DETAILED ACTION
Receipt is acknowledged of applicant’s argument(s)/remark(s) filed April 6, 2026, claims 1-10 and 12-21 are pending and an action on the merits is as follows.
Applicant's arguments with respect to the amended claims have been fully considered but are moot in view of the following new ground(s) of rejection. Applicant has amended claims 1, 12 and 20.
Previously, claim 11 had been canceled.
Response to Argument
Regarding applicant’s arguments with respect to the amendment of the claims, applicant is kindly invited to consider the Office Action below to view the new ground of rejection, cited prior arts’ section(s) and motivation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 of this title, 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.
Claims 1-4, 10, 12-13, 15 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1) and Jeanne et al. (US 2018/0260011 A1)
Regarding claim 1, Ingram et al. disclose a method for tailoring sensor emission power comprising:
receiving, by an autonomy system coupled to a vehicle (e.g., fully-autonomous vehicle (par. 33) and one or more processors 152 of controller 150 (par. 56)), sensor data obtained by one or more sensors as the vehicle navigates a path in an environment (e.g., controller 150 configured to receive / process signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140 (e.g., par. 57-58 and Figures 1-3) ), wherein the autonomy system comprises one or more compute cores and a plurality of sensors (e.g., one or more processors 152 of controller 150 (par. 56) and , multiple sensors of the vehicle (par. 25), for instance, Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140);
determining, by the autonomy system and based on the sensor data (e.g., signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140) , information corresponding to one or more objects detected in the environment (e.g., controller 150 configured to process data from radar sensor(s) 130 to detect and identify nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian, among other features within the environment surrounding the vehicle 110 (par. 43), wherein the controller 140 determines the operation context of the vehicle 110 based on the received information – for instance, using signals from Lidar, Radar and other sensors (par. 65, 64) );
estimating, based on the information corresponding to one or more objects (e.g., based on detected and identified nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian within the environment surrounding the vehicle 110 (par. 43)), a change in a level of environment complexity expected for a threshold duration during subsequent navigation of the path (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) – which covers a change in a level of environment complexity expected for a threshold duration for sensor operation and detection during subsequent navigation of the path);
adjusting, by the autonomy system, an operation of one or more components of the autonomy system (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ) based on the estimated change in a level of environment complexity (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) wherein
adjusting the operation of the one or more components comprises modifying power consumption of one or more sensors by adjusting a sampling frequency, a sensing range, or a state of the one or more sensors (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ); and
controlling the vehicle based on subsequent sensor data obtained after adjusting operation of the one or more components of the autonomy system (e.g., the vehicle control system configured to use radar measurement of aspect of the environment when determining control strategy for the autonomous navigation to avoid obstacle while determining proper path for navigation (par. 43 and Figures 1-2)).
Ingram et al. disclose a method for tailoring sensor emission power based on traffic density (par. 65) and road boundaries and grades (par. 43 and 76).
However, Ingram et al. failed to specifically disclose wherein the level of environment complexity is determined by inputting a subset of the information into a machine learned model and is based on a plurality of environmental factors including at least traffic density and road geometry.
However, Johnson et al. teach a vehicle automated guidance system configured to determine navigation complexity of a zone where the vehicle travels via a trained neural network (par. 61 and 22) based on traffic pattern change (par. 23-34) and pathway shape (par. 27) or route infrastructure / surface quality (par. 22-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the autonomous vehicle’s processor(s) taught by Ingram et al., such that the processors determines navigation complexity of a zone where the vehicle travels via a trained neural network based on traffic pattern change and pathway shape or route infrastructure / surface quality , in view of Johnson et al., with reasonable expectation of success, since doing so would have achieved the benefit of allowing autonomous vehicle to navigate across temporary change to road infrastructure (par. 23) while recognizing a superseded stop indication on a road, overriding other stop indication, and navigating across an area to a destination(par. 24).
However, modified Ingram et al. failed to specifically disclose adjusting the operation of the one or more components comprises modifying power consumption of the one or more computer cores by adjusting a number of powered compute cores or
However, Jeanne et al. teach a technology to increase or decrease the number of processor cores allowed to be used for operating an electronic device based on processor load (par. 30, 32 and 152) and control its power consumption (par. 77, 76).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to further modify the autonomous vehicle’s processor(s) as taught by the combination of Ingram et al. in view of Johnson et al., such that the vehicle processor is configured to increase or decrease the number of processor cores to be used for operation based on processor load and control its power consumption, in view of Jeanne et al., with reasonable expectation of success, since doing so would have achieved the benefit of transition to a more or less powerful processor state based on processor load (Jeanne et al.’s par. 152) while controlling the power consumption of the processor (Jeanne et al.’s par. 5) as the vehicle control system processes environment radar measurement (e.g., load) to avoid obstacle while determining proper path for navigation (Ingram et al.’s par. 43 and Figures 1-2).
Regarding claim 2, Ingram et al. disclose a method for tailoring sensor emission power, wherein receiving sensor data obtained by one or more sensors (e.g., controller 150 configured to receive / process signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140 (e.g., par. 57-58 and Figures 1-3) ) comprises:
receiving sensor data from a set of sensors (e.g., controller 150 configured to receive / process signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140 (e.g., par. 57-58 and Figures 1-3) );
wherein estimating the change in the level of environment complexity expected for the threshold duration comprises:
estimating a decrease in the level of environment complexity based on a decrease in traffic along at least one side of the vehicle for the threshold duration (e.g., determining the vehicle operating context comprising traffic density (par. 65), average speed of other vehicle along adjacent roadway and traffic patterns (par. 101) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D), which covers estimating decrease in level of environment complexity); and
wherein adjusting operation of one or more components of the autonomy system (e.g., reducing power consumption of sensor system (par. 28)) comprises: reducing power consumption by the set of sensors and one or more corresponding compute cores that process sensor data obtained by the set of sensors (e.g., when the vehicle is driving slowly or in certain environment, front face radar is turned off or their emission power be reduced (par. 29). As the object data processing is implemented under low resolution (par. 70 and 72), the controller 150 consumes less power).
Regarding claim 3, Ingram et al. disclose a method for tailoring sensor emission power, wherein adjusting operation of one or more components of the autonomy system comprises: increasing power consumption by one or more particular sensors and one or more corresponding compute cores that process sensor data obtained by the one or more particular sensors (e.g., adjusting the sensor system to provide greater emission power within high priority sectors (par. 105, 132, 24 and Figure 4C); wherein the sensor system comprising radars and Lidar sensor(s) (par. 29 and abstract). As the object data processing is implemented under high resolution (par. 70 and 71), the controller 150 consumes more power).
Regarding claim 4, Ingram et al. disclose method for tailoring sensor emission power wherein increasing power consumption by the one or more particular sensors comprises: switching one or more radar units from a low power state to a high power state (e.g., as the vehicle transition from driving slowly to a high speed / fast (par. 29), the front-facing radars is turned on - increasing power consumption. Plurality of sensor power configuration include radar operating parameter: selection of enable radar unit, selected emitter per enabled radar and other configuration to operate the radar(s) (par. 133)).
Regarding claim 10, Ingram et al. disclose method for tailoring sensor emission power wherein adjusting operation of one or more components of the autonomy system further comprises: reducing a power consumption of one or more actuators (e.g., as side-facing and / or rear-facing radar(s) are turned off when the vehicle is under certain operation and location (par. 29 and 24)), power consumption of sensor’s rotational motor is reduced (par. 82)).
Regarding claim 12, Ingram et al. disclose a fully-autonomous vehicle (par. 33) comprising one or more processors 152 of controller 150 (par. 56) and plurality of sensors (e.g., par. 57-58 and Figures 1-3) configured to
receive sensor data obtained by one or more sensors as the vehicle navigates a path in an environment (e.g., controller 150 configured to receive / process signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140 (e.g., par. 57-58 and Figures 1-3) ), wherein the autonomy system comprises one or more compute cores and a plurality of sensors (e.g., one or more processors 152 of controller 150 (par. 56) and , multiple sensors of the vehicle (par. 25), for instance, Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140);
determine, based on the sensor data (e.g., signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140) , information corresponding to one or more objects detected in the environment (e.g., controller 150 configured to process data from radar sensor(s) 130 to detect and identify nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian, among other features within the environment surrounding the vehicle 110 (par. 43), wherein the controller 140 determines the operation context of the vehicle 110 based on the received information – for instance, using signals from Lidar, Radar and other sensors (par. 65, 64) );
estimate, based on the information corresponding to one or more objects (e.g., based on detected and identified nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian within the environment surrounding the vehicle 110 (par. 43)), a change in a level of environment complexity expected for a threshold duration during subsequent navigation of the path (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) – which covers a change in a level of environment complexity expected for a threshold duration for sensor operation and detection during subsequent navigation of the path);
adjust an operation of one or more components of the autonomy system (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ) based on the estimated change in a level of environment complexity (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) wherein
adjusting the operation of the one or more components comprises modifying power consumption of one or more sensors by adjusting a sampling frequency, a sensing range, or a state of the one or more sensors (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ); and
controlling the vehicle based on subsequent sensor data obtained after adjusting operation of the one or more components of the autonomy system (e.g., the vehicle control system configured to use radar measurement of aspect of the environment when determining control strategy for the autonomous navigation to avoid obstacle while determining proper path for navigation (par. 43 and Figures 1-2)).
