Difference between revisions of "Tesla, Inc. patent applications on March 14th, 2024"

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=Patent Applications by Tesla, Inc. on March 14th, 2024=
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=TRIP PLANNING WITH ENERGY CONSTRAINT=
  
  
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==Organization Name==
  
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[[:Category:Tesla, Inc.|Tesla, Inc.]]: 4 patent applications[[Category:Tesla, Inc.]]
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[[:Category:tesla, inc.|tesla, inc.]]
  
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Tesla, Inc. has applied for patents in the areas of [[:Category:G06N3/02|G06N3/02]] (2), [[:Category:H01Q15/14|H01Q15/14]] (2), [[:Category:B60Q9/00|B60Q9/00]] (2), [[:Category:H04J3/00|H04J3/00]] (2), [[:Category:H01Q1/02|H01Q1/02]] (2)
 
  
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With keywords such as: neural, network, error, antenna, measure, configured, tdm, bus, vehicle, and driving in patent application abstracts.
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[[Category:tesla, inc.]]
  
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==Inventor(s)==
  
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See the following report for Tesla, Inc. patent applications published on March 14th, 2024:
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[[:Category:Andrew D. Baglino of San Francisco CA (US)|Andrew D. Baglino of San Francisco CA (US)]][[Category:Andrew D. Baglino of San Francisco CA (US)]]
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[[:Category:Tesla, Inc. patent applications on March 14th, 2024|Tesla, Inc. patent applications on March 14th, 2024]]
 
  
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[[:Category:Thorsten Hayer of Burlingame CA (US)|Thorsten Hayer of Burlingame CA (US)]][[Category:Thorsten Hayer of Burlingame CA (US)]]
  
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====Patent Applications by Tesla, Inc.====
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[[:Category:Brennan Boblett of San Francisco CA (US)|Brennan Boblett of San Francisco CA (US)]][[Category:Brennan Boblett of San Francisco CA (US)]]
  
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[[:Category:Matthew Fox of Woody Creek CO (US)|Matthew Fox of Woody Creek CO (US)]][[Category:Matthew Fox of Woody Creek CO (US)]]
  
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[[20240085203.TRIP PLANNING WITH ENERGY CONSTRAINT_simplified_abstract_(tesla, inc.)]]
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[[:Category:Vineet H. Mehta of Mountain View CA (US)|Vineet H. Mehta of Mountain View CA (US)]][[Category:Vineet H. Mehta of Mountain View CA (US)]]
  
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Inventor(s): Andrew D. Baglino of San Francisco CA (US) for tesla, inc., Thorsten Hayer of Burlingame CA (US) for tesla, inc., Brennan Boblett of San Francisco CA (US) for tesla, inc., Matthew Fox of Woody Creek CO (US) for tesla, inc., Vineet H. Mehta of Mountain View CA (US) for tesla, inc., Keijiro Ikebe of Cupertino CA (US) for tesla, inc., Kevin Hsieh of Emerald Hills CA (US) for tesla, inc., Craig B. Carlson of Los Altos CA (US) for tesla, inc., Jeffrey B. Straubel of Menlo Park CA (US) for tesla, inc.
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[[:Category:Keijiro Ikebe of Cupertino CA (US)|Keijiro Ikebe of Cupertino CA (US)]][[Category:Keijiro Ikebe of Cupertino CA (US)]]
  
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IPC Code(s): G01C21/34, G01C21/36
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[[:Category:Kevin Hsieh of Emerald Hills CA (US)|Kevin Hsieh of Emerald Hills CA (US)]][[Category:Kevin Hsieh of Emerald Hills CA (US)]]
  
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[[:Category:Craig B. Carlson of Los Altos CA (US)|Craig B. Carlson of Los Altos CA (US)]][[Category:Craig B. Carlson of Los Altos CA (US)]]
  
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[[:Category:Jeffrey B. Straubel of Menlo Park CA (US)|Jeffrey B. Straubel of Menlo Park CA (US)]][[Category:Jeffrey B. Straubel of Menlo Park CA (US)]]
  
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==TRIP PLANNING WITH ENERGY CONSTRAINT - A simplified explanation of the abstract==
  
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Abstract: a method includes: determining, based, at least in part, on a predicted driver characteristic, a first energy-versus-distance measure for a planned driving route of a vehicle, the first energy-versus-distance measure determined using an energy model; presenting the first energy-versus-distance measure on a user interface associated with the vehicle; identifying an already-driven part of the planned driving route; determining, based on information associated with the already-driven part of the planned driving route, a model error associated with the energy model; determining a second energy-versus-distance measure by modifying the first energy-versus-distance measure to account for the model error; and presenting the second energy-versus-distance measure on the user interface associated with the vehicle.[[Category:G01C21/34]][[Category:G01C21/36]][[Category:tesla, inc.]]
 
