Abstract: A system comprises a network and a message management system. The message management system can apply a predictive model to generate a broadcast event message for transmission over the network to solicit message requests for entry into a bidding process. The bidding process can be associated with an event in the region and a bidding timeframe. The message management system can receive message requests for the event during the bidding timeframe, and select one of the message requests from a selected requestor after the expiration of the bidding timeframe. The message management system can receive an event message from the selected requestor. The message management system can present the event message via the communication service. The event message can be provided at an access point for the Wi-Fi service.

Abstract: Systems and methods for synthetic data generation. A system includes at least one processor and a storage medium storing instructions that, when executed by the one or more processors, cause the at least one processor to perform operations including receiving a continuous data stream from an outside source, processing the continuous data stream in real-time, and using machine learning techniques to generating synthetic data to populate the dataset. The operations also include creating a plurality of bins, wherein the plurality of bins occupy a data range between the determined minimum and maximum values without overlapping; and determining a number of samples within each of the created bin, based on a bin edges, wherein the bin edges are bounds within the data range.

Abstract: A method of triggering an event based on occurrence of a POS transaction is provided in which an event processing server receives from a first user device associated with a first account, a first event triggering request. The request includes identification of a desired event to be triggered and first triggering criteria. The desired event is to be initiated by the event processing server upon occurrence of one or more POS transactions collectively meeting the first triggering criteria. The event processing server receives, from a transaction processing server, notification of an occurrence of a merchant POS transaction associated with the first account. The event processing server determines whether the occurrence of the merchant POS transaction results in the first triggering criteria having been met, and, if so initiates the desired event.

Abstract: A device receives information indicating first names and last names of individuals and applies different cursive fonts to each of the first names and the last names to generate images of different cursive first names and different cursive last names. The device applies different transformations to the images of the different cursive first names and the different cursive last names to generate a set of first name images and a set of last name images. The device combines each first name image with each last name image to form a set of signature images and trains a neural network model, with the set of signature images, to generate a trained neural network model. The device receives an image of a signature and processes the image of the signature, with the trained neural network model, to recognize a first name and a last name in the signature.

Abstract: Systems and methods for automatically profiling data a user selects to transfer to a paste area are described. Data may be automatically profiled the at the user selected target paste area to determine if sets of data are of the same data type. There may be a clarification for a target paste area or for identifying the data type. Additionally, there may be reformatting the selected data set to match the target data's format. Machine learning may trigger formatting or prompting actions according to one or more predetermined thresholds.

Abstract: Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Abstract: In some implementations, a device may receive an activation signal transmitted by an activator device. The device may determine that a first signal pattern of the activation signal matches a second signal pattern associated with activating the Bluetooth capability of the device. The device may activate the Bluetooth capability of the device based on determining that the first signal pattern matches the second signal pattern. The device may communicate with a wireless communication device using the Bluetooth capability of the device based on activating the Bluetooth capability of the device.

Abstract: Aspects described herein may relate to systems and methods for automatically adjusting user device settings when a user attends an event or otherwise brings a user device into a context in which an automatic adjustment of settings is appropriate. By providing for automatic adjusting of user device settings, the systems and methods may achieve greater compliance with rules and other policies of airlines, performance venues, schools, and/or other entities associated with events and/or contexts in which user device settings should be adjusted. Aspects described herein may allow for automatic adjustment of user device settings without compromising a user's privacy, security, or control of a user device.

Abstract: Systems and methods for connecting datasets are disclosed. For example, a system may include a memory unit storing instructions and a processor configured to execute the instructions to perform operations. The operations may include receiving a plurality of datasets and a request to identify a cluster of connected datasets among the received plurality of datasets. The operations may include selecting a dataset. In some embodiments, the operations include identifying a data schema of the selected dataset and determining a statistical metric of the selected dataset. The operations may include identifying foreign key scores. The operations may include generating a plurality of edges between the datasets based on the foreign key scores, the data schema, and the statistical metric. The operations may include segmenting and returning datasets based on the plurality of edges.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that provide new capabilities for recommending purchases to a user based on the user's past purchasing history and the purchase history of others. A new descriptor referred to as “purchase embeddings” is disclosed, which are data records in a new multi-dimensional space for describing and tracking purchases of goods and services.

