Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

Abstract: A system for classifying a data item to communicate to authorized users extracts features from the data item, where the features comprise a responsibility feature and a sensitivity feature. The responsibility feature indicates a job responsibility associated with the data item. The sensitivity feature indicates a sensitivity level of the data item. The system determines, based on the responsibility feature, that the data item belongs to a particular responsibility class. The system determines, based on the sensitivity feature, that the data item belongs to a particular sensitivity class. The system determines whether a user to whom the data item is directed belongs to the particular responsibility class and sensitivity class to which the data item belongs. The system sends the data item to the user, if is it determined that the user belongs to the particular responsibility class and sensitivity class to which the data item belongs.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A method for authenticating to a network comprising a plurality of Internet of Things (“IoT”) devices is provided. The method may include using a mobile telephone apparatus, a wrist-worn apparatus and a head-worn apparatus to monitor the level of at least one of a wearer's pulse, body temperature, voice, gait and/or other biorhythmic indicator. One of the aforementioned apparatus may operate as a hub apparatus. The method may further include using the hub apparatus to assign a federated biometric marker based at least in part on the first, second and third biometric markers. The method may also include using artificial intelligence to monitor for one or more outliers with respect to historical monitoring. Each of the one or more outliers may include a magnitude that exceeds a security threshold difference between the current magnitude and the historically monitored magnitude.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: Systems for dynamically controlling data transmissions are provided. In some examples, a system may receive data from one or more computer systems. Data associated with an event may be extracted and the extracted data may be compared to one or more machine learning datasets to determine a likelihood that an issue associated with a computer system may occur. The system may determine whether the likelihood is greater than a predetermined threshold. If so, a modification to avoid the potential issue or mitigate an impact may be identified. The modification may be transmitted to and executed by the computer system to modify one or more aspects of the computer system. In some examples, one or more efficiency modifications may be identified based on the comparison of the extracted data to the machine learning datasets. The identified efficiency modification may be transmitted to and executed by the computer system to modify one or more aspects of the computer system.

Abstract: A method for authenticating to a network comprising a plurality of Internet of Things (“IoT”) devices is provided. The method may include using a mobile telephone apparatus, a wrist-worn apparatus and a head-worn apparatus to monitor the level of at least one of a wearer's pulse, body temperature, voice, gait and/or other biorhythmic indicator. One of the aforementioned apparatus may operate as a hub apparatus. The method may further include using the hub apparatus to assign a federated biometric marker based at least in part on the first, second and third biometric markers. The method may also include using artificial intelligence to monitor for one or more outliers with respect to historical monitoring. Each of the one or more outliers may include a magnitude that exceeds a security threshold difference between the current magnitude and the historically monitored magnitude.

Abstract: A method for authenticating to a network comprising a plurality of Internet of Things (“IoT”) devices is provided. The method may include using a mobile telephone apparatus, a wrist-worn apparatus and a head-worn apparatus to monitor the level of at least one of a wearer's pulse, body temperature, voice, gait and/or other biorhythmic indicator. One of the aforementioned apparatus may operate as a hub apparatus. The method may further include using the hub apparatus to assign a federated biometric marker based at least in part on the first, second and third biometric markers. The method may also include using artificial intelligence to monitor for one or more outliers with respect to historical monitoring. Each of the one or more outliers may include a magnitude that exceeds a security threshold difference between the current magnitude and the historically monitored magnitude.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: A method for authenticating to a network comprising a plurality of Internet of Things (“IoT”) devices is provided. The method may include using a mobile telephone apparatus, a wrist-worn apparatus and a head-worn apparatus to monitor the level of at least one of a wearer's pulse, body temperature, voice, gait and/or other biorhythmic indicator. One of the aforementioned apparatus may operate as a hub apparatus. The method may further include using the hub apparatus to assign a federated biometric marker based at least in part on the first, second and third biometric markers. The method may also include using artificial intelligence to monitor for one or more outliers with respect to historical monitoring. Each of the one or more outliers may include a magnitude that exceeds a security threshold difference between the current magnitude and the historically monitored magnitude.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods and systems for using block chain technology to verify transaction data are described herein. A computing platform may receive data about events related to transactions, personal or corporate information, supply chains, and other relevant information about a person or corporate entity. The event information may be received, aggregated, and processed to determine metadata about the person or corporate entity. The metadata may indicate, for example, a trustworthiness of the person or corporate entity for various purposes. Such event information and/or metadata may be stored as transactions in a block chain that may be accessible by counterparties to a potential transaction involving the person or corporate entity. The automated event processing computing platform may further use automated techniques to implement smart transactions between the person/entity and counterparty based on the trust metadata.

Abstract: Methods, systems, and apparatuses for blockchain-based unexpected data detection are described herein. In some arrangements, a node within a decentralized peer-to-peer (e.g., P2P) network may receive a plurality of network function requests corresponding to the decentralized network. The node may analyze the plurality of network function requests to identify whether the requests included unexpected data and/or irregular data, and/or are associated with flagged wallets and/or smart contracts.