Abstract: Method and system of securing personally identifiable and sensitive information in conversational AI based communication. The method comprises enabling a first service provider device as a communication channel provider of an incoming communication mode and enabling a second service provider device s a communication channel provider of an outgoing communication mode, at least one of the incoming communication and outgoing communication modes comprising an audio communication, storing content of a conversation in the incoming communication mode in a first storage medium accessible to the first service provider device but not the second service provider device, and storing content of the conversation in the outgoing communication mode at a second storage medium accessible to the second service provider device but not the first service provider device, and anonymizing the audio communication wherein personally identifiable audio characteristics of the user are obfuscated from the service provider devices.

Abstract: A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

Abstract: System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

Abstract: Method and system of securing personally identifiable and sensitive information in conversational AI based communication. The method comprises enabling a first service provider device as a communication channel provider of an incoming communication mode and enabling a second service provider device s a communication channel provider of an outgoing communication mode, at least one of the incoming communication and outgoing communication modes comprising an audio communication, storing content of a conversation in the incoming communication mode in a first storage medium accessible to the first service provider device but not the second service provider device, and storing content of the conversation in the outgoing communication mode at a second storage medium accessible to the second service provider device but not the first service provider device, and anonymizing the audio communication wherein personally identifiable audio characteristics of the user are obfuscated from the service provider devices.

Abstract: A method and system of training a machine learning neural network (MLNN) in anatomical degenerative conditions in accordance with anatomical dynamics. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, a first set of mmWave radar point cloud data representing a first gait characteristic of a subject in motion, comprising an arm swing velocity, receiving, in a second layer, a second set of mmWave radar point cloud data representing a second gait characteristic comprising a measure of dynamic postural stability, the input layers being interconnected with an output layer of the MLNN via an intermediate layer, and training a MLNN classifier in accordance with a classification that increases a correlation between a degenerative condition of the subject as generated at the output layer and the sets of mmWave point cloud data.

Abstract: A method and system of training a machine learning neural network (MLNN) in monitoring anatomical positioning. The method comprises receiving, in a first input layer, from millimeter wave (mmWave) radar device, mmWave point cloud data representing spatial positions associated with a medical patient during successive changes in the spatial positions corresponding durations between changes, the mmWave data based upon detecting range and reflected wireless signal strength, receiving, in a second layer of the MLNN, attribute data for the corresponding durations, the f input layers interconnected with an output layer via an intermediate layer, the intermediate layer configured with an initial matrix of weights, training a MLNN classifier using classification that establishes correlation between the mmWave radar point cloud data, the attribute data and likelihood of formation of bodily pressure ulcers (BPUs) generated at the output layer, and producing a trained MLNN based on increasing the correlation.

Abstract: A method and system of training a machine learning neural network (MLNN) in monitoring anatomical positioning causing bodily pressure ulcers (BPUs). The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing anatomical positions of the medical patient in association with corresponding durations; receiving, in at least a second layer of the MLNN, attendant attribute data for the durations, the first and the at least a second input layers being interconnected with an output layer of the MLNN via at least one intermediate layer; training a MLNN classifier in accordance with a supervised classification that establishes a correlation between a likelihood of formation of BPUs with the mmWave point cloud data and attendant attribute data; and adjusting the initial matrix of weights by backpropagation to increase correlation with the likelihood of formation of BPUs as generated at the output layer.

Abstract: A method and system of securing personally identifiable and sensitive information in conversational AI based communication. The method comprises enabling, in response to the identifying a conversation session initiated with a client device, a first service provider device in a set of service providers as communication channel provider of the incoming mode and enabling a second service provider device of the set as communication channel provider of the outgoing mode; and storing at least a portion of content of the incoming conversation in a first storage medium accessible to the first provider but not the second provider, and storing at least a portion of content from the outgoing conversation at a second storage medium accessible to the second provider device but not the first provider device.

