Patents by Inventor Dan Grunspan

Dan Grunspan 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: 11768842
    Abstract: A search engine for objects in a corpus of document dynamically evaluates search rank of the objects through Natural Language Processing and machine learning. When a search query is received for a first object, the search engine identifies search results including a plurality of source values that are tied to the first object in the corpus of published documents. A search rank is computed for each identified search result based on content of direct textual references to each of the plurality of source values within the corpus of published documents, as well as a weight assigned to each published document. The identified search results are returned according to the computed search rank.
    Type: Grant
    Filed: March 14, 2022
    Date of Patent: September 26, 2023
    Assignee: Bioz, Inc.
    Inventors: Karin Lachmi, Daniel Levitt, Ehud Pardo, Dan Grunspan
  • Publication number: 20220197918
    Abstract: A search engine for objects in a corpus of document dynamically evaluates search rank of the objects through Natural Language Processing and machine learning. When a search query is received for a first object, the search engine identifies search results including a plurality of source values that are tied to the first object in the corpus of published documents. A search rank is computed for each identified search result based on content of direct textual references to each of the plurality of source values within the corpus of published documents, as well as a weight assigned to each published document. The identified search results are returned according to the computed search rank.
    Type: Application
    Filed: March 14, 2022
    Publication date: June 23, 2022
    Inventors: Karin Lachmi, Daniel Levitt, Ehud Pardo, Dan Grunspan
  • Patent number: 11347937
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Grant
    Filed: June 30, 2020
    Date of Patent: May 31, 2022
    Assignee: Bioz, Inc.
    Inventors: Daniel Levitt, Karin Lachmi, Dan Grunspan, Ehud Pardo
  • Patent number: 11281678
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Grant
    Filed: January 14, 2019
    Date of Patent: March 22, 2022
    Assignee: Bioz, Inc.
    Inventors: Karin Lachmi, Daniel Levitt, Ehud Pardo, Dan Grunspan
  • Patent number: 10956427
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Grant
    Filed: May 25, 2017
    Date of Patent: March 23, 2021
    Assignee: BIOZ, INC.
    Inventors: Karin Lachmi, Daniel Levitt, Ehud Pardo, Dan Grunspan
  • Publication number: 20200334415
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Application
    Filed: June 30, 2020
    Publication date: October 22, 2020
    Inventors: Daniel Levitt, Karin Lachmi, Dan Grunspan, Ehud Pardo
  • Patent number: 10796101
    Abstract: Disclosed herein are methods, systems, and apparatuses, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining, from a corpus of journals, a plurality of objects. The corpus is analyzed for contextual clues as to the nature of each of these objects. Contextual analysis may include an analysis of particular fields of metadata to do with the objects. Another example of contextual analysis involves identification of an experiment that a group of objects are commonly associated with and merging objects therefrom. A third contextual analysis example makes use of word vectors about each object. Using these contextual analyses, objects are merged together with the context of search results of a search engine.
    Type: Grant
    Filed: July 15, 2019
    Date of Patent: October 6, 2020
    Assignee: Bioz, Inc.
    Inventors: Karin Lachmi, Daniel Levitt, Dan Grunspan, Ehud Pardo
  • Patent number: 10726202
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Grant
    Filed: April 10, 2017
    Date of Patent: July 28, 2020
    Assignee: Bioz, Inc.
    Inventors: Daniel Levitt, Karin Lachmi, Dan Grunspan, Ehud Pardo
  • Publication number: 20190361979
    Abstract: Disclosed herein are methods, systems, and apparatuses, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining, from a corpus of journals, a plurality of objects. The corpus is analyzed for contextual clues as to the nature of each of these objects. Contextual analysis may include an analysis of particular fields of metadata to do with the objects. Another example of contextual analysis involves identification of an experiment that a group of objects are commonly associated with and merging objects therefrom. A third contextual analysis example makes use of word vectors about each object. Using these contextual analyses, objects are merged together with the context of search results of a search engine.
    Type: Application
    Filed: July 15, 2019
    Publication date: November 28, 2019
    Inventors: Karin Lachmi, Daniel Levitt, Dan Grunspan, Ehud Pardo
  • Patent number: 10423724
    Abstract: Disclosed herein are methods, systems, and apparatuses, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining, from a corpus of journals, a plurality of objects. The corpus is analyzed for contextual clues as to the nature of each of these objects. Contextual analysis may include an analysis of particular fields of metadata to do with the objects. Another example of contextual analysis involves identification of an experiment that a group of objects are commonly associated with and merging objects therefrom. A third contextual analysis example makes use of word vectors about each object. Using these contextual analyzes, objects are merged together with the context of search results of a search engine.
