Patents by Inventor Guy Lev
Guy Lev 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).
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Publication number: 20240028913Abstract: An example system includes a processor to receive unlabeled data, few-shot training data, and a pre-trained model. The processor can split the unlabeled data into a number of groups corresponding to different perspectives. The processor can generate weakly labeled data for each of the number of groups using a respective associated heuristic. The processor can inter-train a model for each different perspective based on respective weakly labeled data. The processor can fine-tune each inter-trained model based on the few-shot training data for each different perspective to generate a final model for each different perspective.Type: ApplicationFiled: July 21, 2022Publication date: January 25, 2024Inventors: Benjamin SZNAJDER, Noam SLONIM, Eyal SHNARCH, Guy LEV, Sachindra JOSHI, Chulaka GUNASEKARA
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Patent number: 11790239Abstract: A specification of a property required to be upheld by a computerized machine learning system is obtained. A training data set corresponding to the property and inputs and outputs of the system is built. The system is trained on the training data set. Activity of the system is monitored before, during, and after the training. Based on the monitoring, performance of the system is evaluated to determine whether the system, once trained on the training data set, upholds the property.Type: GrantFiled: December 29, 2018Date of Patent: October 17, 2023Assignee: International Business Machines CorporationInventors: George Kour, Guy Hadash, Yftah Ziser, Ofer Lavi, Guy Lev
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Patent number: 11763132Abstract: Detecting sequences of computer-executed operations, including training a BLSTM to determine forward and backward probabilities of encountering each computer-executed operations within a training set of consecutive computer-executed operations in forward and backward execution directions of the operations, and identifying reference sequences of operations within the training set where for each given one of the sequences the forward probability of encountering a first computer-executed operation in the given sequence is below a predefined lower threshold, the forward probability of encountering a last computer-executed operation in the given sequence is above a predefined upper threshold, the backward probability of encountering the last computer-executed operation in the given sequence is below the predefined lower threshold, and the backward probability of encountering the first computer-executed operation in the given sequence is above the predefined upper threshold, and where the predefined lower thresholdType: GrantFiled: June 11, 2019Date of Patent: September 19, 2023Assignee: International Business Machines CorporationInventors: Guy Lev, Boris Rozenberg, Yehoshua Sagron
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Patent number: 11625609Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.Type: GrantFiled: June 14, 2018Date of Patent: April 11, 2023Assignee: International Business Machines CorporationInventors: Boaz Carmeli, Guy Hadash, Einat Kermany, Ofer Lavi, Guy Lev, Oren Sar-Shalom
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Patent number: 11270061Abstract: Embodiments may provide techniques to generate training data for summarization of complex documents, such as scientific papers, articles, etc., that are scalable to provide large scale training data. For example, in an embodiment, a method may be implemented in a computer system and may comprise collecting a plurality of video and audio recordings of presentations of documents, collecting a plurality of documents corresponding to the video and audio recordings, converting the plurality of video and audio recordings of presentations of documents into transcripts of the plurality of presentations, generating a summary of each document by selecting a plurality of sentences from each document using the transcript of the that document, generating a dataset comprising a plurality of the generated summaries, and training a machine learning model using the generated dataset.Type: GrantFiled: February 25, 2020Date of Patent: March 8, 2022Assignee: International Business Machines CorporationInventors: Jonathan Herzig, Achiya Jerbi, David Konopnicki, Guy Lev, Michal Shmueli-Scheuer
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Publication number: 20210264097Abstract: Embodiments may provide techniques to generate training data for summarization of complex documents, such as scientific papers, articles, etc., that are scalable to provide large scale training data. For example, in an embodiment, a method may be implemented in a computer system and may comprise collecting a plurality of video and audio recordings of presentations of documents, collecting a plurality of documents corresponding to the video and audio recordings, converting the plurality of video and audio recordings of presentations of documents into transcripts of the plurality of presentations, generating a summary of each document by selecting a plurality of sentences from each document using the transcript of the that document, generating a dataset comprising a plurality of the generated summaries, and training a machine learning model using the generated dataset.Type: ApplicationFiled: February 25, 2020Publication date: August 26, 2021Inventors: JONATHAN HERZIG, ACHIYA JERBI, DAVID KONOPNICKI, GUY LEV, MICHAL SHMUELI-SCHEUER
