DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to claims 1, 11, and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Objections
Claim 20 is objected to because of the following informalities:
In claim 20, line 6, “determining” should read “determine”.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Prokhorov (U.S. Publication No. 2016/0358475; hereinafter Prokhorov) and further in view of Boyoung et al. (KR Publication No. 1020210095757; hereinafter Boyoung).
Regarding claim 1, Prokhorov teaches a system comprising: a memory; and one or more processors coupled to the memory (Prokhorov: Par. 27; i.e., The data store 210 can include volatile and/or non-volatile memory… the data store 210 can be operatively connected to the processor),
the one or more processors being configured to: detect, based on at least one of map data and sensor data from at least one sensor on a vehicle, one or more characteristics of an operational context of the vehicle (Prokhorov: Par. 53; i.e., The environment classification module 240 can be configured to identify, classify and/or assess the driving environment of the vehicle… Such information about the driving environment can be obtained from the sensor system);
and determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context, the sensor configuration comprising one or more sensors selected for use by the vehicle in the operational context and one or more different sensors set to an off state or a reduced operating mode while the vehicle is in the operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment… A “subset of the plurality of different sensor types” means less than all of the available types of sensors; Par. 71; i.e., the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; the system may select to use one type of sensor while the remaining sensors operate at a reduced operating mode).
Prokhorov does not explicitly teach prior to the vehicle entering the operational context, determine, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context.
However, in the same field of endeavor, Boyoung teaches prior to the vehicle entering the operational context, determine, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context (Boyoung: Par. 140; i.e., the processor (910) may determine whether the road section on which the vehicle is driving satisfies a specified condition (e.g., presence of a guardrail) based on at least one of map data; Par. 202; i.e., when the completion of driving in a section that satisfies a specified condition is predicted, the processor (910) and/or the sensor module (920) of the vehicle may reactivate at least one camera and perform operation 2601. For example, the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end); and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context (Boyoung: Par. 202; i.e., the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end … and reactivate at least one camera before the vehicle enters a section without a guardrail).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Prokhorov to have further incorporated prior to the vehicle entering the operational context, determine, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context, as taught by Boyoung. Doing so would save energy by only activating necessary sensors (Boyoung: Par. 200; i.e., when the vehicle is driving on a section that satisfies a specified condition, energy can be saved by disabling the cameras and detecting objects using only lidar).
Regarding claim 2, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein the one or more processors are configured to: detect, based on at least one of the map data and additional sensor data from the at least one sensor on the vehicle, one or more different characteristics of a different operational context of the vehicle (Prokhorov: Par. 105; i.e., the method 400 can include determining whether there is a change in at least one of the driving environment complexity);
determine, based on the one or more different characteristics of the different operational context, a different sensor configuration for the vehicle to implement when navigating the different operational context (Prokhorov: Par. 105; i.e., responsive to determining that there is a change in at least one of the driving environment complexity … a different subset of the plurality of different types of sensors can be selected);
and dynamically implement the different sensor configuration at the vehicle when the vehicle is in the different operational context (Prokhorov: Par. 103; i.e., driving environment data acquired by the selected subset of the plurality of different types of sensors can be sent to the remote operation computing system; only data from the selected subset of sensors is acquired).
Regarding claim 3, Prokhorov in view of Boyoung teaches the system according to claim 2. Prokhorov further teaches wherein the different sensor configuration comprises at least one of a first sensor selected for use by the vehicle in the different operational context and a second sensor set to an off state or a reduced operating mode while the vehicle is in the different operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment… A “subset of the plurality of different sensor types” means less than all of the available types of sensors; Par. 71; i.e., the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; the system may select to use one type of sensor while the remaining sensors operate at a reduced operating mode).
