Patents by Inventor Joshua Theodore Goodman

Joshua Theodore Goodman 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: 8839418
    Abstract: Described is a technology by which phishing-related data sources are processed into aggregated data and a given site evaluated the aggregated data using a predictive model to automatically determine whether the given site is likely to be a phishing site. The predictive model may be built using machine learning based on training data, e.g., including known phishing sites and/or known non-phishing sites. To determine whether an object corresponding to a site is likely a phishing-related object are described, various criteria are evaluated, including one or more features of the object when evaluated. The determination is output in some way, e.g., made available to a reputation service, used to block access to a site or warn a user before allowing access, and/or used to assist a hand grader in being more efficient in evaluating sites.
    Type: Grant
    Filed: January 18, 2006
    Date of Patent: September 16, 2014
    Assignee: Microsoft Corporation
    Inventors: Geoffrey John Hulten, Paul Stephen Rehfuss, Robert Rounthwaite, Joshua Theodore Goodman, Gopalakrishnan Seshadrinathan, Anthony P. Penta, Manav Mishra, Roderic C. Deyo, Elliott Jeb Haber, David Aaron Ward Snelling
  • Patent number: 8046832
    Abstract: A system and method facilitating detection of unsolicited e-mail message(s) with challenges is provided. The invention includes an e-mail component and a challenge component. The system can receive e-mail message(s) and associated probabilities that the e-mail message(s) are spam. Based, at least in part, upon the associated probability, the system can send a challenge to a sender of an e-mail message. The challenge can be an embedded code, computational challenge, human challenge and/or micropayment request. Based, at least in part, upon a response to the challenge (or lack of response), the challenge component can modify the associated probability and/or delete the e-mail message.
    Type: Grant
    Filed: June 26, 2002
    Date of Patent: October 25, 2011
    Assignee: Microsoft Corporation
    Inventors: Joshua Theodore Goodman, Robert L. Rounthwaite
  • Publication number: 20110191848
    Abstract: A method disclosed herein includes acts of receiving code at a Just-in-Time compiler executing in an application on a computing device and compiling the code to generate machine code and causing the machine code to be placed on at least one page that is accessible by at least one processor on the computing device, wherein the Just-in-Time compiler compiles the code utilizing at least one technique for preventing a Just-in-Time spraying attack.
    Type: Application
    Filed: February 3, 2010
    Publication date: August 4, 2011
    Applicant: Microsoft Corporation
    Inventors: Benjamin Goth Zorn, Benjamin Livshits, Reid Borsuk, John Joseph Lambert, Matthew Ryan Miller, Louis Lafreniere, Peter Stuart Beck, Joshua Theodore Goodman, Timothy William Burrell, Steven Edward Lucco
  • Patent number: 7266492
    Abstract: A system and method facilitating training machine learning systems utilizing sequential conditional generalized iterative scaling is provided. The invention includes an expected value update component that modifies an expected value based, at least in part, upon a feature function of an input vector and an output value, a sum of lambda variable and a normalization variable. The invention further includes an error calculator that calculates an error based, at least in part, upon the expected value and an observed value. The invention also includes a parameter update component that modifies a trainable parameter based, at least in part, upon the error. A variable update component that updates at least one of the sum of lambda variable and the normalization variable based, at least in part, upon the error is also provided.
    Type: Grant
    Filed: August 16, 2006
    Date of Patent: September 4, 2007
    Assignee: Microsoft Corporation
    Inventor: Joshua Theodore Goodman
  • Patent number: 7107207
    Abstract: A system and method facilitating training machine learning systems utilizing sequential conditional generalized iterative scaling is provided. The invention includes an expected value update component that modifies an expected value based, at least in part, upon a feature function of an input vector and an output value, a sum of lambda variable and a normalization variable. The invention further includes an error calculator that calculates an error based, at least in part, upon the expected value and an observed value. The invention also includes a parameter update component that modifies a trainable parameter based, at least in part, upon the error. A variable update component that updates at least one of the sum of lambda variable and the normalization variable based, at least in part, upon the error is also provided.
    Type: Grant
    Filed: June 19, 2002
    Date of Patent: September 12, 2006
    Assignee: Microsoft Corporation
    Inventor: Joshua Theodore Goodman
  • Publication number: 20040003283
    Abstract: A system and method facilitating detection of unsolicited e-mail message(s) with challenges is provided. The invention includes an e-mail component and a challenge component. The system can receive e-mail message(s) and associated probabilities that the e-mail message(s) are spam. Based, at least in part, upon the associated probability, the system can send a challenge to a sender of an e-mail message. The challenge can be an embedded code, computational challenge, human challenge and/or micropayment request. Based, at least in part, upon a response to the challenge (or lack of response), the challenge component can modify the associated probability and/or delete the e-mail message.
    Type: Application
    Filed: June 26, 2002
    Publication date: January 1, 2004
    Inventors: Joshua Theodore Goodman, Robert L. Rounthwaite
  • Publication number: 20030236662
    Abstract: A system and method facilitating training machine learning systems utilizing sequential conditional generalized iterative scaling is provided. The invention includes an expected value update component that modifies an expected value based, at least in part, upon a feature function of an input vector and an output value, a sum of lambda variable and a normalization variable. The invention further includes an error calculator that calculates an error based, at least in part, upon the expected value and an observed value. The invention also includes a parameter update component that modifies a trainable parameter based, at least in part, upon the error. A variable update component that updates at least one of the sum of lambda variable and the normalization variable based, at least in part, upon the error is also provided.
    Type: Application
    Filed: June 19, 2002
    Publication date: December 25, 2003
    Inventor: Joshua Theodore Goodman