Ingram et al. disclose a method for tailoring sensor emission power based on traffic density (par. 65) and road boundaries and grades (par. 43 and 76).
However, Ingram et al. failed to specifically disclose wherein the level of environment complexity is determined by inputting a subset of the information into a machine learned model and is based on a plurality of environmental factors including at least traffic density and road geometry.
However, Johnson et al. teach a vehicle automated guidance system configured to determine navigation complexity of a zone where the vehicle travels via a trained neural network (par. 61 and 22) based on traffic pattern change (par. 23-34) and pathway shape (par. 27) or route infrastructure / surface quality (par. 22-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the autonomous vehicle’s processor(s) taught by Ingram et al., such that the processors determines navigation complexity of a zone where the vehicle travels via a trained neural network based on traffic pattern change and pathway shape or route infrastructure / surface quality , in view of Johnson et al., with reasonable expectation of success, since doing so would have achieved the benefit of allowing autonomous vehicle to navigate across temporary change to road infrastructure (par. 23) while recognizing a superseded stop indication on a road, overriding other stop indication, and navigating across an area to a destination(par. 24).
However, modified Ingram et al. failed to specifically disclose adjusting the operation of the one or more components comprises modifying power consumption of the one or more computer cores by adjusting a number of powered compute cores or
However, Jeanne et al. teach a technology to increase or decrease the number of processor cores allowed to be used for operating an electronic device based on processor load (par. 30, 32 and 152) and control its power consumption (par. 77, 76).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to further modify the autonomous vehicle’s processor(s) as taught by the combination of Ingram et al. in view of Johnson et al., such that the vehicle processor is configured to increase or decrease the number of processor cores to be used for operation based on processor load and control its power consumption, in view of Jeanne et al., with reasonable expectation of success, since doing so would have achieved the benefit of transition to a more or less powerful processor state based on processor load (Jeanne et al.’s par. 152) while controlling the power consumption of the processor (Jeanne et al.’s par. 5) as the vehicle control system processes environment radar measurement (e.g., load) to avoid obstacle while determining proper path for navigation (Ingram et al.’s par. 43 and Figures 1-2).
Regarding claim 13, Ingram et al. disclose a fully-autonomous vehicle configured to estimate a decrease in traffic in the environment of the vehicle (e.g., determining the vehicle operating context comprising traffic density (par. 65), average speed of other vehicle along adjacent roadway and traffic patterns (par. 101) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D), which covers estimating decrease in traffic in the environment of the vehicle); and
reduce power consumption by both a set of sensors and one or more corresponding compute cores that process sensor data obtained by the set of sensors (e.g., when the vehicle is driving slowly or in certain environment, front face radar is turned off or their emission power be reduced (par. 29). As the object data processing is implemented under low resolution (par. 70 and 72), the controller 150 consumes less power).
Regarding claim 15, Ingram et al. disclose a fully-autonomous vehicle configured wherein the autonomy system is configured to:
estimate a decrease in visibility in the environment (e.g., determining operation context of the vehicle based on current time of day, current sun position and local weather condition (par. 62), which cover decrease in visibility in the vehicle environment based on weather condition and sun position – for instance, poor visibility during snow weather condition at night while the vehicle is traveling); and
based on the estimated decrease in visibility in the environment, transition a radar from a low power consumption state to a high power consumption state (e.g., adjusting the sensor system to provide greater emission power within high priority sectors – for instance, environment with decrease visibility(par. 105, 132, 24 and Figure 4C); wherein the sensor system comprising radars and Lidar sensor(s) (par. 29 and abstract). As the object data processing is implemented under high resolution (par. 70 and 71), the controller 150 consumes more power).
