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 applying a security vulnerability assessment of a software program. The method comprises directing, from a security assessing server, to a software program under execution, a plurality of attack vectors, diagnosing a set of results associated with the software program under execution as comprising a security vulnerability, the set of results produced based at least in part on the plurality of attack vectors, and assessing a monetary premium of a risk insurance policy merited by an enterprise based at least in part on a level of control ceded to an attacker in accordance with the set of results.

Abstract: Method and system of deploying a machine learning neural network (MLNN). The method comprises receiving a set of input features associated with data representative of a patient medical state at input layers of a trained MLNN, the trained MLNN comprising an output layer interconnected to the input layers via intermediate layers configured in accordance with an initial matrix of weights, a subset of the input features being activated responsive to a data sufficiency threshold reached in conjunction with deactivating, from the intermediate layers, a remainder of the input layers, the trained MLNN produced in accordance with adjusting the initial matrix of weights in diminishment of false positives in providing, at the output layer, a patient state diagnosis, and generating, at the output layer, a medical state diagnosis in accordance with the diminishment of false positives.

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 deploying a trained machine learning neural network in dynamic testing of security vulnerability in software applications. The method comprises directing, from a security assessing server computing device, to a software program under execution, a series of attack vectors, deploying a set of results produced in accordance with the software program under execution and the attack vectors to an input layer of the trained machine learning neural network, the trained machine learning neural network comprising an output layer that is interconnected with the input layer via a set of intermediate layers, and identifying, in accordance with a predetermined threshold percentage value of false positive software security vulnerability defects, one or more software security vulnerability defects associated with the results produced, the software security vulnerability defects being generated in accordance with the output layer of the trained machine learning neural network.

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: 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 for dynamic testing and diagnostic assessment of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing a set of results associated with the software execution as comprising one of a security vulnerability and not a security vulnerability, the set of results produced based at least in part on the attack vectors; and assessing a dynamic security vulnerability score for the software program based at least in part on the diagnosing.

Abstract: Method and system of training a machine learning neural network (MLNN). The method comprises receiving a set of input features at respective input layers of the MLNN. The MLNN implemented in a processor and comprises an output layer interconnected to input layers via intermediate layers. The input features are associated with input feature data of a patient medical condition. Then selecting, responsive to a data qualification threshold level, a subset of the input layers while deactivating a remainder of the set of input layers. The intermediate layers are configured with an initial matrix of weights. Then training the MLNN based at least in part upon adjusting the initial matrix of weights based on a supervised classification that provides, via the output layer, one of negative and positive patient diagnostic states.

Abstract: A method and system for implementing AI based neural networks for data analytics in dynamic testing of security vulnerability of cloud-based enterprise software applications. The method comprises directing, to a software program under execution, a series of attack vectors; diagnosing an at least a first set of results associated with the software program under execution as comprising one of a security vulnerability and not a security vulnerability, the at least a first set of results produced based at least in part on the attack vectors; and training a machine learning neural network classifier in accordance with a supervised classification that identifies false positive vulnerability defects of the at least a first set of results to produce a trained classifier, the neural network classifier including an input and an output layers connected via at least one intermediate layer that is configured in accordance with an initial matrix of weights.

Abstract: A method and system of rendering security events in execution of a software application in a communication network. The method comprises receiving, at a memory of the server computing device, a waiver parameter specification identifying at least one waiver parameter in association with at least one recipient client device of the plurality of client computing devices, the at least one waiver parameter based at least in part on an expected security event in the software application execution; during concurrent execution, in a processor of the server computing device, of object code of the software application, generating at least one waiver task automaton that monitors for the at least one waiver parameter; and generating, based on the monitoring, at a client interface of the at least one recipient communication device, a waiver notification interface in accordance with concurrent execution.

Abstract: Disclosed herein are methods and system for content control based on the number of viewers and views. The method may include obtaining, by an audience determination device, a number of viewers watching a selected content being played on a display device. A content receiving device may determine if a determined number of viewers exceeds a purchased number of viewers. If the determined number of viewers exceeds the purchased number of viewers, then the playing of the selected content is paused. An error message may be provided so that corrective action may be taken. The playback of the selected content may be resumed when the determined number of viewers does not exceed the purchased number of viewers. In an implementation, the content receiving device may also determine if there any available purchased views for the selected content.