Patents by Inventor Andrew R. Golding

Andrew R. Golding 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: 20190078905
    Abstract: To generate navigation directions for a driver of a vehicle, a route for guiding the driver to a destination is obtained, visual landmarks corresponding to prominent physical objects disposed along the route are retrieved, and real-time imagery is collected at the vehicle approximately from a vantage point of the driver during navigation along the route. Using (i) the retrieved visual landmarks and (ii) the imagery collected at the vehicle, a subset of the visual landmarks that are currently visible to the driver is selected. Navigation directions describing the route are provided the driver, the navigation directions referencing the selected subset of the visual landmarks and excluding the remaining visual landmarks.
    Type: Application
    Filed: November 12, 2018
    Publication date: March 14, 2019
    Inventors: Andrew R. Golding, Kevin Murphy
  • Patent number: 10126141
    Abstract: To generate navigation directions for a driver of a vehicle, a route for guiding the driver to a destination is obtained, visual landmarks corresponding to prominent physical objects disposed along the route are retrieved, and real-time imagery is collected at the vehicle approximately from a vantage point of the driver during navigation along the route. Using (i) the retrieved visual landmarks and (ii) the imagery collected at the vehicle, a subset of the visual landmarks that are currently visible to the driver is selected. Navigation directions describing the route are provided the driver, the navigation directions referencing the selected subset of the visual landmarks and excluding the remaining visual landmarks.
    Type: Grant
    Filed: May 2, 2016
    Date of Patent: November 13, 2018
    Assignee: GOOGLE LLC
    Inventors: Andrew R. Golding, Kevin Murphy
  • Publication number: 20180128630
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Application
    Filed: November 6, 2017
    Publication date: May 10, 2018
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 9810544
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: March 25, 2016
    Date of Patent: November 7, 2017
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 9810545
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: November 21, 2016
    Date of Patent: November 7, 2017
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Publication number: 20170314954
    Abstract: To generate navigation directions for a driver of a vehicle, a route for guiding the driver to a destination is obtained, visual landmarks corresponding to prominent physical objects disposed along the route are retrieved, and real-time imagery is collected at the vehicle approximately from a vantage point of the driver during navigation along the route. Using (i) the retrieved visual landmarks and (ii) the imagery collected at the vehicle, a subset of the visual landmarks that are currently visible to the driver is selected. Navigation directions describing the route are provided the driver, the navigation directions referencing the selected subset of the visual landmarks and excluding the remaining visual landmarks.
    Type: Application
    Filed: May 2, 2016
    Publication date: November 2, 2017
    Inventors: Andrew R. Golding, Kevin Murphy
  • Publication number: 20170067749
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Application
    Filed: November 21, 2016
    Publication date: March 9, 2017
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Publication number: 20160209228
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Application
    Filed: March 25, 2016
    Publication date: July 21, 2016
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 9297663
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: January 31, 2014
    Date of Patent: March 29, 2016
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 8694499
    Abstract: A system determines query similarity. The system determines a volume per unit time of an issued first query over a time period and determines a volume per unit time of issued other queries over the time period. The system compares the volume per unit time of each of the issued other queries to the volume per unit time of the issued first query. The system identifies ones of the issued other queries as similar to the first query based on the comparison.
    Type: Grant
    Filed: August 19, 2011
    Date of Patent: April 8, 2014
    Assignee: Google Inc.
    Inventors: Shumeet Baluja, Doug Beeferman, Andrew R Golding
  • Patent number: 8682574
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: March 30, 2009
    Date of Patent: March 25, 2014
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 8478515
    Abstract: Methods and systems for generating directions are disclosed. In an embodiment of the invention, there is a system that includes a human-provided directions module for receiving and processing human-provided directions, a database for storing human-provided directions processed by the human-provided directions module, and a directions generator for receiving a directions query from a client. In response to the query, the directions generator accesses the database, retrieves at least one human-provided direction, generates a set of directions based thereupon, and provides the set of generated directions to the client.
    Type: Grant
    Filed: May 23, 2007
    Date of Patent: July 2, 2013
    Assignee: Google Inc.
    Inventors: Trevor Foucher, Andrew R. Golding
  • Patent number: 8024337
    Abstract: A system determines query similarity. The system determines a volume per unit time of an issued first query over a time period and determines a volume per unit time of issued other queries over the time period. The system compares the volume per unit time of each of the issued other queries to the volume per unit time of the issued first query. The system identifies ones of the issued other queries as similar to the first query based on the comparison.
    Type: Grant
    Filed: September 29, 2004
    Date of Patent: September 20, 2011
    Assignee: Google Inc.
    Inventors: Shumeet Baluja, Doug Beeferman, Andrew R. Golding
  • Patent number: 7996345
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: March 15, 2010
    Date of Patent: August 9, 2011
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 7920968
    Abstract: Digital mapping techniques are disclosed that provide visually-oriented information to the user, such as driving directions that include visual data points along the way of the driving route, thereby improving the user experience. The user may preview the route associated with the driving directions, where the preview is based on, for example, at least one of satellite images, storefront images, and heuristics and/or business listings. The visually-oriented information can be presented to the user in a textual, graphical, or verbal format, or some combination thereof.
    Type: Grant
    Filed: August 22, 2006
    Date of Patent: April 5, 2011
    Assignee: Google Inc.
    Inventors: Charles Chapin, Michele Covell, Tiruvilwamalai Venkatraman Raman, Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 7831387
    Abstract: Digital mapping techniques are disclosed that provide visually-oriented information to the user, such as driving directions that include visual data points along the way of the driving route, thereby improving the user experience. The user may preview the route associated with the driving directions, where the preview is based on, for example, at least one of satellite images, storefront images, and heuristics and/or business listings.
    Type: Grant
    Filed: July 13, 2005
    Date of Patent: November 9, 2010
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Publication number: 20100174479
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Application
    Filed: March 15, 2010
    Publication date: July 8, 2010
    Applicant: GOOGLE INC.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Patent number: 7715531
    Abstract: In one embodiment, a system and method for charting audible choices comprises determining if an option is offered; selecting the option if the option is offered; and saving a result, wherein the result is associated with the selected option.
    Type: Grant
    Filed: June 30, 2005
    Date of Patent: May 11, 2010
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Egon Pasztor
  • Patent number: 7680749
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Grant
    Filed: November 2, 2006
    Date of Patent: March 16, 2010
    Assignee: Google Inc.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
  • Publication number: 20090192705
    Abstract: Adaptive navigation techniques are disclosed that allow navigation systems to learn from a user's personal driving history. As a user drives, models are developed and maintained to learn or otherwise capture the driver's personal driving habits and preferences. Example models include road speed, hazard, favored route, and disfavored route models. Other attributes can be used as well, whether based on the user's personal driving data or driving data aggregated from a number of users. The models can be learned under explicit conditions (e.g., time of day/week, driver ID) and/or under implicit conditions (e.g., weather, drivers urgency, as inferred from sensor data). Thus, models for a plurality of attributes can be learned, as well as one or more models for each attribute under a plurality of conditions. Attributes can be weighted according to user preference. The attribute weights and/or models can be used in selecting a best route for user.
    Type: Application
    Filed: March 30, 2009
    Publication date: July 30, 2009
    Applicant: GOOGLE INC.
    Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen