A system and method for assisting drivers of vehicles are described. The systems and methods provide an extended view of the area surrounding the driver's vehicle while providing real-time object trajectory for objects and other vehicles that may enter the driver's reactionary zone. The system and methods capture images of the area surrounding the driver's vehicle and create a composite image of that area in real-time and using Augmented Reality (AR) create a 3-D overlay to warn the driver as objects or other vehicles enter the driver's reactionary zone so that a driver can make more informed driving decisions.

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.

In various example embodiments, a system and method for determining an item that has confirmed characteristics are described herein. An image that depicts an object is received from a client device. Structured data that corresponds to characteristics of one or more items are retrieved. A set of characteristics is determined, the set of characteristics being predicted to match with the object. An interface that includes a request for confirmation of the set of characteristics is generated. The interface is displayed on the client device. Confirmation that at least one characteristic from the set of characteristics matches with the object depicted in the image is received from the client device.

Methods, apparatuses, and systems for transcribing and performing analysis on patient data are disclosed. Data is collected from one or more medical professionals as well as sensors and imaging devices positioned on or oriented towards a patient. An analysis is performed on the patient data and the data is presented to a medical professional via a verbal interface in a conversational manner, allowing the medical professional to provide additional data such as observations or instructions which may be used for further analysis or to perform actions related to the patient's care.

A system for monitoring potential failure in a machine or a component thereof, the system including: at least one optical sensor configured to be fixed on or in vicinity of the machine or the component thereof, at least one processor in communication with the sensor, the processor being executable to: receive signals from the at least one optical sensor, obtain data associated with characteristics of at least one mode of failure of the machine or the component thereof, identify at least one change in the received signals, for an identified change in the received signals, apply the at least one identified change to an algorithm configured to analyze the identified change in the received signals and to classify whether the identified change in the received signals is associated with a mode of failure of the machine or the component thereof, thereby labeling the identified change as a fault, based, at least in part, on the obtained data, and for an identified change is classified as being associated with a mode of failure, outputting a signal indicative of the identified change associated with the mode of failure.

A method and apparatus are provided that analyzing sequence-to-sequence data, such as sequence-to-sequence speech data or sequence-to-sequence machine translation data for example, by minimum Bayes risk (MBR) training a sequence-to-sequence model and within introduction of applications of softmax smoothing to an N-best generation of the MBR training of the sequence-to-sequence model.

Disclosed is a method for self-supervised reinforcement learning (RL) by analogy executed by a computer device, the method including configuring a self-supervised RL with analogical reasoning (SRAR) model; and learning a policy for problem solving in a situation in which a task domain changes using the configured SRAR model.

Disclosed is a method for self-supervised reinforcement learning (RL) by analogy executed by a computer device, the method including configuring a self-supervised RL with analogical reasoning (SRAR) model; and learning a policy for problem solving in a situation in which a task domain changes using the configured SRAR model.

Automated panoramic image connections from outdoor to indoor environments is provided. A system identifies, in a data repository, a virtual tour of an internal portion of a physical building formed from a plurality of images connected with a linear path along a persistent position of a virtual camera. The system receives, from a third-party data repository, image data corresponding to an external portion of the physical building. The system detects, within the image data, an entry point for the internal portion of the physical building. The system generates, responsive to the detection, a step-in transition at the entry point in the image data. The system connects the virtual tour with the step-in transition generated for the image data at the entry point. The system initiates, on a client device responsive to an interaction with the entry point, the step-in transition to cause a stream of the virtual tour.

Automated panoramic image connections from outdoor to indoor environments is provided. A system identifies, in a data repository, a virtual tour of an internal portion of a physical building formed from a plurality of images connected with a linear path along a persistent position of a virtual camera. The system receives, from a third-party data repository, image data corresponding to an external portion of the physical building. The system detects, within the image data, an entry point for the internal portion of the physical building. The system generates, responsive to the detection, a step-in transition at the entry point in the image data. The system connects the virtual tour with the step-in transition generated for the image data at the entry point. The system initiates, on a client device responsive to an interaction with the entry point, the step-in transition to cause a stream of the virtual tour.