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 .
Claim Status
Claim(s) 1-2, 7-9, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Menon (US 20240182112 A1) in view of Jammoussi (US 20200049499 A1).
Claim(s) 3, 6, 10, 13, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Menon (US 20240182112 A1) in view of Jammoussi (US 20200049499 A1) and in further view of Herman (US 20240303862 A1).
Claim 4-5, 11-12, 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1-2, 7-9, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Menon (US 20240182112 A1) in view of Jammoussi (US 20200049499 A1).
Regarding claim 1, Menon discloses A method, comprising: (Menon: ¶2 “The present disclosure relates to systems and methods for calibrating a vehicle steering angle.”)
obtaining, by one or more computing devices of an autonomous machine, (Menon: ¶4 “vehicles may perform automated vehicle marshaling routines (e.g., an autonomous navigation routine)”) an initial rotation value of a control apparatus of the autonomous machine, (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”) wherein an expected path (Menon: ¶52 “navigate the vehicle into a funnel-type wheel alignment system 402 (hereinafter referred to as “the FTWAS”)”) of the autonomous machine is associated with an expected rotation value of the control apparatus; (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration” The FTWAS discloses an expected path )
comparing, for a current path of the autonomous machine, the initial rotation value (Menon: ¶53 “As the vehicle 102 moves through the FTWAS 402, the vehicle driver assistance module 304 determines the vehicle steering pinion angle offset (while power steering is engaged) based on the steering wheel angle/position sensor data (such as steering pinion angle offset information 316 acquired by the on-board sensors 204) and identifies a center rack of travel”) to the expected rotation value; and (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle” Menon discloses that the steering pinion angle is based on the steering wheel angle sensor data)
responsive to determining a difference between the initial rotation value and the expected rotation (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration.”)
Menon fails to specifically disclose value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine.
In related art, Jammoussi discloses value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine. (Jammoussi: ¶47 “The computer 105 can identify a wheel misalignment fault…When the wheels 230 are misaligned, the steering wheel angle x to maintain straight movement of the vehicle 101 defines an offset μ above the offset threshold…Upon identifying the wheel misalignment fault, the computer 105 can, e.g., notify a user over the network 125, notify a repair station over the network 125, etc.”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate generating a notification to check wheel alignment upon the violation of a threshold disclosed by Jammoussi into the method for wheel alignment and calibration disclosed by Menon to alert the user of wheel misalignment that could potentially endanger the vehicle.
Regarding claims 2, 9 and 16, Menon, as modified by Jammoussi, disclose wherein the initial rotation value is obtained using a sensor of the autonomous machine. (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”)
Regarding claim 7, Menon, as modified by Jammoussi, disclose wherein the difference between the initial rotation value and the expected rotation value is at least one of time filtered or averaged. (Menon: ¶53 “As an example, the steering pinion angle offset is averaged over time”)
Regarding claim 8, Menon discloses One or more processors comprising: (Menon: ¶9 “executed by at least one processor”)
processing circuitry to perform operations comprising: (Menon: ¶9 “processor-executable instructions that, when executed by at least one processor”)
obtaining, by one or more computing devices of an autonomous machine, (Menon: ¶4 “vehicles may perform automated vehicle marshaling routines (e.g., an autonomous navigation routine)”) an initial rotation value of a control apparatus of the autonomous machine, (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”) wherein an expected path (Menon: ¶52 “navigate the vehicle into a funnel-type wheel alignment system 402 (hereinafter referred to as “the FTWAS”)”) of the autonomous machine is associated with an expected rotation value of the control apparatus; (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration” The FTWAS discloses an expected path )
comparing, for a current path of the autonomous machine, the initial rotation value (Menon: ¶53 “As the vehicle 102 moves through the FTWAS 402, the vehicle driver assistance module 304 determines the vehicle steering pinion angle offset (while power steering is engaged) based on the steering wheel angle/position sensor data (such as steering pinion angle offset information 316 acquired by the on-board sensors 204) and identifies a center rack of travel”) to the expected rotation value; and (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle” Menon discloses that the steering pinion angle is based on the steering wheel angle sensor data)
responsive to determining a difference between the initial rotation value and the expected rotation (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration.”)
Menon fails to specifically disclose value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine.
In related art, Jammoussi discloses value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine. (Jammoussi: ¶47 “The computer 105 can identify a wheel misalignment fault…When the wheels 230 are misaligned, the steering wheel angle x to maintain straight movement of the vehicle 101 defines an offset μ above the offset threshold…Upon identifying the wheel misalignment fault, the computer 105 can, e.g., notify a user over the network 125, notify a repair station over the network 125, etc.”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate generating a notification to check wheel alignment upon the violation of a threshold disclosed by Jammoussi into the method for wheel alignment and calibration disclosed by Menon to alert the user of wheel misalignment that could potentially endanger the vehicle.
