 , security authentication engines  240 , credit institutions  234 , banking institutions  242 , investment institutions  236 , financial institutions, merchants, service providers, gift card providers, online marketplaces, online stores, brick-and-mortar stores, brick-and-mortar marketplaces, other data sources discussed herein, or combinations thereof. From the historical information sources  228  and/or the CRM engine  226 , the automation engine  216  can receive information about a user (corresponding to a user device  120 ) such as a history of one or more transactions in which one or more assets (e.g., a quantity of funds) was transferred from an account associated with the user to another account (e.g., associated with a second user, with a merchant, with a service provider, with a credit institution, with a banking institution, with an investment institution, and/or with another financial institution). From the historical information sources  228  and/or the CRM engine  226 , the automation engine  216  can receive information about a user (corresponding to a user device  120 ) such as a history of one or more transactions in which one or more assets (e.g., a quantity of funds) was transferred to an account associated with the user from another account (e.g., associated with a second user, with a merchant, with a service provider, with a credit institution, with a banking institution, with an investment institution, and/or with another financial institution). From the historical information sources  228  and/or the CRM engine  226 , the automation engine  216  can receive information about a user (corresponding to a user device  120 ) such as demographic data  810 , psychographic data  815 , behavioral data  820 , geographic data  825 , other categories of data, or combinations thereof. In some examples, at least a subset of the historical information sources  228  may be stored at, and/or may include data stored at, the account automation data structure(s)  135 , the account management data structure(s)  145 , or a combination thereof. In some examples, at least a subset of the historical information sources  228  may be stored at the automation engine  216  and/or at the CRM engine  226 . 
     The automation engine  216  is coupled to the cloud computing engine  244 . The cloud computing engine  244  includes cloud computing server(s)  246 . The cloud computing engine  244  can perform computationally intensive tasks for the automation engine  216 . For instance, the cloud computing engine  244  can include a machine learning (ML) engine  248 . The ML engine  248  can generate, train, run, and/or use one or more ML models. Examples of the one or more ML models include the one or more trained ML models of operation  310  and operation  312  of  FIG.  3 B , the first trained ML model of operations  322 - 328  of  FIG.  3 C , the second trained ML model of operations  330 - 334  of  FIG.  3 C , the ML models  435  of  FIGS.  4  and  5 A- 5 B , one or more ML models of the engagement AI/ML engine  612 , one or more ML models of the AI/ML engine  660 , the neural network  1400  of  FIG.  14   , and combinations thereof. Examples of the ML engine  248  include the ML engine associated with the one or more trained machine learning models of operation  310  and operation  312  of  FIG.  3 B , the ML engine associated with the first trained ML model of operations  322 - 328  of  FIG.  3 C , the ML engine associated with the trained ML model of operations  330 - 334  of  FIG.  3 C , the ML engine  430  of  FIGS.  4  and  5 A- 5 B , the engagement AI/ML engine  612 , the AI/ML engine  660 , the AI/ML engine  925 , the neural network  1400  of  FIG.  14   , and combinations thereof. In some examples, at least a subset of the cloud computing engine  244  may be run as part of the automation engine  216 . For instance, the automation engine  216  can include at least a subset of the cloud computing servers  246 , the ML engine  248 , and/or the one or more ML models. 
     The automation engine  216  is coupled to the user-agent communication engine  250 . The user-agent communication engine  250  includes communication infrastructure that allows a user device  120  to communicate with an agent device  260  through the automation engine  216  and/or through the user-agent communication engine  250  and/or through the web server(s)  208 . The user-agent communication engine  250  includes a voice-based communication engine  252  and a text-based communication engine  254 . The voice-based communication engine  252  can connect the user device  120  and the agent device  260  together via voice-based communication, such as telephony, voice over internet protocol (VOIP), teleconferencing, video conferencing, cloud phone, webinars, video teleconferencing, or a combination thereof. In some examples, the voice-based communication engine  252  can call APIs for, include plugins for, and/or run instances of, services such as Zoom®, Skype®, Microsoft® Teams®, Cisco® WebEx®, Google® Hangouts®, Google® Duo®, Google® Voice®, Google® Meet®, Apple® Facetime®, Viber®, and the like. The text-based communication engine  254  can connect the user device  120  and the agent device  260  together via a text-based messaging platform. In some examples, the text-based communication engine  254  can call APIs for, include plugins for, and/or run instances of, services such as short message service (SMS), multimedia messaging service (MMS), rich communication services (RCS), Apple® iMessage®, Apple® Business Chat®, Google® RCS for Business Messaging®, Google® Jibe®, Google® Hangouts®, Google® Chat®, Google® Docs® Chat®, Google® Talk®, Facebook® Messenger®, Twitter® Direct Messages, Instagram® Direct Messages, WhatsApp®, Slack®, Slack® Channels, Cisco® Jabber®, Microsoft® Teams® Chat, and the like. In some examples, the user-agent communication engine  250  can include a voice-to-text interpreter engine, a text-to-speech synthesizer, or a combination thereof. In some examples, the user-agent communication engine  250  can include, and/or can be a part of, the voice interface gateway server(s)  125 . 
     The request engine  222  of the automation engine  216  receives, interprets, and/or handles requests from the user device(s)  120  and/or from the agent device(s)  260 . In some examples, the requests may include, for example, requests for recommended transactions or interactions. To fulfill the requests, the automation engine  216  can retrieve information associated with the user in question from the historical information source(s)  228  and/or CRM engine  226 . For example, the automation engine  216  can retrieve historical information identifying one or more transactions from a transaction history associated with the user, the user&#39;s account, and/or the user device  120  corresponding to the user. The automation engine  216  can classify the transactions into categories or classifications using the transaction classifier  220 . In some examples, the automation engine  216  can determine an intent for one or more of the transactions by inputting the historical information, and/or the classification(s), into one or more ML models of the ML engine  248  that are trained to output the intent. In an illustrative example, the automation engine  216  can determine that a user&#39;s intent behind a transaction in which the user purchase automotive products or services (e.g., motor oil, gasoline, windshield wipers, tires, oil change, tire rotation, car wash) was for the transaction to be for a vehicle that the user owns, leases, drives, or otherwise has access to operate. The automation engine  216  can generate a recommended transaction based on this intent, for example by inputting the intent, and/or the historical information, into one or more ML models of the ML engine  248  that are trained to output the recommended transaction. The recommended transaction can be based on a transaction in the historical information, for instance being complementary to the transaction in the historical information and sharing the intent of the transaction in the historical information. For instance, if the historical information includes a recent transaction for an oil change service, the recommended transaction might be for a tire rotation service. If the historical information includes a recent transaction for a car wash, the recommended transaction might be for a car waxing. 
     In another example, the automation engine  216  can determine that a user&#39;s intent behind a transaction in which the user purchase home-related products or services (e.g., recessed lighting, floorboards, cabinets, chairs, carpet cleaning service) was for the transaction to be for a house, home, apartment, condo, or other residence that the user owns, rents, leases, resides in, or otherwise has access to. The automation engine  216  can generate a recommended transaction based on this intent. For instance, if the historical information includes a recent transaction for wooden floorboards, the recommended transaction might be for a carpet that complements the wooden floorboards. If the historical information includes a recent transaction for a lawnmower, the recommended transaction might be for a weed puller. 
     In another example, the automation engine  216  can determine that a user&#39;s intent behind a transaction in which the user donated to a particular charity was for a cause that the charity advances. The automation engine  216  can generate a recommended transaction based on this intent, for instance by providing a recommended transaction for a donation to another charity that advances a similar cause. 
     In this way, the automation engine  216  can generate determinations. The determinations by the automation engine  216  can include intent for transactions and/or recommended transactions as discussed herein. The determinations by the automation engine  216  can also include determinations as to whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. In some examples, the automation engine  216  can provide its determinations to a user device  120  via the user front-end  202 , and can request feedback regarding its determinations (e.g., approval or disapproval) from the user device  120  (e.g., from the user) via the user front-end  202 . Similarly, in some examples, the automation engine  216  can provide its determinations to an agent device  260  via the agent back-end  210 , and can request, via the agent back-end  210 , that the agent using the agent device  260  request and convey feedback from the user device  120  (from the user device) regarding the automation engine  216 &#39;s determinations (e.g., approval or disapproval). For example, the automation engine  216  can send a message to the agent using the agent device  260  indicating that the agent using the agent device  260  has determined that the user likely has a car (e.g., because the user purchased motor oil and paid for a car wash), and can request the agent ask a question of the user (e.g., “do you have a car?”) to verify this determination. 
     The rules engine  218  can include rules that may be used by the automation engine  216  for fulfilling certain requests. The rules may include general rules as well as personalized rules that are specific to certain users. General rules may include rules preventing recommendation of certain types of transactions at certain times, such as preventing recommendations for transactions at restaurants or merchants while those restaurants or merchants are closed. Personalized rules can be personalized to users regionally. For instance, personalized rules can include weather rules  1110  that encourage some types of transactions to be used as recommended transactions in certain types of weather, while discouraging or prohibiting other types of transactions to be used as recommended transactions during the same types of weather. For instance, if the automation engine  216  identifies that the weather is currently raining heavily in the region that the user is in, the weather rules  1110  can encourage transactions for umbrellas for the recommended transactions, while discouraging or prohibiting transactions for steep hiking trails. Personalized rules can also include rules relating to a user&#39;s interests, preferences, habits, patterns, accounts, and the like. Example of rule types of the rules engine  218  include the rule types  1105  of  FIG.  11   . 
     The security engine  224  can verify the authenticity of requests, transactions, user accounts, user identifiers, credit card information, debit card information, and the like. In some cases, the security engine  224  can verify authenticity based on rules in the rules engine  218 . For instance, if user identity has been verified via digital certificate against a trusted certificate authority, the user identity can be verified as authentic. Similarly, the security engine  224  can flag as inauthentic or unauthorized requests, transactions, user accounts, user identifiers, credit card information, debit card information, and the like. For instance, if repeated requests are received from the same device with slightly different card information, address information, passwords, or some other piece of information, this may suggest an attempted brute-force attack, and any resulting requests or transactions may be treated as suspicious, inauthentic, and/or unauthorized. 
       FIGS.  3 A- 3 C  are flow diagrams  300 A- 300 C illustrating processes for automated account interactions. Each of the processes of  FIGS.  3 A- 3 C  may be performed by an account management system. The account management system(s) of  FIGS.  3 A- 3 C  can include, for example, the one or more account automation servers  130 , the one or more account management servers  140 , the one or more account automation data structures  135 , the one or more account management data structures  145 , the one or more voice interface gateway servers  125 , the user device  120 , the user front-end  202 , the web server(s)  208 , the agent back-end  210 , the automation engine  216 , the CRM engine  226 , the historical information sources  228 , the cloud computing engine  244 , the user-agent communication engine  250 , the ML engine  430 , the user device  602 , the agent platform  606 , the historical information engine  618 , the data analysis engine  654 , the CRM engine  614 , the interaction engine  662 , the analysis engine  702 , the classifiers  704 , the question service  712 , the question data structure  714 , the device  718 , the question gateway API  722 , the feedback data structure  724 , the analysis engine  910 , the AI/ML engine  925 , the analysis engine  1004 , the classifiers  1010 , the profile data structure  1024 , neural network  1400 , the computing system  1500 , the processor  1510 , or a combination thereof. In some examples, at least two of  FIGS.  3 A,  3 B, and  3 C  may use the same account management system(s) as one another. In some examples, at least two of  FIGS.  3 A,  3 B, and  3 C  may use different account management systems than one another. 
       FIG.  3 A  is a flow diagram  300 A illustrating a process for automated account interactions. The process of  FIG.  3 A  may be performed by an account management system as discussed above. 