  
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This abstract first appeared for US patent application 20240085203 titled 'TRIP PLANNING WITH ENERGY CONSTRAINT
  
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==Simplified Explanation==
  
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[[20240085915.AUTONOMOUS DRIVING SYSTEM EMERGENCY SIGNALING_simplified_abstract_(tesla, inc.)]]
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The method described in the abstract involves predicting a driver characteristic to determine an energy-versus-distance measure for a planned driving route of a vehicle. This measure is then presented on a user interface associated with the vehicle. The method also involves identifying an already-driven part of the route, determining a model error associated with the energy model based on this information, modifying the energy-versus-distance measure to account for the model error, and presenting the modified measure on the user interface.
  
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Inventor(s): Michael D. Cave of Austin TX (US) for tesla, inc.
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* Determining energy-versus-distance measure based on predicted driver characteristic
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* Presenting the measure on a user interface
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* Identifying already-driven part of the route
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* Determining model error associated with the energy model
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* Modifying the measure to account for the model error
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* Presenting the modified measure on the user interface
  
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IPC Code(s): G05D1/02, B60Q9/00, H04J3/00, H04J3/16
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== Potential Applications ==
 +
This technology could be applied in:
 +
* Electric vehicles to optimize energy consumption during driving
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* Fleet management systems to improve route planning and efficiency
  
 +
== Problems Solved ==
 +
This technology helps in:
 +
* Reducing energy consumption during driving
 +
* Providing accurate energy predictions for planned routes
  
 +
== Benefits ==
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The benefits of this technology include:
 +
* Increased fuel efficiency
 +
* Enhanced driver awareness of energy consumption
 +
* Improved overall driving experience
  
 +
== Potential Commercial Applications ==
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Potential commercial applications of this technology include:
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* Integration into electric vehicle systems
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* Incorporation into navigation apps for energy-efficient driving routes
  
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Abstract: a vehicular autonomous driving system includes a time division multiplexed (tdm) bus, an autonomous driving (ad) controller coupled to the tdm bus, and a plurality of ad sensors coupled to the tdm bus. the ad sensors are configured to collect ad data and transmit collected ad data to the ad controller on the tdm bus in an assigned time slot at a first power level. a first ad sensor of the plurality of ad sensors is configured to, based upon collected ad data, detect an ad emergency event. in response to the detection, the first ad sensor is configured to transmit an ad emergency message on the tdm bus in a non-assigned time slot and at a second power level that exceeds the first power level. the ad sensor may transmit the ad emergency message in a particular sub-slot of the non-assigned time slot.[[Category:G05D1/02]][[Category:B60Q9/00]][[Category:H04J3/00]][[Category:H04J3/16]][[Category:tesla, inc.]]
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== Possible Prior Art ==
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One possible prior art for this technology could be existing energy modeling systems used in electric vehicles or route planning software.
  
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=== Unanswered Questions ===
  
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=== How does this technology handle real-time changes in driving conditions? ===
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The article does not mention how the method adapts to sudden changes in driving conditions that may affect energy consumption.
  
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[[20240086270.SYSTEM AND METHOD FOR HANDLING ERRORS IN A VEHICLE NEURAL NETWORK PROCESSOR_simplified_abstract_(tesla, inc.)]]
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=== What data sources are used to predict driver characteristics? ===
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The abstract does not specify the data sources or methods used to predict driver characteristics for determining the energy-versus-distance measure.
  
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Inventor(s): Christopher Hsiong of San Jose CA (US) for tesla, inc., Emil Talpes of San Mateo CA (US) for tesla, inc., Debjit Das Sarma of San Jose CA (US) for tesla, inc., Peter Bannon of Woodside CA (US) for tesla, inc., Kevin Hurd of Redwood City CA (US) for tesla, inc., Benjamin Floering of San Jose CA (US) for tesla, inc.
 
  
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IPC Code(s): G06F11/07, G06F11/30, G06F11/32, G06N3/02
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==Original Abstract Submitted==
  
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a method includes: determining, based, at least in part, on a predicted driver characteristic, a first energy-versus-distance measure for a planned driving route of a vehicle, the first energy-versus-distance measure determined using an energy model; presenting the first energy-versus-distance measure on a user interface associated with the vehicle; identifying an already-driven part of the planned driving route; determining, based on information associated with the already-driven part of the planned driving route, a model error associated with the energy model; determining a second energy-versus-distance measure by modifying the first energy-versus-distance measure to account for the model error; and presenting the second energy-versus-distance measure on the user interface associated with the vehicle.
  
  
  
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Abstract: a system for handling errors in a neural network includes a neural network processor for executing a neural network associated with use of a vehicle. the neural network processor includes an error detector configured to detect a data error associated with execution of the neural network and a neural network controller configured to receive a report of the data error from the error detector. in response to receiving the report, the neural network controller is further configured to signal that a pending result of the neural network is tainted, without terminating execution of the neural network.[[Category:G06F11/07]][[Category:G06F11/30]][[Category:G06F11/32]][[Category:G06N3/02]][[Category:tesla, inc.]]
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[[Category:G01C21/34]]
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[[Category:G01C21/36]]
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[[20240088547.MULTI-BAND ANTENNA_simplified_abstract_(tesla, inc.)]]
 
