Computer-based systems configured for machine-learning context-aware curation and methods of use thereof

A system comprising:

• a non-transient computer memory, storing software instructions;
• and
• at least one processor of a computing device;
• wherein, when the at least one processor executes the software instructions, the computing device is programmed to:
• obtain a trained activity completion time estimation machine learning model that determines activity completion time prediction data for an activity of an entity based on at least one pattern between entity-specific user activity data of at least one user and entity activity data of the entity, wherein the entity-specific user activity data is indicative of past device-executed interactions by the at least one user with the entity, wherein the activity completion time prediction data is indicative of when the entity activity is to be completed by the entity;
• receive, from a first computing device of a user, current entity-specific device-executed user activity data of a current entity-specific user activity associated with a particular user and a particular entity, wherein the current entity-specific user activity is associated with a current entity activity to be completed at a future point of time by a particular service provider associated with the particular entity, and wherein the current entity-specific device-executed user activity data is indicative of one or more current actions of the user with the first computing device;
• receive from a second computing device associated with the particular entity, current user-specific entity activity data associated with a current user-specific entity activity, related to the current entity-specific user activity, wherein the current user-specific entity activity data is associated with data of the particular entity, data of the particular service, or both;
• utilize the trained activity completion time prediction machine learning model to determine current user-specific entity activity completion time prediction data for the current user-specific entity activity based at least in part on:
• i) the current entity-specific device-executed user activity data,
• ii) the current user-specific entity activity data, or
• iii) a combination of both the current entity-specific device-executed activity data and the current user-specific entity activity data,
• wherein the trained activity completion time prediction machine learning model is configured to capture one or more real-time events via a browser extension associated with the first computing device;
• determine a current displaying context associated with one or more current actions based on the one or more real-time events and performed by the user with the first computing device so as to notify to the user of the current user-specific entity activity completion time prediction;
• and
• instruct the first computing device of the user to generate a graphical user interface (GUI), wherein the GUI comprises at least one GUI element that is configured to display the displaying context to the user so as to notify the user regarding the current user-specific entity activity completion time prediction data for the current user-specific entity activity.

A computing device comprising:

• a non-transient computer memory, storing software instructions;
• and
• at least one processor;
• wherein, when the at least one processor executes the software instructions, the computing device is programmed to:
• obtain a trained activity completion time estimation machine learning model that determines activity completion time prediction data for an activity of an entity based on at least one pattern between entity-specific user activity data of at least one user and entity activity data of the entity, wherein the entity-specific user activity data is indicative of past device-executed interactions by the at least one user with the entity, wherein the activity completion time prediction data is indicative of when the entity activity is to be completed by the entity;
• receive, from a first computing device of a user, current entity-specific device-executed user activity data of a current entity-specific user activity associated with a particular user and a particular entity, wherein the current entity-specific user activity is associated with a current entity activity to be completed at a future point of time by a particular service provider associated with the particular entity, and wherein the current entity-specific device-executed user activity data is indicative of one or more current actions of the user with the first computing device;
• receive from a second computing device associated with the particular entity, current user-specific entity activity data associated with a current user-specific entity activity, related to the current entity-specific user activity, wherein the current user-specific entity activity data is associated with data of the particular entity, data of the particular service, or both;
• utilize the trained activity completion time prediction machine learning model to determine current user-specific entity activity completion time prediction data for the current user-specific entity activity based at least in part on:
• i) the current entity-specific device-executed user activity data,
• ii) the current user-specific entity activity data, or
• iii) a combination of both the current entity-specific device-executed activity data and the current user-specific entity activity data,
• wherein the trained activity completion time prediction machine learning model is configured to capture one or more real-time events via a browser extension associated with the first computing device;
• determine a current displaying context associated with one or more current actions based on the one or more real-time events and performed by the user with the first computing device so as to notify to the user of the current user-specific entity activity completion time prediction;
• and
• instruct the first computing device of the user to generate a graphical user interface (GUI), wherein the GUI comprises at least one GUI element that is configured to display the displaying context to the user so as to notify the user regarding the current user-specific entity activity completion time prediction data for the current user-specific entity activity.