Patents by Inventor Hassan SAWAF

Hassan SAWAF 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).

  • Publication number: 20210298711
    Abstract: A mobile device application prompts and conducts audio and/or video tests using a microphone on a smartphone, tablet or laptop in order to record and analyze a patient's speech, cough, breathing and other sounds in order to diagnose the patient with Covid 19, another ailment, or as having normal ranges not indicative of disease. The mobile device's tests and protocols use program instructions, AI processing and other automated tools to facilitate the speed and reliability of the testing.
    Type: Application
    Filed: March 25, 2021
    Publication date: September 30, 2021
    Applicant: Applications Technology (AppTek), LLC
    Inventors: Shahnaz MIRI, Yasar Torres YAGHI, Fernando PAGAN, Mudar YAGHI, Sanjeev KHUDANPUR, Jan TRMAL, Hassan SAWAF, Jintao JIANG, Mazda EBRAHIMI
  • Patent number: 10891673
    Abstract: A semantic analysis can be performed to determine an intent of a received query. The intent can relate to a primary object of the query, which can be identified through the semantic analysis. Other attributes can be determined from the query that help to focus the object of the intent. A query vector is generated, based on the intent and primary object, and used to search a multi-dimensional semantic space including semantic representations of possible matches. The attributes are used to adjust the query vector in the semantic space. Objects having vectors ending proximate the query vector are identified as potential search results, with the distance from the query vector being used as a ranking mechanism. If refinement is needed, a dialog is used to obtain additional information from the user. Once results are obtained with sufficient confidence, results can be returned as search results for the query.
    Type: Grant
    Filed: December 22, 2016
    Date of Patent: January 12, 2021
    Assignee: A9.com, Inc.
    Inventor: Hassan Sawaf
  • Publication number: 20200193098
    Abstract: In a flow of computer actions, a computer system (110) receives a request involving a machine translation. In performing the translation (160, 238), or in using the translation in subsequent computer operations (242, 1110), the computer system takes into account known statistical relationships (310), obtained from previously accumulated click-through data (180), between a machine translation performed in a flow, the flow's portions preceding the translation, and success indicators pertaining to the flow's portion following the translation. The statistical relationships are derived by data mining of the click-through data. Further, normal actions can be suspended to use a random option to accumulate the click-through data and/or perform statistical AB testing. Other features are also provided.
    Type: Application
    Filed: October 21, 2019
    Publication date: June 18, 2020
    Inventor: Hassan SAWAF
  • Publication number: 20200151793
    Abstract: A method of propagating annotations of content items to a search query is disclosed. A strength of a correspondence between a search query and a listing of an item on a network-based publication system is determined. The strength of the correspondence is based on an analysis of a set of actions by a set of users who submitted the search query. A set of annotations is generated. The set of annotations is propagated to a search engine and used to enhance search results.
    Type: Application
    Filed: January 3, 2020
    Publication date: May 14, 2020
    Inventors: Jean-David Ruvini, Sunil Mohan, Smruthi Mukund, Hassan Sawaf
  • Patent number: 10650442
    Abstract: Information is provided a user of a mobile device. Media content is sensed by the mobile device and sent to an information processing server. The information processing server also obtains media content from another source and associates at least a portion of the obtained media content with a product or service offer. The sensed media content is correlated to the obtained media content such that an associated buy or service offer is selected. The buy or service offer is sent to the mobile device for display to the user.
    Type: Grant
    Filed: March 15, 2013
    Date of Patent: May 12, 2020
    Inventors: Amro Shihadah, Mohammad Shihadah, Hassan Sawaf
  • Publication number: 20200133948
    Abstract: According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.
    Type: Application
    Filed: December 23, 2019
    Publication date: April 30, 2020
    Inventors: Selcuk Kopru, Sanjika Hewavitharana, Hassan Sawaf
  • Patent number: 10586265
    Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.
    Type: Grant
    Filed: April 10, 2018
    Date of Patent: March 10, 2020
    Assignee: PAYPAL, INC.
