Enhanced value component predictions using contextual machine-learning models
The present disclosure generally relates to systems and methods that intelligently generate reassignment value condition for reassigning access rights. The systems and methods include executing a trained contextual machine-learning model to generate predictions of value components of the reassignment value condition, which once satisfied, enables an access-right requestor to have an assigned access right reassigned to the access-right requestor.
1. A computer-implemented method for generating value prediction for a reassignment value condition, comprising:
receiving an assigned access right information of a plurality of assigned access rights at a secondary load management system from an access-right holder device, the assigned access right information includes a user identifier of an access-right holder;
generating a visual indicator based on the assigned access right information;
displaying the visual indicator on an interface of the secondary load management system;
enabling user to request reassignment of the assigned access right;
receiving a query for the plurality of assigned access rights available for reassignment process that satisfy a particular constraint, wherein the query is received from an access-right requestor device;
generating a signal on initiating reassignment process by the access-right requestor device on receiving the query;
initiating, by the secondary load management system, reassignment of the plurality of assigned access rights in response to the signal;
receiving, by a contextual bandit learner, the signal indicating that the reassignment process has been initiated, wherein the contextual bandit learner enables real-time collection of attributes from the plurality of assigned access rights;
generating a user vector by the contextual bandit learner to represent a contextual information of the access-right requestor device;
inputting the user vector into a contextual machine-learning model associated with the contextual bandit learner;
generating, by the contextual machine-learning model, an output corresponding to a prediction of a value component of the reassignment value condition for the assigned access right;
calculating the reassignment value condition by a value server;
transferring the assigned access right from the access-right holder to an access-right requestor associated with the access-right requestor device based on the reassignment value condition with a digital access-enabling code associated with the assigned access right.
2. The method for generating value prediction for a reassignment value condition according to claim 1 , further comprising, processing, via the secondary load management system, a request for reassigning the assigned access right from one user to another user.
3. The method for generating value prediction for a reassignment value condition according to claim 1 , wherein the visual indicator is a post on the interface associated with the plurality of assigned access rights.
4. The method for generating value prediction for a reassignment value condition according to claim 1 , wherein the contextual machine-learning model includes the constraint on values selectable for the value component of the reassignment value condition.
5. The method for generating value prediction for a reassignment value condition according to claim 1 , wherein the constraint is determined based on real-time data collected from the reassignment of the plurality of assigned access rights, wherein the reassignment of a plurality of access rights request, that are assigned, is received from the user.
6. The method for generating value prediction for a reassignment value condition according to claim 1 , wherein the contextual machine-learning model is generated using one or more contextual multi-armed bandit algorithms.
7. The method for generating value prediction for a reassignment value condition according to claim 1 , wherein the contextual machine-learning model is trained by data sets collected by a primary load management system.
8. A system for generating value prediction for a reassignment value condition, the system comprising:
one or more processors; and
a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more processors, cause the one or more processors to perform operations including:
receive an assigned access right information of a plurality of assigned access rights at a secondary load management system from an access-right holder device, the assigned access right information includes a user identifier of an access-right holder;
generate a visual indicator based on the assigned access right information;
display the visual indicator on an interface of the secondary load management system;
enable user to request reassignment of the assigned access right;
receive a query for the plurality of assigned access rights available for reassignment process that satisfy a particular constraint, wherein the query is received from an access-right requestor device;
generate a signal on initiating reassignment process by the access-right requestor device on receiving the query;
initiate, by the secondary load management system, reassignment of the plurality of assigned access rights in response to the signal;
receive, by a contextual bandit learner, the signal indicating that the reassignment process has been initiated, wherein the contextual bandit learner enables real-time collection of attributes from the plurality of assigned access rights;
generate a user vector by the contextual bandit learner to represent a contextual information of the access-right requestor device;
input the user vector into a contextual machine-learning model associated with the contextual bandit learner;
generate, by the contextual machine-learning model, an output corresponding to a prediction of a value component of the reassignment value condition for the assigned access right;
calculate the reassignment value condition by a value server;
transfer the assigned access right from the access-right holder to an access-right requestor associated with the access-right requestor device based on the reassignment value condition with a digital access-enabling code associated with the assigned access right.
9. The system as recited in claim 8 , wherein the secondary load management system processes a request for reassigning the assigned access right from one user to another user.
10. The system as recited in claim 8 , wherein the visual indicator is a post on the interface associated with the plurality of assigned access rights.
11. The system as recited in claim 8 , wherein the contextual machine-learning model includes the constraint on values selectable for the value component of the reassignment value condition.
12. The system as recited in claim 8 , wherein the constraint is determined based on real-time data collected from the reassignment of the plurality of assigned access rights, wherein the reassignment of a plurality of access rights request, that are assigned, is received from the user.
13. The system as recited in claim 8 , wherein the contextual machine-learning model is generated using one or more contextual multi-armed bandit algorithms.
14. The system as recited in claim 8 , wherein the contextual machine-learning model is trained by data sets collected by a primary load management system.
15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a processing apparatus to perform operation for generating value prediction for a reassignment value condition, including:
receiving an assigned access right information of a plurality of assigned access rights at a secondary load management system from an access-right holder device, the assigned access right information includes a user identifier of an access-right holder;
generating a visual indicator based on the assigned access right information;
displaying the visual indicator on an interface of the secondary load management system;
enabling user to request reassignment of the assigned access right;
receiving a query for the plurality of assigned access rights available for reassignment process that satisfy a particular constraint, wherein the query is received from an access-right requestor device;
generating a signal on initiating reassignment process by the access-right requestor device on receiving the query;
initiating, by the secondary load management system, reassignment of the plurality of assigned access rights in response to the signal;
receiving, by a contextual bandit learner, the signal indicating that the reassignment process has been initiated, wherein the contextual bandit learner enables real-time collection of attributes from the plurality of assigned access rights;
generating a user vector by the contextual bandit learner to represent a contextual information of the access-right requestor device;
inputting the user vector into a contextual machine-learning model associated with the contextual bandit learner;
generating, by the contextual machine-learning model, an output corresponding to a prediction of a value component of the reassignment value condition for the assigned access right;
calculating the reassignment value condition by a value server; and
transferring the assigned access right from the access-right holder to an access-right requestor associated with the access-right requestor device based on the reassignment value condition with a digital access-enabling code associated with the assigned access right.
16. The computer-program product, as recited in claim 15 , wherein the secondary load management system processes a request for reassigning the assigned access right from one user to another user.
17. The computer-program product, as recited in claim 15 , wherein the visual indicator is a post on the interface associated with the plurality of assigned access rights.
18. The computer-program product, as recited in claim 15 , wherein the contextual machine-learning model includes the constraint on values selectable for the value component of the reassignment value condition.
19. The computer-program product, as recited in claim 15 , wherein the constraint is determined based on real-time data collected from the reassignment of the plurality of assigned access rights, wherein the reassignment of a plurality of access rights request, that are assigned, is received from the user.
20. The computer-program product, as recited in claim 15 , wherein the contextual machine-learning model is generated using one or more contextual multi-armed bandit algorithms.