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).
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Publication number: 20230043557Abstract: 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: ApplicationFiled: August 12, 2022Publication date: February 9, 2023Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 11415426Abstract: 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: GrantFiled: December 6, 2019Date of Patent: August 16, 2022Assignee: GOOGLE LLCInventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Publication number: 20200109961Abstract: 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: ApplicationFiled: December 6, 2019Publication date: April 9, 2020Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 10533868Abstract: 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: GrantFiled: November 6, 2017Date of Patent: January 14, 2020Assignee: Google LLCInventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Publication number: 20190078905Abstract: 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: ApplicationFiled: November 12, 2018Publication date: March 14, 2019Inventors: Andrew R. Golding, Kevin Murphy
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Patent number: 10126141Abstract: 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: GrantFiled: May 2, 2016Date of Patent: November 13, 2018Assignee: GOOGLE LLCInventors: Andrew R. Golding, Kevin Murphy
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Publication number: 20180128630Abstract: 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: ApplicationFiled: November 6, 2017Publication date: May 10, 2018Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 9810544Abstract: 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: GrantFiled: March 25, 2016Date of Patent: November 7, 2017Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 9810545Abstract: 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: GrantFiled: November 21, 2016Date of Patent: November 7, 2017Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Publication number: 20170314954Abstract: 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: ApplicationFiled: May 2, 2016Publication date: November 2, 2017Inventors: Andrew R. Golding, Kevin Murphy
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Publication number: 20170067749Abstract: 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: ApplicationFiled: November 21, 2016Publication date: March 9, 2017Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Publication number: 20160209228Abstract: 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: ApplicationFiled: March 25, 2016Publication date: July 21, 2016Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 9297663Abstract: 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: GrantFiled: January 31, 2014Date of Patent: March 29, 2016Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 8694499Abstract: 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: GrantFiled: August 19, 2011Date of Patent: April 8, 2014Assignee: Google Inc.Inventors: Shumeet Baluja, Doug Beeferman, Andrew R Golding
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Patent number: 8682574Abstract: 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: GrantFiled: March 30, 2009Date of Patent: March 25, 2014Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 8478515Abstract: 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: GrantFiled: May 23, 2007Date of Patent: July 2, 2013Assignee: Google Inc.Inventors: Trevor Foucher, Andrew R. Golding
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Patent number: 8024337Abstract: 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: GrantFiled: September 29, 2004Date of Patent: September 20, 2011Assignee: Google Inc.Inventors: Shumeet Baluja, Doug Beeferman, Andrew R. Golding
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Patent number: 7996345Abstract: 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: GrantFiled: March 15, 2010Date of Patent: August 9, 2011Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 7920968Abstract: 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: GrantFiled: August 22, 2006Date of Patent: April 5, 2011Assignee: Google Inc.Inventors: Charles Chapin, Michele Covell, Tiruvilwamalai Venkatraman Raman, Andrew R. Golding, Jens Eilstrup Rasmussen
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Patent number: 7831387Abstract: 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: GrantFiled: July 13, 2005Date of Patent: November 9, 2010Assignee: Google Inc.Inventors: Andrew R. Golding, Jens Eilstrup Rasmussen