Abstract: A method for generating a cluster-based machine learning model based on federated learning with cluster feedback includes providing a current machine learning model to a plurality of user devices that train the current machine learning model, receiving respective model states, generating updated model states, causing the plurality of user devices to obtain a respective instance of an updated machine learning model based on the updated model states, receiving an applicability feedback for the updated machine learning model for each of the plurality of user devices, determining a plurality of user clusters including a subset of the plurality of user devices, identifying a first user cluster and a second user cluster, the first user cluster having a higher cluster applicability feedback than the second user cluster, receiving the additional model states from the clusters and updating the updated machine learning model to generate the cluster-based machine learning model.

Abstract: Disclosed are methods and systems for determining the likelihood that a vehicle is under autonomous control. For instance, a method may include receiving a request for an autonomous operation score of the vehicle; obtaining one or more permissions to access observation data from one or more sources; and generating a vehicle observation profile based on the observation data from the one or more sources. The method may further include determining whether the vehicle observation profile is indicative of one or more reference patterns; generating an autonomous control score based on the determination that the vehicle observation profile is indicative of the one or more reference patterns; and providing the autonomous operation score to a user.

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: Disclosed herein are system, method, and computer program product embodiments for generating non-fungible tokens (NFTs) representing digital assets. In various embodiments, a server generates a first NFT comprising one or more hash values. Each hash value of the one or more hash values corresponds with at least one cryptographically verifiable asset of one or more cryptographically verifiable assets. The server further receives a first request to obtain a first portion of the first NFT for a user and determines a first value of the first NFT based on a crypto-value of each of the one or more cryptographically verifiable assets. The server generates a second NFT comprising a first NFT hash value, wherein a second value of the second NFT matches the first portion of the first value and issues the second NFT to the user.

Abstract: A user device may obtain sensor data related to a physical environment. The user device may process the sensor data to determine that a potential threat exists in the physical environment and a threat score associated with the potential threat. The user device may determine that the threat score satisfies a threat score threshold. The user device may obtain, based on determining that the threat score satisfies the threat score threshold, additional information related to the physical environment. The user device may generate, based on the additional information related to the physical environment and at least one of the sensor data, information indicating the potential threat in the physical environment, or the threat score associated with the potential threat, threat determination information associated with the physical environment. The user device may perform, based on the threat determination information associated with the physical environment, one or more actions.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to orient webpage content based on user attention. The system may determine a user has navigated to a webpage. The system may generate an attention level based on received user feature(s) associated with activity of the user on the webpage. Responsive to determining the attention level exceeds a predetermined threshold, the system may identify a duration associated with the attention level, transmit an offer to one or more entities to submit predetermined content based on the duration, receive predetermined content from the one or more entities, identify, based on a predetermined amount of predetermined content space, selected content of the received predetermined content for placement on the webpage, and cause a GUI of the user device to display the selected content.

Abstract: A system may receive user interface information that indicates an image, associated with a web page, that depicts an object for which a counterfeit estimation is to be determined, text associated with the web page, or an entity identifier that identifies an entity associated with the web page and the object. The system may determine a first estimation that the object is counterfeit based on performing an image analysis of the image, a second estimation that the object is counterfeit based on performing text analysis of the text, or a third estimation that the object is counterfeit based on performing an entity analysis of the entity. The system may determine the counterfeit estimation based on the first estimation, the second estimation, or the third estimation. The counterfeit estimation may indicate a likelihood that the object is counterfeit. The system may transmit information that identifies the counterfeit estimation.

Abstract: Systems and methods for architecture embeddings for efficient dynamic synthetic data generation are disclosed. The disclosed systems and methods may include a system for generating synthetic data configured to perform operations. The operations may include retrieving a set of rules associated with a first data profile and generating, by executing a hyperparameter search, a plurality of hyperparameter sets for generative adversarial networks (GANs) that satisfy the set of rules. The operations may include generating mappings between the hyperparameter sets and the first data profile and storing the mappings in a hyperparameter library. The operations may include receiving a request for synthetic data, the request indicating a second data profile and selecting, from the mappings in the hyperparameter library, a hyperparameter set mapped to the second data profile. The operations may include building a GAN using the selected hyperparameter set and generating, using the GAN, a synthetic data set.

Abstract: Disclosed embodiments may include a method and system for automated incremental payments. The system may identify recurring charges from historical account data. Based on the recurring charges and an incremental period, the system may calculate an incremental amount and expected amount. At each iteration of the incremental period, the incremental amount may be assigned to a savings bucket. The value of the savings bucket may be subtracted from an actual account balance to calculate a reduced account balance. The system may generate and transmit a graphical user interface to a user device showing the reduced account balance. The system may receive current data containing a charge that corresponds to the recurring charges. The system may reduce the value of the savings bucket by the amount of the current data charge. If the current data charge is different from the expected amount, the system may adjust the incremental amount accordingly.

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: In some implementations, a user device may determine that the user device was granted access to a first set of resources by a first authentication process. The user device may receive a request to access a second set of resources. The user device may generate, based on the request, a token that indicates that the user device was granted access to the first set of resources by the first authentication process. The user device may transmit, based on the request, the token to a server device to cause a second authentication process to be performed on the token. The user device may receive, from the server device, access information indicating whether access to the second set of resources is granted or denied based on transmitting the token to the server device.

Abstract: Methods and systems are described herein for improvements to authenticate users, particularly authenticating a user based on data known to the user. For example, methods and systems allow for users to be securely authenticated based on data known to the users over remote communication networks without storing the data known to the users. Specifically, methods and systems authenticate users by requiring users to select images that are known to the users. For example, the methods and systems may generate synthetic images based on the user's own images and require the user to select the synthetic image, from a set of a set of images, that is known to the user to authenticate the user. Moreover, the methods and systems alleviate storage and privacy concerns by not storing the data known to the users.

Abstract: A system may include processor(s), and memory in communication with the processor(s) and storing instructions configured to cause the system to orient webpage content based on user attention. The system may determine a user has navigated to a webpage. The system may generate an attention level based on received user feature(s) associated with activity of the user on the webpage. Responsive to determining the attention level exceeds a predetermined threshold, the system may identify a duration associated with the attention level, transmit an offer to one or more entities to submit predetermined content based on the duration, receive predetermined content from the one or more entities, identify, based on a predetermined amount of predetermined content space, selected content of the received predetermined content for placement on the webpage, and cause a GUI of the user device to display the selected content.

Abstract: Methods and systems are described herein for improvements to authenticate users, particularly authenticating a user based on data known to the user. For example, methods and systems allow for users to be securely authenticated based on data known to the users over remote communication networks without storing the data known to the users. Specifically, methods and systems authenticate users by requiring users to select images that are known to the users. For example, the methods and systems may generate synthetic images based on the user's own images and require the user to select the synthetic image, from a set of a set of images, that is known to the user to authenticate the user. Moreover, the methods and systems alleviate storage and privacy concerns by not storing the data known to the users.