Mm&r Products, Inc.

Trade Box, LLC

Banana Bros, LLC

International Business Machines Corporation

Other

Trade Boxy, LLC

Tubetech, LLC

Total Tiger, Inc.

Modern Media

Caldo Caldo LLC

David Joseph Richmond

Howard C Richmond

Michael A Ragosta

Claudio Marino

Bruno Meglio

Manuel A Montano

Maria Victoria Sainz De Cea

Henry K Yu

Rentong Wang

Zhiwu Yu

Zhiyong Yu

Wenrui Dai

Juan M Fernandez

Marwan Sati

Ruiju Liu

Yiyan Feng

Qianli Zhou

Jeffery Kunkler

Matthew W Tivnon

Jefferson McCrory

Paul McGrath

Paul Thomas McGrath

Craig R Steele

Gregory Steven Thüne

Howard Richmond

Emily Lindemer

Jefferson H McCrory

Mark D Bronkalla

Sun Young Park

Dustin Michael Sargent

Kurt Koptis

Grant Covell

Luke James Ritchie

Greg Thüne

Linda Flowers

Jeffery Scott Kunkler

Paul Robert Goetz

Aly Said Mohamed

Amanda Long

Shi Kai Tan

Chao Song

An AI system may receive an image. The AI system may include a first AI model trained using labeled training images including images from prior mammograms to predict cancer and a second AI model trained using labeled training images including images from current mammograms to classify mammogram images. The second AI model may be initialized using the weights of the first AI model using transfer learning. The AI system may receive a classification output indicating a likely current breast cancer diagnosis or a likelihood of the user to develop breast cancer in the future.

Deep learning modeling using health screening images

An apparatus for preparing a smokeable product is provided. The apparatus includes an upper chamber configured to receive feed material, a feed material cutting mechanism comprising a size reducer configured to rotate in two directions, a motor configured to rotate the feed material cutting mechanism, a controller configured to selectively reverse direction of rotation of the feed material cutting mechanism, a power source configured to drive the motor, a switch for turning the power source off and on, and a fan in the uppermost chamber configured to move feed material within the uppermost chamber to a lowermost chamber.

Compact system for preparing personal smokable products

Systems and methods for selectively processing image studies with an artificial intelligence system. One system includes an electronic processor configured to select an image study awaiting review and update a workflow status of the image study to a first status indicating that the image study has been claimed for review by the artificial intelligence system. The electronic processor is also configured to apply at least one of the plurality of rules to the image study to determine whether the image study is applicable for processing by the artificial intelligence system, and, in response to determining the image study is not applicable for processing by the artificial intelligence system based on the at least one of the plurality of rules, update the workflow status associated with the image study to a second status to make the image study available for claiming by a manual reviewer or another artificial intelligence system.

Enhancing medical imaging workflows using artificial intelligence

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Hair styling apparatus

A hair styling device comprises a base having a rear handle portion and a forward styling portion, the forward styling portion comprising a plurality of projections, the plurality of projections comprising one or more of the following: bristles, comb teeth or spaced apart, plates. The device comprises a retainer arm opposed to the base, and pivotally connected to the base so that the retainer arm can pivot from an open position to receive hair and a closed position for retaining hair in the plurality of projections. The forward section of the retainer arm does not have projections and has sufficient open space that at least 50% of the hair being styled is visible to a user.

Safely single-handed hair curler

A method, computer system, and a computer program product for automatic labeling to train a machine learning algorithm is provided. The present invention may include labeling a medical image with at least one finding from a corresponding medical report. The present invention may include determining a localization information from the labeled medical image. The present invention may include training the machine learning algorithm with the determined localization information. The present invention may include detecting at least one candidate in a test medical image. The present invention may include generating a discrepancy list between the at least one detected candidate in the test medical image and at least one human-reported finding in a corresponding test medical report. The present invention may include, in response to determining that the generated discrepancy list is above a threshold, retraining the trained machine learning algorithm until the generated discrepancy list is below the threshold.