Regarding claim 4, Prokhorov in view of Boyoung teaches the system according to claim 2. Boyoung further teaches wherein the one or more processors are configured to: prior to detecting the one or more different characteristics of the different operational context, determine, based on at least one of map data, route planning data, and external planning data available before the vehicle enters the different operational context, determine that the vehicle is in the different operational context or predicted to be in the different operational context during a trip of the vehicle (Boyoung: Par. 140; i.e., the processor (910) may determine whether the road section on which the vehicle is driving satisfies a specified condition (e.g., presence of a guardrail) based on at least one of map data; Par. 202; i.e., when the completion of driving in a section that satisfies a specified condition is predicted, the processor (910) and/or the sensor module (920) of the vehicle may reactivate at least one camera and perform operation 2601. For example, the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end).
and Prokhorov further teaches in response to determining that the vehicle is in the different operational context or predicted to be in the different operational context during the trip, detect the one or more different characteristics of the different operational context using sensor data from at least one sensor on the vehicle after the determination (Prokhorov: Par. 53; i.e., the classification of the driving environment can be performed continuously; Par. 55; i.e., For instance, the driving environment can be classified as low complexity if a relatively small number of objects is detected in the driving environment; the system is continuously monitoring environment characteristics to determine changes in complexity of the driving environment).
Regarding claim 5, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein the sensor data from at least one sensor on the vehicle comprises at least one of a speed measurement associated with the vehicle, a measurement of a heading of the vehicle, image data, a distance or range measurement of one or more objects in a scene of the vehicle, a motion measurement of the one or more objects in the scene, and a measured trajectory of at least one of the vehicle and the one or more objects in the scene (Prokhorov: Par. 38; i.e., The LIDAR sensors 222 can be configured and/or used to detect … various things about the driving environment of the vehicle 200. Non-limiting examples of such things include … the distance between each detected object and the vehicle 200 in one or more directions, the speed of each detected object, and/or the movement of each detected object).
Regarding claim 6, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein at least one of the operational context and the one or more characteristics of the operational context comprises at least one of a type of a driving environment associated with the vehicle, a driving intent of the vehicle, a weather associated with the driving environment, a light or brightness level in the driving environment, traffic conditions in the driving environment, a traffic rule associated with the driving environment, a type of road associated with the driving environment, and a geography associated with the driving environment (Prokhorov: Par. 53; i.e., The driving environment can include any information about the external environment, including, for example, the presence and/or location of one or more objects in the environment, the identity and/or nature of the objects, traffic conditions and/or weather conditions).
Regarding claim 8, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein the one or more sensors in the sensor configuration comprise a set of sensors selected for the operational context based on capabilities of the set of sensors and the one or more characteristics of the operational context, and wherein the one or more different sensors in the sensor configuration comprise at least one different sensor selected to be set to the off state or the reduced operating mode while the vehicle is in the operational context based on a mismatch between one or more capabilities of the at least one different sensor and one or more desired capabilities identified for the operational context based on the one or more characteristics of the operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment; Par. 71; i.e., as an example, the driving environment can include a road with a 45 MPH legal speed limit. It may be nighttime, and there can be relatively few vehicles on the road. In such case, the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; Par. 93; i.e., a sensor representation can be based on data from one or more different types of sensors of the sensor system 220 that are most suitable for the current driving environment; the system may select to use LIDAR due to the operational context, while the remaining sensors operate at a reduced operating mode because they do not have the desired capabilities in the operational context).
Regarding claim 9, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein at least one of the one or more sensors and the at least one sensor on the vehicle comprises a visible-light camera sensor, a radio detection and ranging (RADAR) sensor, a time-of-flight (TOF) sensor, a light detection and ranging (LIDAR) sensor, a speedometer, an inertial measurement unit, a thermal camera sensor, and an ultrasonic sensor (Prokhorov: Par. 101; i.e., The sensor system 220 includes a plurality of different types of sensors (e.g., LIDAR sensors 221, RADAR sensors 222, ultrasound sensors 223, cameras 224, etc.)).
Regarding claim 10, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein the vehicle comprises an autonomous vehicle (Prokhorov: Par. 19; i.e., the vehicle 200 can be an autonomous vehicle).
