Patents by Inventor Oren Sar-Shalom

Oren Sar-Shalom 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: 11893608
    Abstract: A method may be used to predict a business' category by analyzing the business' vendors. A neural network architecture may be trained via supervised learning to predict categories for businesses based on listed vendors. The neural network may be used to classify uncategorized businesses within an accounting software database. A list of factors associated with a business' success may be generated by analyzing, aggregating and ranking factors determined to be relevant to a business based on its categorization. The factors associated with the business' success may be related to the products and/or services offered by the business and the format of which those products and/or services are offered by the business. The factors may also be related to the products and/or services purchased by the business from a vendor and the format of which those products and/or services are purchased from the vendor.
    Type: Grant
    Filed: March 13, 2020
    Date of Patent: February 6, 2024
    Assignee: INTUIT INC.
    Inventors: Shlomi Medalion, Yair Horesh, Yehezkel Shraga Resheff, Sigalit Bechler, Oren Sar Shalom, Daniel Ben David
  • Patent number: 11893351
    Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.
    Type: Grant
    Filed: August 22, 2022
    Date of Patent: February 6, 2024
    Assignee: Intuit Inc.
    Inventors: Oren Sar Shalom, Yehezkel Shraga Resheff
  • Publication number: 20240037342
    Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
    Type: Application
    Filed: October 6, 2023
    Publication date: February 1, 2024
    Applicant: INTUIT INC.
    Inventors: Rami COHEN, Noa HAAS, Oren Sar SHALOM, Alexander ZHICHAREVICH
  • Patent number: 11875116
    Abstract: A method including inputting, into a phrase recognition model comprising a neural network, a vector comprising a plurality of ngrams of text. The method also includes applying, using the phrase recognition model, a filter to the plurality of ngrams during execution. The filter has a skip word setting of at least one. The method also includes determining, based on the skip word setting, at least one ngram in the vector to be skipped to form at least one skip word. The method also includes outputting an intermediate score for a set of ngrams that match the filter. The method also includes calculating a scalar number representing a semantic meaning of the at least one skip word. The method also includes generating based on the scalar number and the intermediate score, a final score for the set of ngrams. A computer action is performed using the final score.
    Type: Grant
    Filed: December 20, 2019
    Date of Patent: January 16, 2024
    Assignee: Intuit Inc.
    Inventors: Oren Sar Shalom, Alexander Zhicharevich, Adi Shalev, Yehezkel Shraga Resheff
  • Patent number: 11860949
    Abstract: Automatic keyphrase labeling and machine learning training may include a processor extracting a plurality of keywords from at least one search query that resulted in a selection of a document appearing in a search result. For each of the plurality of keywords, the processor may determine a probability that the keyword describes the document. The processor may generate one or more keyphrases by performing processing including selecting each of the plurality of keywords having a probability greater than a predetermined threshold value for insertion into at least one of the one or more keyphrases and assembling the one or more keyphrases from the selected plurality of keywords. The processor may label the document with the keyphrase.
    Type: Grant
    Filed: January 4, 2022
    Date of Patent: January 2, 2024
    Assignee: INTUIT INC.
    Inventors: Yair Horesh, Yehezkel Shraga Resheff, Oren Sar Shalom, Alexander Zhicharevich
  • Patent number: 11822891
    Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
    Type: Grant
    Filed: March 7, 2023
    Date of Patent: November 21, 2023
    Assignee: INTUIT INC.
    Inventors: Rami Cohen, Noa Haas, Oren Sar Shalom, Alexander Zhicharevich
  • Patent number: 11736428
    Abstract: An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.
    Type: Grant
    Filed: June 24, 2019
    Date of Patent: August 22, 2023
    Assignee: International Business Machines Corporation
    Inventors: Devin A. Conley, Priscilla S. Moraes, Lakshminarayanan Krishnamurthy, Oren Sar-Shalom
  • Patent number: 11727058
    Abstract: A method involves receiving search queries, having search terms, submitted to at least one computerized search engine. For each query, a corresponding pairwise relation in the search queries is calculated. The corresponding pairwise relation is a corresponding probability of a potential edge relationship between at least two terms. Thus, potential edges are formed. A general graph of the terms is constructed by selecting edges from the potential edges. The general graph is nodes representing the terms used in the search queries. The general graph also is edges representing semantic relationships among the nodes. A hierarchical graph is constructed from the general graph by altering at least one of the edges among the nodes in the general graph to form the hierarchical graph.