     At operation  302 , the account management system is configured to, and can, receive first historical information associated with a first account corresponding to a first user. The first historical information identifies a transaction involving the first account. In some examples, the first historical information can include historical information from the historical information sources  228 , the CRM engine  226 , the historical information engine  618 , and the like. Examples of the first historical information include the historical information  405 , historical information from the historical information sources  228 , historical information from the CRM engine  226 , historical information from the historical information engine  618 , or a combination thereof. The first historical information can identify a variety of transactions involving the first user and/or the first account, such as transactions with different entities (e.g., transactions with merchants to purchase products, with service providers to receive services, with financial institutions such as banks and credit unions and lenders for financial services, and the like). The first historical information can include, for example, demographic data  810 , psychographic data  815 , behavioral data  820 , geographic data  825 , transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the first user and/or using the first user account, other types of historical information, or combinations thereof. 
     At operation  304 , the account management system is configured to, and can, automatically identify, based on the first historical information, an intent for the transaction. In some examples, the account management system identifies the intent for the transaction using one or more ML models and based on the first historical information, for instance as discussed with respect to operation  310  of  FIG.  3 B , as discussed with respect to at least one of operations  322 - 328  of  FIG.  3 C , as discussed with respect to  FIG.  4   , or some combination thereof. 
     At operation  306 , the account management system is configured to, and can, automatically generate, based on the intent, a recommended transaction. The recommended transaction can involve purchase, rental, and/or transfer of one or more assets, such as funds, stocks, bonds, points, store credit, in-game credit, gift card credit, cryptocurrencies, non-fungible tokens (NFTs), other digital assets, or combinations thereof. 
     In some examples, the account management system generates the recommended transaction using one or more ML models and based on the intent and/or the first historical information, for instance as discussed with respect to operation  312  of  FIG.  3 B , as discussed with respect to at least one of operations  330 - 334  of  FIG.  3 C , as discussed with respect to  FIG.  5 A , or some combination thereof. 
     At operation  308 , the account management system is configured to, and can, automatically perform the recommended transaction. Performance of the recommended transaction in operation  308  can include imitation, processing, and/or completion of the transaction by the account management system. Performance of the recommended transaction in operation  308  can include sending a communication, by the account management system, to a transaction processing system to request that the transaction processing system initiate, process, and/or complete the recommended transaction. The communication can include details of the transaction, for example identifying the first user, the first account, a transferee account to which funds or other assets are to be transferred to from the first account, a transferor account to which funds or other assets are to be transferred from to the first account, a good or service to be purchased or rented or otherwise licensed, or a combination thereof. Performance of the recommended transaction in operation  308  can include, for example, at least one of operations  314 - 318  of the process of  FIG.  3 B , at least one of operations  336 - 338  of the process of  FIG.  3 C , or a combination thereof. 
     Performance of the recommended transaction as in operation  308  can include an automatic transfer of one or more assets, such as an automatic donation, purchase, or transfer of funds. Based on the recommended transaction, the account management system automatically selects a second account of a second user, a merchant, a service provider, a financial institution, a donor, a donation recipient, or another entity discussed herein. The account management system can automatically trigger transfer of a quantity of the one or more assets (e.g., funds, stocks, bonds, points, store credit, in-game credit, gift card credit, cryptocurrencies, non-fungible tokens (NFTs), other digital assets, etc.) between the second account and first account, and can communicate an indicator indicating that the transfer has been performed. 
     In some examples, the process illustrated in the flow diagram  300 A of  FIG.  3 A  can be used to recommend offers instead of or in addition to recommending transactions. Offers may refer to an offered transaction. Offers may also refer to a discount, promotion, coupon, and/or promotion associated with a transaction. In some examples, the process illustrated in the flow diagram  300 A of  FIG.  3 A  can be used to recommend other elements instead of or in addition to recommending transactions, such as online communities (e.g., on a social media platform, financial platform, forum, or other community platform), for instance as discussed with respect to  FIG.  5 B  and  FIG.  13   . It should be understood that references to transactions (recommended or otherwise) in the diagram and discussion of  FIG.  3 A  may also refer to offers and/or communities. For instance, the system can use historical information (e.g., about transactions involving the account, offers involving the account, and/or communities that the account has joined or otherwise interacted with) to determine an intent behind specified transaction(s) and/or offer(s) and/or communitie(s), and can generate and execute on a recommended transaction and/or offer and/or community. 
       FIG.  3 B  is a flow diagram  300 B illustrating a process for automated account interactions using one or more trained machine learning models. The process of  FIG.  3 B  may be performed by an account management system as discussed above. 
     Operation  302 , previously discussed with respect to the process of  FIG.  3 A , is also part of the process of  FIG.  3 B . At operation  302 , the account management system is configured to, and can, receive first historical information associated with a first account corresponding to a first user. The first historical information identifies a transaction involving the first account. The first historical information can include historical information can include any of the types of historical information discussed with respect to operation  302  within the process of  FIG.  3 A . 
     At operation  310 , the account management system is configured to, and can, use the one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models. Examples of the one or more ML models include the first trained ML model of at least one of operations  322 - 328  of  FIG.  3 C , the second trained ML model of at least one of operations  330 - 334  of  FIG.  3 C , the ML models  435  of at least one of  FIGS.  4  and  5 A- 5 B , the one or more ML models of the engagement AI/ML engine  612 , he one or more ML models of the AI/ML engine  660 , the neural network  1400  of  FIG.  14   , and combinations thereof. An illustrative example of use of one or more trained ML models to determine an intent for the transaction as in operation  310  is illustrated in  FIG.  4   , where the one or more ML models  435  determine the predicted intent  460  for the first transaction  415  based on the historical information  405 . 
     In some examples, the training data for the one or more ML models can include historical information and an intent for the transaction identified in the historical information. During a validation stage of training, the account management system can use the one or more ML models to generate a predicted intent for the transaction based on the historical information, and the account management system can update and/or further train the one or more ML models based on whether or not the predicted intent for the transaction matches the intent for the transaction from the training data. 
     In some examples, the account management system requests and/or receives feedback from the user regarding the intent for the transaction, and updates and/or further trains the one or more ML models based on the feedback. 
     At operation  312 , the account management system is configured to, and can, use the one or more trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the one or more trained machine learning models. An illustrative example of use of one or more trained ML models to generate a recommended transaction as in operation  312  is illustrated in  FIG.  5 A , where the one or more ML models  435  generate the recommended transaction  525  based on the intent  505  and/or the historical information  405 . 
     In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the account management system can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the account management system can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data. 
     In some examples, the account management system uses the a first trained ML engine to identify the intent for the transaction and uses a second trained ML engine to generate the recommended transaction. In some examples, the account management system uses a first trained ML engine both to identify the intent for the transaction and to generate the recommended transaction. 
     In an illustrative example, outputting the recommended transaction includes sending a message identifying the recommended transaction to a user device associated with the first user, and receiving the confirmation regarding the recommended transaction includes receiving an approval to initiate the recommended transaction from a user device associated with the first user. 
     In another illustrative example, outputting the recommended transaction includes automatically initiating execution of the recommended transaction on behalf of the first user, and receiving the confirmation regarding the recommended transaction includes receiving a transaction completion confirmation indicating that the recommended transaction has been processed. 
     In some examples, generating the recommended transaction includes determining an eligibility of the first user for a line of credit. In some aspects, the recommended transaction includes a recommendation to open the line of credit for the first user based on the eligibility. 
     In some examples, the recommended transaction includes a recommendation to make at least one of an appointment and a reservation with a service provider. In some examples, the recommended transaction includes a recommendation for a purchase (e.g., of one or more goods and/or one or more services) from a merchant. 
     In some examples, the account management system is configured to, and can, receive schedule information associated with the first user. For example, the account management system can receive the schedule information from a calendar, a to-do-list, a schedule, an itinerary, an email, a text-based message, a phone call, a video call, a set of notes, or a combination thereof. Each of these sources that the account management system can receive the schedule information from can be locally stored on the user device associated with the user, can be stored remotely on a server (e.g., associated with a cloud service), or a combination thereof. In some aspects, the account management system identifies a scheduled event based on the schedule information, and generates the recommended transaction so that the recommended transaction is associated with the scheduled event. For instance, the scheduled event can be the first user&#39;s birthday, and the recommended transaction can be for a treat that the first user likes to treat themselves to periodically. The scheduled event can be a birthday of a second user that the first user is in a relationship with, and the recommended transaction can be for a fancy dinner with the first user and the second user. The scheduled event can be a doctor&#39;s appointment, and the recommended transaction can be for a prescription from the doctor. 
     In some examples, the account management system is configured to, and can, generate profiles associated with the user based on the intent for the transaction and/or based on additional intents determined for additional transactions. Each of the profiles identifies one or more preferences of the user with respect a category of transactions of a set of different categories of transactions. Examples of the profiles include, for instance, the taste profile  706 , the style profile  708 , the entertainment profile  710 , the location profile  1018 , the hobby profile  1020 , the transportation profile  1022 , and the like. The account management system can inputting the intent for the transaction to the one or more trained machine learning models in operation  312  by inputting at least one of the profiles into the one or more trained machine learning models. 
     At operation  314 , the account management system is configured to, and can, output the recommended transaction. In some examples, the account management system outputs the recommended transaction by displaying the recommended transaction using a display. In some examples, the account management system outputs the recommended transaction by sending the recommended transaction to the user device associated with the user via a communication transceiver of the account management system. In some examples, the account management system outputs the recommended transaction by initiating, processing, and/or completing the transaction. In some examples, the account management system outputs the recommended transaction by communicating with a second system to request that the second system that initiate, process, and/or complete the transaction. 
     At operation  316 , the account management system is configured to, and can, receive a confirmation regarding the recommended transaction. In some examples, the confirmation is approval from the user to initiate the recommended transaction. In response to the confirmation, the account management system can initiate, process, and/or complete the transaction. In response to the confirmation, the account management system can communicate with a second system to request that the second system initiate, process, and/or complete the transaction. In some examples, the confirmation is a confirmation received from a second system (or a component of the account management system) that confirms that the transaction has been initiated, processed, and/or completed. The account management system can output a message based on the confirmation, for instance by displaying the message using a display and/or sending the message to the user device associated with the user. 
     At operation  318 , the account management system is configured to, and can, use, based on the confirmation, the intent and the recommended transaction to update the one or more machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. In some examples, the account management system requests and/or receives feedback from the user regarding the intent and/or recommended transaction, and updates and/or further trains the one or more ML models based on the feedback. For instance, if the feedback is positive, then the update to the ML models can reinforce weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction and/or to the generation of the recommended transaction. If the feedback is negative, then the update can weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the determination of the intent for the transaction and/or to the generation of the recommended transaction. The account management system requesting the feedback can include the account management system sending, to a user device associated with the first user, a query requesting the feedback. 
     In an illustrative example, the account management system is configured to, and can, receive feedback from a user device associated with the first user. The feedback can be associated with the intent for the transaction and/or for the recommended transaction. The account management system is configured to, and can, update the one or more trained machine learning models at least in part by using the feedback as training data for the one or more trained machine learning models. 
     In some examples, the account management system is configured to, and can, automatically initiate execution of the recommended transaction on behalf of the first user in response to receipt of the confirmation. In some examples, the account management system is configured to, and can, automatically output a transaction completion confirmation in response to execution of the recommended transaction. 
     In some examples, the account management system is configured to, and can, receive second historical information associated with a second account. Identifying the intent for the transaction includes identifying a link between the first account and the second account. Identifying the intent for the transaction is also based on the second historical information. In some examples, the recommended transaction includes a recommendation to transfer one or more assets from the first account to the second account. In some examples, the recommended transaction includes a recommendation to transfer one or more assets from the second account to the first account. In some examples, the second account is associated with a merchant. In some examples, the second account is associated with a second user. In some examples, the recommended transaction is for at least one of a product and a service associated with the second user. In some examples, the recommended is for a gift for the second user. In some examples, the link between the first account and the second account corresponds to a relationship between the first user and the second user. 
