Hewlett-packard Development Company, L.p.

Entit Software LLC

General Electric Company

Ge Precision Healthcare LLC

Micro Focus Lcc

Hewlett-packard Indigo B.v.

Ruth Bergman

Omer Barkol

Sagi Schein

Gitit Ruckenstein

Doron Shaked

Carl Staelin

Hila Nachlieli

Shlomo Harush

Mani Fischer

Darryl Greig

Shahar Golan

Ayelet Pnueli

Michael Pogrebisky

Ira Cohen

Renato Keshet

Pavel Kisilev

Michal Aharon

Marie Vans

Eyal Shelef

Gregory Braverman

Daniel Freedman

Ludovic Avot

Gidi Amir

Anastasia Dubrovina

Jeffrey Hersh

Gideon Amir

Menachem Halmann

Ron Maurer

Thomas Malzbender

Eli Mordechai

Naomi Sato

Mark Majette

Paul Lawrence Mullen

David Lehavi

Sandeep N Bhatt

Boris Oicherman

Patrick J Chase

Marianne Hickey

Arik Sityon

Yacov Hel-Or

Mohammed J Zaki

Justin A Tehrani

Yifat Felder

Paul Mullen

Tsahi Rosenbaom

Inbal Tadeski

Pranay Anchuri

Yonathan Livny

Oren Ariel

Christopher Peltz

Alexander Spivakovsky

Antonio Zaitoun

Vitaly Burshtein

Edwar Bkheet

Ayelel Pnueli

Leonid Vygodner

Andrey Iossifov

Gregory Braveman

Alexei Lenedev

Ram Arad

Gal Noy

Inbal Marhaim

Claudio Bantolini

Oliver Wang

Eyol Shalef

Gennady Karvitsky

Ole Jakob Utkilen

Eyal Hayun

Various systems and devices are provided for facilitating communication in a healthcare setting. In one example, a system for providing medical information can include a processor that, in response to executing machine-readable instructions, can generate a patient dashboard comprising the medical information for a patient and a patient communication thread. The processor can also output the patient dashboard to a display, wherein the patient dashboard further comprises a first link proximate to a displayed parameter from the medical information and the processor can obtain communication among care providers monitoring the patient and virtual healthcare assistants configured to communicate on the communication thread. The processor can also output the communication from the care providers, the virtual healthcare assistances, or any combination thereof, in the patient communication thread of the patient dashboard provided by the display.

Devices and method for a healthcare collaboration space

Techniques are described that facilitate online monitoring of clinical data streams in association with detecting missing data and other suspicious data deviations. According to an embodiment, a computer implemented comprises receiving, by a system comprising a processor, a data stream from a plurality of different clinical data information systems configured to report defined clinical events within the data stream and recording arrival times of received events of the defined clinical events. The method further comprises detecting, by the system, data failure events associated with the data stream based on time differences between the arrival times for defined clinical events of the same type and estimated probabilities that the time differences are expected, wherein the detecting comprises estimating the probabilities that the time differences are expected using time-to-event models developed for each of the defined clinical events of the same type.

Online monitoring of clinical data drifts

Methods and systems are provided for tracking anatomical features across multiple images. One example method includes outputting, for display on a display device, an annotation indicative of a first location of an identified anatomical feature of a first ultrasound image, the annotation generated based on a first output of a model and outputting, for display on the display device, an adjusted annotation based on a second output of the model, the second output of the model generated based on a second ultrasound image and further based on the first output of the model, the adjusted annotation indicative of a second location of the identified anatomical feature in the second ultrasound image.

System and methods for tracking anatomical features in ultrasound images

Various systems and devices are provided for facilitating communication in a healthcare setting. In one example, a system includes a display and a computing device operably coupled to the display. The computing device stores instructions executable to output, to the display, a patient-specific dashboard including medical information specific to the patient, the dashboard further including a displayed link to a patient-specific communication thread; and responsive to a user selecting the link, output to the display a portion of the communication thread, where the communication thread includes communication among one or more care providers monitoring the patient and one or more virtual healthcare assistants.