Abstract: Method and system of training a machine learning neural network (MLNN) monitoring anatomical dynamics of a subject in motion. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing a first gait characteristic; receiving, in a second layer of the MLNN, from the mmWave radar sensing device, mmWave radar point cloud data representing a second gait characteristic; the first and the at least a second input layers being interconnected with an output layer via an intermediate layer having an initial matrix of weights; training a MLNN classifier based on a supervised classification establishing correlation between a degenerative condition of the subject at the output layer and the point cloud data; and adjusting the initial matrix of weights by backpropagation to increase correlation between the degenerative condition and the sets of point cloud data.

Abstract: System and method of deploying a trained machine learning neural network (MLNN) in generating a fall injury condition of a subject. The method comprises receiving, at input layers of the trained MLNN, millimeter wave (mmWave) radar point cloud data representing fall attributes from monitoring the subject via mmWave radar sensing device, the input layers associated with the fall attributes, receiving, at a second set of input layers, personal attributes of the subject associated with ones of the second set of input layers, the first and second sets of input layers interconnected with an output layer of the trained MLNN via intermediate layers, the trained MLNN produced by establishing a correlation between an injury condition of prior subjects and mmWave point cloud data and personal attributes associated with the prior subjects, and generating, at the output layer, the fall injury condition attributable to the subject.

Abstract: Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.

Abstract: Training a machine learning neural network (MLNN) in radiowave based monitoring of fall characteristics in diagnosing injury. The method comprises receiving, in a first set of input layers of the MLNN, from a millimeter wave (mmWave) radar sensing device, a set of mmWave radar point cloud data representing fall attributes associated with a subject, each of the first set associated with a respective fall attribute; receiving, at a second set of input layers of the MLNN, a set of personal attributes of the subject, training a MLNN classifier based on supervised training that establishes a correlation between an injury condition of the subject as generated at the output layer, the mmWave point cloud data, and personal attributes; and adjusting an initial matrix of weights by backpropagation to increase correlation between the injury condition, the mmWave point cloud data, and the personal attributes.

Abstract: A method and system of securing personally identifiable and sensitive information in conversational AI based communication. The method comprises enabling, in response to the identifying a conversation session initiated with a client device, a first service provider device in a set of service providers as communication channel provider of the incoming mode and enabling a second service provider device of the set as communication channel provider of the outgoing mode; and storing at least a portion of content of the incoming conversation in a first storage medium accessible to the first provider but not the second provider, and storing at least a portion of content from the outgoing conversation at a second storage medium accessible to the second provider device but not the first provider device.

Abstract: Method and system of training a machine learning neural network (MLNN) monitoring anatomical dynamics of a subject in motion. The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing a first gait characteristic; receiving, in a second layer of the MLNN, from the mmWave radar sensing device, mmWave radar point cloud data representing a second gait characteristic; the first and the at least a second input layers being interconnected with an output layer via an intermediate layer having an initial matrix of weights; training a MLNN classifier based on a supervised classification establishing correlation between a degenerative condition of the subject at the output layer and the point cloud data; and adjusting the initial matrix of weights by backpropagation to increase correlation between the degenerative condition and the sets of point cloud data.

Abstract: A method and system of training a machine learning neural network (MLNN) in monitoring anatomical positioning causing bodily pressure ulcers (BPUs). The method comprises receiving, in a first input layer of the MLNN, from a millimeter wave (mmWave) radar sensing device, mmWave radar point cloud data representing anatomical positions of the medical patient in association with corresponding durations; receiving, in at least a second layer of the MLNN, attendant attribute data for the durations, the first and the at least a second input layers being interconnected with an output layer of the MLNN via at least one intermediate layer; training a MLNN classifier in accordance with a supervised classification that establishes a correlation between a likelihood of formation of BPUs with the mmWave point cloud data and attendant attribute data; and adjusting the initial matrix of weights by backpropagation to increase correlation with the likelihood of formation of BPUs as generated at the output layer.

Abstract: A method and system of generating a patient medical record dataset. The method comprises receiving, at a server computing device, image data acquired at a mobile computing device in association with medical monitoring of a patient state, deriving patient diagnostic data based on interpreting the image data, and formatting the patient diagnostic data in accordance with a longitudinal dataset associated with a patient medical record.