Regarding claim 11, Prokhorov teaches a method comprising: detecting, based on at least one of map data and sensor data from at least one sensor on a vehicle, one or more characteristics of an operational context of the vehicle (Prokhorov: Par. 53; i.e., The environment classification module 240 can be configured to identify, classify and/or assess the driving environment of the vehicle… Such information about the driving environment can be obtained from the sensor system);
and determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context, the sensor configuration comprising one or more sensors selected for use by the vehicle in the operational context and one or more different sensors set to an off state or a reduced operating mode while the vehicle is in the operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment… A “subset of the plurality of different sensor types” means less than all of the available types of sensors; Par. 71; i.e., the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; the system may select to use one type of sensor while the remaining sensors operate at a reduced operating mode).
Prokhorov does not explicitly teach prior to the vehicle entering the operational context, determining, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implementing the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context.
However, in the same field of endeavor, Boyoung teaches prior to the vehicle entering the operational context, determining, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context (Boyoung: Par. 140; i.e., the processor (910) may determine whether the road section on which the vehicle is driving satisfies a specified condition (e.g., presence of a guardrail) based on at least one of map data; Par. 202; i.e., when the completion of driving in a section that satisfies a specified condition is predicted, the processor (910) and/or the sensor module (920) of the vehicle may reactivate at least one camera and perform operation 2601. For example, the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end); and prior to the vehicle entering the operational context, dynamically implementing the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context (Boyoung: Par. 202; i.e., the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end … and reactivate at least one camera before the vehicle enters a section without a guardrail).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Prokhorov to have further incorporated prior to the vehicle entering the operational context, determining, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implementing the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context, as taught by Boyoung. Doing so would save energy by only activating necessary sensors (Boyoung: Par. 200; i.e., when the vehicle is driving on a section that satisfies a specified condition, energy can be saved by disabling the cameras and detecting objects using only lidar).
Regarding claim 12, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches detecting, based on at least one of the map data and additional sensor data from the at least one sensor on the vehicle, one or more different characteristics of a different operational context of the vehicle (Prokhorov: Par. 105; i.e., the method 400 can include determining whether there is a change in at least one of the driving environment complexity);
determining, based on the one or more different characteristics of the different operational context, a different sensor configuration for the vehicle to implement when navigating the different operational context (Prokhorov: Par. 105; i.e., responsive to determining that there is a change in at least one of the driving environment complexity … a different subset of the plurality of different types of sensors can be selected);
and dynamically implementing the different sensor configuration at the vehicle when the vehicle is in the different operational context (Prokhorov: Par. 103; i.e., driving environment data acquired by the selected subset of the plurality of different types of sensors can be sent to the remote operation computing system; only data from the selected subset of sensors is acquired).
Regarding claim 13, Prokhorov in view of Boyoung teaches the method according to claim 12. Prokhorov further teaches wherein the different sensor configuration comprises at least one of a first sensor selected for use by the vehicle in the different operational context and a second sensor set to an off state or a reduced operating mode while the vehicle is in the different operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment… A “subset of the plurality of different sensor types” means less than all of the available types of sensors; Par. 71; i.e., the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; the system may select to use one type of sensor while the remaining sensors operate at a reduced operating mode).
Regarding claim 14, Prokhorov in view of Boyoung teaches the method according to claim 12. Boyoung further teaches prior to detecting the one or more different characteristics of the different operational context, determining, based on at least one of map data, route planning data, and external planning data available before the vehicle enters the different operational context, that the vehicle is in the different operational context or predicted to be in the different operational context during a trip of the vehicle (Boyoung: Par. 140; i.e., the processor (910) may determine whether the road section on which the vehicle is driving satisfies a specified condition (e.g., presence of a guardrail) based on at least one of map data; Par. 202; i.e., when the completion of driving in a section that satisfies a specified condition is predicted, the processor (910) and/or the sensor module (920) of the vehicle may reactivate at least one camera and perform operation 2601. For example, the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end);
and Prokhorov further teaches in response to determining that the vehicle is in the different operational context or predicted to be in the different operational context during the trip, detecting the one or more different characteristics of the different operational context (Prokhorov: Par. 53; i.e., the classification of the driving environment can be performed continuously; Par. 55; i.e., For instance, the driving environment can be classified as low complexity if a relatively small number of objects is detected in the driving environment; the system is continuously monitoring environment characteristics to determine changes in complexity of the driving environment).
