Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting data anomalies using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple tasks associated with at least one enterprise; detecting one or more data anomalies by predicting one or more values within the obtained data by processing at least a portion of the obtained data using one or more artificial intelligence techniques, and comparing the one or more predicted values to one or more corresponding portions of the obtained data; generating one or more data recommendations associated with at least a portion of the one or more detected data anomalies; and performing one or more automated actions based at least in part on at least one of the one or more detected data anomalies and the one or more generated data recommendations.

Abstract: Systems, methods and interfaces are provided for processing natural language queries. A machine learning-powered search interface may process complex natural language queries across specialized domains. The method may utilize a transformer-based natural language processing model that analyzes user intent by examining both current and historical user inputs. A graph data structure may be used to represent interconnected domains like patient records, insurance claims, and business metrics enables dynamic information retrieval. The system may use an A* search algorithm to navigate domain nodes and identify relevant answers. An abbreviation expansion mechanism resolves technical shorthand by referencing domain-specific mapping databases, ensuring precise interpretation of user queries. The method may identify keywords, traces optimal answer pathways, and adapt by updating graph relationships and abbreviation mappings.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: Methods, apparatus, and processor-readable storage media for security-related risk detection using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data related to at least one user associated with at least one interaction; identifying information pertaining to the at least one user by processing at least a portion of the obtained data using one or more artificial intelligence techniques; determining, based at least in part on at least a portion of the identified information, one or more security risks associated with the at least one user within a context of the at least one interaction; and performing one or more automated actions based at least in part on the one or more determined security risks.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: Methods, apparatus, and processor-readable storage media for determining configurable component parameters using machine learning techniques are provided herein. An example computer-implemented method includes forecasting demand data for at least one component in connection with one or more temporal periods by processing component-related data using one or more machine learning techniques; determining information pertaining to one or more modifications associated with the at least one component; determining, by processing at least a portion of the demand data and at least a portion of the information pertaining to the one or more modifications using at least one designated algorithm, one or more configurable component parameter values attributed to the at least one component and at least a portion of the one or more modifications; and performing one or more automated actions based at least in part on at least one of the one or more configurable component parameter values.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: Cryptographic systems, methods and communication network comprising thereof are disclosed, including numerous industry applications. Embodiments of the present invention can generate and regenerate the same symmetric key. The cryptographic systems and methods include a key generator configured to use two or more inputs to reproducibly generate the symmetric key and a cryptographic engine configured to use the symmetric key for encrypting and decrypting data.

Abstract: The present disclosure relates to systems and methods for identifying vehicle asset pairings.

Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting data anomalies using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple tasks associated with at least one enterprise; detecting one or more data anomalies by predicting one or more values within the obtained data by processing at least a portion of the obtained data using one or more artificial intelligence techniques, and comparing the one or more predicted values to one or more corresponding portions of the obtained data; generating one or more data recommendations associated with at least a portion of the one or more detected data anomalies; and performing one or more automated actions based at least in part on at least one of the one or more detected data anomalies and the one or more generated data recommendations.

Abstract: Methods, apparatus, and processor-readable storage media for resource forecasting using artificial intelligence techniques are provided herein.

Abstract: Methods, apparatus, and processor-readable storage media for dynamic action classification using machine learning techniques are provided herein. An example computer-implemented method includes generating at least one resource-related forecast by processing, using at least one regression model, resource-related data within at least one predetermined temporal period; converting at least a portion of the at least one resource-related forecast to at least one resource-related action forecast using one or more machine learning techniques in conjunction with one or more temporal lag values; classifying at least one resource-related action associated with at least a portion of the at least one predetermined temporal period by processing at least a portion of the at least one resource-related action forecast using at least one classification model; and performing one or more automated actions based at least in part on the at least one classified resource-related action.

Abstract: Methods, apparatus, and processor-readable storage media for resource-related forecasting using machine learning techniques are provided herein. An example computer-implemented method includes obtaining multiple items of data related to one or more resources associated with an enterprise; correlating at least a portion of the multiple items of data with at least one target variable using one or more correlation techniques; generating one or more forecasts pertaining to the at least one target variable and at least a portion of the one or more resources by processing at least a portion of the correlated data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the one or more forecasts.

Abstract: Described are systems and methods for client side enrichment and transform via dynamic logic for analytics across various platforms for improved performance, features, and uses. Analytics data collected in client applications is transformed and enriched before being sent to the downstream pipeline using native code and logic bundled into the core application code. The additional logic specific to manipulation of analytics may be unbundled from client-side application code and still be executed on on-device to achieve the same result. The logic may be written in a single language, such as JavaScript, and run across all clients including web browser and mobile operating systems.

Abstract: A system includes a bus interface and circuitry. The bus interface is configured to communicate with an external device over a peripheral bus. The circuitry is configured to support a plurality of widgets that perform primitive operations used in implementing peripheral-bus devices, to receive a user-defined configuration, which specifies a user-defined peripheral-bus device as a configuration of one or more of the widgets, and to implement the user-defined peripheral-bus device toward the external device over the peripheral bus, in accordance with the user-defined configuration.

Abstract: Cryptographic systems, methods and communication network comprising thereof are disclosed, including numerous industry applications. Embodiments of the present invention can generate and regenerate the same symmetric key. The cryptographic systems and methods include a key generator configured to use two or more inputs to reproducibly generate the symmetric key and a cryptographic engine configured to use the symmetric key for encrypting and decrypting data.

Abstract: One-to-many cryptographic systems and methods are disclosed, and a network employing the same, including numerous industry applications. The embodiments of the present invention can generate and regenerate the same symmetric key from a random token. The one-to-many cryptographic systems and methods include a central location and a cryptographic module being in communication with each other. The cryptographic module is configured to encrypt and/or decrypt data received a remote location and output encrypted and/or decrypted data. The cryptographic module includes a key generator configured to use two or more inputs to reproducibly generate the symmetric key and a cryptographic engine configured to use the symmetric key for encrypting and decrypting data. Corresponding methods, and network employing the same, are also provided.

Abstract: Cryptographic systems and methods are disclosed, including numerous industry applications. Embodiments of the present invention can generate and regenerate the same symmetric key. The cryptographic systems and methods include a key generator configured to use two or more inputs to reproducibly generate the symmetric key and a cryptographic engine configured to use the symmetric key for encrypting and decrypting data.