    Type: Grant
    Filed: September 17, 2019
    Date of Patent: August 15, 2023
    Assignee: Intuit Inc.
    Inventors: Oren Sar Shalom, Alexander Zhicharevich, Rami Cohen, Yonatan Ben-Simhon
  • Publication number: 20230222292
    Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
    Type: Application
    Filed: March 7, 2023
    Publication date: July 13, 2023
    Applicant: INTUIT INC.
    Inventors: Rami COHEN, Noa HAAS, Oren Sar SHALOM, Alexander ZHICHAREVICH
  • Patent number: 11687612
    Abstract: A method for mitigating cold starts in recommendations includes receiving a request that identifies a requested page and identifying a content vector of the requested page. The content vector is generated based on providing text of the requested page to a neural network text encoder. The method further includes selecting, based on the content vector, a link to a cold start page that does not satisfy a threshold level of interaction data. The selected link is ranked above a second link to a warm page that does satisfy the threshold level of the interaction data. The method further includes presenting the requested page with the selected link.
    Type: Grant
    Filed: November 19, 2021
    Date of Patent: June 27, 2023
    Assignee: INTUIT INC
    Inventors: Elik Sror, Oren Sar Shalom, Rami Cohen
  • Patent number: 11688393
    Abstract: A method including embedding, by a trained issue MLM (machine learning model), a new natural language issue statement into an issue vector. An inner product of the issue vector with an actions matrix is calculated. The actions matrix includes centroid-vectors calculated using a clustering method from a second output of a trained action MLM which embedded prior actions expressed in natural language action statements taken as a result of prior natural issue statements. Calculating the inner product results in probabilities associated with the prior actions. Each of the probabilities represents a corresponding estimate that a corresponding prior action is relevant to the issue vector. A list of proposed actions relevant to the issue vector is generated by comparing the probabilities to a threshold value and selecting a subset of the prior actions with corresponding probabilities above the threshold. The list of proposed actions is transmitted to a user device.
    Type: Grant
    Filed: December 30, 2021
    Date of Patent: June 27, 2023
    Assignee: INTUIT INC
    Inventors: Shlomi Medalion, Alexander Zhicharevich, Yair Horesh, Oren Sar Shalom, Elik Sror, Adi Shalev
  • Patent number: 11677705
    Abstract: An approach is provided that receives a message and applies a deep analytic analysis to the message. The deep analytic analysis results in a set of enriched message embedding (EME) data that is passed to a trained neural network. Based on a set of scores received from the trained neural network, a conversation is identified from a number of available conversations to which the received message belongs. The received first message is then associated with the identified conversation.
    Type: Grant
    Filed: April 23, 2019
    Date of Patent: June 13, 2023
    Assignee: International Business Machines Corporation
    Inventors: Devin A. Conley, Priscilla S. Moraes, Lakshminarayanan Krishnamurthy, Oren Sar-Shalom
  • Publication number: 20230118323
    Abstract: In one embodiment, one or more computing systems may determine a first set of bins that collectively cover a pre-determined numerical range with each bin covering a sub-range of the pre-determined range. The system may determine a second set of bins that collectively cover the pre-determined range with each covers a different but overlapping sub-range with respect to a corresponding bin in the first bin set. The system may access a value that falls within the pre-determined range. The system may determine that the value falls within a first bin of the first bin set and a second bin of the second bin set. The system may determine a positive value for each the first and second bins. The positive values indicate an association level of the value with the first and second bins. The system may determine a representation of the value based on the positive values.