Regarding claim 15, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches wherein the sensor data from at least one sensor on the vehicle comprises at least one of a speed measurement associated with the vehicle, a measurement of a heading of the vehicle, image data, a distance or range measurement of one or more objects in a scene of the vehicle, a motion measurement of the one or more objects in the scene, and a measured trajectory of at least one of the vehicle and the one or more objects in the scene (Prokhorov: Par. 38; i.e., The LIDAR sensors 222 can be configured and/or used to detect … various things about the driving environment of the vehicle 200. Non-limiting examples of such things include … the distance between each detected object and the vehicle 200 in one or more directions, the speed of each detected object, and/or the movement of each detected object).
Regarding claim 16, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches wherein at least one of the operational context and the one or more characteristics of the operational context comprises at least one of a type of a driving environment associated with the vehicle, a driving intent of the vehicle, a weather associated with the driving environment, a light or brightness level in the driving environment, traffic conditions in the driving environment, a traffic rule associated with the driving environment, a type of road associated with the driving environment, and a geography associated with the driving environment (Prokhorov: Par. 53; i.e., The driving environment can include any information about the external environment, including, for example, the presence and/or location of one or more objects in the environment, the identity and/or nature of the objects, traffic conditions and/or weather conditions).
Regarding claim 18, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches wherein the one or more sensors in the sensor configuration comprise a set of sensors selected for the operational context based on capabilities of the set of sensors and the one or more characteristics of the operational context, and wherein the one or more different sensors in the sensor configuration comprise at least one different sensor selected to be set to the off state or the reduced operating mode while the vehicle is in the operational context based on a mismatch between one or more capabilities of the at least one different sensor and one or more desired capabilities identified for the operational context based on the one or more characteristics of the operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment; Par. 71; i.e., as an example, the driving environment can include a road with a 45 MPH legal speed limit. It may be nighttime, and there can be relatively few vehicles on the road. In such case, the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; Par. 93; i.e., a sensor representation can be based on data from one or more different types of sensors of the sensor system 220 that are most suitable for the current driving environment; the system may select to use LIDAR due to the operational context, while the remaining sensors operate at a reduced operating mode because they do not have the desired capabilities in the operational context).
Regarding claim 19, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches wherein at least one of the one or more sensors and the at least one sensor on the vehicle comprises a visible-light camera sensor, a radio detection and ranging (RADAR) sensor, a time-of-flight (TOF) sensor, a light detection and ranging (LIDAR) sensor, a speedometer, an inertial measurement unit, a thermal camera sensor, and an ultrasonic sensor (Prokhorov: Par. 101; i.e., The sensor system 220 includes a plurality of different types of sensors (e.g., LIDAR sensors 221, RADAR sensors 222, ultrasound sensors 223, cameras 224, etc.)).
Regarding claim 20, Prokhorov teaches a non-transitory computer-readable medium having stored thereon instructions which, when executed by one or more processors, cause the one or more processors to (Prokhorov: Par. 114; i.e., arrangements described herein may take the form of a computer program product embodied in one or more computer-readable media having computer-readable program code embodied or embedded, e.g., stored, thereon):
detect, based on at least one of map data and sensor data from at least one sensor on a vehicle, one or more characteristics of an operational context of the vehicle (Prokhorov: Par. 53; i.e., The environment classification module 240 can be configured to identify, classify and/or assess the driving environment of the vehicle… Such information about the driving environment can be obtained from the sensor system);
and determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context, the sensor configuration comprising one or more sensors selected for use by the vehicle in the operational context and one or more different sensors set to an off state or a reduced operating mode while the vehicle is in the operational context (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment… A “subset of the plurality of different sensor types” means less than all of the available types of sensors; Par. 71; i.e., the vehicle 200 can determine that a representation of a driving environment might be more suitable based on data from the LIDAR sensors 222 than data from the cameras 224 and/or RADAR sensors 221; the system may select to use one type of sensor while the remaining sensors operate at a reduced operating mode);
Prokhorov does not explicitly teach prior to the vehicle entering the operational context, determining, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determine, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context.