     In some examples, identifying the intent for the transaction includes identifying that the first user has operational access to a vehicle. For instance, the transaction can be for a product and/or a service associated with the vehicle. In some aspects, the recommended transaction is for a second product and/or a second service associated with the vehicle. 
     In some examples, wherein identifying the intent for the transaction includes identifying that the first user resides in a residence. For instance, the transaction can be for a product and/or a service associated with the residence. In some aspects, the recommended transaction is for a second product and/or a second service associated with the residence. 
     In some examples, identifying the intent for the transaction includes identifying that the first user works in a profession. For instance, the transaction can be for a product and/or a service associated with the profession. In some aspects, the recommended transaction is for a second product and/or a second service associated with the profession. 
     In some examples, identifying the intent for the transaction includes identifying that the first user has a relationship with a second user. For instance, the transaction can be for a product and/or a service associated with the second user. In some aspects, the recommended transaction is for a second product and/or a second service associated with the second user. 
     In some examples, the account management system is configured to, and can, receive additional information associated with the first account, and use one or more trained machine learning models to update the intent for the transaction based on the additional information. In some examples, in response to updating the intent, the account management system is configured to, and can, use one or more trained machine learning models to update the recommended transaction based on the intent. In some examples, the account management system is configured to, and can, receive additional information associated with the first account, and use one or more trained machine learning models to update the recommended transaction based on the additional information. 
     In some examples, the account management system is configured to, and can, receive a question associated with the first user, determine an answer to the question based on the intent for the transaction, and output the answer to the question. In some examples, the account management system is configured to, and can, receive a question associated with the first user, determine an answer to the question based on the recommended transaction, and output the answer to the question. 
     In some examples, the account management system is configured to, and can, identify that the first user is characterized by a characteristic based on the intent for the transaction. Use of the one or more trained machine learning models to generate the recommended transaction as in operation  312  can include inputting the characteristic to the one or more trained machine learning models. Examples of the characteristic can include ownership, rental, or operational access to a vehicle, such as a car. Examples of the characteristic can include ownership, rental, residence in, or access to a home or house. Examples of the characteristic can include a relationship between the user and a second user, such as a marriage or partnership. 
     In some examples, the process illustrated in the flow diagram  300 B of  FIG.  3 B  can be used to recommend offers instead of or in addition to recommending transactions. Offers may refer to an offered transaction. Offers may also refer to a discount, promotion, coupon, and/or promotion associated with a transaction. In some examples, the process illustrated in the flow diagram  300 B of  FIG.  3 B  can be used to recommend other elements instead of or in addition to recommending transactions, such as online communities (e.g., on a social media platform, financial platform, forum, or other community platform), for instance as discussed with respect to  FIG.  5 B  and  FIG.  13   . It should be understood that references to transactions (recommended or otherwise) in the diagram and discussion of  FIG.  3 B  may also refer to offers and/or communities. For instance, the system can use historical information (e.g., about transactions involving the account, offers involving the account, and/or communities that the account has joined or otherwise interacted with) to determine (using the one or more trained ML model(s)) an intent behind specified transaction(s) and/or offer(s) and/or communitie(s), and can (using the one or more trained ML model(s)) generate and execute on a recommended transaction and/or offer and/or community. 
       FIG.  3 C  is a flow diagram  300 C illustrating a process for automated account interactions using multiple trained machine learning models. The process of  FIG.  3 C  may be performed by an account management system as discussed above. 
     Operation  302 , previously discussed with respect to the process of  FIGS.  3 A- 3 B , is also part of the process of  FIG.  3 C . At operation  302 , the account management system is configured to, and can, receive first historical information associated with a first account corresponding to a first user. The first historical information identifies a transaction involving the first account. 
     At operation  320 , the account management system is configured to, and can, train one or more machine learning models using training data and machine learning engine. Training of the one or more machine learning models can include training of the first trained ML model of operations  322 - 328  (e.g., as in  FIG.  4   ) and/or training of the second trained ML model of operations  330 - 334  (e.g., as in  FIGS.  5 A- 5 B ). Training of the one or more machine learning models can include training stages (e.g., using training data  440  and/or training data  540 ) and validation stages (e.g., as in validation  445  and/or validation  545 ). 
     At operation  322 , the account management system is configured to, and can, input the historical information into first machine learning model of machine learning engine. At operation  326 , the account management system is configured to, and can, receive second historical information associated with a second account (e.g., associated with a second user, with a merchant, etc.) and input the second historical information into the first machine learning model. At operation  324 , the account management system is configured to, and can, use the first machine learning model to identify an intent for the transaction. In some examples, operation  310  of the process of  FIG.  3 B  can include operation  322 , operation  326 , and/or operation  324 . 
     At operation  328 , the account management system is configured to, and can, receive first feedback about the intent from a user device associated with the first user, and train the first machine learning model further based on first feedback. In some examples, operation  310  of the process of  FIG.  3 B  can include operation  328 . In some examples, operation  318  of the process of  FIG.  3 B  can include operation  328 . 
     At operation  330 , the account management system is configured to, and can, input the intent and/or the historical information into second machine learning model of machine learning engine. At operation  326 , the account management system is configured to, and can, receive second historical information associated with a second account (e.g., associated with a second user, with a merchant, etc.) and input the second historical information into the second machine learning model. At operation  332 , the account management system is configured to, and can, use the second machine learning model to automatically generate a recommended transaction corresponding to the intent. In some examples, operation  312  of the process of  FIG.  3 B  can include operation  330 , operation  326 , and/or operation  332 . 
     At operation  334 , the account management system is configured to, and can, receive second feedback about the recommended transaction from the user device associated with the first user, and train the second machine learning model further based on second feedback. In some examples, operation  312  of the process of  FIG.  3 B  can include operation  334 . In some examples, operation  318  of the process of  FIG.  3 B  can include operation  334 . 
     At operation  336 , the account management system is configured to, and can, automatically initiate and/or process the recommended transaction. In some examples, the outputting of the recommended transaction of operation  314  of the process of  FIG.  3 B  includes operation  336 . 
     At operation  338 , the account management system is configured to, and can, automatically send, to the user device associated with the first user, a confirmation confirming that the recommended transaction has been initiated, processed, and/or completed. 
     In some examples, the process illustrated in the flow diagram  300 C of  FIG.  3 C  can be used to recommend offers instead of or in addition to recommending transactions. Offers may refer to an offered transaction. Offers may also refer to a discount, promotion, coupon, and/or promotion associated with a transaction. In some examples, the process illustrated in the flow diagram  300 C of  FIG.  3 C  can be used to recommend other elements instead of or in addition to recommending transactions, such as online communities (e.g., on a social media platform, financial platform, forum, or other community platform), for instance as discussed with respect to  FIG.  5 B  and  FIG.  13   . It should be understood that references to transactions (recommended or otherwise) in the diagram and discussion of  FIG.  3 C  may also refer to offers and/or communities. For instance, the system can use historical information (e.g., about transactions involving the account, offers involving the account, and/or communities that the account has joined or otherwise interacted with) to determine (using the one or more trained ML model(s)) an intent behind specified transaction(s) and/or offer(s) and/or communitie(s), can (using the one or more trained ML model(s)) generate and execute on a recommended transaction and/or offer and/or community, and can update and/or train the one or more trained ML model(s). 
       FIG.  4    is a block diagram  400  illustrating using one or more machine learning models  435  of a machine learning engine  430  to determine a predicted intent  460  for a first transaction  415  based on historical information  405  associated with a user  410 . Examples of the ML engine  430  include the ML engine  248 , the ML engine associated with the one or more trained machine learning models of operation  310  and/or operation  312  of  FIG.  3 B , the ML engine associated with the first trained ML model of at least one of operations  322 - 328  of  FIG.  3 C , the ML engine associated with the trained ML model of at least one of operations  330 - 334  of  FIG.  3 C , the engagement AI/ML engine  612 , the AI/ML, engine  660 , the AI/ML engine  925 , the neural network  1400  of  FIG.  14   , and combinations thereof. 
     The ML engine  430  generates, trains, and uses the one or more ML models  435 . Examples of the one or more ML models  435  include the one or more ML models of the ML engine  248 , the one or more trained ML models of operation  310  and/or operation  312  of  FIG.  3 B , the first trained ML model of at least one of operations  322 - 328  of  FIG.  3 C , the second trained ML model of at least one of operations  330 - 334  of  FIG.  3 C , the one or more ML models of the engagement AI/ML engine  612 , the one or more ML models of the AI/ML engine  660 , the neural network  1400  of  FIG.  14   , and combinations thereof. 
     Once trained via initial training  465 , the one or more ML models  435  receive, as an input, historical information  405  about a user  410 . The historical information  405  identifies a first transaction  415  involving an account associated with the user  410 . In response to receiving the historical information  405  as an input, the one or more ML models  435  determine a predicted intent  460  for the first transaction  415 . Examples of the historical information  405  include demographic data  810 , psychographic data  815 , behavioral data  820 , geographic data  825 , transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the first user and/or using the first user account, other types of historical information, or combinations thereof. Examples of the predicted intent  460  for the first transaction  415  include vehicle ownership, home ownership, food preferences, entertainment preferences, style preferences, financial preferences, product/merchant/brand preferences, relationship status, and the like. The predicted intent  460  can be referred to as the intent. 
     Determination of the predicted intent  460  as in  FIG.  4    can correspond to operations in at least  FIGS.  3 A,  3 B, and  3 C . For instance, the determination of the predicted intent  460  can correspond to identifying the intent in operation  304 , identifying the intent in operation  310 , identifying the intent in operation  324 , or combinations thereof. 
     Once the one or more ML models  435  determine the predicted intent  460 , the predicted intent  460  can be used to determine a recommended transaction as illustrated and/or described with respect to at least  FIGS.  3 A,  3 B,  3 C,  5 A, and  5 B . For instance, the predicted intent  460  can be used as the intent basis in operation  306 . The predicted intent  460  can be used as the intent that is input to the one or more trained machine learning models in operation  312 . The predicted intent  460  and the historical information  405  can be used as the intent and the historical information that are input to the second trained machine learning model in operation  330 . 
     Before using the one or more ML models  435  to determine predicted intents  460 , the ML engine  430  performs initial training  465  of the one or more ML models  435  using training data  440 . The training data  440  includes examples of historical information  405  about the user  410 , and corresponding examples of intent  420  for the first transaction  415 . During an initial training stage of the initial training  465 , the one or more ML models  435  form connections and weights based on the training data  440 . 
     During a validation stage of the initial training  465 , exemplary historical information  405  is input into the one or more ML models  435  to generate a predicted intent  460  as described above. The ML engine  430  performs validation  445  at least in part by determining whether the predicted intent  460  matches the intent  420 . If the predicted intent  460  matches the intent  420  during validation  445 , then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the determination of the predicted intent  460 . If the predicted intent  460  does not matches the intent  420  during validation  445 , then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the determination of the predicted intent  460 . 
     Validation  445  and further training  455  of the one or more ML models  435  can continue once the one or more ML models  435  are in use based on feedback  450  received on the predicted intent  460  (e.g., from the user  410  via a user device  120  associated with the user  410 ). The one or more ML models  435  generate the predicted intent  460  as described above. If the feedback  450  is positive (e.g., expresses, indicates, and/or suggests approval of the predicted intent  460 ), then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the determination of the predicted intent  460 . If the feedback  450  is negative (e.g., expresses, indicates, and/or suggests disapproval of the predicted intent  460 ) then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the determination of the predicted intent  460 . 
