Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Disclosed embodiments pertain to filtering application notifications from one or more applications on an electronic device. A request can be received from an application installed on the electronic device to present a notification. In response, a machine learning model is invoked that is trained to generate a relevancy score for a notification based on historical behavior of the user with respect to previous notifications from the application as well as the active context of the user. The relevancy score can be compared with a threshold to determine whether the threshold is satisfied. Notification filtering can be performed based on whether or not the threshold is satisfied. The method can filter out the notification from presentation if the threshold is satisfied and present the notification if the threshold is unsatisfied, or vice versa.

Abstract: Disclosed embodiments pertain to filtering application notifications from one or more applications on an electronic device. A request can be received from an application installed on the electronic device to present a notification. In response, a machine learning model is invoked that is trained to generate a relevancy score for a notification based on historical behavior of the user with respect to previous notifications from the application as well as the active context of the user. The relevancy score can be compared with a threshold to determine whether the threshold is satisfied. Notification filtering can be performed based on whether or not the threshold is satisfied. The method can filter out the notification from presentation if the threshold is satisfied and present the notification if the threshold is unsatisfied, or vice versa.

Abstract: A computing device may determine that a condition exists with respect to a user of an application. Based on the determined condition, the computing device may determine an action for the user to perform via the application, and an associated importance level of the action. The computing device may identify a user interface element of the application that is associated with the action and a color that is associated with the importance level. The computing device may change the appearance of the identified user interface element such that the user interface element has the color that is associated with the importance level of the action.

Abstract: In some implementations, a device of a user may receive user input indicative of a selection of an application implemented by the device. The user input may be received via a graphical user interface presented by the device. The user input may be received while the application is in an unlaunched state in which the user is unauthenticated for use of the application. The device may obtain, while the application is in the unlaunched state, one or more messages of the application. The device may cause, while the application is in the unlaunched state, information indicative of at least one message, of the one or more messages, to be displayed in the graphical user interface.

Abstract: Disclosed are methods and systems for inactivity-based prompting of task completion. For instance, a task related to an account may be identified as an incomplete task along with a task status related to the task as incomplete. The task and task status may be stored in a data store in association with an account identifier. In response to receiving a session inactivity indication for an account session established via an application executing on a user computing device, the data store may be queried using the account identifier to determine the task status for the task. The task status may be determined as remaining incomplete, and a notification indicating the incompletion may be generated. Additionally, a communication channel may be determined for transmission of the notification that increases a likelihood of notification engagement to prompt completion of the task. The notification may be transmitted via the communication channel.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: In some implementations, a device of a user may receive user input indicative of a selection of an application implemented by the device. The user input may be received via a graphical user interface presented by the device. The user input may be received while the application is in an unlaunched state in which the user is unauthenticated for use of the application. The device may obtain, while the application is in the unlaunched state, one or more messages of the application. The device may cause, while the application is in the unlaunched state, information indicative of at least one message, of the one or more messages, to be displayed in the graphical user interface.

Abstract: In some implementations, a trend prediction system may identify, using a machine learning model, one or more consumers having a historical tendency to adopt one or more trends near a beginning of one or more trend adoption curves. The trend prediction system may predict, using the machine learning model, a consumer trend near a beginning of a trend adoption curve based on a subset of consumer data associated with the one or more consumers having the historical tendency to adopt the trends near the beginning of the trend adoption curves. The trend prediction system may determine, based on the consumer trend that is near the beginning of the trend adoption curve, local trend information in an area associated with a client. The trend prediction system may provide, to a device associated with the client, the local trend information.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: The innovation disclosed and claimed herein, in one aspect thereof, comprises systems and methods of keystone activity based suggestions. The innovation detects a keystone activity of a user. The keystone activity is a planned event for the user such as recently purchased tickets to a specific show. Customer data of a financial institution is accessed where the customer data includes data of customers of the financial institution. A set of similar customers to the user is determined. Transaction data of the set of similar customers is determined and analyzed for likelihood of the user wanting to attend a secondary activity that is similar the set of similar customers. The secondary activity can be automatically scheduled for the user based on the keystone activity and the transaction data.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: A computing device may determine that a condition exists with respect to a user of an application. Based on the determined condition, the computing device may determine an action for the user to perform via the application, and an associated importance level of the action. The computing device may identify a user interface element of the application that is associated with the action and a color that is associated with the importance level. The computing device may change the appearance of the identified user interface element such that the user interface element has the color that is associated with the importance level of the action.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of a customized price rating for the merchant that is specific to the user, and then to generate information about the merchant for display that includes the customized price rating.

Abstract: In some implementations, a system may identify, using a machine learning model, a pattern of events based on account information associated with a user. The system may generate a prediction of a time period during which a predicted event, associated with the pattern of events, is predicted to occur. The system may receive an indication that a vehicle, associated with the user, is to travel a route from a starting location to an ending location during the time period. The system may identify a physical location associated with the predicted event based on a location associated with the vehicle or based on the route. The system may generate a new route that includes the starting location, the physical location as a waypoint along the new route, and the ending location. The system may transmit information that identifies the new route to a device associated with the vehicle.

Abstract: Aspects described herein may allow for generating a customized price rating using a machine learning algorithm. This may have the effect of improving the display of information about merchants by including customized, personalized price ratings that better reflect the tastes and preferences of a user or group of users. According to some aspects, these and other benefits may be achieved by using a machine learning model, trained to receive input corresponding to both user data and merchant data and output an indication of customized price rating(s) for the merchant that is specific to one or more users, and then to generate information about the merchant for display that includes the customized price rating(s).