Regarding claim 19, Ingram et al. disclose a fully-autonomous vehicle wherein the autonomy system is further configured to: adjust the threshold duration based on a speed of the vehicle (e.g., when the vehicle is moving at highway speed or slowly changing lane, the vehicle senses (i) stopped traffic far ahead in the same lane / adjacent lanes or (ii) fast oncoming traffic from the rear respectively (par. 20 and 99) – which cover adjust threshold duration for sensor operation and detection as the vehicle travels along a route (par. 97 and Figures 4B – 4D)).
Regarding claim 20, Ingram et al. disclose a memory comprising non-transitory computer-readable medium to store executable instruction and be executed by one or more processors 152 of controller 150 (par. 55-56) to perform operation comprising:
receive sensor data obtained by one or more sensors as the vehicle navigates a path in an environment (e.g., controller 150 configured to receive / process signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140 (e.g., par. 57-58 and Figures 1-3) ), wherein the autonomy system comprises one or more compute cores and a plurality of sensors (e.g., one or more processors 152 of controller 150 (par. 56) and , multiple sensors of the vehicle (par. 25), for instance, Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140);
determine, based on the sensor data (e.g., signals from Lidar sensor(s) 120, Radar Sensor 130 and other sensor(s) 140) , information corresponding to one or more objects detected in the environment (e.g., controller 150 configured to process data from radar sensor(s) 130 to detect and identify nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian, among other features within the environment surrounding the vehicle 110 (par. 43), wherein the controller 140 determines the operation context of the vehicle 110 based on the received information – for instance, using signals from Lidar, Radar and other sensors (par. 65, 64) );
estimate, based on the information corresponding to one or more objects (e.g., based on detected and identified nearby vehicles, road boundaries, weather condition, traffic signs and signal and pedestrian within the environment surrounding the vehicle 110 (par. 43)), a change in a level of environment complexity expected for a threshold duration during subsequent navigation of the path (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) – which covers a change in a level of environment complexity expected for a threshold duration for sensor operation and detection during subsequent navigation of the path);
adjust an operation of one or more components of the autonomy system (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ) based on the estimated change in a level of environment complexity (e.g., sensing stopped traffic far ahead in the same lane / adjacent lanes to sensing fast oncoming traffic from the rear when the vehicle is moving at highway speed or slowly changing lane respectively (par. 20 and 99). Estimating / predicting no object or no hazards to arise within low priority sectors (par. 72) to estimating detection of pedestrian, vehicle(s), building or other obstacles within high priority sector (par. 71) while the vehicle is traveling along a route (par. 97 and Figures 4B – 4D) wherein
adjusting the operation of the one or more components comprises modifying power consumption of one or more sensors by adjusting a sampling frequency, a sensing range, or a state of the one or more sensors (e.g., reducing power consumption of sensor system (par. 28) – for instance, (i)” side-facing radars could be turned off” when the vehicle is not at an intersection or (ii) “rear-facing radar could be turned-off” or their power be reduced when the vehicle is driving quickly (par. 29 and 24) and / or dynamically adjust power of Lidar and radar sensors based on priority of a given sector (par. 73) and desired sensor power configuration (par. 77) ); and
controlling the vehicle based on subsequent sensor data obtained after adjusting operation of the one or more components of the autonomy system (e.g., the vehicle control system configured to use radar measurement of aspect of the environment when determining control strategy for the autonomous navigation to avoid obstacle while determining proper path for navigation (par. 43 and Figures 1-2)).
Ingram et al. disclose a method for tailoring sensor emission power based on traffic density (par. 65) and road boundaries and grades (par. 43 and 76).
However, Ingram et al. failed to specifically disclose wherein the level of environment complexity is determined by inputting a subset of the information into a machine learned model and is based on a plurality of environmental factors including at least traffic density and road geometry.
However, Johnson et al. teach a vehicle automated guidance system configured to determine navigation complexity of a zone where the vehicle travels via a trained neural network (par. 61 and 22) based on traffic pattern change (par. 23-34) and pathway shape (par. 27) or route infrastructure / surface quality (par. 22-23).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to modify the autonomous vehicle’s processor(s) taught by Ingram et al., such that the processors determines navigation complexity of a zone where the vehicle travels via a trained neural network based on traffic pattern change and pathway shape or route infrastructure / surface quality , in view of Johnson et al., with reasonable expectation of success, since doing so would have achieved the benefit of allowing autonomous vehicle to navigate across temporary change to road infrastructure (par. 23) while recognizing a superseded stop indication on a road, overriding other stop indication, and navigating across an area to a destination(par. 24).
However, modified Ingram et al. failed to specifically disclose adjusting the operation of the one or more components comprises modifying power consumption of the one or more computer cores by adjusting a number of powered compute cores or
However, Jeanne et al. teach a technology to increase or decrease the number of processor cores allowed to be used for operating an electronic device based on processor load (par. 30, 32 and 152) and control its power consumption (par. 77, 76).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to further modify the autonomous vehicle’s processor(s) as taught by the combination of Ingram et al. in view of Johnson et al., such that the vehicle processor is configured to increase or decrease the number of processor cores to be used for operation based on processor load and control its power consumption, in view of Jeanne et al., with reasonable expectation of success, since doing so would have achieved the benefit of transition to a more or less powerful processor state based on processor load (Jeanne et al.’s par. 152) while controlling the power consumption of the processor (Jeanne et al.’s par. 5) as the vehicle control system processes environment radar measurement (e.g., load) to avoid obstacle while determining proper path for navigation (Ingram et al.’s par. 43 and Figures 1-2).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1), Jeanne et al. (US 2018/0260011 A1) and Iwama (Pub. No.: US 2014/0076513 A1).
Regarding claim 5, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose wherein adjusting one or more components of the autonomy system comprises: adjusting a frequency of a compute cooling system, wherein the compute cooling system is configured to reduce an operational temperature of at least one compute core.
However, Iwama teaches a control unit configured to control a rotational frequency of a cooling fan to regulate the temperature of electronic device (e.g., CPU) (par. 4 and 3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the controller taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the controller is configured to control the rotational frequency of a cooling fan to regulate the temperature of controller, in view of Iwama, with reasonable expectation of success, since doing so would have achieved the benefit of avoiding adverse heat effect on the performance of the electronic device / controller (par. 3) while regulating power consumption (par. 12).
Claim 6, 14, 16 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1), Jeanne et al. (US 2018/0260011 A1) and Rice et al. (US. No.: US 10,173,646 A1).
Regarding claims 6 and 16, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose (i) adjusting a frequency of a sensor cleaning system, wherein the sensor cleaning system is configured to clean one or more sensors of the plurality of sensors (claim 6) and (ii) detect precipitation in the environment; and adjust, based on detecting precipitation in the environment, a frequency of a sensor cleaning process at one or more sensors (claim 16).
However, Rice et al. teach a computing system configured to determine the type and level of precipitation experience by the autonomous vehicle (col. 37, lines 3-6) and determine a sensor cleaning sequence based on the intensity of the precipitation – for instance, adjust to a higher frequency sensor cleaning sequence during heavy rain conditions than during light rain condition (col. 36, lines 47-55 and col. 37, lines 11-19 and abstract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the controller taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the controller is configured to determine the type and level of precipitation experience by the autonomous vehicle and adjust to a higher frequency sensor cleaning sequence during heavy rain conditions than during light rain condition, in view of Rice et al., with reasonable expectation of success, since doing so would have achieved the benefit of reducing degrading quality of data collected by the sensors during precipitation (col. 1, lines 38-42) while allowing the vehicle to properly identify an appropriate motion path through a surrounding environment (col. 1, lines 17-24).
Regarding claim 14, Ingram et al. failed to specifically disclose to estimate an increase in visibility in the environment and responsively decrease power consumption by one or more sensors and one or more corresponding compute cores that process sensor data obtained by the one or more sensors.
However, Ingram et al., as modified by Johnson et al. and Rice et al., teach a vehicle control system / controller configured to detect weather condition surrounding the vehicle using radar sensor(s) (Ingram et al.’s par. 43) and determine the type and level of precipitation experience by the vehicle (Rice et al.’s col. 37, lines 3-6) for controlling a sensor cleaning sequence based on the intensity of the precipitation – for instance, adjust to a higher frequency sensor cleaning sequence during heavy rain conditions than during light rain condition (Rice et al.’s col. 36, lines 47-55 and col. 37, lines 11-19 and abstract).
Under these disclosure, It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to reasonable conclude that as the weather condition improves from a heavy rain condition to a sunny condition, the environment visibility increases and the vehicle’s controller stops the operation of the sensor cleaning sequence to reduce power consumption as the vehicle travels along a route while also reducing the power consumption of the controller as it no longer require the allocation of computer resource to operate the sensor cleaning sequence.
Regarding claim 18, Ingram et al., as further modified by Jeanne et al.,
failed to specifically disclose wherein the autonomy system is further configured to: based on estimating a transition from a low confidence environment to a high confidence environment, transitioning one or more lidars from a high-power consumption state to a low power consumption state. In other word, when the environment changes from raining / snowing condition to a clear sky / sunny condition, the lidars transitions from high power consumption to a lower power consumption.
However, Ingram et al., as further modified by Rice et al., teach a vehicle control system / controller configured to detect weather condition surrounding the vehicle via radar sensor(s) (Ingram et al.’s par. 43) operating at a desired power configuration (Ingram et al.’s par. 133) and determine the type and level of precipitation experience by the vehicle (Rice et al.’s col. 37, lines 3-6) for controlling a sensor cleaning sequence based on the intensity of the precipitation – for instance, adjust to a higher frequency sensor cleaning sequence during heavy rain conditions than during light rain condition and vice versa (Rice et al., col. 36, lines 47-55 and col. 37, lines 11-19 and abstract).
Under these disclosure, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to reasonable conclude that as the weather condition improves from a heavy rain condition to a sunny condition, the environment visibility increases from low confidence to a high confidence environment(s) and the vehicle’s controller adjusts the radar operating parameter to reduce the power consumption of the radar based on the operating context of the vehicle.
Claims 7, 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1), Jeanne et al. (US 2018/0260011 A1) and O’Donnell (Pub. No.: US 2023/0168351 A1).
Regarding claim 7, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose determining a battery level corresponding to the vehicle, wherein the vehicle is an electric vehicle; and wherein adjusting operation of one or more components of the autonomy system further comprises: adjusting operation of the one or more components of the autonomy system further based on the battery level corresponding to the vehicle and a total distance remaining until the vehicle reaches a predefined destination.
However, O’Donnell teaches a method for vehicle sensor management comprising a vehicle power management module 820 configured to manage energy resources (par. 80) – which cover determining a battery level corresponding to the vehicle - for a battery electric autonomous vehicle (par. 22) to travel toward a destination (par. 61). O’Donnell also teaches a technique to reduce power usage of the vehicle by deactivating particular sensors that are not used during certain operation (par. 22 and 82) based on energy resources and trajectory to a destination that a vehicle requires to travel (par. 61 and 64) to avoid / reduce vehicle range degradation from sensors consuming large amount of energy (par. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the controller taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the controller is configured to reduce power usage of the vehicle by deactivating particular sensors that are not used during certain operation based on energy resources and vehicle’s trajectory to a destination, in view of O’Donnell, with reasonable expectation of success, since doing so would have achieved the benefit of avoiding vehicle range degradation from sensors consuming large amount of energy and reducing energy wasted due to sensor operation that are not used for operation of the vehicle (par. 1).
Regarding claim 9, Ingram et al. disclose a method for tailoring sensor emission power wherein adjusting operation of one or more components of the autonomy system (e.g., reducing power consumption of sensor system (par. 28)) comprises: reducing one or both of a frequency or a range of the lidar for at least the threshold duration (e.g., Lidar operating parameters includes “dynamic sector angle ranges” (pare. 67), “specific elevation range” (par. 76) and variable “sensing range” (par. 31) configurations to reduce the overall power consumption of the Lidar sensor system) – note: alternative limitation. Ingram et al. disclose the controller 150 configured to receive data / signal from Lidar sensor (par. 58).
However, Ingram et al., as further modified by Jeanne et al., fails to specifically disclose receiving point cloud data from a Lidar coupled to the vehicle.