    Inventors: Marc Delingat, Hassan Sawaf, Kiran Reddy Nagarur, Yoram Vardi, Alex Cozzi
  • Patent number: 10559018
    Abstract: A method of propagating annotations of content items to a search query is disclosed. A strength of a correspondence between a search query and a listing of an item on a network-based publication system is determined. The strength of the correspondence is based on an analysis of a set of actions by a set of users who submitted the search query. A set of annotations is generated. The set of annotations is propagated to a search engine and used to enhance search results.
    Type: Grant
    Filed: October 19, 2017
    Date of Patent: February 11, 2020
    Assignee: EBAY INC.
    Inventors: Jean-David Ruvini, Sunil Mohan, Smruthi Mukund, Hassan Sawaf
  • Patent number: 10552548
    Abstract: A method of forming parallel corpora comprises receiving sets of items in first language and second languages, each of the sets having one or more associated descriptions and metadata. The metadata is collected from the two sets of items and are aligned using the metadata. The aligned metadata are mapped from the first language to the second language for each of the sets. The descriptions of two items are fetched and the structural similarity of the descriptions is measured to assess whether two items are likely to be translations of each other. For mapped items with structurally similar descriptions, the mapped item descriptions are formed into respective sentences in first language and in the second language. The sentences are parallel corpora which may be used to translate an item from the first language to the second language, and also to train a machine translation system.
    Type: Grant
    Filed: January 30, 2018
    Date of Patent: February 4, 2020
    Assignee: PAYPAL, INC.
    Inventors: Jean-David Ruvini, Hassan Sawaf, Derek Barnes
  • Patent number: 10540357
    Abstract: According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.
    Type: Grant
    Filed: March 21, 2016
    Date of Patent: January 21, 2020
    Assignee: EBAY INC.
    Inventors: Selcuk Kopru, Sanjika Hewavitharana, Hassan Sawaf
  • Publication number: 20190362401
    Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.
    Type: Application
    Filed: May 7, 2019
    Publication date: November 28, 2019
    Inventors: Sanjika Hewavitharana, Evgeny Matusov, Robinson Piramuthu, Hassan Sawaf
  • Patent number: 10452786
    Abstract: In a flow of computer actions, a computer system (110) receives a request involving a machine translation. In performing the translation (160, 238), or in using the translation in subsequent computer operations (242, 1110), the computer system takes into account known statistical relationships (310), obtained from previously accumulated click-through data (180), between a machine translation performed in a flow, the flow's portions preceding the translation, and success indicators pertaining to the flow's portion following the translation. The statistical relationships are derived by data mining of the click-through data. Further, normal actions can be suspended to use a random option to accumulate the click-through data and/or perform statistical AB testing. Other features are also provided.
    Type: Grant
    Filed: December 29, 2014
    Date of Patent: October 22, 2019
    Assignee: PayPal, Inc.
    Inventor: Hassan Sawaf
  • Patent number: 10319019
    Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.
    Type: Grant
    Filed: September 14, 2016
    Date of Patent: June 11, 2019
    Assignee: eBay Inc.
    Inventors: Sanjika Hewavitharana, Evgeny Matusov, Robinson Piramuthu, Hassan Sawaf
  • Patent number: 10255910
    Abstract: Deep Neural Networks (DNN) are time shifted relative to one another and trained. The time-shifted networks may then be combined to improve recognition accuracy. The approach is based on an automatic speech recognition (ASR) system using DNN and using time shifted features. Initially, a regular ASR model is trained to produce a first trained DNN. Then a top layer (e.g., SoftMax layer) and the last hidden layer (e.g., Sigmoid) are fine-tuned with same data set but with a feature window left- and right-shifted to create respective second and third left-shifted and right-shifted DNNs. From these three DNN networks, four combination networks may be generated: left- and right-shifted, left-shifted and centered, centered and right-shifted, and left-shifted, centered, and right-shifted. The centered networks are used to perform the initial (first-pass) ASR. Then the other six networks are used to perform rescoring.
    Type: Grant
    Filed: September 18, 2017
    Date of Patent: April 9, 2019
    Assignee: AppTek, Inc.