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Publication number: 20200394496Abstract: Detecting sequences of computer-executed operations, including training a BLSTM to determine forward and backward probabilities of encountering each computer-executed operations within a training set of consecutive computer-executed operations in forward and backward execution directions of the operations, and identifying reference sequences of operations within the training set where for each given one of the sequences the forward probability of encountering a first computer-executed operation in the given sequence is below a predefined lower threshold, the forward probability of encountering a last computer-executed operation in the given sequence is above a predefined upper threshold, the backward probability of encountering the last computer-executed operation in the given sequence is below the predefined lower threshold, and the backward probability of encountering the first computer-executed operation in the given sequence is above the predefined upper threshold, and where the predefined lower thresholdType: ApplicationFiled: June 11, 2019Publication date: December 17, 2020Inventors: GUY LEV, Boris Rozenberg, YEHOSHUA SAGRON
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Publication number: 20200210848Abstract: A specification of a property required to be upheld by a computerized machine learning system is obtained. A training data set corresponding to the property and inputs and outputs of the system is built. The system is trained on the training data set. Activity of the system is monitored before, during, and after the training. Based on the monitoring, performance of the system is evaluated to determine whether the system, once trained on the training data set, upholds the property.Type: ApplicationFiled: December 29, 2018Publication date: July 2, 2020Inventors: GEORGE KOUR, GUY HADASH, YFTAH ZISER, OFER LAVI, GUY LEV
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Patent number: 10678821Abstract: An example system includes a processor to receive a plurality of object aspects of an object to be evaluated using a process, a structure of the process, a plurality of extracted facts from documents, a tree related to the plurality of object aspects and the structure, and a thesis for each leaf in the tree. The processor is also to relate the extracted facts to the theses in the tree. The processor is to generate a score for each leaf corresponding to a fact in the tree. The processor is to generate a thesis score and a thesis summary for each thesis based on the scores and the summaries of related facts for each thesis. The processor is to further generate a final score for the object based on the thesis scores.Type: GrantFiled: June 6, 2017Date of Patent: June 9, 2020Assignee: International Business Machines CorporationInventors: Boaz Carmeli, Einat Kermany, Ofer Lavi, Guy Lev, Elad Mezuman
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Publication number: 20190385060Abstract: During end-to-end training of a Deep Neural Network (DNN), a differentiable estimator subnetwork is operated to estimate a functionality of an external software application. Then, during inference by the trained DNN, the differentiable estimator subnetwork is replaced with the functionality of the external software application, by enabling API communication between the DNN and the external software application.Type: ApplicationFiled: June 14, 2018Publication date: December 19, 2019Inventors: BOAZ CARMELI, Guy Hadash, Einat Kermany, Ofer Lavi, Guy Lev, Oren Sar-Shalom
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Publication number: 20190147343Abstract: A method, system and computer program product, the method comprising: mutually training, using feedback, a generator and a discriminator of a conditional adversarial generative adversarial networks using training item groups, each item group representing events in a time window, the generator comprises a generator Recurrent Neural Network (RNN), the discriminator comprises a discriminator RNN; receiving by the discriminator, discrete sequential data comprising a sequence of item groups comprising an item group representing events in a time window, and item groups representing events in preceding time windows; altering the sequence of item groups into collections of real numbers and providing them to the discriminator RNN; processing the collections by the discriminator RNN to obtain a probability for the item group to comprise an anomaly, in an unsupervised manner; and providing output to a user, the output based on the probability and indicative of a label for the discrete sequential data.Type: ApplicationFiled: November 15, 2017Publication date: May 16, 2019Inventors: GUY LEV, Matan Ninio, Oren Sar Shalom
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Publication number: 20180349476Abstract: An example system includes a processor to receive a plurality of object aspects of an object to be evaluated using a process, a structure of the process, a plurality of extracted facts from documents, a tree related to the plurality of object aspects and the structure, and a thesis for each leaf in the tree. The processor is also to relate the extracted facts to the theses in the tree. The processor is to generate a score for each leaf corresponding to a fact in the tree. The processor is to generate a thesis score and a thesis summary for each thesis based on the scores and the summaries of related facts for each thesis. The processor is to further generate a final score for the object based on the thesis scores.Type: ApplicationFiled: June 6, 2017Publication date: December 6, 2018Inventors: BOAZ CARMELI, EINAT KERMANY, OFER LAVI, GUY LEV, ELAD MEZUMAN
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Publication number: 20070178885Abstract: A method for challenge-based authentication of a communication entity to an access network. The access network uses a password-based communication protocol. The method comprises a) pre-supplying to the communication entity a challenge, thereby allowing the communication entity to provide a challenge response, b) supplying to the communication entity a password request, c) receiving the challenge response via the password request, and d) authenticating the communication entity if the challenge response is correct. Presupplying may be during a previous IP session, wherein communication entities are simply given challenges for next time they connect to the hotspot. Alternatively presupplying could be during a brief probationary connection that the access network gives to its users.Type: ApplicationFiled: November 28, 2006Publication date: August 2, 2007Applicant: StarHome GmbHInventor: Guy Lev