       FIG.  5 A  is a block diagram  500 A illustrating using the one or more machine learning models  435  of the machine learning engine  430  to generate a recommended transaction  525  based on an intent  505  for the first transaction  415  and/or based on historical information  405  associated with the user  410 . Once trained via initial training  565 , the one or more ML models  435  receive, as an input, an intent  505  for the first transaction  415 . In some examples, the intent  505  is a predicted intent, such as the predicted intent  460 . In some examples, the intent  505  is received (e.g., from the user  410 ) or predetermined, such as the intent  420 . In some examples, the one or more ML models  435  also receive, as a second input, historical information  405  about a user  410 . The historical information  405  identifies the first transaction  415  involving the account associated with the user  410 . 
     In response to receiving the intent  505  for the first transaction  415  and/or the historical information  405  as input(s), the one or more ML models  435  generate a recommended transaction  525 . The recommended transaction  525  can align with the intent  505  for the first transaction  415 . In some examples, the recommended transaction  525  can be for a good or service that is completely to another good or service corresponding to first transaction  415 . Examples of the recommended transaction  525  include purchasing of a product (e.g., a good or service), making an appointment and/or reservation with a service provider, recommending a gift for a second user who is linked to the user  410  (e.g., via a relationship), opening line of credit for the user  410 , transferring one or more assets to the user account associated with the user  410 , transfer one or more assets from the user account associated with the user  410 , other recommended transactions discussed herein, other transaction types discussed herein, or a combination thereof. 
     Generating the recommended transaction  525  as in  FIG.  5 A  can correspond to operations in at least  FIGS.  3 A,  3 B, and  3 C . For instance, generating the recommended transaction  525  can correspond to generating the recommended transaction in operation  306 , generating the recommended transaction in operation  312 , generating the recommended transaction in operation  332 , or combinations thereof. 
     Once the one or more ML models  435  generate the recommended transaction  525 , the recommended transaction  525  can be output as in operation  314 . Once the oneor more ML models  435  generate the recommended transaction  525 , the recommended transaction  525  can be automatically initiated processed, performed, and/or completed, for instance as in operation  336  and/or operation  338 . 
     Before using the one or more ML models  435  to generate recommended transactions  525 , the ML engine  430  performs initial training  565  of the one or more ML models  435  using training data  540 . The training data  540  can include examples of historical information  405  about the user  410 , corresponding examples of intent  505  for the first transaction  415 , and/or a examples of a second transaction  520  corresponding to the intent  505 . In some examples, the second transaction  520  is a transaction performed by the user  410  after the first transaction  415 . In some examples, the second transaction  520  is a transaction performed by the user  410  before the first transaction  415 . During an initial training stage of the initial training  465 , the one or more ML models  435  form connections and weights based on the training data  540 . 
     During a validation stage of the initial training  565 , exemplary intent  505  and/or historical information  405  is input into the one or more ML models  435  to generate a recommended transaction  525  as described above. The ML engine  430  performs validation  545  at least in part by determining whether the recommended transaction  525  matches the second transaction  520 . If the recommended transaction  525  matches the second transaction  520  during validation  445 , then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the generation of the recommended transaction  525 . If the recommended transaction  525  does not match the second transaction  520  during validation  445 , then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the generation of the recommended transaction  525 . 
     Validation  545  and further training  555  of the one or more ML models  435  can continue once the one or more ML models  435  are in use based on feedback  550  received on the recommended transaction  525  (e.g., from the user  410  via a user device  120  associated with the user  410 ). The one or more ML models  435  generate the recommended transaction  525  as described above. If the feedback  550  is positive (e.g., expresses, indicates, and/or suggests approval of the recommended transaction  525 ), then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the generation of the recommended transaction  525 . If the feedback  550  is negative (e.g., expresses, indicates, and/or suggests disapproval of the recommended transaction  525 ) then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the generation of the recommended transaction  525 . 
       FIG.  5 B  is a block diagram  500 B illustrating using the one or more machine learning models  435  of the machine learning engine  430  to generate a recommended community  560  based on an intent  575  for a first community  580  and/or based on historical information  405  associated with the user  410 . In short, similarly to the generation of the recommended transaction  525  as in  FIG.  5 A , the ML models  435  of  FIG.  5 B  can also generate a recommended community  560  for a user to join, such as an online community associated with a particular interest and/or demographic group. Once trained via initial training  565 , the one or more ML models  435  receive, as an input, an intent  575  for a first community  580  (e.g., that the user has already joined). In some examples, the intent  575  is a predicted intent, such as the predicted intent  460 . In some examples, the intent  575  is received (e.g., from the user  410 ) or predetermined, such as the intent  420 . In some examples, the one or more ML models  435  also receive, as a second input, historical information  405  about a user  410 . The historical information  405  identifies the first community  580  that the account associated with the user  410  has joined or is otherwise a part of. 
     In response to receiving the intent  575  for the first community  580  and/or the historical information  405  as input(s), the one or more ML models  435  generate a recommended community  560 . The recommended community  560  can align with the intent  575  for the first community  580 . In some examples, the recommended community  560  can be associated with a particular interest (e.g., a sport, a genre of music, a genre of movies or TV shows, a genre of literature, a geographic community or neighborhood, a type of merchant or business, charity, cause, and the like). In an illustrative example, a recommended community  560  can be a community interested in soccer in a particular geographic region, a community interested in classical music, and the like. 
     Generating the recommended community  560  and in  FIG.  5 B  can correspond to operations in at least  FIGS.  3 A,  3 B, and  3 C . For instance, generating the recommended community  560  can correspond to generating the recommended transaction in operation  306 , generating the recommended transaction in operation  312 , generating the recommended transaction in operation  332 , generating the recommended transaction  525  of  FIG.  5 B , generating the recommendation(s)  1310  of  FIG.  13   , or combinations thereof. 
     Once the one or more ML models  435  generate the recommended community  560 , the recommended community  560  can be output as in operation  314 . Once the one or more ML models  435  generate the recommended community  560 , the recommended community  560  can be automatically initiated processed, performed, and/or completed, for instance as in operation  336  and/or operation  338 . 
     Before using the one or more ML models  435  to generate the recommended community  560 , the ML engine  430  performs initial training  565  of the one or more ML models  435  using training data  540 . The training data  540  can include examples of historical information  405  about the user  410 , corresponding examples of intent  575  for the first community  580 , and/or a examples of a second community  570  corresponding to the intent  575 . In some examples, the second community  570  is a community joined by the user  410  after the first community  580 . In some examples, the second community  570  is a community joined by the user  410  before the first community  580 . During an initial training stage of the initial training  465 , the one or more ML models  435  form connections and weights based on the training data  540 . 
     During a validation stage of the initial training  565 , exemplary intent  575  and/or historical information  405  is input into the one or more ML models  435  to generate a recommended community  560  as described above. The ML engine  430  performs validation  545  at least in part by determining whether the recommended community  560  matches the second community  570 . If the recommended community  560  matches the second community  570  during validation  445 , then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the generation of the recommended community  560 . If the recommended community  560  does not match the second community  570  during validation  445 , then the ML engine  430  performs further training  455  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the generation of the recommended community  560 . 
     Validation  545  and further training  555  of the one or more ML models  435  can continue once the one or more ML models  435  are in use based on feedback  550  received on the recommended community  560  (e.g., from the user  410  via a user device  120  associated with the user  410 ). The one or more ML models  435  generate the recommended community  560  as described above. If the feedback  550  is positive (e.g., expresses, indicates, and/or suggests approval of the recommended community  560 ), then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to reinforce weights and/or connections within the one or more ML models  435  that contributed to the generation of the recommended community  560 . If the feedback  550  is negative (e.g., expresses, indicates, and/or suggests disapproval of the recommended community  560 ) then the ML engine  430  performs further training  555  of the one or more ML models  435  by updating the one or more ML models  435  to weaken, remove, and/or replace weights and/or connections within the one or more ML models that contributed to the generation of the recommended community  560 . 
       FIG.  6    is a block diagram  600  illustrating a system architecture of a system for intent-based recommendations. The system includes a user device  602 , an agent platform  606 , a historical information engine  618 , a CRM engine  614 , a data analysis engine  654 , and an interaction engine  662 . 
     The user device  602  is an example of a user device  120 . The user device  602  includes an operating system (OS), a global navigation satellite system (GNSS) receiver, a network connection, and an internet protocol (IP) address to use over the network. The user device  602  runs one or more apps  604 , which can include apps that associated with, and/or that communicate with, the agent platform  606 , the historical information engine  618 , the CRM engine  614 , the data analysis engine  654 , and an interaction engine  662 . In some aspects, at least one of the apps  604  is an example of the user front-end  202 . In some aspects, the user front-end  202  is an example of at least one of the apps  604 . 
     In some aspects, the agent platform  606  is an example of the agent back-end  210 . In some aspects, the agent back-end  210  is an example of the agent platform  606 . The agent platform  606  includes agent devices  608 , which may correspond to the agent devices  260 . The agent platform  606  includes a concierge engine  610 , which may connect a user using the user device  602  to a particular agent device  608  of an agent whose expertise and/or knowledge matches the subject matter of a query or request that the user of the user device  602  has expressed to the concierge engine  610 . The concierge engine  610  may, for example, ask the user to state, through the user device  602  either verbally or through text, a reason for their call, message, or other form of communication with the agent. The concierge engine  610  can parse this reason and determine the subject matter of a query or request of the user of the user device  602 . 
     The agent platform  606  includes an engagement AI/ML engine  612 . The engagement AI/ML engine  612  can suggest, to agent devices  608  (e.g., to agents) information to request from the user, for instance in the form of questions. The data analysis engine  654  may make certain determinations about the user of the user device  602 , for instance using the AI/ML engine  660 . The engagement AI/ML engine  612  can be used to request feedback on these determinations. The user device  602  can provide the feedback to the agent platform  606  (e.g., to the agent device  608 ). In some cases, the feedback from the user device  602  can verify or approve of these determinations. The data analysis engine  654  can thus proceed with using these determinations further, for example to generate recommended transactions and/or recommended community as in  FIGS.  5 A- 5 B . In some cases, the feedback from the user device  602  can refute or disapprove of these determinations. The data analysis engine  654  can thus halt any further use of these determinations. Examples of the determinations include determinations as to intent for a transaction, as in operation  304 , operation  310 , operation  324 , and/or determination of the predicted intent  460 . Examples of the determinations include generation of a recommended transaction, as in operation  306 , operation  312 , operation  332 , and/or generation of the recommended transaction  525 . Examples of the determinations include determinations as to characteristics of the user, such as whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. 
     In some aspects, the historical information engine  618  is an example of the historical information sources  228 . In some aspects, the historical information sources  228  are an example of the historical information engine  618 . The historical information engine  618  includes historical information about the user of the user device  602 , and in some cases about other users as well. The historical information engine  618  includes historical information associated with donations  620 , including charities  622  that the user has donated to, a donation score  624  related to frequency of donations and/or quantity of assets (e.g., funds) donated, and/or reviews  626  of the user and/or of the charities related to the user&#39;s donations. The historical information engine  618  includes historical information associated with transactions  628  involving the user, including peer-to-peer (P2P) transactions  630  (e.g., from the user&#39;s to a transferee account, from a transferor account to the user&#39;s account), purchases  632  (e.g., from merchants, service providers, marketplaces, stores), rentals, leases, and the like. The historical information engine  618  includes historical information associated with accounts  634  of the user, including checking accounts  636 , savings accounts  638 , other accounts  640  (e.g., money market), and the like. The historical information engine  618  includes historical information associated with banking  642  by the user, including investments  644  (e.g., stocks, bonds, cryptocurrencies, NFTs, other digital assets), budgeting  646 , and the like. The historical information engine  618  includes historical information associated with user identification  648  for the user, including preferences  650  (e.g., preferred name, preferred pronoun, prefer calls or messaging, etc.), user profile information  652  (e.g., name, address, phone number, email address, username, etc.), and the like. 
     In some aspects, the CRM Engine  614  is an example of the CRM Engine  226 . In some aspects, the CRM Engine  226  is an example of the CRM Engine  614 . The CRM Engine  614  can include conversational data  616  regarding conversation(s) between the user device  602  and agent device(s)  608  and any related data. The CRM Engine  614  can pull information for the conversational data  616  from the user device  602 , the agent platform  606 , the historical information engine  618 , the data analysis engine  654 , the interaction engine  662 , or a combination thereof. The CRM Engine  614  can parse, reformat, normalize, and/or organize this information to generate the conversational data  616 . 
     In some aspects, the data analysis engine  654  is an example of the automation engine  216 . In some aspects, the automation engine  216  is an example of the data analysis engine  654 . The data analysis engine  654  includes an operational database  656  that ingests information from the historical information engine  618  and the CRM engine  614  (e.g., the conversational data  616 ). The data analysis engine  654  includes a data lake  658  with vast quantities of data. The data analysis engine  654  can parse, reformat, normalize, and/or organize the information from the operational database  656 , and can import the information from the operational database  656  into the data lake  658 . The data lake  658  can include information about other users than the user. The information about the other users may include the same types of information as about the user (e.g., the types of information in the historical information engine  618 , in the historical data sources  228 , and/or in the CRM engine  614 ). The information about the other users may be anonymized by the data analysis engine  654  before entry into the data lake  658 , in some examples. 
     The data analysis engine  654  includes an AI/ML engine  660  that receives data from the data lake  658  and/or the operational database  656  as input(s). The AI/ML engine  660  can generate determinations based on the receipt of the data from the data lake  658  and/or the operational database  656  as input(s). Examples of the determinations include determinations as to intent for a transaction, as in operation  304 , operation  310 , operation  324 , and/or determination of the predicted intent  460 . Examples of the determinations include generation of a recommended transaction, as in operation  306 , operation  312 , operation  332 , and/or generation of the recommended transaction  525 . Examples of the determinations include determinations as to characteristics of the user, such as whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. 
     The AI/ML engine  660  can include, for example, the ML engine  248 , the ML engine associated with the one or more trained machine learning models of operation  310  and/or operation  312  of  FIG.  3 B , the ML engine associated with the first trained ML model of at least one of operations  322 - 328  of  FIG.  3 C , the ML engine associated with the trained ML model of at least one of operations  330 - 334  of  FIG.  3 C , the ML engine  430 , the neural network  1400  of  FIG.  14   , and combinations thereof. 
     The determinations generated by the AI/ML engine  660  based on the data from the data lake  658  and/or the operational database  656  can be received by, and/or used by, the interaction engine  662 . The interaction engine  662  can provide services to the user of the user device  602 , to agents associated with the agent devices  608 , to financial entities, and/or to entities. For instance, the interaction engine  662  includes a loan credit service  664 , an emergency fund service  666 , a financial planning service  668 , a fraud protection service  670 , an offers service  672 , a financial automation service  674 , and an enhanced interactions service  676 . 
     The interaction engine  662  includes a loan credit service  664  that can provide credit products such as credit cards, mortgages, or loans. For the loan credit service  664 , the AI/ML engine  660  can generate a determination as to creditworthiness for a credit product based on the data from the data lake  658  and/or the operational database  656 . The interaction engine  662  includes an emergency fund service  666  that can quickly loan or provide emergency funds to users in urgent need (e.g., due to a natural disaster or serious accident). For the emergency fund service  666 , the AI/ML engine  660  can generate a determination as to whether a user is in an emergency and/or in urgent need and should receive funds from the emergency fund service  666  based on the data from the data lake  658  and/or the operational database  656 . The interaction engine  662  includes a financial planning service  668  that can provide financial planning for users. For the financial planning service  668 , the AI/ML engine  660  can generate at least a portion of a financial plan for a user based on the data from the data lake  658  and/or the operational database  656 . 
     The interaction engine  662  includes a fraud protection service  670  that can prevent fraud. For the fraud protection service  670 , the AI/ML engine  660  can generate a determination as to whether or not a transaction attempt and/or login attempt is fraudulent or authorized based on the data from the data lake  658  and/or the operational database  656 . The interaction engine  662  includes an offers service  672  that can provide offers for users, such as coupons, discounts, and rebates. For the offers service  672 , the AI/ML engine  660  can generate a determination as to whether the user is eligible for any offers based on the data from the data lake  658  and/or the operational database  656 . 
     The interaction engine  662  includes a financial automation service  674  that can automate certain periodic financial tasks for the user. For the financial automation service  674 , the AI/ML engine  660  can identify periodic financial tasks that the user performs, and that can be automated using the financial automation service  674 , based on the data from the data lake  658  and/or the operational database  656 . The interaction engine  662  includes an enhanced interactions service  676  that can provide enhanced interactions, such as recommended transactions based on user intent. For the enhanced interactions service  676 , the AI/ML engine  660  can determine user intent and/or generate a recommended transaction based on the data from the data lake  658  and/or the operational database  656 . 
     In some cases, the CRM engine  614  can receive and store the determinations made by the AI/ML engine  660  for the various services  664 - 676  of the interaction engine  662  as part of the conversational data  616  of the CRM engine  614 . 
       FIG.  7    is a block diagram  700  illustrating a process for requesting feedback from a user. An analysis engine  702  can make a determination about a user. The analysis engine  702  can include, for example, the one or more account automation servers  130 , the one or more account management servers  140 , the one or more account automation data structures  135 , the one or more account management data structures  145 , the automation engine  216 , the cloud computing engine  244 , the account management system(s) of  FIGS.  3 A- 3 C , the ML engine  430 , the data analysis engine  654 , the CRM engine  614 , the interaction engine  662 , the analysis engine  910 , the AI/ML engine  925 , the analysis engine  1004 , the classifiers  1010 , the profile data structure  1024 , neural network  1400 , the computing system  1500 , the processor  1510 , or a combination thereof. Examples of the determinations include determinations as to intent for a transaction, as in operation  304 , operation  310 , operation  324 , and/or determination of the predicted intent  460 . Examples of the determinations include generation of a recommended transaction, as in operation  306 , operation  312 , operation  332 , and/or generation of the recommended transaction  525 . Examples of the determinations include determinations as to characteristics of the user, such as whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. 
     The analysis engine  702  can generate various profiles for the user based on the various determinations. The profiles may be based on the intents determined for various transactions, as in the determination of the predicted intent  460  of  FIG.  4   . The analysis engine  702  can generate recommended transactions and/or recommended communities, as in  FIGS.  5 A- 5 B , based on these profiles instead of or in addition to being based on the intent  505 . For instance, the analysis engine  702  can provide information from one or more of these profiles in place of the intent  505  as one or more inputs to the one or more ML models  435  to generate the recommended transaction  525 . The analysis engine  702  can classify determinations as to different aspects of the user into the appropriate profile using the classifier(s)  704 . 
     For instance, the profiles include a taste profile  706  that the analysis engine  702  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to restaurants, dishes, food items, meals, beverages, cooking, grocery shopping, and the like. The profiles include a style profile  708  that the analysis engine  702  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to clothing, fashion, style, and the like. The profiles include an entertainment profile  710  that the analysis engine  702  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to movies, television, video games, art, music, and the like. In some examples, the taste profile  706 , the style profile  708 , the entertainment profile  710 , and other similar profiles can correspond to transactions that the user is involved in, offers that the user has purchased, communities (online or otherwise) that the user has joined or is otherwise involved in, or a combination thereof. 
     In some examples, one or more of the profiles store information meeting conditions that trigger a question. The question service  712  can determine the question. For example, is a determination about the user has been determined by the analysis engine  702  with a low confidence or probability, the question service  712  can seek to approve or refute the determination via a question that the question service  712  generates to ask the user if the determination is correct. The question service  712  can store the question in a question data structure  714 . From the question data structure  714 , the question can be sorted into a question queue  716 . In some examples, questions may be entered into the question queue  716  in a first-in first-out (FIFO) order. In some examples, questions may be sorted in the question queue  716  by priority or weight, with some questions (e.g., more urgent or important questions) being higher priority or having higher weight than others. For instance, a medical question such as “are you allergic to peanuts?” may be ranked as a higher priority, higher weight, and/or higher urgency than a stylistic question such as “is your favorite color blue?” 
     The question queue  716  may be part of a functionality of an app  720  running on the device  718 . In some examples, the device  718  is an example of the user device  120  and/or the user device  602 . In some examples, the device  718  is an example of the agent device  260  and/or the agent device  608 . The question queue  716  may be generated, sorted, and/or asked locally by the device  718  in the course of the device  718  running the app  720 . The question queue  716  may be generated, sorted, and/or asked remotely by a server (e.g., the analysis engine  702 ) by request from the device  718  in the course of the device  718  running the app  720 . 
     A question gateway API  772  may be used to send and/or ask the question to the user via the user device (e.g., user device  120  and/or user device  602 ), and/or to receive and/or record feedback from the user in response to the question. The feedback may be stored, by analysis engine  702  and/or by the device  718 , in a feedback data structure  724 . The feedback may include, for example, the confirmation of operations  316 - 318 , the first feedback of operation  328 , the second feedback of operation  334 , the feedback  450 , and/or the feedback  550 . 
       FIG.  8    is a block diagram  800  illustrating exemplary of data categories  805  of historical data about users. The data categories  805  include demographic data  810 , psychographic data  815 , behavioral data  820 , and geographic data  825 . Demographic data  810  includes, for instance, age, sex, gender, income, marital status, relationship status, ethnic background, and/or sexual orientation. Psychographic data  815  includes, for instance, activities, attitudes, personality, values, political views, and/or religious views. Behavioral data  820  includes, for example, benefits, transaction history, usage rates, patterns, and/or browsing history. Geographic data  825  includes, for example, local data, regional data, national data, and/or international data. The data categories  805  can correspond to transactions that the user is involved in, offers that the user has purchased, communities (online or otherwise) that the user has joined or is otherwise involved in, or a combination thereof. 
       FIG.  9    is a block diagram  900  illustrating generation of recommendations  915  for users  905  by an analysis engine  910  based on interactions  920 . The analysis engine  910  includes an AI/ML engine  925 . 
     Examples of the AI/ML engine  925  include the ML engine  248 , the ML engine associated with the one or more trained machine learning models of operation  310  and/or operation  312  of  FIG.  3 B , the ML engine associated with the first trained ML model of at least one of operations  322 - 328  of  FIG.  3 C , the ML engine associated with the trained ML model of at least one of operations  330 - 334  of  FIG.  3 C , the ML engine  430 , the engagement AI/ML engine  612 , the AI/ML engine  660 , the neural network  1400  of  FIG.  14   , and combinations thereof. 
     The users  905  are users of user devices  120 . The users  905  perform interactions  920  with other users and/or with merchants, service providers, financial institutions, and the like. The interactions  920  can include transactions. The analysis engine  910  receives indications of the interactions  920  from the user devices  120 , from devices of other uses, from devices of merchants, from devices of service providers, from devices of financial institutions, or a combination thereof. 
     The analysis engine  910  can include, for example, the one or more account automation servers  130 , the one or more account management servers  140 , the one or more account automation data structures  135 , the one or more account management data structures  145 , the automation engine  216 , the cloud computing engine  244 , the account management system(s) of  FIGS.  3 A- 3 C , the ML engine  430 , the data analysis engine  654 , the CRM engine  614 , the interaction engine  662 , the analysis engine  702 , the AI/ML engine  925 , the analysis engine  1004 , the classifiers  1010 , the profile data structure  1024 , neural network  1400 , the computing system  1500 , the processor  1510 , or a combination thereof. 
