Microsoft Technology Licensing, LLC

Nuance Communications, Inc.

Jeremy Martin Jancsary

Joel Praveen Pinto

Uwe Helmut Jost

William F Ganong, Iii

Paul Joseph Vozila

Donald E Owen

Reinhard Sebastian Bernhard Nowozin

Mehmet Mert Öz

Guido Remi Marcel Gallopyn

Matthias Helletzgruber

Kumar Abhinav

Andrew William Fitzgibbon

Carsten Curt Eckard Rother

Bruce Justin Lindbloom

Danyal Khashabi

A method, computer program product, and computing system for: monitoring a meeting between a patient and a medical entity during a medical encounter; gathering information during the medical encounter, thus generating gathered encounter information, wherein the gathered encounter information includes video-based content of the patient; generating video-based content information via artificial intelligence, wherein the video-based content information is based at least in part upon the video-based content and/or the gathered encounter information and is configured to provide guidance to the medical entity concerning the video-based content; and providing the video-based content information to the medical entity.

Medical intelligence system and method

A method, computer program product, and computing system for: monitoring a meeting between a patient and a medical entity during a telehealth medical encounter; gathering information during the telehealth medical encounter, thus generating gathered encounter information; and rendering an informational window concerning the telehealth medical encounter for review by the patient and/or the medical entity, wherein the informational window is configured to provide supplemental information based, at least in part, upon the gathered encounter information.

A method, computer program product, and computing system for: monitoring a meeting between a patient and a medical entity during a medical encounter; gathering information during the medical encounter, thus generating gathered encounter information; generating medical encounter topical information via artificial intelligence, wherein the medical encounter topical information is based at least in part upon the gathered encounter information and is configured to provide guidance to the medical entity concerning one or more topics to be discussed during the medical encounter; and providing the medical encounter topical information to the medical entity.

A method, computer program product, and computing system for obtaining, by a computing device, encounter information of a patient encounter, wherein the encounter information may include audio encounter information obtained from at least a first encounter participant. The audio encounter information obtained from at least the first encounter participant may be processed. A user interface may be generated displaying a plurality of layers associated with the audio encounter information obtained from at least the first encounter participant. A user input may be received from a peripheral device to navigate through each of the plurality of layers associated with the audio encounter information displayed on the user interface.

System and method for review of automated clinical documentation from recorded audio

Image demosaicing is described, for example, to enable raw image sensor data, where image elements have intensity values in only one of three color channels, to be converted into a color image where image elements have intensity values in three color channels. In various embodiments a trained machine learning component is used to carry out demosaicing optionally in combination with denoising. In some examples the trained machine learning system comprises a cascade of trained regression tree fields. In some examples the machine learning component has been trained using pairs of mosaiced and demosaiced images where the demosaiced images have been obtained by downscaling natural color digital images. For example, the mosaiced images are obtained from the demosaiced images by subsampling according to one of a variety of color filter array patterns.

Image demosaicing

A new tractable model solves labeling problems using regression tree fields, which represent non-parametric Gaussian conditional random fields. Regression tree fields are parameterized by non-parametric regression trees, allowing universal specification of interactions between image observations and variables. The new model uses regression trees corresponding to various factors to map dataset content (e.g., image content) to a set of parameters used to define the potential functions in the model. Some factors define relationships among multiple variable nodes. Further, the training of regression trees is scalable, both in the training set size and in the fact that the training can be parallelized. In one implementation, maximum pseudolikelihood learning provides for joint training of various aspects of the model, including feature test selection and ordering (i.e., the structure of the regression trees), parameters of each factor in the graph, and the scope of the interacting variable nodes used in the graph.

Growing community of inventors

Vienna, Austria

Jeremy Martin Jancsary