    Type: Grant
    Filed: May 19, 2017
    Date of Patent: September 24, 2019
    Assignee: BIOZ, INC.
    Inventors: Karin Lachmi, Daniel Levitt, Dan Grunspan, Ehud Pardo
  • Publication number: 20190146976
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Application
    Filed: January 14, 2019
    Publication date: May 16, 2019
    Inventors: Karin Lachmi, Daniel Levitt, Ehud Pardo, Dan Grunspan
  • Publication number: 20180336182
    Abstract: Disclosed herein are methods, systems, and apparatuses, including computer programs encoded on computer storage media, for computing numeric representations of words. One of the methods includes obtaining, from a corpus of journals, a plurality of objects. The corpus is analyzed for contextual clues as to the nature of each of these objects. Contextual analysis may include an analysis of particular fields of metadata to do with the objects. Another example of contextual analysis involves identification of an experiment that a group of objects are commonly associated with and merging objects therefrom. A third contextual analysis example makes use of word vectors about each object. Using these contextual analyses, objects are merged together with the context of search results of a search engine.
    Type: Application
    Filed: May 19, 2017
    Publication date: November 22, 2018
    Inventors: Karin Lachmi, Daniel Levitt, Dan Grunspan, Ehud Pardo
  • Publication number: 20180276303
    Abstract: Taught is a search engine for science tools which dynamically evaluates search rank of said science tools through Natural Language Processing and machine learning. The search engine accepts into a corpus of public and private materials, which references individual science tools. Each item of the corpus is evaluated both as to how much that given item should be trusted, and what that item says about individual science tools. Each science tool is evaluated based on what the whole corpus of input data contains concerning those science tools, taking into account how valuable the source of the data is in order to render an overall score and search rank. The search engine generates a judgement of each individual science tool, which is dynamically updated as new information becomes available in the corpus of input data.
    Type: Application
    Filed: April 10, 2017
    Publication date: September 27, 2018
    Inventors: Daniel Levitt, Karin Lachmi, Dan Grunspan, Ehud Pardo
  • Patent number: 7676391
    Abstract: The present invention describes the scheduling requirements if a business to an automatic scheduling system in a way which allows efficient automatic scheduling of appointments for resources of the business. In one embodiment, the scheduling system schedules the resources based not only on total capacity, but based on how many appointments can be initiated at a given time.
    Type: Grant
    Filed: June 30, 2003
    Date of Patent: March 9, 2010
    Assignee: Microsoft Corporation
    Inventors: Dan Grunspan, Ehud Pardo, Derik Stenerson, Tobin Zerba
  • Patent number: 7502747
    Abstract: Each job is defined in terms of the tasks/services required to do the jobs, using resources needed to carry out those tasks/services at different times during a scheduling period. Each available resource needed for each task/service is associated with a job ID and different start times for a job, forming proposals. The proposals are preferably created prior to the time that any appointments are made to do the job. If an appointment time requested by a customer to have the job done is available among the proposals created, the appointment is scheduled using the proposal. As each appointment is scheduled, changed, or canceled, the available proposals for the job are automatically modified to reflect the changes in the time that each resource is available to do the tasks/services required for the job.
    Type: Grant
    Filed: November 29, 2001
    Date of Patent: March 10, 2009
    Assignee: Microsoft Corporation
    Inventors: Ehud Pardo, Dan Grunspan
  • Publication number: 20060167725
    Abstract: A method and apparatus for scheduling is disclosed. The method may include g a service for which a scheduling strategy is to be applied, selecting the scheduling to be applied to the service selected, selecting an action to be applied to the selected accessing a calendar related to the selected service, in view of the service selected, g the constraints to be applied as required by the scheduling strategy and returning a applying the constraints.
    Type: Application
    Filed: January 25, 2005
    Publication date: July 27, 2006
    Applicant: MICROSOFT CORPORATION
    Inventors: Dan Grunspan, Graham Sheldon, Michael Ott, Naveen Garg, Ilya Baimetov, Tsvi Reiter