Integrating data from multiple unrelated data structures

A method of integrating data from multiple unrelated data structures, comprising:

• retrieving, from one or more databases, one or more of:
• exchange data relating to exchanges associated with a user, account data relating to an account associated with the user, record data relating to informational records associated with the user, interaction data relating to online interactions associated with the user, or metaverse data relating to metaverse exchanges associated with the user;
• obtaining at least one of location data or wireless network data associated with a user device associated with the user,
• wherein the location data indicates a location of the user device that is associated with a first vehicle-related entity, and
• wherein the wireless network data indicates a wireless network to which the user device has connected that is associated with a second vehicle-related entity;
• determining, using a machine learning model, a machine learning model output based on at least one of the exchange data, the account data, the record data, the interaction data, or the metaverse data, and at least one of first information relating to the first vehicle-related entity or second information relating to the second vehicle-related entity,
• wherein each type of data, that the machine learning model output is based on, are incompatible from each other and from different data structures, and
• wherein the machine learning model output indicates a probability of the user acquiring a vehicle in a future time interval;
• and
• performing one or more actions based on the machine learning model output, wherein the one or more actions are associated with at least one of:
• configuring an extended reality content based on the machine learning model output, or
• generating a new communication session based on the machine learning model output.

A non-transitory computer-readable medium storing a set of instructions for integrating data from multiple unrelated data structures, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• retrieve one or more of:
• exchange data relating to exchanges associated with a user, account data relating to an account associated with the user, record data relating to informational records associated with the user, interaction data relating to online interactions associated with the user, or metaverse data relating to metaverse exchanges associated with the user;
• obtain at least one of location data or wireless network data associated with a user device associated with the user,
• wherein the location data indicates a location of the user device that is associated with a first entity, and
• wherein the wireless network data indicates a wireless network to which the user device has connected that is associated with a second entity;
• determine, based on using a machine learning model, a probability of the user acquiring an item in a future time interval based on at least one of the exchange data, the account data, the record data, the interaction data, or the metaverse data, and at least one of first information relating to the first entity or second information relating to the second entity,
• wherein each type of data, that output of the machine learning model is based on, are incompatible from each other and from different data structures;
• transmit information based on the probability of the user acquiring the item;
• and
• perform, based on transmitting the information, at least one action associated with:
• configuring an extended reality content based on the machine learning model output, or
• generating a new communication session based on the machine learning model output.