However, in the same field of endeavor, Boyoung teaches prior to the vehicle entering the operational context, determine, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context (Boyoung: Par. 140; i.e., the processor (910) may determine whether the road section on which the vehicle is driving satisfies a specified condition (e.g., presence of a guardrail) based on at least one of map data; Par. 202; i.e., when the completion of driving in a section that satisfies a specified condition is predicted, the processor (910) and/or the sensor module (920) of the vehicle may reactivate at least one camera and perform operation 2601. For example, the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end); and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context (Boyoung: Par. 202; i.e., the processor (910) and/or the sensor module (920) of the vehicle may predict in advance that the guardrail section will end … and reactivate at least one camera before the vehicle enters a section without a guardrail).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Prokhorov to have further incorporated prior to the vehicle entering the operational context, determine, based at least in part on map data and/or route planning data, that the vehicle is predicted to enter the operational context during a trip of the vehicle, and in response determining, based on the one or more characteristics of the operational context, a sensor configuration for the vehicle to implement when navigating the operational context; and prior to the vehicle entering the operational context, dynamically implement the sensor configuration at the vehicle so that the sensor configuration is active when the vehicle enters the operational context, as taught by Boyoung. Doing so would save energy by only activating necessary sensors (Boyoung: Par. 200; i.e., when the vehicle is driving on a section that satisfies a specified condition, energy can be saved by disabling the cameras and detecting objects using only lidar).
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Prokhorov in view of Boyoung and further in view of Ewert (U.S. Publication No. 2022/0126832).
Regarding claim 7, Prokhorov in view of Boyoung teaches the system according to claim 1. Prokhorov further teaches wherein the one or more different sensors in the sensor configuration are set to the reduced operating mode (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment; the remaining sensors that are not selected operate at a reduced operating mode).
Prokhorov does not explicitly teach wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors.
However, in the same field of endeavor, Ewert teaches wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors (Ewert: Par. 35; i.e., the at least one sensor may also be set to a standby mode. In this connection, the at least one sensor may be operated at a lower transmission rate or at a lower sampling rate; Par. 69; i.e., In spite of an interrupted supply of power, this may allow camera sensor 12 to be operated at a reduced sampling rate or to at least maintain rotation).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Prokhorov and Boyoung to have further incorporated wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors, as taught by Ewert. Doing so would allow the sensor to reactivate faster than if it were turned off (Ewert: Par. 69; i.e., Through this, camera sensor 12 may be reactivated more rapidly).
Regarding claim 17, Prokhorov in view of Boyoung teaches the method according to claim 11. Prokhorov further teaches wherein the one or more different sensors in the sensor configuration are set to the reduced operating mode (Prokhorov: Par. 70; i.e., The vehicle sensor selection module 250 can be configured to select a subset of the plurality of different sensor types based on the complexity of the current driving environment; the remaining sensors that are not selected operate at a reduced operating mode).
Prokhorov does not explicitly teach wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors.
However, in the same field of endeavor, Ewert teaches wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors (Ewert: Par. 35; i.e., the at least one sensor may also be set to a standby mode. In this connection, the at least one sensor may be operated at a lower transmission rate or at a lower sampling rate; Par. 69; i.e., In spite of an interrupted supply of power, this may allow camera sensor 12 to be operated at a reduced sampling rate or to at least maintain rotation).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Prokhorov and Boyoung to have further incorporated wherein the reduced operating mode comprises at least one of a reduced data collection frequency relative to a different data collection frequency of the one or more different sensors, a reduced power mode relative to a different power mode of the one or more different sensors, a reduced resolution relative to a different resolution of the one or more different sensors, a reduced framerate relative to a different framerate of the one or more different sensors, and a setting configured to reduce a resource consumption by the one or more different sensors, as taught by Ewert. Doing so would allow the sensor to reactivate faster than if it were turned off (Ewert: Par. 69; i.e., Through this, camera sensor 12 may be reactivated more rapidly).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRANDON Z WILLIS/Examiner, Art Unit 3665
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