Automated labeling of images to train machine learning

An approach for training, on a computer, one or more deep learning algorithms with a plurality of mammograms with known outcomes based, at least in part, on using a set of mammograms of each patient in the plurality of mammograms. The approach includes receiving a first set of mammograms of a first patient. The first set of mammograms includes an unevaluated mammogram of the first patient and a set of prior mammograms of the first patient. The approach includes the trained convolutional neural network extracting the set of features from each mammogram of the set of mammograms of the first patient. Furthermore, the approach includes using a second deep learning algorithm of the one or more deep learning algorithms to perform an evaluation of the unevaluated mammogram of the first patient based, at least in part, on the set of prior mammograms of the first patient.

Evaluating a mammogram using a plurality of prior mammograms and deep learning algorithms

Mechanisms are provided for implementing a patient complexity classification (PCC) computing system. The PCC computing system receives medical image study data for a patient that comprises one or more medical image data structures and one or more corresponding medical image metadata data structures. A natural language processing engine of the PCC computing system performs natural language processing on the medical image metadata data structure to extract features indicative of at least one of patient or medical image characteristics. A complexity classifier of the PCC computing system evaluates the extracted features to determine a patient complexity indicating a complexity of a medical condition of the patient. Routing logic associated with the PCC computing system routes the one or more medical image data structures and one or more corresponding medical image metadata data structures to one or more downstream patient evaluation computing systems based on the determined patient complexity.

Automated patient complexity classification for artificial intelligence tools

The invention includes a device for smoking smokeable material. The device has a housing, a rotatable shaft for driving smokeable material towards the first end of the housing as the smokeable material is smoked, at least one radial hole through the shaft for passage of smokeable material from the burning section of the housing into the longitudinal hole of the rotatable shaft.

System utilizing compressed smokable product

An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from an imaging device. The image may be a medical image. The computing device may detect one or more potential indicators of disease in the image using a first algorithm and determine areas of potential disease in the image using an artificial intelligence algorithm. The computing device may determine a correlation between the determined areas of potential disease in the image and the one or more potential indicators of disease for the image. The computing device may, in response to determining a positive correlation, identify one or more of the potential indicators of disease for annotation and generate a report indicating one or more potential indicators of disease was found in the image.

Image analysis and annotation

An embodiment of the invention may include a method, computer program product and computer system for image artifact removal. The method, computer program product and computer system may include computing device which may receive a primary image and analyze the primary image for global artifacts and local artifacts. The computing device may, in response to identifying a global artifact in the primary image, generate a secondary image with the global artifact removed utilizing a first generative adversarial network. The computing device may, in response to identifying a local artifact in the primary image, generate a patch with the local artifact removed utilizing a second generative adversarial network. The computing device may generate a hybrid image containing a reduction of global artifacts and a reduction of local artifacts by combining the secondary image and the patch utilizing a hybrid generative adversarial network.

Artifact removal in medical imaging

A method, computer system, and a computer program product for case-adaptive image quality assessment is provided. The present invention may include detecting a current set of features in a current exam associated with a patient. The present invention may also include calculating a current set of quality measurements for the current exam based on the detected current set of features. The present invention may further include in response to determining that the calculated current set of quality measurements for the current exam is below a patient-specific image quality threshold defined by at least one prior exam associated with the patient, automatically registering a negative quality assessment for the current exam associated with the patient.

Case-adaptive medical image quality assessment

An electronic cigarette comprising an atomizer is provided. The electronic cigarette includes an inner core assembly configured to receive liquid and provide the liquid through at least one opening in the inner core assembly. The inner core assembly includes a heater having a noncylindrical shape, in one embodiment having hexagonal shape in a sinusoidal wave pattern with rounded corners.

Inner core element for use with electronic cigarette

A hair styling apparatus is provided including a brush head, a plurality of tines located on the brush head, and a series of movable linearly arranged prongs having an angled orientation with respect to the brush head. A user causing movement of the linearly arranged prongs serves to secure the user' s hair against a subset of the plurality of tines.

Growing community of inventors

Los Angeles, CA, United States of America

David Joseph Richmond