Abstract: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

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: Communications over short-range connections are used to facilitate whether access to resources is to be granted. For example, upon device discovery of one of an electronic user device and an electronic client device by the other device over a Bluetooth Low Energy connection, an access-enabling code associated with a user device or account can be evaluated for validity and applicability with respect to one or more particular resource specifications. User identity can be verified by comparing the user against previously obtained biometric information.

Abstract: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

Abstract: Communications over short-range connections are used to facilitate whether access to resources is to be granted. For example, upon device discovery of one of an electronic user device and an electronic client device by the other device over a Bluetooth Low Energy connection, an access-enabling code associated with a user device or account can be evaluated for validity and applicability with respect to one or more particular resource specifications. User identity can be verified by comparing the user against previously obtained biometric 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: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

Abstract: Communications over short-range connections are used to facilitate whether access to resources is to be granted. For example, upon device discovery of one of an electronic user device and an electronic client device by the other device over a Bluetooth Low Energy connection, an access-enabling code associated with a user device or account can be evaluated for validity and applicability with respect to one or more particular resource specifications. User identity can be verified by comparing the user against previously obtained biometric 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: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

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: 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: Communications over short-range connections are used to facilitate whether access to resources is to be granted. For example, upon device discovery of one of an electronic user device and an electronic client device by the other device over a Bluetooth Low Energy connection, an access-enabling code associated with a user device or account can be evaluated for validity and applicability with respect to one or more particular resource specifications. User identity can be verified by comparing the user against previously obtained biometric information.

Abstract: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

Abstract: Communications over short-range connections are used to facilitate whether access to resources is to be granted. For example, upon device discovery of one of an electronic user device and an electronic client device by the other device over a Bluetooth Low Energy connection, an access-enabling code associated with a user device or account can be evaluated for validity and applicability with respect to one or more particular resource specifications. User identity can be verified by comparing the user against previously obtained biometric 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: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

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: Authentication systems and methods can selectively authenticate a request to access a resource data store storing access rights associated with a user device. The systems and methods can scalably execute challenges workflows as part of the authentication process. For example, a request to access one or more access rights stored in the data store can be received from the user device. The user device can be authenticated using challenge workflows selected based on a device identifier of the user device. The selected challenge workflows can be executed to determine whether or not to grant access to the access rights stored in the resource data store.

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.