     The analysis engine  910  generates the recommendations  915  for transactions, offers, and/or communities based on interaction data  935  from the interactions  920 . Generation of the recommendations  915  by the analysis engine  910  can include generation of a recommended transaction, as in operation  306 , operation  312 , operation  332 , and/or generation of the recommended transaction  525 . Generation of the recommendations  915  by the analysis engine  910  can include generation of a recommended community, as in the generation of the recommended community  560 . The recommendations  915  can be used by the analysis engine to provide enhanced interactions  930  for users  905 . The enhanced interactions  930  can include interactions and/or services described with respect to the interaction engine  662 , such as the loan credit service  664 , the emergency fund service  666 , a financial planning service  668 , the fraud protection service  670 , the offers service  672 , the financial automation service  674 , and the enhanced interactions service  676 . 
       FIG.  10    is a block diagram  1000  illustrating generation of profiles associated with a user by one or more classifiers  1010 . An analysis engine  1004  can make a determination about a user, for instance based on geographic data  1002 , mobile analytic data  1006 , and/or transaction data  1008 . The analysis engine  1004  can include, for example, the one or more account automation servers  130 , the one or more account management servers  140 , the one or more account automation data structures  135 , the one or more account management data structures  145 , the automation engine  216 , the cloud computing engine  244 , the account management system(s) of  FIGS.  3 A- 3 C , the ML engine  430 , the data analysis engine  654 , the CRM engine  614 , the interaction engine  662 , the analysis engine  702 , the classifiers  704 , the analysis engine  910 , the AI/ML engine  925 , the neural network  1400 , the computing system  1500 , the processor  1510 , or a combination thereof. Examples of the determinations include determinations as to intent for a transaction, as in operation  304 , operation  310 , operation  324 , and/or determination of the predicted intent  460 . Examples of the determinations include generation of a recommended transaction, as in operation  306 , operation  312 , operation  332 , and/or generation of the recommended transaction  525 . Examples of the determinations include determinations as to characteristics of the user, such as whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. 
     The analysis engine  1004  can generate various profiles for the user based on the various determinations. The analysis engine  1004  can generate recommended transactions and/or recommended communities, similarly to  FIGS.  5 A- 5 B , based on these profiles. For instance, the analysis engine  1004  can provide information from one or more of these profiles in place of the intent  505  as one or more inputs to the one or more ML models  435  to generate the recommended transaction  525  and/or recommended community  560 . The analysis engine  1004  can classify determinations as to different aspects of the user into the appropriate profile using the classifier(s)  704 . 
     As in  FIG.  7   , the profiles of  FIG.  10    include a taste profile  706  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to restaurants, dishes, food items, meals, beverages, cooking, grocery shopping, and the like. The profiles include a style profile  708  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to clothing, fashion, style, and the like. The profiles include an entertainment profile  710  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to movies, television, video games, art, music, and the like. 
     The profiles of  FIG.  10    also include a location profile  1018  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to travel locations, home locations, vacation destinations, and the like. The profiles also include a hobby profile  1020  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to hobbies. The profiles also include a hobby profile  1022  that the analysis engine  1004  can pull data from to use as input(s) to the one or more ML models  435  to generate recommended transactions related to transportation preferences (e.g., car, walk, bike, train, plane), ridesharing service, public transit service, and the like. The analysis engine  1004  can store the profiles in a profile data structure  1024 . 
     In some examples, the taste profile  706 , the style profile  708 , the entertainment profile  710 , the location profile  1018 , the hobby profile  1020 , the transportation profile  1022 , and other similar profiles can correspond to transactions that the user is involved in, offers that the user has purchased, communities (online or otherwise) that the user has joined or is otherwise involved in, or a combination thereof. 
       FIG.  11    is a block diagram  1100  illustrating rule types associated with the rules engine  218 . The rule types include weather rules  1110 , sports rules  1115 , saving habit rules  1120 , fitness/health rules  1125 , and the like. 
     The weather rules  1110  can encourage some types of transactions to be used as recommended transactions in certain types of weather, while discouraging or prohibiting other types of transactions to be used as recommended transactions during the same types of weather. For instance, if the automation engine  216  identifies that the weather is currently raining heavily in the region that the user is in, the weather rules  1110  can encourage transactions for umbrellas for the recommended transactions, while discouraging or prohibiting transactions for steep hiking trails. 
     The sports rules  1115  can encourage some types of transactions to be used as recommended transactions based on the user&#39;s sports preferences, while discouraging or prohibiting other types of transactions to be used as recommended transactions based on the user&#39;s sports preferences. For instance, if the automation engine  216  identifies that the user is a fan of a particular sports team, the sports rules  1115  can encourage recommended transactions related to that sports team, and can discourage recommended transactions related to rival sports teams. 
     The saving habit rules  1120  can encourage some types of transactions to be used as recommended transactions based on the user&#39;s saving habits, while discouraging or prohibiting other types of transactions to be used as recommended transactions based on the user&#39;s saving habits. For instance, if the automation engine  216  identifies that the user is very frugal, the saving habit rules  1120  can encourage recommended transactions related to more affordable, and can discourage recommended transactions that are extravagant. 
     The fitness/health rules  1125  can encourage some types of transactions to be used as recommended transactions based on the user&#39;s fitness/health habits, while discouraging or prohibiting other types of transactions to be used as recommended transactions based on the user&#39;s fitness/health habits. For instance, if the automation engine  216  identifies that the user is very fit, the fitness/health rules  1125  can encourage recommended transactions related to fitness activities and/or healthy food, and can discourage recommended transactions that are related to sedentary activities and/or junk food. 
     In some examples, the weather rules  1110 , the sports rules  1115 , the saving habit rules  1120 , the fitness/health rules  1125 , and other similar rulesets can correspond to transactions that the user is involved in, offers that the user has purchased, communities (online or otherwise) that the user has joined or is otherwise involved in, or a combination thereof. 
       FIG.  12    is a block diagram  1200  illustrating considerations  1210  for generating recommendations  1205 . In some examples, the recommendations  1205  include recommended transactions  525 , recommended communities  560 , recommended offers, and/or other recommendations. The considerations  1210  can include categories  1215  of transactions, money habits  1220  of the user (e.g., as in the saving habit rules  1120 ), responses to questions or suggestions  1225  (e.g., feedback as in  FIG.  7   ), ability of the user to complete actions associated with the recommendations on time  1230 , or combinations thereof. In some examples, the categories  1215  of transactions, money habits  1220  of the user (e.g., as in the saving habit rules  1120 ), responses to questions or suggestions  1225  (e.g., feedback as in  FIG.  7   ), ability of the user to complete actions associated with the recommendations on time  1230 , and other considerations  1210  can correspond to transactions that the user is involved in, offers that the user has purchased, communities (online or otherwise) that the user has joined or is otherwise involved in, or a combination thereof. 
       FIG.  13    is a block diagram  1300  illustrating a system architecture of a system that provides intent-based recommendations. The system includes an intent-based recommendation service  1305  that is configured to generate one or more intent-based recommendation(s)  1310 , for instance as discussed with respect to the processes  300 A- 300 C of  FIGS.  3 A- 3 C  and/or otherwise herein. The recommendation(s)  1310  can recommend communities, offers, or combinations thereof to a user of the system based on an intent of the user (e.g., as determined based on previous transaction(s) and/or other activity by the user). 
     The system provides various user experiences  1315  to users, such as discovery  1320  of communities and/or offers, purchases  1325  (e.g., using the offers and/or between community members), messaging  1330  with community members. A user can interface with the system&#39;s discovery user interface(s) to view various communities, community-specific offerings, and activity of by other users of the system (e.g., dynamically in real-time or near-real-time). A user can select any community or offer in the recommendation(s)  1310  to further the user&#39;s engagement with the community or offer, for instance to learn more about the recommended community or offer, to conduct further discovery  1320 , to engage in messaging  1330 , to make purchases  1325 , and the like. In some examples, the system can also provide an interface of the user through which the user can view the intent-based recommendation service  1305 &#39;s logic behind particular recommendation(s)  1310 , such as what intent the intent-based recommendation service  1305  determined the user to have, what data the intent-based recommendation service  1305  used to determine the user&#39;s intent, and the like. In this way, the system can provide the user with further transparency and awareness. In some examples, the user&#39;s data that are used by the intent-based recommendation service  1305  to determine the user&#39;s intent may be stored in the user profile data data store (DS)  1340 . The user profile data DS  1340  may also be updated using a profile enrichment service  1335  to store the user&#39;s interactions with recommendation(s)  1310  provided by the intent-based recommendation service  1305 , which may in turn influence future determinations of the user&#39;s intent and future recommendation(s)  1310  for the user by the intent-based recommendation service  1305 . The profile enrichment service  1335  may track these interactions and edit the user profile data DS  1340 , and may initiate further updates to the trained ML model(s)  435  of the intent-based recommendation service  1305  using the new data in the user profile data DS  1340  (in some cases with the recommendation(s)  1310  that the user is interacting with) as training data. These updates to the trained ML model(s)  435  can refine future recommendation(s)  1310 . 
     In some examples, the user profile data DS  1340  can store various actions that a user takes, such as interactions with various communities, purchases, offers, and/or recommendation(s)  1310 . For instance, the user profile data DS  1340  can store a user identifiers, a name, a phone number, an address, an email address, a wallet hash for a digital wallet (e.g., for a cryptocurrency wallet or a web3 wallet) associated with the user, data from at least one distributed ledger (e.g., a blockchain ledger or a DAG ledger) associated with the user and/or the wallet hash, user behavioral data, communities joined by the user, assets purchased or sold by the user, the user&#39;s activity within different communities, what was recommended in intent-based recommendation(s)  1310  provided to the user, how a user interacted with intent-based recommendation(s)  1310  provided to the user (e.g., were the recommendation(s)  1310  viewed or selected, did the user make a recommended purchase or join a recommended community, did the user message within the recommended community), types of assets purchased and/or owned by the user, stage of investments by the user (e.g., early or late stage), user preferences or affinities (e.g., sports, fine art, street art, fashion, etc.), average purchase price of assets purchased by the user, length of time asset(s) are held by the user after purchase (e.g., is the user a long-term supporter or a short-term asset flipper), types of communities joined, how active a user is within each community, frequency of usage of different assets and/or services, other user data described herein, or a combination thereof. 
     The profile enrichment service  1335  is configured to capture user-specific data and route the data accordingly. In some examples, the profile enrichment service  1335  can capture user behavior data (and other forms of user data described above) from interface(s) between the intent-based recommendation service  1305  and the user (e.g., including from interfaces corresponding to the recommendation(s)  1310 ) to capture user behavior data (and other forms of user data described above) and route the data to be stored in the user profile data DS  1340 . In some examples, the profile enrichment service  1335  can capture user behavior data (and other forms of user data described above) from the user profile data DSs  1340 . In some examples, the profile enrichment service  1335  can access data from various distributed ledgers (e.g., via the distributed ledger data service  1350  and/or the asset authenticator service  1345 ), such as a distributed ledger  1355 , a distributed ledger  1360 , and a distributed ledger  1365 . The profile enrichment service  1335  can store the data from the distributed ledger(s) in the user profile data DS  1340  and/or use the data for training and/or updating the trained ML model(s)  435 . The profile enrichment service  1335  can store any generated recommendation(s)  1310 , and/or information about any user interactions therewith, in the user profile data DS  1340  and/or use the data for training and/or updating the trained ML model(s)  435 . In some examples, some user experiences  1315  in the system (e.g., discovery  1320 , purchases  1325 , and/or messaging  1330 ) by the user can trigger the profile enrichment service  1335  to interact with the intent-based recommendation service  1305  to generate new recommendation(s)  1310  for the user. 
