Abstract: Disclosed embodiments may include a system for curating online vehicle reservations. The system may determine, from received vehicle data including first timing data and indicating a selected vehicle, that the selected vehicle is electric. The system may receive accommodation data including second timing data and indicating a selected accommodation. The system may determine, from comparing the first timing data to the second timing data, that the selected vehicle will likely require charging at the selected accommodation. The system may determine whether the accommodation data includes an indication of electric vehicle charging facilities. Responsive to determining that the selected accommodation does not provide the indication of electric vehicle charging facilities, the system may generate a first graphical user interface warning that no charging station is available for the selected vehicle at the selected accommodation and transmit the first graphical user interface to a user device for display.

Abstract: Disclosed embodiments may include a method for generating meal recommendations by receiving first data and user input data. The system may generate a food profile and estimate of ingredients based on the first data and the user input data. The system may generate, based on the estimate of ingredients, one or more cooking instructions or an output that the user does not have sufficient ingredients. If cooking instructions are generated, the system may generate and transmit a graphical user interface comprising the one or more cooking instructions based on the estimate of ingredients for display. If it is determined the user does not have sufficient ingredients, the system may generate and transmit a graphical user interface comprising one or more dining options for the user for display.

Abstract: Disclosed embodiments may include a method for generating meal recommendations by receiving first data and user input data. The system may generate a food profile and estimate of ingredients based on the first data and the user input data. The system may generate, based on the estimate of ingredients, one or more cooking instructions or an output that the user does not have sufficient ingredients. If cooking instructions are generated, the system may generate and transmit a graphical user interface comprising the one or more cooking instructions based on the estimate of ingredients for display. If it is determined the user does not have sufficient ingredients, the system may generate and transmit a graphical user interface comprising one or more dining options for the user for display.

Abstract: A method for identifying correlated users. The method may include: receiving connection data for each of one or more other users; determining a degree of connection of the one or more other users to the first user; receiving query information from one or more electronic devices associated with the first user; receiving authentication information for each of the one or more other users; determining a destination of the first user based on the query information; determining one or more other user destinations for the one or more other users; identifying one or more correlated other users from the one or more other users by comparing the destination of the first user with user destinations of the one or more other users and further based on the degree of connection of the one or more other users; and providing the identified one or more correlated other users to the first user.

Abstract: Disclosed embodiments may include a system for controlling smart home devices. The system may receive, from a router, a list of devices connected to a home network in a home. The system may determine, from the list of devices, a presence of one or more controllable smart devices, the one or more controllable smart devices operating on a schedule. The system may categorize the one or more controllable smart devices into function-based categories. The system may receive user data. The system may determine a duration of an absence of a user in the home based on the user data. The system may change the schedule of the one or more controllable smart devices based on the function-based category and the duration of the absence of the user.

Abstract: In some implementations, a device may obtain a first image of a first note, and identify a first identifier associated with the first note and a first set of visual characteristics of the first note indicating an appearance of the first note. The device may obtain a second image of a second note, and identify a second identifier associated with the second note and a second set of visual characteristics of the second note indicating an appearance of the second note. The second identifier may correspond to the first identifier, indicating that the second note is purported to be the first note. The device may determine whether the second note is counterfeit based on the first set of visual characteristics of the first note and the second set of visual characteristics of the second note. The device may perform action(s) based on a determination that the second note is counterfeit.

Abstract: In some implementations, a device may determine, using a machine learning model and based on interaction data relating to interactions between a plurality of entities and a user, a machine learning prediction of a behavior of the user at a location. The device may determine a distance and a transportation mode for each of one or more predicted locations associated with the behavior of the user and a lodging unit of the location. The distance and the transportation mode may indicate a transportation amount. The device may transmit, to a user device of the user, information indicating a total amount associated with the lodging unit. The total amount includes a lodging amount associated with the lodging unit and transportation amounts for the one or more predicted locations.

Abstract: Disclosed embodiments may include a system for curating online vehicle reservations. The system may determine, from received vehicle data including first timing data and indicating a selected vehicle, that the selected vehicle is electric. The system may receive accommodation data including second timing data and indicating a selected accommodation. The system may determine, from comparing the first timing data to the second timing data, that the selected vehicle will likely require charging at the selected accommodation. The system may determine whether the accommodation data includes an indication of electric vehicle charging facilities. Responsive to determining that the selected accommodation does not provide the indication of electric vehicle charging facilities, the system may generate a first graphical user interface warning that no charging station is available for the selected vehicle at the selected accommodation and transmit the first graphical user interface to a user device for display.

Abstract: Disclosed embodiments may include a system configured to perform digital image analysis. The system may receive transaction data and image data associated with a user. The system may identify, from the transaction data, first travel feature(s). The system may identify, from the image data via computer vision, second travel feature(s). The system may train a machine learning model (MLM) to generate trip recommendation(s) for the user based on the first travel feature(s) and the second travel feature(s). The system may determine, via the trained MLM, whether at least a first trip recommendation of the trip recommendation(s) exceeds a predetermined threshold indicating a likelihood the user will be interested in the first trip recommendation. Responsive to determining the first trip recommendation exceeds the predetermined threshold, the system may provide the first trip recommendation to the user.