Abstract: Due diligence of documents is faster and simpler. An electronic mortgage application, for example, often contains or references a collection of many separate electronic documents. Electronic data representing an original version of an electronic document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic document has not changed since its creation. However, if the digital signatures do not match, then the electronic document has changed since its creation. The auditor may thus flag the electronic document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Due diligence of documents is faster and simpler. An electronic mortgage application, for example, often contains or references a collection of many separate electronic documents. Electronic data representing an original version of an electronic document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic document has not changed since its creation. However, if the digital signatures do not match, then the electronic document has changed since its creation. The auditor may thus flag the electronic document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Methods and apparatuses for authentication and/or provisioning of wireless network devices, and in particular, methods and apparatuses for authentication and/or provisioning of wireless network devices that are communicating with and may be monitored and/or controlled by a remote (e.g., cloud) server.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Phone systems, phone devices (including hardware, software and firmware), and methods of operating a phone. Described herein are dissociated phone systems including one or a plurality of local client phones with basic call handling capability that communicate though a remote phone server but are locally controlled by a controller gateway that manages the local network including the local client phones.

Abstract: Methods and apparatuses for authentication and/or provisioning of wireless network devices, and in particular, methods and apparatuses for authentication and/or provisioning of wireless network devices that are communicating with and may be monitored and/or controlled by a remote (e.g., cloud) server.

Abstract: Phone systems, phone devices (including hardware, software and firmware), and methods of operating a phone. Described herein are dissociated phone systems including one or a plurality of local client phones with basic call handling capability that communicate though a remote phone server but are locally controlled by a controller gateway that manages the local network including the local client phones.

Abstract: Authentication of electronic document is based on multiple digital signatures incorporated into a blockchain. Structured data, metadata, and instructions may be hashed to generate the multiple digital signatures for distribution via the blockchain. Any peer receiving the blockchain may then verify an authenticity of an electronic document based on any one or more of the multiple digital signatures incorporated into the blockchain.

Abstract: Confidential, secret data may be shared via one or more blockchains. Mortgage applications, medical records, financial records, and other electronic documents often contain social security numbers, names, addresses, account information, and other personal data. A secret sharing algorithm is applied to any secret data to generate shares. The shares may then be integrated or written to one or more blockchains for distribution.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: Due diligence of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly conduct the due diligence by comparing the digital signatures. If the digital signatures match, then the due diligence reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional due diligence. Regardless, a result of the due diligence may be incorporated into one or more blockchains.

Abstract: Auditing of mortgage documents is faster and simpler. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Electronic data representing an original version of an electronic mortgage document and its current version may be hashed to generate digital signatures. Any auditor may then quickly compare the digital signatures. If the digital signatures match, then the audit reveals that the electronic mortgage document has not changed since its creation. However, if the digital signatures do not match, then the electronic mortgage document has changed since its creation. The auditor may thus flag the electronic mortgage document for additional auditing processes.

Abstract: Indexing of mortgage documents is faster and simpler for auditing purposes. An electronic mortgage application often contains or references a collection of many separate electronic mortgage documents. Indexing data describing the individual electronic mortgage documents and/or the electronic mortgage application may be hashed and integrated into a blockchain. Any auditor receiving the blockchain may thus perform a reverse lookup to generate an index describing the sections and/or pages within the electronic mortgage application. Moreover, the auditor may also verify a current version of the index to an original version created at creation.