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: 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: 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: 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: 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: 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 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: 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: 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: 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.

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: Systems and methods are disclosed herein for building contextual transcripts. A computing system may receive a textual transcript of a meeting that contains a variety of statements made by various attendees of the meeting, select the first statement made during the meeting, and determine which meeting attendee made the statement. A machine learning model corresponding to the particular attendee that has been trained using previously received statements by the particular attendee may be used on the utterance to determine the tone of the utterance. That tone may be recorded within the transcript and this process may be repeated for each utterance to build a contextual transcript.

Abstract: Systems as described herein may determine a plurality of content items posted to a mesh network and each content item may be associated with a signature stored in a blockchain. A search engine may query each content item based on the corresponding signature in the blockchain. The search engine may parse each content item to obtain a label and store the label, the signature and content associated with each content item in a database. The search engine may query the blockchain to obtain a frequency that each content item has been queried in a predetermined period of time. The search engine may rank the content items based on the frequencies, and determine a subset of the content items as frequently searched content. Accordingly, the search engine may distribute the frequently searched content to a plurality of cached nodes in the mesh network.

Abstract: An enterprise platform may retrieve, from a collaboration data store, information about a selected enterprise team. The enterprise platform may then transmit, from a set of integration tools to a workflow tool of a remote cloud-based environment, a data payload including a notification request associated with a channel. The enterprise platform may also transmit, from a runtime environment to a business communication platform of the cloud-based environment, a request to post a notification to the channel and arrange for a member of the selected enterprise team to receive an adaptive information card, pushed by the business communication platform via the channel, containing information associated with at least one of the integration tools. The member who receives the adaptive information card may also, via selection of a graphical icon on the card, respond to the business communication platform via bi-directional communication.

Abstract: In some implementations, a device may train the model to generate embeddings for log files associated with an application, and to enable the model to generate embeddings for sensitive information included in a set of training log files. The device may receive a log file associated with the application. The device may generate a compressed log file including a set of embedding vectors associated with records included in the log file, where a record that includes sensitive information is associated with one or more embedding vectors for the sensitive information and one or more embedding vectors for other information included in the record. The device may store the compressed log file including the set of embedding vectors where a size of the compressed file is less than a size of the log file, and the embedding vectors obfuscate the records included in the log file.

Abstract: A computer-implemented method may include: receiving financial information regarding a user; categorizing transaction information of the user based on the financial information; displaying the categorized transaction information of the user; receiving information regarding at least one financial preference and at least one transaction preference of the user; training a machine learning engine based on the at least one financial preference and at least one transaction preference of the user to determine one or more activities available to the user; calculating, for each of the one or more activities available to the user, an estimated influence on the at least one financial preference; displaying the estimated influence on the at least one financial preference based on a user selected one of the one or more activities available to the user; filtering the one or more activities available to the user with a positive estimated influence to the at least one financial preference; and presenting a recommendation of