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Inventor(s): Anand S. Konanur of San Jose CA (US) for tesla, inc., Shreya Singh of La Jolla CA (US) for tesla, inc., Richard Breden of Mountain View CA (US) for tesla, inc., Yasutaka Horiki of Santa Clara CA (US) for tesla, inc., Aycan Erentok of Mountain View CA (US) for tesla, inc., George Zucker of San Francisco CA (US) for tesla, inc., Nagarjun Bhat of Sunnyvale CA (US) for tesla, inc., Rui Moreira of San Jose CA (US) for tesla, inc., Aydin Nabovati of Toronto  (CA) for tesla, inc., Rishabh Bhandari of San Carlos CA (US) for tesla, inc., Austin Rothschild of Palo Alto CA (US) for tesla, inc., Jae Hoon Yoo of San Jose CA (US) for tesla, inc., Loic Le Toumelin of Palo Alto CA (US) for tesla, inc.
 
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IPC Code(s): H01Q1/32, B60R1/12, H01Q1/02, H01Q13/10, H01Q15/14
 
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Abstract: a multi-band antenna system is provided. the antenna system can be placed under and embedded within a glass exterior surface of a vehicle. such an antenna system can include a capacitively coupled metallic element on or adjacent to the glass exterior surface, which can serve as both a parasitic element to enhance gain and as a heating element to melt snow and/or ice accumulation over the glass area that covers the antenna. in certain applications, the antenna's structure itself can be used as a heater to improve performance in adverse weather conditions while the heating elements are positioned away from the thermally sensitive electronics. the antenna system with integrated heating can include a spiral antenna.[[Category:H01Q1/32]][[Category:B60R1/12]][[Category:H01Q1/02]][[Category:H01Q13/10]][[Category:H01Q15/14]][[Category:tesla, inc.]]
 
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[[Tesla, Inc. patent applications on March 14th, 2024]]
 
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[[Category:Tesla, Inc.]] [[Category:G06N3/02]] [[Category:H01Q15/14]] [[Category:B60Q9/00]] [[Category:H04J3/00]] [[Category:H01Q1/02]]
 

Revision as of 02:36, 19 March 2024

TRIP PLANNING WITH ENERGY CONSTRAINT

Organization Name

tesla, inc.

Inventor(s)

Andrew D. Baglino of San Francisco CA (US)

Thorsten Hayer of Burlingame CA (US)

Brennan Boblett of San Francisco CA (US)

Matthew Fox of Woody Creek CO (US)

Vineet H. Mehta of Mountain View CA (US)

Keijiro Ikebe of Cupertino CA (US)

Kevin Hsieh of Emerald Hills CA (US)

Craig B. Carlson of Los Altos CA (US)

Jeffrey B. Straubel of Menlo Park CA (US)

TRIP PLANNING WITH ENERGY CONSTRAINT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240085203 titled 'TRIP PLANNING WITH ENERGY CONSTRAINT

Simplified Explanation

The method described in the abstract involves predicting a driver characteristic to determine an energy-versus-distance measure for a planned driving route of a vehicle. This measure is then presented on a user interface associated with the vehicle. The method also involves identifying an already-driven part of the route, determining a model error associated with the energy model based on this information, modifying the energy-versus-distance measure to account for the model error, and presenting the modified measure on the user interface.

  • Determining energy-versus-distance measure based on predicted driver characteristic
  • Presenting the measure on a user interface
  • Identifying already-driven part of the route
  • Determining model error associated with the energy model
  • Modifying the measure to account for the model error
  • Presenting the modified measure on the user interface

Potential Applications

This technology could be applied in:

  • Electric vehicles to optimize energy consumption during driving
  • Fleet management systems to improve route planning and efficiency

Problems Solved

This technology helps in:

  • Reducing energy consumption during driving
  • Providing accurate energy predictions for planned routes

Benefits

The benefits of this technology include:

  • Increased fuel efficiency
  • Enhanced driver awareness of energy consumption
  • Improved overall driving experience

Potential Commercial Applications

Potential commercial applications of this technology include:

  • Integration into electric vehicle systems
  • Incorporation into navigation apps for energy-efficient driving routes

Possible Prior Art

One possible prior art for this technology could be existing energy modeling systems used in electric vehicles or route planning software.

Unanswered Questions

How does this technology handle real-time changes in driving conditions?

The article does not mention how the method adapts to sudden changes in driving conditions that may affect energy consumption.

What data sources are used to predict driver characteristics?

The abstract does not specify the data sources or methods used to predict driver characteristics for determining the energy-versus-distance measure.


Original Abstract Submitted

a method includes: determining, based, at least in part, on a predicted driver characteristic, a first energy-versus-distance measure for a planned driving route of a vehicle, the first energy-versus-distance measure determined using an energy model; presenting the first energy-versus-distance measure on a user interface associated with the vehicle; identifying an already-driven part of the planned driving route; determining, based on information associated with the already-driven part of the planned driving route, a model error associated with the energy model; determining a second energy-versus-distance measure by modifying the first energy-versus-distance measure to account for the model error; and presenting the second energy-versus-distance measure on the user interface associated with the vehicle.

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