Patents by Inventor Aviad Zlotnick

Aviad Zlotnick has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11409992
    Abstract: A method and a computer program product for identification and improvement of machine learning (ML) under-performance The method comprises slicing data of ML model based on a functional model representing requirements of a system utilizing the ML model. The functional model comprises a set of attributes and respective domain of values. Each data slice is associated with a different valuation of one or more attributes of the functional model. Each data instance of the ML model is mapped to one or more data slices, based on valuation of the attributes. A performance measurement of the ML model over is computed for each data slice, based on an application of the ML model on each data instance that is mapped to the data slice. A Determination whether ML model adheres to a target performance requirement may be performed based on the performance measurements of the data slices.
    Type: Grant
    Filed: June 10, 2019
    Date of Patent: August 9, 2022
    Assignee: International Business Machines Corporation
    Inventors: Rachel Brill, Eitan Farchi, Orna Raz, Aviad Zlotnick
  • Publication number: 20220245427
    Abstract: There is provided a computer-implemented method of identifying a finding in an input sub-dataset of an input dataset using an ensemble of machine learning (ML) models, comprising: obtaining outcomes of an ensemble of ML models generated in response to feeding each input sub-dataset of the input dataset into each ML model of the ensemble trained to generate an outcome indicative of likelihood of a finding depicted in a respective input sub-dataset, creating a two dimensional (2D) outcome dataset storing outcomes of the ensemble, wherein a first dimension denotes the respective ML model and a second dimension denotes the respective input sub-dataset, computing a filtered 2D dataset by applying a filter function to the 2D outcome dataset, and analyzing the filtered 2D dataset to identify specific input sub-dataset(s) likely depicting the finding.
    Type: Application
    Filed: February 4, 2021
    Publication date: August 4, 2022
    Inventor: Aviad Zlotnick
  • Patent number: 11393587
    Abstract: Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes accessing a database storing associations between objects included in medical images and classifications of the objects. A risk assessment model adapted to determine a risk condition for an object is accessed, the assessment based on features of the object. Risk conditions associated with respective objects are determined based on the risk assessment model. A group of objects associated with a first risk condition is identified. An interactive user interface is generated for display, the user interface concurrently displaying images of the group of objects. The interactive user interface enables a user to select subsets of images to be concurrently assigned a user-selected classification. User selected classifications are provided to a machine learning system adapted to update the risk assessment model based on the classifications to increase accuracy of the model.
    Type: Grant
    Filed: December 4, 2017
    Date of Patent: July 19, 2022
    Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
    Inventors: Aviad Zlotnick, Ayelet Akselrod-Ballin, Murray A. Reicher, Sivan Ravid
  • Publication number: 20220207288
    Abstract: Aspects of an embodiment of the present invention disclose a method, computer program product, and computing system for selecting representative scores. A processor receives first scores generated by a plurality of object part classifiers classifying a first part of an object. A processor also receives second scores generated by the plurality of object part classifiers classifying a second part of the object. A processor also determines a first aggregation of the first scores. A processor also determines a second aggregation of the second scores. A processor also selects the representative scores from the first scores and the second scores based on a comparison between the first aggregation and the second aggregation.
    Type: Application
    Filed: December 26, 2020
    Publication date: June 30, 2022
    Inventors: Aviad Zlotnick, Flora Gilboa-Solomon, Michal Flato
  • Publication number: 20220101068
    Abstract: Outlier detection using a Deep Neural Network (DNN) includes running a trained DNN model on an received input item. A first feature vector is extracted from the input item and quantized to discrete values. A first number of special t-way feature combinations are computed in the input item and compared against a computed threshold. Based on the comparison, the input item is flagged as an outlier and an alert is generated notifying of the flagged input item.