    Type: Application
    Filed: October 19, 2022
    Publication date: April 20, 2023
    Inventors: Hagay Lupesko, Chuan Jiang, Andrey Malevich, Oren Sar Shalom
  • Patent number: 11625609
    Abstract: 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: Grant
    Filed: June 14, 2018
    Date of Patent: April 11, 2023
    Assignee: International Business Machines Corporation
    Inventors: Boaz Carmeli, Guy Hadash, Einat Kermany, Ofer Lavi, Guy Lev, Oren Sar-Shalom
  • Patent number: 11625541
    Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
    Type: Grant
    Filed: April 27, 2021
    Date of Patent: April 11, 2023
    Assignee: INTUIT INC.
    Inventors: Rami Cohen, Noa Haas, Oren Sar Shalom, Alexander Zhicharevich
  • Patent number: 11551282
    Abstract: This disclosure provides systems, methods and apparatuses for recommending items to users of a recommendation system. In some implementations, the recommendation system determines a plurality of contribution values based on interactions between a plurality of users and a plurality of items. Each of the plurality of contribution values represents a confidence level that a respective user prefers a respective item. The recommendation system further determines a capacity of each of the plurality of items. The capacity of each item represents a maximum number of users to which the item can be recommended. The recommendation system recommends one or more items of the plurality of items to each of the plurality of users based at least in part on the plurality of contribution values and the capacities of the plurality of items.
    Type: Grant
    Filed: July 27, 2020
    Date of Patent: January 10, 2023
    Assignee: Intuit Inc.
    Inventors: Shlomi Medalion, Sigalit Bechler, Oren Sar Shalom, Guy Maman
  • Publication number: 20220405476
    Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.
    Type: Application
    Filed: August 22, 2022
    Publication date: December 22, 2022
    Applicant: Intuit Inc.
    Inventors: Oren Sar Shalom, Yehezkel Shraga Resheff
  • Patent number: 11494701
    Abstract: A method includes generating recommendations and user structures by applying a recommender machine learning model to training user information and item information, and generating, from the user structures and by applying a demographic machine learning model, demographic predictions of users represented by the user structures. The method further includes generating a first accuracy measure of the demographic machine learning model based on a first comparison of the demographic predictions with demographics of the users. A recommender loss function is generated based on the first accuracy measure and a second comparison of the recommendations with selections of users, where the recommender loss function uses the first accuracy measure to suppress detectability by the demographic machine learning model. The method further includes updating the recommender machine learning model according to the recommender loss function.
    Type: Grant
    Filed: November 29, 2018
    Date of Patent: November 8, 2022
    Assignee: Intuit Inc.
    Inventors: Yehezkal Shraga Resheff, Shimon Shahar, Oren Sar Shalom, Yanai Elazar
  • Publication number: 20220343080
    Abstract: A computing system identifies an incoming voice call from a user device to an agent device associated with the computing system. The computing system generates a transcription of the incoming voice call using one or more natural language processing techniques. The computing system extracts a problem description from the transcription. The problem description indicates a topic for the incoming voice call. A first machine learning model estimates a situation vector from the problem description. A second machine learning model identifies a pre-existing situation vector that closely matches the estimated situation vector. The computing system retrieves a situation description that corresponds to the identified pre-existing situation vector.
    Type: Application
    Filed: April 27, 2021
    Publication date: October 27, 2022
    Applicant: INTUIT INC.
    Inventors: Rami COHEN, Noa HAAS, Oren Sar SHALOM, Alexander ZHICHAREVICH
  • Patent number: 11436413
    Abstract: A method including receiving, in a machine learning model (MLM), a corpus including words. The MLM includes layers configured to extract keywords from the corpus, plus a retrospective layer. A first keyword and a second keyword from the corpus are identified in the layers. The first and second keywords are assigned first and second probabilities. Each probability is a likelihood that a keyword is to be included in a key phrase. A determination is made, in the retrospective layer, of a first probability modifier that modifies the first probability based on a first dependence relationship between the second keyword being placed after the first keyword. The first probability is modified using the first probability modifier. The first modified probability is used to determine whether the first keyword and the second keyword together form the key phrase. The key phrase is stored in a non-transitory computer readable storage medium.
    Type: Grant
    Filed: February 28, 2020
    Date of Patent: September 6, 2022
    Assignee: Intuit Inc.
    Inventors: Oren Sar Shalom, Yehezkel Shraga Resheff