     The profile enrichment service  1335  can interact with an asset authenticator service  1345  and/or distributed ledger service  1350 , for instance to receive updated distributed ledger data from one or more distributed ledgers, such as the distributed ledger  1355 , the distributed ledger  1360 , and/or the distributed ledger  1365 . Each of these distributed ledgers may be blockchain ledgers, DAG ledgers, hashgraph ledgers, private ledgers, public ledgers, permissioned ledgers, permissionless ledgers, or a combination thereof. The asset authenticator service  1345  receives a digital wallet hash of a digital wallet (e.g., a cryptocurrency wallet and/or a web3 wallet) associated with a user, and/or a user-specific authentication token. The asset authenticator service  1345  uses the digital wallet hash and/or the authentication token to ensure that a specified user is the true owner of a digital wallet. Once the asset authenticator service  1345  authenticates ownership of the digital wallet, information regarding the assets within the wallet and/or the wallet hash are sent from the asset authenticator service  1345  to the distributed ledger service  1350 . 
     The distributed ledger service  1350  pulls current data and/or historical data from one or more distributed ledgers (e.g., the distributed ledger  1355 , the distributed ledger  1360 , and/or the distributed ledger  1365 ) that are relevant to the digital wallet associated with a user. Examples of the distributed ledgers include Ethereum, Polygon, Bitcoin, Solana, and/or other distributed ledger types. The information queried and/or retrieved by the distributed ledger service  1350  is sent from the distributed ledger service  1350  to the asset authenticator service  1345 , the profile enrichment service  1335 , and/or the user data DS  1340 , and can be used to update training of the trained ML model(s)  435  to enhance future recommendation(s)  1310 . 
     The intent-based recommendation service  1305  can use user-specific profile data (e.g., from the user profile data DS  1340  and/or from various distributed ledger(s) as described herein) to output personalized and user-relevant recommendation(s)  1310  regarding communities for the user to join, transactions for the user to participate in, and/or offers for the user to purchase. The intent-based recommendation service  1305  can generate the recommendation(s)  1310  dynamically in real-time (or near-real-time) as further user-specific profile data is received, for instance as the user interacts with other recommendations, as the user interacts with communities, as the user participates in transactions, as the user purchases offers, and the like. The intent-based recommendation service  1305  can use the user-specific profile data (e.g., from the user profile data DS  1340  and/or from various distributed ledger(s) as described herein) to train and/or update the trained ML model(s)  435  that the intent-based recommendation service  1305  uses to determine intent (e.g., the predicted intent  460 ) and/or to generate the recommendation(s)  1310 . The recommended transactions of  FIGS.  3 A- 3 C , the recommended transaction  535 , and the recommended community  560  are examples of the recommendation(s)  1310 . In some examples, a user can automatically receive a reward (e.g., an asset) for interacting with the recommendation(s)  1310 , for instance by joining a recommended community, purchasing a recommended offer, or participating in a recommended transaction. 
       FIG.  14    is a block diagram illustrating an example of a neural network (NN)  1400  that can be used by a machine learning engine to determine intent for transactions and/or to generate recommended transactions, recommended communities, and/or recommended offers. Examples of the machine learning engine and/or the NN  1400  include the ML engine  248 , the ML engine associated with the one or more trained machine learning models of operation  310  and operation  312  of  FIG.  3 B , the ML engine associated with the first trained ML model of operations  322 - 328  of  FIG.  3 C , the ML engine associated with the trained ML model of operations  330 - 334  of  FIG.  3 C , the ML engine  430  of  FIGS.  4  and  5 A- 5 B , the engagement AI/ML engine  612 , the AI/ML engine  660 , the AI/ML engine  925 , and combinations thereof. The neural network  1400  can include any type of deep network, such as a convolutional neural network (CNN), an autoencoder, a deep belief net (DBN), a Recurrent Neural Network (RNN), a Generative Adversarial Networks (GAN), and/or other type of neural network. 
     Examples of the determinations as to intent for a transaction using the NN  1400  include operation  304 , operation  310 , operation  324 , and/or determination of the predicted intent  460 . Examples of the generation of a recommended transaction, recommended community, and/or recommended offer using the NN  1400  include operation  306 , operation  312 , operation  332 , generation of the recommended transaction  525 , generation of the recommended community  560 , generation of the recommendations  1205 , and/or generation of the recommendation(s)  1310 . Examples of other determinations made using the NN  1400  include determinations as to characteristics of the user, such as whether the user has a car, has a house, is in a relationship, is interested in a particular charity, has a particular political affiliation, and the like. 
     An input layer  1410  of the neural network  1400  includes input data. The input data of the input layer  1410  can include data representing, for example, historical information  405  about a user  410  that identifies a first transaction  415 , intent  505  for the first transaction  415 , data from the data lake  658 , data from the operational database  656 , or a combination thereof. In an illustrative example, the input data of the input layer  1410  can include data representing the historical information  405  about a user  410  that identifies a first transaction  415 . In another illustrative example, the input data of the input layer  1410  can include data representing the intent  505  for the first transaction  415 . In another illustrative example, the input data of the input layer  1410  can include data representing the intent  575  for the first community  580 . 
     The neural network  1400  includes multiple hidden layers  1412 A,  1412 B, through  1412 N. The hidden layers  1412 A,  1412 B, through  1412 N include “N” number of hidden layers, where “N” is an integer greater than or equal to one. The number of hidden layers can be made to include as many layers as needed for the given application. The neural network  1400  further includes an output layer  1414  that provides an output resulting from the processing performed by the hidden layers  1412 A,  1412 B, through  1412 N. In some examples, the output layer  1414  can provide a predicted intent  460  for the first transaction  415 , a recommended transaction  525 , a recommended community  506 , a determination as to a characteristic of the user, a determination associated with one of the services of the interaction engine  662 , a determination associated with one of the profiles  706 - 710  and/or  1018 - 1022 , another determination, or a combination thereof. 
     The neural network  1400  is a multi-layer neural network of interconnected filters. Each filter can be trained to learn a feature representative of the input data. Information associated with the filters is shared among the different layers and each layer retains information as information is processed. In some cases, the neural network  1400  can include a feed-forward network, in which case there are no feedback connections where outputs of the network are fed back into itself. In some cases, the network  1400  can include a recurrent neural network, which can have loops that allow information to be carried across nodes while reading in input. 
     In some cases, information can be exchanged between the layers through node-to-node interconnections between the various layers. In some cases, the network can include a convolutional neural network, which may not link every node in one layer to every other node in the next layer. In networks where information is exchanged between layers, nodes of the input layer  1410  can activate a set of nodes in the first hidden layer  1412 A. For example, as shown, each of the input nodes of the input layer  1410  can be connected to each of the nodes of the first hidden layer  1412 A. The nodes of a hidden layer can transform the information of each input node by applying activation functions (e.g., filters) to this information. The information derived from the transformation can then be passed to and can activate the nodes of the next hidden layer  1412 B, which can perform their own designated functions. Example functions include convolutional functions, downscaling, upscaling, data transformation, and/or any other suitable functions. The output of the hidden layer  1412 B can then activate nodes of the next hidden layer, and so on. The output of the last hidden layer  1412 N can activate one or more nodes of the output layer  1414 , which provides a processed output image. In some cases, while nodes (e.g., node  1416 ) in the neural network  1400  are shown as having multiple output lines, a node has a single output and all lines shown as being output from a node represent the same output value. 
     In some cases, each node or interconnection between nodes can have a weight that is a set of parameters derived from the training of the neural network  1400 . For example, an interconnection between nodes can represent a piece of information learned about the interconnected nodes. The interconnection can have a tunable numeric weight that can be tuned (e.g., based on a training dataset), allowing the neural network  1400  to be adaptive to inputs and able to learn as more and more data is processed. 
     The neural network  1400  is pre-trained to process the features from the data in the input layer  1410  using the different hidden layers  1412 A,  1412 B, through  1412 N in order to provide the output through the output layer  1414 . 
       FIG.  15    is a diagram illustrating an example of a system for implementing certain aspects of the present technology. In particular,  FIG.  15    illustrates an example of computing system  1500 , which can be for example any computing device making up internal computing system, a remote computing system, a camera, or any component thereof in which the components of the system are in communication with each other using connection  1505 . Connection  1505  can be a physical connection using a bus, or a direct connection into processor  1510 , such as in a chipset architecture. Connection  1505  can also be a virtual connection, networked connection, or logical connection. 
     In some embodiments, computing system  1500  is a distributed system in which the functions described in this disclosure can be distributed within a datacenter, multiple data centers, a peer network, etc. In some embodiments, one or more of the described system components represents many such components each performing some or all of the function for which the component is described. In some embodiments, the components can be physical or virtual devices. 
     Example system  1500  includes at least one processing unit (CPU or processor)  1510  and connection  1505  that couples various system components including system memory  1515 , such as read-only memory (ROM)  1520  and random access memory (RAM)  1525  to processor  1510 . Computing system  1500  can include a cache  1512  of high-speed memory connected directly with, in close proximity to, or integrated as part of processor  1510 . 
     Processor  1510  can include any general purpose processor and a hardware service or software service, such as services  1532 ,  1534 , and  1536  stored in storage device  1530 , configured to control processor  1510  as well as a special-purpose processor where software instructions are incorporated into the actual processor design. Processor  1510  may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric. 
     To enable user interaction, computing system  1500  includes an input device  1545 , which can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech, etc. Computing system  1500  can also include output device  1535 , which can be one or more of a number of output mechanisms. In some instances, multimodal systems can enable a user to provide multiple types of input/output to communicate with computing system  1500 . Computing system  1500  can include communications interface  1540 , which can generally govern and manage the user input and system output. The communication interface may perform or facilitate receipt and/or transmission wired or wireless communications using wired and/or wireless transceivers, including those making use of an audio jack/plug, a microphone jack/plug, a universal serial bus (USB) port/plug, an Apple® Lightning® port/plug, an Ethernet port/plug, a fiber optic port/plug, a proprietary wired port/plug, a BLUETOOTH® wireless signal transfer, a BLUETOOTH® low energy (BLE) wireless signal transfer, an IBEACON® wireless signal transfer, a radio-frequency identification (RFID) wireless signal transfer, near-field communications (NFC) wireless signal transfer, dedicated short range communication (DSRC) wireless signal transfer, 802.11 Wi-Fi wireless signal transfer, wireless local area network (WLAN) signal transfer, Visible Light Communication (VLC), Worldwide Interoperability for Microwave Access (WiMAX), Infrared (IR) communication wireless signal transfer, Public Switched Telephone Network (PSTN) signal transfer, Integrated Services Digital Network (ISDN) signal transfer, 3G/4G/5G/LTE cellular data network wireless signal transfer, ad-hoc network signal transfer, radio wave signal transfer, microwave signal transfer, infrared signal transfer, visible light signal transfer, ultraviolet light signal transfer, wireless signal transfer along the electromagnetic spectrum, or some combination thereof. The communications interface  1540  may also include one or more Global Navigation Satellite System (GNSS) receivers or transceivers that are used to determine a location of the computing system  1500  based on receipt of one or more signals from one or more satellites associated with one or more GNSS systems. GNSS systems include, but are not limited to, the US-based Global Positioning System (GPS), the Russia-based Global Navigation Satellite System (GLONASS), the China-based BeiDou Navigation Satellite System (BDS), and the Europe-based Galileo GNSS. There is no restriction on operating on any particular hardware arrangement, and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed. 
     Storage device  1530  can be a non-volatile and/or non-transitory and/or computer-readable memory device and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, a floppy disk, a flexible disk, a hard disk, magnetic tape, a magnetic strip/stripe, any other magnetic storage medium, flash memory, memristor memory, any other solid-state memory, a compact disc read only memory (CD-ROM) optical disc, a rewritable compact disc (CD) optical disc, digital video disk (DVD) optical disc, a blu-ray disc (BDD) optical disc, a holographic optical disk, another optical medium, a secure digital (SD) card, a micro secure digital (microSD) card, a Memory Stick® card, a smartcard chip, a EMV chip, a subscriber identity module (SIM) card, a mini/micro/nano/pico SIM card, another integrated circuit (IC) chip/card, random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash EPROM (FLASHEPROM), cache memory (L1/L2/L3/L4/L5/L #), resistive random-access memory (RRAM/ReRAM), phase change memory (PCM), spin transfer torque RAM (STT-RAM), another memory chip or cartridge, and/or a combination thereof. 
