Integrating data from multiple unrelated data structures
In some implementations, a device may retrieve one or more of: exchange data, account data, record data, interaction data, or metaverse data. The device may obtain at least one of location data or wireless network data, where the location data indicates a location, of a user device, associated with a first entity, and where the wireless network data indicates a wireless network, to which the user device has connected, associated with a second entity. The device may determine 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. The device may transmit information based on the probability of the user acquiring the item.
1 . A system for integrating data from multiple unrelated data structures, the system comprising:
one or more memories; and
one or more processors, communicatively coupled to the one or more memories, configured to:
retrieve, from one or more databases, one or more records indicating 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, and metaverse data relating to metaverse exchanges associated with the user;
obtain location data indicating one or more locations of a user device associated with the user;
determine, based on the location data, that a location, of the one or more locations, is associated with a first vehicle-related entity;
obtain wireless network data indicating identifiers of one or more wireless networks to which the user device has connected;
determine, based on an identifier of a wireless network of the one or more wireless networks indicated by the wireless network data, that the wireless network is associated with a second vehicle-related entity;
determine, using a machine learning model, a machine learning model output based on the exchange data, the account data, the record data, the interaction data, the metaverse data, first information relating to the first vehicle-related entity, and 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;
transmit, to the user device or to another user device associated with the user, content based on the machine learning model output; and
perform, based on transmitting the content, 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.
2 . The system of claim 1 , wherein the first information relating to the first vehicle-related entity indicates at least one of a first identifier of the first vehicle-related entity, a first category associated with the first vehicle-related entity, or a first frequency of user interactions with the first vehicle-related entity, and
wherein the second information relating to the second vehicle-related entity indicates at least one of a second identifier of the second vehicle-related entity, a second category associated with the second vehicle-related entity, or a second frequency of user interactions with the second vehicle-related entity.
3 . The system of claim 1 , wherein the one or more processors, to retrieve the one or more records indicating the exchange data, the account data, the record data, the interaction data, and the metaverse data, are configured to:
retrieve the exchange data from a first database, the account data from a second database, the record data from a third database, the interaction data from a fourth database, and the metaverse data from a fifth database,
wherein the first database, the second database, the third database, the fourth database, and the fifth database are unrelated.
4 . The system of claim 1 , wherein the machine learning model is trained to determine the machine learning model output based on a feature set that includes one or more features relating to life events of the user, a wealth level of the user, a use of public transportation by the user, or problems associated with a current vehicle of the user.
5 . The system of claim 1 , wherein the content includes information relating to the vehicle.
6 . The system of claim 1 , wherein the content is extended reality content relating to the vehicle.
7 . The system of claim 1 , wherein the one or more processors are further configured to:
determine whether the user acquired the vehicle; and
provide, to the machine learning model, feedback data for training the machine learning model indicating whether the user acquired the vehicle.
8 . The system of claim 1 , wherein the machine learning model output indicates the probability of the user acquiring the vehicle, a type of the vehicle, and an amount range of the vehicle.
9 . The system of claim 1 , wherein the one or more processors are configured to:
update the machine learning model based on feedback data indicating whether the user completed a transaction using the machine learning model output.
10 . 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.
11 . The method of claim 10 , further comprising:
transmitting content based on the machine learning model output.
12 . The method of claim 10 , wherein performing the one or more actions comprises:
determining, based on the machine learning model output indicating the probability of the user acquiring the vehicle, an approval for the user to receive one or more services in connection with acquiring the vehicle; and
transmitting information indicating the approval for the user.
13 . The method of claim 10 , further comprising:
performing natural language processing of a name of the wireless network; and
determining that the wireless network is associated with the second vehicle-related entity based on performing natural language processing of the name.
14 . The method of claim 10 , wherein the machine learning model is trained to determine the machine learning model output based on a feature set that includes one or more features relating to life events of the user, a wealth level of the user, a use of public transportation by the user, or problems associated with a current vehicle of the user.
15 . The method of claim 10 , further comprising:
updating the machine learning model based on feedback data indicating whether the user completed a transaction using the machine learning model output.
16 . 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.
17 . The non-transitory computer-readable medium of claim 16 , wherein the item is a vehicle.
18 . The non-transitory computer-readable medium of claim 16 , wherein the one or more instructions, that cause the device to obtain at least one of the location data or the wireless network data, cause the device to:
obtain at least one of the location data or the wireless network data from a user device of the user.
19 . The non-transitory computer-readable medium of claim 16 ,
wherein the information identifies the user, the future time interval, and the item.
20 . The non-transitory computer-readable medium of claim 16 ,
wherein the metaverse data relates to at least one of an exchange of a non-fungible token or an exchange for a digital item.