Abstract: 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.

Abstract: 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.

Abstract: The present disclosure generally relates to systems and methods that intelligently generate reassignment value conditions 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.

Abstract: Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.

Abstract: A method for transmitting ticket information to user devices based on interactions of the user devices with a ticket system, is disclosed. The method involves identifying interactions of a first user device and a second user device with the ticket system within a predefined time period. The interactions are associated with ticket requests for a resource. Interaction scores are generated and updated based on the number of interactions. Upon interaction with the first user device, a first interface containing first ticket information is transmitted within the predefined time period, determined by the first interaction score. Simultaneously, in response to the second user device interaction, a second interface with second ticket information is transmitted, determined by the second interaction score. Notably, the second ticket information includes a second quantity of available tickets for the resource, which is lesser than a first quantity in the first ticket information.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Systems and methods for facilitating shared access-right evaluation using linked communication channels are provided. A first communication can be received over a first communication link from a first user device, and a second communication can be received over a second communication link from a second user device. The first and second communications can include requests for the assignment of access rights. Map data can be generated and transmitted to each of the first and second user devices. Each user device can display a visual representation of access-right data. Further, a communication session can be facilitated between the first user device and the second user device. The communication session can be presented on the visual representation for each user device so that the first user and the second user can collaboratively evaluate access rights.

Abstract: The present disclosure generally relates to systems and methods that intelligently generate reassignment value conditions 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.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.

Abstract: A system and method for scheduling tasks associated with controlling access to databases. The system and method relate to scheduling tasks for data requesting systems that satisfy particular conditions. For example, data requesting systems that satisfy the conditions may have associated tasks stored in a queue during a first processing phase. Data requesting systems that do not satisfy the conditions may have associated tasks inhibited from being stored in the queue during the first processing phase, but these tasks may be stored in the queue during a later second processing phase. Tasks stored in the queue during the first processing phase may be processed before tasks stored in the queue during the second processing phase. For example, the tasks may correspond to accessing a database for querying data representing access rights to a resource.

Abstract: Systems and methods for facilitating shared access-right evaluation using linked communication channels are provided. A first communication can be received over a first communication link from a first user device, and a second communication can be received over a second communication link from a second user device. The first and second communications can include requests for the assignment of access rights. Map data can be generated and transmitted to each of the first and second user devices. Each user device can display a visual representation of access-right data. Further, a communication session can be facilitated between the first user device and the second user device. The communication session can be presented on the visual representation for each user device so that the first user and the second user can collaboratively evaluate access rights.

Abstract: Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.

Abstract: Systems and methods for facilitating shared access-right evaluation using linked communication channels are provided. A first communication can be received over a first communication link from a first user device, and a second communication can be received over a second communication link from a second user device. The first and second communications can include requests for the assignment of access rights. Map data can be generated and transmitted to each of the first and second user devices. Each user device can display a visual representation of access-right data. Further, a communication session can be facilitated between the first user device and the second user device. The communication session can be presented on the visual representation for each user device so that the first user and the second user can collaboratively evaluate access rights.

Abstract: A system and method for scheduling tasks associated with changing a personality of a ticketing interface. One or more processors generate interaction scores for each of the plurality of user devices based on receiving interactions between the ticketing engine and a plurality of user devices. The system further generate interaction patterns for each of the plurality of user devices that include a relation between the interaction scores generated for each of the plurality of user devices with the interactions from the plurality of user devices. The system further classify each of the plurality of user devices based on the generated interaction patterns to identify whether a user device from the plurality of user devices is a fraudulent or a non-fraudulent user device and modify interface of the ticketing engine based on the classification of each of the plurality of user devices.

Abstract: The present disclosure generally relates to systems and methods that intelligently generate reassignment value conditions 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.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: The present disclosure generally relates to systems and methods that intelligently generate reassignment value conditions 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.

Abstract: Certain aspects and features of the present disclosure relate to systems and methods that generate machine-learning models to predict whether user devices are likely to meet defined objectives. For example, a machine-learning model can be generated to predict whether or not a user device is likely to access a resource. In some implementations, a semi-supervised model can be used to determine to what extent user devices are predicted to satisfy the defined objective(s). For example, a resource-affinity parameter can be generated as a result of inputting various data points into a semi-supervised model. The various data points can be access from a plurality of data sources, and can represent one or more activities or attributes associated with a user. The value of the resource-affinity parameter can be evaluated to determine the extent to which the user is likely to meet an objective.

Abstract: Techniques herein attempt to provide actors with more flexible and satisfactory experiences regarding obtaining tickets for an event. A learning model may identify attributes indicative of whether a particular actor (e.g., attempting to purchase tickets to an event) possesses a desirable characteristic (e.g., is likely to attend the event). Each actor can then be evaluated to estimate whether she is a good actor (possesses the characteristic). If so, favored opportunities may be made available, such as the opportunity to buy high-demand tickets. An actor may further have the opportunity to hold or reserve tickets for a period time, during which other actors cannot purchase them. A fee for holding or reserving tickets (and/or maintaining the hold or reserve) can be dynamically set based on market factors. Opportunities to modify seat assignments to allow a group of friends to sit together may also be provided.