Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods for identifying data labels for submitting to additional data labeling routines based on embedding clusters are described. For example, the system may retrieve an unlabeled dataset, wherein the unlabeled dataset comprises a plurality of unlabeled samples. The system may generate a plurality of embeddings based on the unlabeled dataset, wherein the plurality of embeddings comprises a respective embedding for each unlabeled sample in the plurality of unlabeled samples. The system may cluster the plurality of embeddings into a plurality of clusters. The system may, based on the first cluster, determine a first labeled sample of the first labeled dataset corresponding to the first cluster and determine to submit the first labeled sample to a second data labeling routine. The system may generate, using the second data labeling routine, a second labeled sample based on the first labeled sample.

Abstract: Systems and methods for providing variable and temporary access to account content for a user in a secured manner through the use of an access token with variable properties are described. The systems and methods provide improved navigability to account content accessed via the access token through the customization of user interfaces. For example, the system and methods may generate user interface templates that comprise a recommended selection and organization of user input fields and/or user interface pages.

Abstract: Systems and methods for detecting unauthorized access are disclosed herein. In some aspects, the system receives a combined activity dataset. The system updates a base breach detection model based on the combined activity dataset to generate a combined breach detection model. The system duplicates the combined breach detection model to generate a first breach detection model for a first user. The system receives a first activity dataset for the first user and trains a labeling model to associate activities from the first activity dataset with the first user. The system processes the combined activity dataset using the labeling model to associate activities from a portion of the combined activity dataset with the first user. The system updates the base breach detection model based on activities from the first activity dataset and the portion of the combined activity dataset to generate a second breach detection model to detect breach activity.

Abstract: A method includes obtaining a first dataset associated with a first set of classes and a second dataset associated with a second set of classes, where the first and second datasets share features. The method also includes creating an aggregated dataset having a quality score for the second set of classes that satisfy a first threshold. Creating the aggregated dataset includes determining a first and second set of clusters based on the first and second datasets. The method includes determining a distance based on a record of the first set of clusters and a second record of the second set of clusters in a feature space. The method further includes determining a set of analysis scores based on the distance and generating an indication for a set of records associated with the set of analysis scores based on the set of analysis scores satisfying a second threshold.

Abstract: Systems and methods for providing variable and temporary access to account content for a user in a secured manner through the use of an access token with variable properties are described. The systems and methods provide improved navigability to account content accessed via the access token through the customization of user interfaces. For example, the system and methods may generate user interface templates that comprise a recommended selection and organization of user input fields and/or user interface pages.

Abstract: Systems and methods for a predictive cache of machine learning models. In some aspects, the system, in response to a user request, trains one or more machine learning models using a training dataset and inserts the machine learning models into a user library. The system, without input from the user, based on a distribution of values for a first feature in the training dataset, generates a search request for labeled data having a distribution of values within a threshold of the distribution of values of the first feature. The system adds to the training dataset values from the labeled data to generate an updated training dataset. The system trains a proximate machine learning model based on the updated training dataset and inserts the proximate machine learning model into a predictive cache of machine learning models.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Methods and systems for reducing network traffic between a service and client devices. In some aspects, the system receives a first data stream for a first type of communication between a service and a client device for a user. In response to determining that the first data stream includes an unresolved user query, the system determines that a second type of communication occurred between the service and the user. The system processes a second data stream for the second type of communication to determine that the second type of communication includes the unresolved user query and a service response to the unresolved user query. The system provides the unresolved user query and the service response to update a machine learning model used by the service to generate service responses to one or more user queries during a future communication of the first type.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Methods and systems for reducing network traffic between a service and client devices. In some aspects, the system receives a first data stream for a first type of communication between a service and a client device for a user. In response to determining that the first data stream includes an unresolved user query, the system determines that a second type of communication occurred between the service and the user. The system processes a second data stream for the second type of communication to determine that the second type of communication includes the unresolved user query and a service response to the unresolved user query. The system provides the unresolved user query and the service response to update a machine learning model used by the service to generate service responses to one or more user queries during a future communication of the first type.

Abstract: Methods and systems for reducing network traffic between a service and client devices. In some aspects, the system receives a first data stream for a first type of communication between a service and a client device for a user. In response to determining that the first data stream includes an unresolved user query, the system determines that a second type of communication occurred between the service and the user. The system processes a second data stream for the second type of communication to determine that the second type of communication includes the unresolved user query and a service response to the unresolved user query. The system provides the unresolved user query and the service response to update a machine learning model used by the service to generate service responses to one or more user queries during a future communication of the first type.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Embodiments relate to enhancing data labeling for machine learning model training using context data. The context data is provided to data labelers to improve accuracy of labels assigned by the data labelers, such as those assigned for ambiguous or unclear target objects. In an example, a system determines and provides context sets for target objects at user devices. The system generates a first training dataset and a second training dataset with labels obtained in connection with a first subset and a second subset of the context sets, respectively. The system trains a first and second instance of a machine learning model on the first training dataset and the second training dataset, respectively, and determines a respective accuracy score for the model instances. If the first instance is more accurate than the second instance, the system generates subsequent context sets based on characteristics of the first subset of context sets.

Abstract: The invention relates to generating cryptographic tokens based on received records. The system may receive a blockchain operation request comprising a plurality of records, encrypt a plurality of records into an encrypted payload, generate a cryptographic token based on the encrypted payload, and cause the cryptographic token to be committed to a blockchain.

Abstract: The invention relates to generating cryptographic tokens based on characteristic determination. The system may generate and transmit a request for encrypted records from a blockchain node, receive the encrypted records from the blockchain node, decrypt the encrypted records into payload identifiers, retrieve sets of item identifiers based on the payload identifiers, determine characteristics associated with the sets of items, and generate a new cryptographic token that indicates the characteristics.

Abstract: A method includes obtaining a first dataset associated with a first set of classes and a second dataset associated with a second set of classes, where the first and second datasets share features. The method also includes creating an aggregated dataset having a quality score for the second set of classes that satisfy a first threshold. Creating the aggregated dataset includes determining a first and second set of clusters based on the first and second datasets. The method includes determining a distance based on a record of the first set of clusters and a second record of the second set of clusters in a feature space. The method further includes determining a set of analysis scores based on the distance and generating an indication for a set of records associated with the set of analysis scores based on the set of analysis scores satisfying a second threshold.