    Type: Application
    Filed: September 30, 2020
    Publication date: March 31, 2022
    Inventor: Aviad Zlotnick
  • Patent number: 11288797
    Abstract: Embodiments may include techniques to choose a model based on a similarity of computed features of an input to computed features of several models in order to improve feature analysis using Machine Learning models. A method of image analysis may comprise extracting a training feature vector corresponding to each of the plurality of machine learning models from each validation image from a plurality of machine learning models trained using a plurality of validation images, extracting from a new image a new feature vector corresponding to each of the plurality of machine learning models, comparing each new feature vector corresponding to each machine learning model with the training feature vector corresponding to each of the plurality of machine learning models, and selecting and outputting an inference for the new image generated by the machine learning model for which the new feature vector and the training feature vector are most similar.
    Type: Grant
    Filed: July 8, 2020
    Date of Patent: March 29, 2022
    Assignee: International Business Machines Corporation
    Inventors: Flora Gilboa-Solomon, Efrat Hexter, Dana Levanony, Aviad Zlotnick
  • Publication number: 20220035489
    Abstract: There is provided a method for monitoring interaction with 3D medical images, comprising: dividing the 3D image into a sequence of a 2D images, arranging the sequence into slabs each including at least one 2D image, computing, for each respective slab, a minimal amount of viewing time a user is predicted to spend viewing the respective slab, monitoring, while the 3D medical image is presented on a display, an amount of time a user actually spent viewing portions of the 3D medical image corresponding to each of the of slabs, in response to the amount of time spent viewing a certain portion of the 3D medical image being less than the computed minimal amount of viewing time of a certain slab corresponding to the certain portion, generating instructions for implementation by a user interface indicative of the amount of time spent being less than the computed minimal amount of time.
    Type: Application
    Filed: July 30, 2020
    Publication date: February 3, 2022
    Inventors: FLORA GILBOA-SOLOMON, AVIAD ZLOTNICK
  • Publication number: 20220036612
    Abstract: There is provided a computer implemented method for generating summary images from 3D medical images, comprising: receiving a 3D medical image, dividing the 3D medical images into a sequence of a plurality 2D images, computing a similarity dataset indicative of an amount of similarity between each pair of the plurality of 2D images, segmenting the similarity dataset into a plurality of slabs by minimizing the amount of similarity between consecutive slabs and maximizing the amount of similarity within each slab, aggregating, for each respective slab, the plurality of 2D images into a respective summary image, and presenting on a display, the respective summary image.
    Type: Application
    Filed: July 30, 2020
    Publication date: February 3, 2022
    Inventors: FLORA GILBOA-SOLOMON, Aviad Zlotnick
  • Publication number: 20220012872
    Abstract: Embodiments may include techniques to choose a model based on a similarity of computed features of an input to computed features of several models in order to improve feature analysis using Machine Learning models. A method of image analysis may comprise extracting a training feature vector corresponding to each of the plurality of machine learning models from each validation image from a plurality of machine learning models trained using a plurality of validation images, extracting from a new image a new feature vector corresponding to each of the plurality of machine learning models, comparing each new feature vector corresponding to each machine learning model with the training feature vector corresponding to each of the plurality of machine learning models, and selecting and outputting an inference for the new image generated by the machine learning model for which the new feature vector and the training feature vector are most similar.
    Type: Application
    Filed: July 8, 2020
    Publication date: January 13, 2022
    Inventors: FLORA GILBOA-SOLOMON, EFRAT HEXTER, DANA LEVANONY, AVIAD ZLOTNICK
  • Publication number: 20210225466
    Abstract: There is provided a system and a method of generating an annotated structured dataset, comprising: receiving a medical classification term, searching over the unstructured patient data for extracting unclassified unstructured text fragments, presenting a subset of the unclassified unstructured text fragments, receiving an indication of a selection of none or at least one of the text fragments, and one of: (i) classifying non-selected unclassified unstructured text fragments according to the medical classification term, and classifying selected text fragments as not satisfying the medical classification term, and (ii) classifying selected unclassified unstructured text fragments according to the medical classification term, and classifying non-selected unclassified unstructured text fragments as not satisfying the medical classification term, and iterating the searching, and/or the presenting, until no text fragments are obtained by the search, wherein the annotated structured dataset is created by the classif
    Type: Application
    Filed: January 20, 2020
    Publication date: July 22, 2021
    Inventors: ELLA BARKAN, AVIAD ZLOTNICK
  • Publication number: 20210150763
    Abstract: A method and system for reducing the number of colors per pixel present in an image to increase the ability to detect objects or anomalies in the image. A final number of colors per pixel to reduce the image to is determined, wherein the final number of colors is a number of colors less than the number of colors per pixel in the original image. A corresponding threshold value for each of the final number of colors is identified, such that the corresponding threshold values optimize an arithmetic combination of separation score functions applied to the plurality of pixels and the threshold values. The image is reduced to the final number of colors per pixel, by creating an output image where a value of each output pixel is equal to the number of threshold values that are less than the value of the corresponding input pixel.
