Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

Abstract: Aspects of the disclosure relate to automated pairing of customers and businesses. A computing platform may determine, based on historical user activity of a user, a pattern of the user activity, and may identify, based on the pattern of the user activity, an anticipated purchase activity of the user. Then, the computing platform may determine a sales offering by a vendor. Then, the computing platform may match the anticipated purchase activity with the sales offering. Then, the computing platform may retrieve user-defined preference rules associated with the anticipated purchase activity. Then, the computing platform may determine whether the preference rules apply to one or more attributes of the anticipated purchase activity. Subsequently, the computing platform may trigger, based on a determination that the preference rules apply to the one or more attributes of the anticipated purchase activity, an action associated with the anticipated purchase activity.

Abstract: Aspects of the disclosure relate to detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Abstract: Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

Abstract: Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

Abstract: Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

Abstract: Aspects of the disclosure relate to detecting usage issues on enterprise systems and dynamically providing user assistance. In some embodiments, a computing platform may receive, from an automated teller system, user presence information indicating that a user has been detected at an automated teller machine associated with the automated teller system. Subsequently, the computing platform may identify, based on the user presence information received from the automated teller system, that the user has a need for assistance with the automated teller machine associated with the automated teller system. In response to identifying that the user has the need for assistance, the computing platform may generate and send one or more commands directing the automated teller machine associated with the automated teller system to execute one or more automated assistance actions corresponding to the need for assistance.

Abstract: Aspects of the disclosure relate to a decision model to open and close enterprise facilities. A computing platform may receive one or more attributes associated with an operation of an enterprise facility. Subsequently, the computing platform may identify, based on the one or more attributes, a business operation rule for the enterprise facility. Then, the computing platform may determine, based on the one or more attributes and the business operation rule, an operational parameter for the enterprise facility. Then, the computing platform may compare the operational parameter to a threshold parameter, where the threshold parameter may be indicative of whether to keep the enterprise facility open for business. Then, the computing platform may, upon a determination that the operational parameter fails to exceed the threshold parameter, determine to temporarily close the enterprise facility. Then, the computing platform may redistribute user traffic to one or more open customer resource facilities.

Abstract: This application describes apparatus and methods for using edge-computing to control resource distribution among access channels, such as a retail banking center. Edge-nodes may be configured to move a product display in response to detected or expected customer traffic flow in or near a retail location. Edge-nodes may be configured to redirect resources provided by a cloud computing environment to or away from the retail location. Based on customer traffic flow, edge-nodes may direct customers/resources to a retail location and ensure the retail location provides a predetermined quality of service.

Abstract: According to certain embodiments, a wearable device comprises a memory operable to store menus comprising menu options, and a processor operably coupled to a network interface and the memory. The processor is configured to present a first menu comprising a first menu option associated with a data processing transaction available at a current location, determine an updated location, obtain a second menu comprising a second menu option associated with a data processing transaction available at the updated location, and present the second menu. The processor is further configured to receive user input indicating a selection of the second menu option and facilitate the data processing transaction available at the updated location based on the selection.

Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: According to certain embodiments, a wearable device comprises a memory operable to store menus comprising menu options, and a processor operably coupled to a network interface and the memory. The processor is configured to present a first menu comprising a first menu option associated with a data processing transaction available at a current location, determine an updated location, obtain a second menu comprising a second menu option associated with a data processing transaction available at the updated location, and present the second menu. The processor is further configured to receive user input indicating a selection of the second menu option and facilitate the data processing transaction available at the updated location based on the selection.

Abstract: Aspects of the disclosure relate to use of supervised, unsupervised, semi-supervised, or reinforcement-based machine learning algorithm(s) to perform pattern recognition and/or cluster detection on historical transactional data in order to predict events that may occur in the future and are candidates for automated delivery of items from a source to a customer in a contactless manner, via a secure delivery device, to minimize health risk(s) to the customer. The items may be securely delivered in an automated manner such as, for example, by use of a drone or autonomous vehicle, which may have advanced sensors to facilitate various aspects of the delivery. The items may be contained in a secure carrier coupled to the delivery device and may be unlocked locally or remotely using mechanical or digital means. Novel logical systems, architectures, machines, platforms, delivery devices and components thereof, and methods are disclosed.

Abstract: Systems for using cognitive automation techniques to generate resource recommendations are provided. In some examples, current resource data may be received. The current resource data may include current staffing data, skills associated with current staff, and the like. In some examples, environmental condition data may also be received. The environmental condition data may include current weather information, predicted weather information, power outage information, and the like. The resource data and environmental data may be analyzed using cognitive automation techniques to identify and/or generate one or more resource recommendations. The resource recommendations may include adjustments to staffing, modification of locations at which processes are performed, and the like. The identified or generated recommendations may be transmitted to one or more computing devices.

Abstract: Aspects of the disclosure relate to detecting and identifying devices at enterprise locations to protect enterprise-managed information and resources. In some embodiments, a computing platform may capture information identifying devices present at a first enterprise location during a malicious event. Then, the computing platform may generate alerts when one of the devices present at the first enterprise location during the malicious event is detected at a second enterprise location. In other embodiments, when such a device is detected at the second enterprise location, an enterprise center monitoring system may generate commands that cause an augmented reality device at the second enterprise location to present one or more augmented-reality user interfaces that include one or more augmented-reality elements identifying a user in possession of the detected device.

Abstract: Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.

Abstract: Aspects of the disclosure relate to monitoring devices at enterprise locations using machine-learning models to protect enterprise-managed information and resources. In some embodiments, a computing platform may receive, from one or more data source computer systems, passive monitoring data. Based on applying a machine-learning classification model to the passive monitoring data received from the one or more data source computer systems, the computing platform may determine to trigger a data capture process at an enterprise center. In response to determining to trigger the data capture process, the computing platform may initiate an active monitoring process to capture event data at the enterprise center. Thereafter, the computing platform may generate one or more alert messages based on the event data captured at the enterprise center. Then, the computing platform may send the one or more alert messages to one or more enterprise computer systems.