Abstract: The disclosed systems and methods may receive a first salted password having a first password, a first user device identifier, and a first browser identifier, extract the first password, the first user device identifier, and the first browser identifier from the first salted password, and determine whether the first password, the first user device identifier, and the first browser identifier respectively match a stored first password, a stored first user device identifier, and a stored first browser identifier. The systems may grant the request to access the one or more resources for the first user device or perform other actions depending on whether the first password, the first user device identifier, and the first browser identifier respectively match the stored first password, the stored first user device identifier, and the stored first browser identifier.

Abstract: An exemplary system, method, and computer-accessible medium can include, for example, receiving an original dataset(s), receiving a synthetic dataset(s), training a model(s) using the original dataset(s) and the synthetic dataset(s), and evaluating the synthetic dataset(s) based on the training of the model(s). The model(s) can include a first model and a second model, and the first model can be trained using the original dataset(s) and the second model can be trained using the synthetic dataset(s). The synthetic dataset(s) can be evaluated by comparing first results from the training of the first model to second results from the training of the second model.

Abstract: Example embodiments of biodegradable cards and systems and methods making the same are provided. A biodegradable card can comprise a biodegradable substrate, a magnetic particle slot within the biodegradable substrate configured to receive magnetic particles, a high coercivity magnetic stripe comprising magnetic particles printed on the biodegradable substrate and encoded with payment account data, and a biodegradable water resistant coating, wherein the coating covers at least the magnetic stripe.

Abstract: Disclosed embodiments include systems and methods for providing a secure physical storage system. The system may receive a request to register a first physical storage box from an entity including location information and security information. The request can be validated by comparing the security information to a predetermined security threshold. The location information associated with the first physical storage box can be recorded to a blockchain. The first registration information can be received from a first user device. The first physical storage box can be assigned to the first user device and a first identity token associated with the first physical storage box. The assignment information associated with the first physical storage box can be recorded to the blockchain. A first identity token can be received from the first user device. Instructions can be transmitted to the first physical storage box to transition from a locked to an unlocked state.

Abstract: Systems as described herein may include detecting live browser information that a user navigates to a first website displayed in a first open browser tab and a second website displayed in a second open browser tab. A data sharing server may provide the live browser information to a machine learning model as input. Based on feedback from the machine learning model, one or more similar products displayed in the first website and the second website may be determined. The data sharing server may detect an update on the one or more similar products, and cause a user device to display an alert indicating the update on the similar products.

Abstract: Systems and methods for generating real-time location-based notifications are described. In some aspects, the system processes, using a first machine learning model, a real-time video stream to determine a measure of significance of an event depicted in the real-time video stream. The system determines whether the user was present at the event. Based on determining that the user was present at the event, the system processes a user profile for the user and the measure of significance of the event to determine whether to generate for the user a cryptographic token associated with the event. Based on determining to generate for the user the cryptographic token associated with the event, the system generates a notification to the user including information for accessing the cryptographic token for the user.

Abstract: The invention relates to executing an action for a cryptographic storage application by comparing a movement dataset, which may contain positional and inertial datasets, with a user profile dataset. The system may receive a first cryptographic storage application address, retrieve a first movement dataset corresponding to a first time period, retrieve a user profile dataset, compare the first movement dataset with the user profile dataset, and execute an action for the first cryptographic storage application.

Abstract: The disclosed methods may receive a first request for access to a first system memory from a new user and a first justification, the first request includes first role information and first current access information of the new user, determine whether the first request is within a request cluster based on first role history information and current access history information. When the first request is within the request cluster, generate a matrix from the first role information and the first current access information, and determine using a first neural network, whether to grant the first request based on the matrix. When the first request is granted by the first neural network, determine, using a second neural network, whether the first justification is similar to first justification history information. When the first justification is similar to the first justification history information, grant the new user access to the first system memory.

Abstract: A method includes storing, by one or more processors of one or more computing devices, a plurality of datasets in a non-transitory computer memory associated with the one or more computing devices. A plurality of index representations is generated where each one of the plurality of index representations includes a compressed representation of a respective one of the plurality of datasets. The plurality of index representations is stored in the non-transitory computer memory. A sample dataset is received by the one or more processors. A sample dataset representation is generated that includes a compressed representation of the sample dataset. A determination that at least one of the plurality of datasets is most similar to the sample dataset based on the sample dataset representation and the plurality of index representations is performed.

Abstract: The present disclosure provides systems for encoding transforms based on intent to be useable by developers. The system receives input datasets, generates output datasets, and identifies characteristics of the input or output datasets. Using these characteristics, the system can encode transforms associated with the datasets based on intent. The system stores and/or associates the intent-encoded transforms in data profiles, so that the system enables the transforms to be searched, recommended, and/or combined.