A method for discovering representative composite configuration item (CI) patterns in an IT system that includes a plurality of configuration items may include data mining a graph representing the IT system to extract extended frequent composite CI patterns. The method may also include clustering the extended frequent composite CI patterns into clusters based on similarity between the maximal frequent composite CI patterns. The method may further include extracting a representative composite CI pattern for each of the clusters, and using an output device, outputting the representative composite CI pattern for each of the clusters.

Discovering representative composite CI patterns in an it system

Methods and systems of aligning annotation of fields of documents are provided. Training information that includes first measurement information pertaining to features of each of a plurality of fields associated with training clusters for documents of a document type is accessed. A first training cluster is annotated with a first name and the second training cluster is annotated with a second name. An electronic classification model is generated based on the training information. Second measurement information for features of fields associated with new clusters of a new document is accessed. Each of the new clusters is automatically annotated based on the second measurement information using the classification model. For example, a first new cluster that has fields of the first field type is annotated with the first name and a second new cluster that has fields of the second field type is annotated with the second name.

Aligning annotation of fields of documents

In one implementation, a plurality of signature vectors from a multi-dimensional representation of a graphical object is generated. Each of the signature vectors comprises attributes that vary little in response to changes in shape, size, orientation, and visual layer appearance of the graphical object, and each of the signature vectors includes attributes based on operations of integration, differentiation, and transforms on the multi-dimensional representation of the graphical object. Each signature vector is composited into multiple portions from the plurality of signature vectors to define an appearance-invariant signature of the graphical object.

Graphical object appearance-invariant signature

In one implementation, an image-based automation process includes identifying a graphical object of a graphical user interface and performing an action relative to the graphical object at the graphical user interface. The identifying is based on an image including the graphical object.

Image-based automation systems and methods

Provided in an example is a method of compressing a digital image includes dividing the image into cells of constant physical size regardless of resolution of the image to produce cell data for each cell which defines content of the cell and compressing the cell data.

Compressing image data

A method for compressing a digital image to be printed, the image consisting of pixels. It is determined, for blocks of pixels, whether a block contains a graphical structure that is to be maintained without loss because it is of a type that can be accurately perceived by a human observer. The block is encoded losslessly into a less voluminous format, if such a graphical structure has been found. Otherwise, the block is compressed lossily, if no such graphical structure has been found.

Image compression

In one implementation, a graphical object classification system includes an acquisition module, a signature generation module, and a classification module. The acquisition module accesses a representation of a graphical object. The signature generation module generates an appearance-invariant signature of the graphical object based on the representation. The classification module classifies the graphical object based on the appearance-invariant signature.

Graphical object classification

A method of compressing a digital image comprises dividingthe image into cells of constant physical size regardless of resolution of the image to produce cell data for each cell which defines content of the cell and compressingthe cell data.

Systems, methods, and other embodiments associated with graphical user interface (GUI) component classification are described. One example method includes generating a first vector. The first vector may be generated based on image data describing a GUI component. The example method may also include assigning a GUI component classifier to the GUI component. Assigning the GUI component classifier may comprise comparing the first vector to members of a vector set. Members of the vector set may describe GUI elements. The example method may also include providing the GUI component classifier.

Graphical user interface component classification

According to an order, a plurality of restricted top down model (RTDM) distances between a plurality of nodes residing on at least one tree related structure is determined. The determining includes determining a first RTDM distance between nodes of a first node pair of the plurality of nodes, wherein the determining the first RTDM distance comprises comparing prior determined RTDM distance measurements with the first node pair, wherein the prior determined RTDM distance measurements relate to node pairs preceding the first node pair within the order. The first RTDM distance measurement, the prior determined RTDM distance measurements and a second node pair of the plurality of nodes are compared. Based on the comparing, generating a second RTDM distance measurement.

Determining a similarity between tree related structures

Method and system embodiments of the present invention are directed to automated identification of regions within digitally-encoded images that correspond to objects and features of scenes captured in the digitally-encoded images, a process referred to as 'perceptual segmentation' of an image. Regions or segments within an image are first identified by any of various region-identifying or segmentation methods. For each region or segment, features of pixels within the region or segment are employed to compute one or more segment features. The segment features are used, in turn, to identify the region or segment as belonging to a particular type of region or segment, and the region is then accordingly labeled or tagged as a region or segment of the determined type.

Perceptual segmentation of images

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Haifa, Israel

Ruth Bergman