     The storage device  1530  can include software services, servers, services, etc., that when the code that defines such software is executed by the processor  1510 , it causes the system to perform a function. In some embodiments, a hardware service that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as processor  1510 , connection  1505 , output device  1535 , etc., to carry out the function. 
     As used herein, the term “computer-readable medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A computer-readable medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-readable medium may have stored thereon code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, or the like. 
     In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se. 
     Specific details are provided in the description above to provide a thorough understanding of the embodiments and examples provided herein. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software. Additional components may be used other than those shown in the figures and/or described herein. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments. 
     Individual embodiments may be described above as a process or method which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function. 
     Processes and methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer-readable media. Such instructions can include, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or a processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, source code, etc. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on. 
     Devices implementing processes and methods according to these disclosures can include hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof, and can take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a computer-readable or machine-readable medium. A processor(s) may perform the necessary tasks. Typical examples of form factors include laptops, smart phones, mobile phones, tablet devices or other small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example. 
     The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are example means for providing the functions described in the disclosure. 
     In the foregoing description, aspects of the application are described with reference to specific embodiments thereof, but those skilled in the art will recognize that the application is not limited thereto. Thus, while illustrative embodiments of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art. Various features and aspects of the above-described application may be used individually or jointly. Further, embodiments can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive. For the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate embodiments, the methods may be performed in a different order than that described. 
     One of ordinary skill will appreciate that the less than (“&lt;”) and greater than (“&gt;”) symbols or terminology used herein can be replaced with less than or equal to (“≤”) and greater than or equal to (“≥”) symbols, respectively, without departing from the scope of this description. 
     Where components are described as being “configured to” perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof. 
     The phrase “coupled to” refers to any component that is physically connected to another component either directly or indirectly, and/or any component that is in communication with another component (e.g., connected to the other component over a wired or wireless connection, and/or other suitable communication interface) either directly or indirectly. 
     Claim language or other language reciting “at least one of” a set and/or “one or more” of a set indicates that one member of the set or multiple members of the set (in any combination) satisfy the claim. For example, claim language reciting “at least one of A and B” means A, B, or A and B. In another example, claim language reciting “at least one of A, B, and C” means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language “at least one of” a set and/or “one or more” of a set does not limit the set to the items listed in the set. For example, claim language reciting “at least one of A and B” can mean A, B, or A and B, and can additionally include items not listed in the set of A and B. 
     The various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, firmware, or combinations thereof. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. 
     The techniques described herein may also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such techniques may be implemented in any of a variety of devices such as general purposes computers, wireless communication device handsets, or integrated circuit devices having multiple uses including application in wireless communication device handsets and other devices. Any features described as modules or components may be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, the techniques may be realized at least in part by a computer-readable data storage medium comprising program code including instructions that, when executed, performs one or more of the methods described above. The computer-readable data storage medium may form part of a computer program product, which may include packaging materials. The computer-readable medium may comprise memory or data storage media, such as random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, and the like. The techniques additionally, or alternatively, may be realized at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer, such as propagated signals or waves. 
     The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, an application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Such a processor may be configured to perform any of the techniques described in this disclosure. A general purpose processor may be a microprocessor; but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Accordingly, the term “processor,” as used herein may refer to any of the foregoing structure, any combination of the foregoing structure, or any other structure or apparatus suitable for implementation of the techniques described herein. In addition, in some aspects, the functionality described herein may be provided within dedicated software modules or hardware modules configured for encoding and decoding, or incorporated in a combined video encoder-decoder (CODEC). 
     Illustrative aspects of the disclosure include: 
     Aspect 1. A method of automated account interaction, the method comprising: receiving historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; using one or more trained machine learning models to identify an intent for the transaction by inputting the historical information to the one or more trained machine learning models; using the one or more trained machine learning models to generate a recommended transaction by inputting the intent for the transaction to the one or more trained machine learning models; outputting the recommended transaction; receiving a confirmation regarding the recommended transaction; and based on the confirmation, using the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
     Aspect 2. The method of Aspect 1, further comprising: automatically initiating execution of the recommended transaction on behalf of the first user in response to receipt of the confirmation. 
     Aspect 3. The method of Aspect 2, further comprising: automatically outputting a transaction completion confirmation in response to execution of the recommended transaction. 
     Aspect 4. The method of any of Aspects 1 to 3, further comprising: receiving second historical information associated with a second account, wherein identifying the intent for the transaction includes identifying a link between the first account and the second account, wherein identifying the intent for the transaction is also based on the second historical information. 
     Aspect 5. The method of Aspect 4, wherein the recommended transaction includes a recommendation to transfer one or more assets from the first account to the second account. 
     Aspect 6. The method of any of Aspects 4 to 5, wherein the recommended transaction includes a recommendation to transfer one or more assets from the second account to the first account. 
     Aspect 7. The method of any of Aspects 4 to 6, wherein the second account is associated with at least one of a merchant or a second user. 
     Aspect 8. The method of any of Aspects 4 to 7, wherein the recommended transaction is for at least one of a product and a service associated with the second account. 
     Aspect 9. The method of any of Aspects 1 to 8, wherein identifying the intent for the transaction includes identifying that the first user has operational access to a vehicle, wherein the transaction is for at least one of a product and a service associated with the vehicle, and wherein the recommended transaction is for at least one of a second product and a second service associated with the vehicle. 
     Aspect 10. The method of any of Aspects 1 to 9, wherein identifying the intent for the transaction includes identifying that the first user resides in a residence, wherein the transaction is for at least one of a product and a service associated with the residence, and wherein the recommended transaction is for at least one of a second product and a second service associated with the residence. 
     Aspect 11. The method of any of Aspects 1 to 10, wherein identifying the intent for the transaction includes identifying that the first user works in a profession, wherein the transaction is for at least one of a product and a service associated with the profession, and wherein the recommended transaction is for at least one of a second product and a second service associated with the profession. 
     Aspect 12. The method of any of Aspects 1 to 11, wherein identifying the intent for the transaction includes identifying that the first user has a relationship with a second user, wherein the transaction is for at least one of a product and a service associated with the second user, and wherein the recommended transaction is for at least one of a second product and a second service associated with the second user. 
     Aspect 13. The method of any of Aspects 1 to 12, further comprising: receiving additional information associated with the first account; and using one or more trained machine learning models to update the intent for the transaction based on the additional information. 
     Aspect 14. The method of Aspect 13, further comprising: in response to updating the intent, using one or more trained machine learning models to update the recommended transaction based on the intent. 
     Aspect 15. The method of any of Aspects 1 to 14, further comprising: receiving additional information associated with the first account; and using one or more trained machine learning models to update the recommended transaction based on the additional information. 
     Aspect 16. The method of any of Aspects 1 to 15, further comprising: receiving a question associated with the first user; determining an answer to the question based on at least one of the intent for the transaction or the recommended transaction; and outputting the answer to the question. 
     Aspect 17. The method of any of Aspects 1 to 16, further comprising: identifying that the first user is characterized by a characteristic based on the intent for the transaction, wherein using the one or more trained machine learning models to generate the recommended transaction includes inputting the characteristic to the one or more trained machine learning models. 
     Aspect 18. The method of any of Aspects 1 to 17, wherein outputting the recommended transaction includes sending a message identifying the recommended transaction to a user device associated with the first user, wherein receiving the confirmation regarding the recommended transaction includes receiving an approval to initiate the recommended transaction from a user device associated with the first user. 
     Aspect 19. The method of any of Aspects 1 to 18, wherein outputting the recommended transaction includes automatically initiating execution of the recommended transaction on behalf of the first user, wherein receiving the confirmation regarding the recommended transaction includes receiving a transaction completion confirmation indicating that the recommended transaction has been processed. 
     Aspect 20. The method of any of Aspects 1 to 19, further comprising: receiving feedback from a user device associated with the first user, wherein the feedback is associated with at least one of the intent for the transaction or the recommended transaction; and updating the one or more trained machine learning models by using the feedback as training data for the one or more trained machine learning models. 
     Aspect 21. The method of any of Aspects 1 to 20, wherein generating the recommended transaction includes determining an eligibility of the first user for a line of credit, wherein the recommended transaction includes a recommendation to open the line of credit for the first user based on the eligibility. 
     Aspect 22. The method of any of Aspects 1 to 21, wherein the recommended transaction includes a recommendation to make at least one of an appointment or a reservation with a service provider. 
     Aspect 23. The method of any of Aspects 1 to 22, wherein the one or more trained machine learning models include a first trained machine learning model and a second trained machine learning model, wherein using the one or more trained machine learning models to identify the intent for the transaction includes using the first trained machine learning model to identify the intent for the transaction by inputting the historical information to the first trained machine learning model, wherein using the one or more trained machine learning models to generate the recommended transaction includes using the second trained machine learning model to generate the recommended transaction by inputting the intent for the transaction to the second trained machine learning model. 
     Aspect 24. The method of any of Aspects 1 to 23, wherein the one or more trained machine learning models include a first trained machine learning model, wherein using the one or more trained machine learning models to identify the intent for the transaction includes using the first trained machine learning model to identify the intent for the transaction by inputting the historical information to the first trained machine learning model, wherein using the one or more trained machine learning models to generate the recommended transaction includes using the first trained machine learning model to generate the recommended transaction by inputting the intent for the transaction to the first trained machine learning model. 
     Aspect 25. The method of any of Aspects 1 to 24, wherein outputting the recommended transaction includes causing the recommended transaction to be displayed using a display. 
     Aspect 26. The method of any of Aspects 1 to 25, wherein outputting the recommended transaction includes causing the recommended transaction to be transmitted a user device associated with the first user via a communication transceiver. 
     Aspect 27. The method of any of Aspects 1 to 26, further comprising: receiving schedule information associated with the first user; and identifying a scheduled event based on the schedule information, wherein the recommended transaction is associated with the scheduled event. 
     Aspect 28. The method of any of Aspects 1 to 27, further comprising: generating one or more profiles associated with the user based on the intent for the transaction and one or more intents determined for one or more additional transactions, wherein each of the one or more profiles identifies one or more preferences of the user with respect a category of transactions of a set of different categories of transactions; wherein using the one or more trained machine learning models to generate the recommended transaction by inputting the intent for the transaction to the one or more trained machine learning models includes inputting at least one of the one or more profiles to the one or more trained machine learning models. 
     Aspect 29. The method of any of Aspects 1 to 28, further comprising: using the one or more trained machine learning models to select a recommended community from a plurality of communities by inputting the intent to the one or more trained machine learning models, wherein the recommended community is associated with the intent; outputting the recommended community. 
     Aspect 30. An apparatus for automated account interaction, the apparatus comprising: a memory; and one or more processors coupled to the memory, the one or more processors configured to: receive historical information associated with a first account corresponding to a first user, wherein the historical information identifies a transaction involving the first account; use one or more trained machine learning models to identify an intent for the transaction by inputting the historical information to the one or more trained machine learning models; use the one or more trained machine learning models to generate a recommended transaction by inputting the intent for the transaction to the one or more trained machine learning models; output the recommended transaction; receive a confirmation regarding the recommended transaction; and use, based on the confirmation, the intent and the recommended transaction to update the one or more trained machine learning models for use in identifying one or more additional intents and one or more additional recommended transactions. 
     Aspect 31. The apparatus of Aspect 30, further comprising any of Aspects 2-29. 
     Aspect 32. A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause the one or more processors to perform operations according to any of Aspects 1-29. 
     Aspect 33. An apparatus for image processing, the apparatus comprising one or more means for performing operations according to any of Aspects 1-29.