    Inventors: Mudar Yaghi, Hassan Sawaf, Jinato Jiang
  • Patent number: 10235991
    Abstract: A hybrid frame, phone, diphone, morpheme, and word-level Deep Neural Networks (DNN) in model training and applications-is based on training a regular ASR system, which can be based on Gaussian Mixture Models (GMM) or DNN. All the training data (in the format of features) are aligned with the transcripts in terms of phonemes and words with the timing information and new features are formed in terms of phonemes, diphones, morphemes, and up to words. Regular ASR produces a result lattice with timing information for each word. A feature is then extracted and sent to the word-level DNN for scoring Phoneme features are sent to corresponding DNNs for training. Scores are combined to form the word level scores, a rescored lattice and a new recognition result.
    Type: Grant
    Filed: August 9, 2017
    Date of Patent: March 19, 2019
    Assignee: AppTek, Inc.
    Inventors: Jintao Jiang, Hassan Sawaf, Mudar Yaghi
  • Publication number: 20180357698
    Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.
    Type: Application
    Filed: April 10, 2018
    Publication date: December 13, 2018
    Inventors: Marc Delingat, Hassan Sawaf, Kiran Reddy Nagarur, Yoram Vardi, Alex Cozzi
  • Publication number: 20180253421
    Abstract: A method of forming parallel corpora comprises receiving sets of items in first language and second languages, each of the sets having one or more associated descriptions and metadata. The metadata is collected from the two sets of items and are aligned using the metadata. The aligned metadata are mapped from the first language to the second language for each of the sets. The descriptions of two items are fetched and the structural similarity of the descriptions is measured to assess whether two items are likely to be translations of each other. For mapped items with structurally similar descriptions, the mapped item descriptions are formed into respective sentences in first language and in the second language. The sentences are parallel corpora which may be used to translate an item from the first language to the second language, and also to train a machine translation system.
    Type: Application
    Filed: January 30, 2018
    Publication date: September 6, 2018
    Inventors: Jean-David Ruvini, Hassan Sawaf, Derek Barnes
  • Patent number: 9940658
    Abstract: A user query for items is received in a first language and translated from the first language to a second language. A result set in the second language that meets the query is obtained and is translated into the first language for presentation to the user. User feedback is used to build an ontology for optimizing the translation from the first language to the second language based on query context and the feedback. Query context may include information determined by learning semantic relationships between keywords in the query. Optimizing may include building an ontology used by a machine translator to translate key words from the first language to the second language. The number of items in the result set are measured or information is abstracted from the feedback and correlated to ontological information of the result set. The system adapts to changes in meanings in the first language over time.
    Type: Grant
    Filed: December 30, 2014
    Date of Patent: April 10, 2018
    Assignee: PAYPAL, INC.
    Inventors: Marc Delingat, Hassan Sawaf, Kiran Reddy Nagarur, Yoram Vardi, Alex Cozzi
  • Publication number: 20180082677
    Abstract: Deep Neural Networks (DNN) are time shifted relative to one another and trained. The time-shifted networks may then be combined to improve recognition accuracy. The approach is based on an automatic speech recognition (ASR) system using DNN and using time shifted features. Initially, a regular ASR model is trained to produce a first trained DNN. Then a top layer (e.g., SoftMax layer) and the last hidden layer (e.g., Sigmoid) are fine-tuned with same data set but with a feature window left- and right-shifted to create respective second and third left-shifted and right-shifted DNNs. From these three DNN networks, four combination networks may be generated: left- and right-shifted, left-shifted and centered, centered and right-shifted, and left-shifted, centered, and right-shifted. The centered networks are used to perform the initial (first-pass) ASR. Then the other six networks are used to perform rescoring.
    Type: Application
    Filed: September 18, 2017
    Publication date: March 22, 2018
    Applicant: Apptek, Inc.
    Inventors: Mudar YAGHI, Hassan SAWAF, Jinato JIANG
  • Publication number: 20180075508
    Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.
    Type: Application
    Filed: September 14, 2016
    Publication date: March 15, 2018
    Inventors: Sanjika Hewavitharana, Evgeny Matusov, Robinson Piramuthu, Hassan Sawaf