However, O’Donnell teaches a method for vehicle sensor management comprising Lidar sensors (202b) configured to generate “point cloud” image / data of object on a field of view (par. 83 and Figure 2), wherein the autonomous vehicle compute (202f) / localization system 406 receives and processes said “point cloud” image / data of object to determine vehicle location (62 and 42 and Figures 2 and 4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the Lidar sensor and controller taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the Lidar sensor is configured to generate “point cloud” image / data of object on a vehicle field of view and be processed by the controller, in view of O’Donnell, with reasonable expectation of success, since doing so would have achieved the benefit of determining the location of the vehicle (par. 62) and reducing power usage of the vehicle by deactivating particular sensors that are not used during certain operations (par. 22).
Regarding claim 17, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose a vehicle further comprising a battery; and wherein the autonomy system is further configured to: determine an energy level remaining in the battery; and adjust power consumption at one or more components of the autonomy system based on the energy level remaining in the battery.
However, O’Donnell teaches a method for vehicle sensor management comprising a vehicle power management module 820 configured to manage energy resources (par. 80) for a battery electric autonomous vehicle (par. 22) to travel toward a destination (par. 61). O’Donnell also teaches a technique to reduce power usage of the vehicle by deactivating particular sensors that are not used during certain operation (par. 22 and 82) based on energy resources and trajectory to a destination that a vehicle requires to travel (par. 61 and 64) to avoid / reduce vehicle range degradation from sensors consuming large amount of energy (par. 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the controller taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the controller is configured to reduce power usage of the vehicle by deactivating particular sensors that are not used during certain operation based on energy resources and vehicle’s trajectory to a destination, in view of O’Donnell, with reasonable expectation of success, since doing so would have achieved the benefit of avoiding vehicle range degradation from sensors consuming large amount of energy and reducing energy wasted due to sensor operation that are not used for operation of the vehicle (par. 1).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1), Jeanne et al. (US 2018/0260011 A1) and Vickers (Pub. No.: US 2004/0204797 A1).
Regarding claim 8, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose determining a fuel level corresponding to the vehicle; and wherein adjusting operation of one or more components of the autonomy system further comprises: adjusting operation of the one or more components of the autonomy system further based on the fuel level corresponding to the vehicle and a total distance remaining until the vehicle reaches a predefined destination.
However, Vickers teaches a vehicle’s circuit 18 configured to monitor and regulate engine 12 and motor 14 (limitation: one or more components) based on fuel level indication (par. 14) and a calculated distance to a destination (par. 15 and 26).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the controller for tailoring sensor emission power taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the controller monitors and regulates vehicle’s engine and motor based on fuel level indication and a calculated distance to a destination, in view of Vickers, with reasonable expectation of success, since doing so would have achieved the benefit of maximize / increase fuel efficiency by shutting down the engine and using a remaining energy stored in a cell to maximize use of recharging the device at the destination rather than using a likely less efficient source – fossil / consumable fuel (par. 27 and 3).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Ingram et al. (Pub. No.: US 2019/0277962 A1) in view of Johnson et al. (US 20210247199 A1), Jeanne et al. (US 2018/0260011 A1) and Thivierge, JR. et al. (Pub. No.: US 2021/0096629 A1).
Regarding claim 21, Ingram et al., as further modified by Jeanne et al., fail to specifically disclose adjusting a state or performance of a wireless communication system of the vehicle based on a determined need for wireless communication.
However, Thivierge, JR. et al. teach a vehicle telematics control unit (TCU) configured to limit wireless communication between the vehicle TCU and other devices and prevent other type of wireless communication with the TCU during lower-energy state (par. 41).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention (AIA ) to furthermore modify the vehicle controller for tailoring sensor emission power taught by the combination of Ingram et al. in view of Johnson et al. and Jeanne et al., such that the vehicle controller limits wireless communication between the vehicle TCU and other devices and prevent other type of wireless communication with the TCU during lower-energy state, in view of Thivierge, JR. et al., with reasonable expectation of success, since doing so would have achieved the benefit of conserving battery energy while allowing and / or limit vehicle functionality (par. 41).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chen et al. (Pub. No.: US 2022/0300064 A1) directed to in-vehicle processor for predicting and determining a load amount of the processor and operating frequency respectively based on the collected current load amount and reducing processor power consumption (par. 101, 132, 144), and
Viswanathan (Pub. No.: US 2020/0204440 A1) directed to the implementation of a machine learning algorithm to dynamically adjust the power consumption of sensors of a vehicle based on received / processed vehicle sensor data (par. 47, 24, 23, and 27).
THIS ACTION IS MADE FINAL. 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 extension fee 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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/J.O.P/Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656
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