Abstract: In some embodiments, the present description provides a distributed blockchain ledger system that includes at least: a first distributed blockchain ledger, configured to storing first cryptographically-secured data representative of a plurality of tokenized assets; a second distributed blockchain ledger, configured to storing second cryptographically-secured data representative of a plurality of transactions related to the plurality of tokenized assets; and a plurality of smart contracts that is configured to self-execute to at least: store the first cryptographically-secured data on the first distributed blockchain ledger, store the second cryptographically-secured data on the second distributed blockchain ledger, and maintain a plurality of digital associations between the first cryptographically-secured data of the first distributed blockchain ledger and the second cryptographically-secured data of the second distributed blockchain ledger.

Abstract: In some embodiments, the present description provides a distributed blockchain ledger system that includes at least: a first distributed blockchain ledger, configured to storing first cryptographically-secured data representative of a plurality of tokenized assets; a second distributed blockchain ledger, configured to storing second cryptographically-secured data representative of a plurality of transactions related to the plurality of tokenized assets; and a plurality of smart contracts that is configured to self-execute to at least: store the first cryptographically-secured data on the first distributed blockchain ledger, store the second cryptographically-secured data on the second distributed blockchain ledger, and maintain a plurality of digital associations between the first cryptographically-secured data of the first distributed blockchain ledger and the second cryptographically-secured data of the second distributed blockchain ledger.

Abstract: In some embodiments, the present description provides a computer-based system having a dual-exchange cryptographically-secured platform (DECSP); where the DECSP includes: a first-type cryptographically-secured platform (first-type CSP) and a second-type cryptographically-secured platform (second-type CSP); where the first-type CSP includes a first computing device; where the second-type CSP includes a second computing device; where the first computing device is connected to a blockchain and configured to issue crypto-tokens associated with a non-crypto asset, perform blockchain-based activities, and automatically transmit an instruction associated with the non-crypto asset to a second computing device in response to an issuance of the crypto-token by the first computing device; where the second computing device is configured to: receive the instruction associated with the one non-crypto asset from the first computing device and automatically communicate with a non-crypto asset electronic marketplace to execut

Abstract: In some embodiments, the present description provides a computer-based system having a dual-exchange cryptographically-secured platform (DECSP); where the DECSP includes: a first-type cryptographically-secured platform (first-type CSP) and a second-type cryptographically-secured platform (second-type CSP); where the first-type CSP includes a first computing device; where the second-type CSP includes a second computing device; where the first computing device is connected to a blockchain and configured to issue crypto-tokens associated with a non-crypto asset, perform blockchain-based activities, and automatically transmit an instruction associated with the non-crypto asset to a second computing device in response to an issuance of the crypto-token by the first computing device; where the second computing device is configured to: receive the instruction associated with the one non-crypto asset from the first computing device and automatically communicate with a non-crypto asset electronic marketplace to execut

Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model

Abstract: In some embodiments, an exemplary inventive computer-implemented method may include steps, performed by a processor, of: obtaining training real representations of a real subject; obtaining a training synthetic representation having a visual effect applied to a synthetic subject; training a first neural network and a second neural network by: presenting the first neural network with training real representation and candidate meta-parameters of latent variables for the visual effect to generate a training photorealistic-imitating synthetic representation of the real subject with the visual effect; presenting the second neural network with the training photorealistic-imitating synthetic representation and the training synthetic representation to determine actual meta-parameters of the latent variables of the visual effect, where the actual meta-parameters are meta-parameters at which the second neural network has identified that the training photorealistic-imitating synthetic representation is realistic, and pr

Abstract: The exemplary inventive instant messaging system may include a sending client that accesses encryption data associated with a receiving client on a distributed mesh network where the encryption data is signed by a receiver public key of the receiving client, forms a non-interactive message exchange session on the distributed mesh network, generates a first session key based on the encryption data and a sender secret key, encrypts a message using the first session key, encrypts session information using the receiver public key, produces a session state including the encrypted message and the encrypted session information and stores the session state in the non-interactive message exchange session. The receiving client accesses the session state, decrypts the encrypted session information with a receiver secret key, generates a second session key using the session information and a sender public key, and decrypts the message using the second session key.

Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model

Abstract: In some embodiments, the present invention provides for an exemplary system that may include at least the following components: a camera component, where the camera component is configured to acquire a visual input, where the visual input includes a face of a person; a processor configured to: obtain the visual input; apply a face detection algorithm to detect a presence of the face within the visual input; extract a vector of at least one feature of the face; match the vector to a stored profile of the person to identify the person; fit, based on person-specific meta-parameters, a three-dimensional morphable face model (3DMFM) to obtain a person-specific 3DMFM of the one person; apply a facial expression detection algorithm to the person-specific 3DMFM to determine a person-specific facial expression; and cause to perform at least one activity associated with the person based at least in part on the person-specific facial expression of the person.

Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model

Abstract: In some embodiments, the present invention provides for an exemplary inventive system, icluding: a communication pipeline, including: at a first end of the communication pipeline: a first processor configured to: obtain a plurality of original content data units having a representative content associated with a subject; apply a trained artificial intelligence algorithm to identify: the representative content of the subject and original background content that is not associated with the subject; remove the original background content to reduce a volume of data being transmitted resulting in an increased capacity of the communication channel; encode and transmit each respective modified content data unit from the first end of the communication pipeline to a second end; a second processor configured to: receive and decode each respective modified content data unit; generate a respective artificial background content; and combine the representative content associated with the subject and the respective artificial

Abstract: In some embodiments, the present invention provides for an exemplary system that may include at least the following components: a camera component, where the camera component is configured to acquire a visual input, where the visual input includes a face of a person; a processor configured to: obtain the visual input; apply a face detection algorithm to detect a presence of the face within the visual input; extract a vector of at least one feature of the face; match the vector to a stored profile of the person to identify the person; fit, based on person-specific meta-parameters, a three-dimensional morphable face model (3DMFM) to obtain a person-specific 3DMFM of the ne person; apply a facial expression detection algorithm to the person-specific 3DMFM to determine a person-specific facial expression; and cause to perform at least one activity associated with the person based at least in part on the person-specific facial expression of the person.

Abstract: In some embodiments, the present invention provides for an exemplary system that may include at least the following components: a camera component, where the camera component is configured to acquire a visual input, where the visual input includes a face of a person; a processor configured to: obtain the visual input; apply a face detection algorithm to detect a presence of the face within the visual input; extract a vector of at least one feature of the face; match the vector to a stored profile of the person to identify the person; fit, based on person-specific meta-parameters, a three-dimensional morphable face model (3DMFM) to obtain a person-specific 3DMFM of the ne person; apply a facial expression detection algorithm to the person-specific 3DMFM to determine a person-specific facial expression; and cause to perform at least one activity associated with the person based at least in part on the person-specific facial expression of the person.

Abstract: In some embodiments, an exemplary inventive computer-implemented method may include steps, performed by a processor, of: obtaining training real representations of a real subject; obtaining a training synthetic representation having a visual effect applied to a synthetic subject; training a first neural network and a second neural network by: presenting the first neural network with training real representation and candidate meta-parameters of latent variables for the visual effect to generate a training photorealistic-imitating synthetic representation of the real subject with the visual effect; presenting the second neural network with the training photorealistic-imitating synthetic representation and the training synthetic representation to determine actual meta-parameters of the latent variables of the visual effect, where the actual meta-parameters are meta-parameters at which the second neural network has identified that the training photorealistic-imitating synthetic representation is realistic, and pr

Abstract: In some embodiments, the present invention provides for an exemplary inventive system, including: a communication pipeline, including: at a first end of the communication pipeline: a first processor configured to: obtain a plurality of original content data units having a representative content associated with a subject; apply a trained artificial intelligence algorithm to identify: the representative content of the subject and original background content that is not associated with the subject; remove the original background content to reduce a volume of data being transmitted resulting in an increased capacity of the communication channel; encode and transmit each respective modified content data unit from the first end of the communication pipeline to a second end; a second processor configured to: receive and decode each respective modified content data unit; generate a respective artificial background content; and combine the representative content associated with the subject and the respective artificia

Abstract: In some embodiments, the present invention provides for an exemplary inventive system, including: a communication pipeline, including: at a first end of the communication pipeline: a first processor configured to: obtain a plurality of original content data units having a representative content associated with a subject; apply a trained artificial intelligence algorithm to identify: the representative content of the subject and original background content that is not associated with the subject; remove the original background content to reduce a volume of data being transmitted resulting in an increased capacity of the communication channel; encode and transmit each respective modified content data unit from the first end of the communication pipeline to a second end; a second processor configured to: receive and decode each respective modified content data unit; generate a respective artificial background content; and combine the representative content associated with the subject and the respective artificia

Abstract: In some embodiments, the present invention provides for a computer system which includes a content database storing initial content data and a vocabulary data set; a processor configured to applying a machine learning model to transform the initial content data into a N-dimensional vector space; self-training the machine learning model based on the vocabulary data set; applying a clustering technique to the N-dimensional vector space to generate a cluster model of clusters, where each cluster includes a plurality of word representations; associating each cluster with a cluster identifier; obtaining subsequent content data; associating each data element of the subsequent content data with each cluster to generate a content data cluster mapping model; continuously tracking, for each user, each respective cluster identifier of each respective cluster associated with each action performed by each user with each data element to continuously self-adapt each user-specific, time-specific dynamic cluster mapping model