    Type: Application
    Filed: November 25, 2020
    Publication date: May 20, 2021
    Inventor: Aviad Zlotnick
  • Patent number: 11003567
    Abstract: A method, system and computer program product, the method comprising: receiving a user document describing at least a part of a system to be tested; computing a similarity measurement between the user document and documents in a document corpus, each describing at least part of a system and associated with a combinatorial model representing the at least part of the system, wherein the combinatorial model comprises a set of attributes, a respective domain for each attribute defining possible values for the attribute, and restrictions, wherein each restriction comprises at least one attribute, a respective value and a logical operator, based upon the similarity measurement, selecting selected documents from the document corpus; obtaining one or more combinatorial models corresponding to the selected documents; selecting elements from the combinatorial models; generating an initial combinatorial model for the user document, wherein said generating comprises adding the elements to the initial combinatorial model.
    Type: Grant
    Filed: December 6, 2017
    Date of Patent: May 11, 2021
    Assignee: International Business Machines Corporation
    Inventors: Howard Hess, Eitan Farchi, Orna Raz, Rachel Tzoref-Brill, Aviad Zlotnick
  • Publication number: 20210125080
    Abstract: A method, system and computer program product, the method comprising: creating a model representing underperforming cases; from a case collection having a total performance, and which comprises for each of a multiplicity of records: a value for each feature from a collection of features, a ground truth label and a prediction of a machine learning (ML) engine, obtaining one or more features; dividing the records into groups, based on values of the features in each record; for one group of the groups, calculating a performance parameter of the ML engine over the portion of the records associated with the group; subject to the performance parameter of the group being below the total performance in at least a predetermined threshold: determining a characteristic for the group; adding the characteristic of the group to the model; and providing the model to a user, thus indicating under-performing parts of the test collection.
    Type: Application
    Filed: October 24, 2019
    Publication date: April 29, 2021
    Inventors: ORNA RAZ, Marcel Zalmanovici, Aviad Zlotnick
  • Publication number: 20210117775
    Abstract: An example system includes a processor to train a neural network model using annotated training data to generate features. The processor is to select a feature vector of the neural network model. The processor is to execute an inference stage on the annotated training data via the neural network model to generate a first set of values corresponding to the annotated training data for features in the selected feature vector. The processor is to execute the inference stage on unannotated data to generate a second set of values corresponding to the unannotated data for the features in the selected feature vector. The processor is to select an item in unannotated data that matches an uncovered combination of feature values in the annotated training data. The processor is to send the selected item for annotation and receive a corresponding additional annotated item to be added to the annotated training data.
    Type: Application
    Filed: October 22, 2019
    Publication date: April 22, 2021
    Inventor: Aviad Zlotnick
  • Publication number: 20210004671
    Abstract: A method, apparatus and a computer program product for automated data slicing based on an Artificial Neural Network (ANN). The method comprising: obtaining an ANN, wherein the ANN is configured to provide a prediction for a data instance, wherein the ANN comprises a set of nodes having interconnections therebetween; determining an attribute vector based on a subset of the nodes of the ANN; determining, based on the attribute vector, a plurality of data slices; obtaining a testing dataset comprising testing data instances; computing, for each data slice, a performance measurement of the ANN over the data slice, wherein said computing is based on an application of the ANN on each testing data instance that is mapped to the data slice; and performing an action based on at least a portion of the performance measurements of the data slices.
    Type: Application
    Filed: July 3, 2019
    Publication date: January 7, 2021
    Inventors: RACHEL BRILL, Eitan Farchi, Orna Raz, Aviad Zlotnick
  • Patent number: 10878596
    Abstract: A method and system for reducing the number of colors per pixel present in an image to increase the ability to detect objects or anomalies in the image. A final number of colors per pixel to reduce the image to is determined, wherein the final number of colors is a number of colors less than the number of colors per pixel in the original image. A corresponding threshold value for each of the final number of colors is identified, such that the corresponding threshold values optimize an arithmetic combination of separation score functions applied to the plurality of pixels and the threshold values. The image is reduced to the final number of colors per pixel, by creating an output image where a value of each output pixel is equal to the number of threshold values that are less than the value of the corresponding input pixel.
    Type: Grant
    Filed: November 29, 2018
    Date of Patent: December 29, 2020
    Assignee: International Business Machines Corporation
    Inventor: Aviad Zlotnick
  • Publication number: 20200387753
    Abstract: A method and a computer program product for identification and improvement of machine learning (ML) under-performance The method comprises slicing data of ML model based on a functional model representing requirements of a system utilizing the ML model. The functional model comprises a set of attributes and respective domain of values. Each data slice is associated with a different valuation of one or more attributes of the functional model. Each data instance of the ML model is mapped to one or more data slices, based on valuation of the attributes. A performance measurement of the ML model over is computed for each data slice, based on an application of the ML model on each data instance that is mapped to the data slice. A Determination whether ML model adheres to a target performance requirement may be performed based on the performance measurements of the data slices.
    Type: Application
    Filed: June 10, 2019
    Publication date: December 10, 2020
    Inventors: Rachel Brill, Eitan Farchi, Orna Raz, Aviad Zlotnick
  • Patent number: 10832407
    Abstract: In some examples, a system for training a neural network can include a processor to detect a trained neural network application. The processor can also detect a set of images, wherein the neural network application is not trained with the set of images. Additionally, the processor can train an adapter network based on the trained neural network application and the set of images, wherein the adapter network is to be trained by freezing weights of the trained neural network and modifying weights of the adapter network. Furthermore, the processor can use the trained adapter network to process at least one additional image, the processed additional image to be transmitted to the trained neural network to generate an output signal.
    Type: Grant
    Filed: March 6, 2019
    Date of Patent: November 10, 2020
    Assignee: International Business Machines Corporation
    Inventors: Alon Hazan, Yoel Shoshan, Vadim Ratner, Aviad Zlotnick, Flora Gilboa
  • Publication number: 20200342310
    Abstract: A method, apparatus and product for identifying data drifts. The method comprising: obtaining a seen dataset, wherein the seen dataset comprises seen instances, each of which comprising feature values in a feature space; determining a first measurement of a statistical metric of the seen dataset; obtaining an unseen dataset, wherein the unseen dataset comprises unseen instances, each of which comprising features values in the feature space; determining a second measurement of the statistical metric of the unseen dataset; identifying a data drift in the unseen dataset with respect to the seen dataset based on the first and second measurements of the statistical metric; and performing a responsive action based on the identification of the data drift.
    Type: Application
    Filed: April 28, 2019
    Publication date: October 29, 2020
    Inventors: Eitan Farchi, Orna Raz, Marcel Zalmanovici, Aviad Zlotnick
  • Patent number: 10810737
    Abstract: A method comprising receiving a digital mammogram of a human breast; automatically extracting a contour boundary of said breast in said digital mammogram; automatically calculating a convex hull for said contour boundary; automatically comparing said contour boundary to said convex hull, to detect a plurality of gap segments, wherein each of said plurality of gap segments has an associated inflection point; and automatically determining a nipple location along said contour boundary as between a pair of said inflection points.
    Type: Grant
    Filed: October 18, 2018
    Date of Patent: October 20, 2020
    Assignee: International Business Machines Corporation
    Inventors: Guy Amit, Aviad Zlotnick