Regarding claim 14, Menon, as modified by Jammoussi, disclose wherein the processor is comprised in at least one of: (Menon: ¶9 “executed by at least one processor”)
a control system for an autonomous or semi-autonomous machine; (Menon: ¶27 “The vehicle controller 200 includes or may be communicatively coupled to (e.g., via a vehicle communications bus) one or more processors”)
a perception system for an autonomous or semi-autonomous machine; (Menon: ¶31 “the sensors 204 may include object detection sensors such as lidar sensor(s) disposed on or in the vehicles 102 that provide relative locations, sizes, and shapes of one or more targets surrounding the vehicles 102”)
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system for generating or presenting at least one of virtual reality content,
augmented reality content, or mixed reality content;
a system implemented using a robot;
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
(Menon: ¶21 “To be able to implement the automated marshaling system within the factory infrastructure, the automated vehicle marshaling is operable to enable automated steering immediately at the end-of-line”)
Regarding claim 15, Menon discloses A system comprising: (Menon: ¶2 “The present disclosure relates to systems and methods for calibrating a vehicle steering angle.”)
one or more processing units; and (Menon: ¶9 “executed by at least one processor”)
one or more memory units storing instructions that, when executed by the one or more processing units, cause the one or more processing units to execute operations comprising: (Menon: ¶63 “a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit;”)
obtaining, by one or more computing devices of an autonomous machine, Menon: ¶4 “vehicles may perform automated vehicle marshaling routines (e.g., an autonomous navigation routine)”) an initial rotation value of a control apparatus of the autonomous machine, (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”) wherein an expected path (Menon: ¶52 “navigate the vehicle into a funnel-type wheel alignment system 402 (hereinafter referred to as “the FTWAS”)”) of the autonomous machine is associated with an expected rotation value; (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration” The FTWAS discloses an expected path )
comparing, for a current path of the autonomous vehicle, the initial rotation value Menon: ¶53 “As the vehicle 102 moves through the FTWAS 402, the vehicle driver assistance module 304 determines the vehicle steering pinion angle offset (while power steering is engaged) based on the steering wheel angle/position sensor data (such as steering pinion angle offset information 316 acquired by the on-board sensors 204) and identifies a center rack of travel”)to the expected rotation value; and (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle” Menon discloses that the steering pinion angle is based on the steering wheel angle sensor data)
responsive to determining a difference between the initial rotation value and the expected rotation (Menon: ¶53 “As such, the vehicle driver assistance module 304 determines a deviation between a rack center 404 of the FTWAS 402 based on the steering pinion angle to provide coarse calibration.”)
Menon fails to specifically disclose value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine.
In related art, Jammoussi discloses value being greater than a threshold value, generating a notification to perform an alignment check of the autonomous machine. (Jammoussi: ¶47 “The computer 105 can identify a wheel misalignment fault…When the wheels 230 are misaligned, the steering wheel angle x to maintain straight movement of the vehicle 101 defines an offset μ above the offset threshold…Upon identifying the wheel misalignment fault, the computer 105 can, e.g., notify a user over the network 125, notify a repair station over the network 125, etc.”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate generating a notification to check wheel alignment upon the violation of a threshold disclosed by Jammoussi into the method for wheel alignment and calibration disclosed by Menon to alert the user of wheel misalignment that could potentially endanger the vehicle.
Regarding claim 20, Menon, as modified by Jammoussi, disclose wherein the system is comprised in at least one of: (Menon: ¶2 “The present disclosure relates to systems and methods for calibrating a vehicle steering angle.”)
a control system for an autonomous or semi-autonomous machine; (Menon: ¶27 “The vehicle controller 200 includes or may be communicatively coupled to (e.g., via a vehicle communications bus) one or more processors”)
a perception system for an autonomous or semi-autonomous machine; (Menon: ¶31 “the sensors 204 may include object detection sensors such as lidar sensor(s) disposed on or in the vehicles 102 that provide relative locations, sizes, and shapes of one or more targets surrounding the vehicles 102”)
a system for performing simulation operations;
a system for performing digital twin operations;
a system for performing light transport simulation;
a system for performing collaborative content creation for 3D assets;
a system for performing deep learning operations;
a system implemented using an edge device;
a system implemented using a robot;
a system for performing conversational AI operations;
a system for generating synthetic data;
a system incorporating one or more virtual machines (VMs);
a system implemented at least partially in a data center; or
a system implemented at least partially using cloud computing resources.
(Menon: ¶21 “To be able to implement the automated marshaling system within the factory infrastructure, the automated vehicle marshaling is operable to enable automated steering immediately at the end-of-line”)
Claim(s) 3, 6, 10, 13, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Menon (US 20240182112 A1) in view of Jammoussi (US 20200049499 A1) and in further view of Herman (US 20240303862 A1).
Regarding claim 3, Menon, as modified by Jammoussi, disclose wherein obtaining the initial rotation value comprises: (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”)
Menon, as modified by Jammoussi, fail to specifically disclose estimating the initial rotation value based at least on analyzing one or more images of at least a portion of the control apparatus captured during operation of the autonomous machine.
In related art, Herman discloses estimating the initial rotation value based at least on analyzing one or more images of at least a portion of the control apparatus captured during operation of the autonomous machine. (Herman: ¶34 “The system may further determine an “estimated” steering wheel location in the driver image based on the steering wheel rotation angle, camera 118 pitch, roll and yaw information, and the steering wheel 120 geometry (that may be pre-stored in the vehicle memory).”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate utilizing an image to determine the rotation angle of the steering disclosed by Herman into the method of wheel alignment and calibration disclosed by Menon, as modified by to aid in determining the position of the steering wheel such that the angle of the steering wheel can be determined.
Regarding claims 6 and 13, Menon, as modified by Jammoussi and Herman, disclose wherein the images are captured using one or more interior cameras of the autonomous machine, and wherein at least a portion of the control apparatus is within a field of view of the one or more interior cameras. (Herman: ¶65 “When the driver starts to rotate the steering wheel 120, a steering wheel 120 view may get included in the driver image, as show in images 302b and 302c”)
Regarding claims 10 and 17, Menon, as modified by Jammoussi, disclose wherein obtaining the initial rotation value comprises: (Menon: ¶45 “As another example, the one or more on-board sensors 204 include a steering wheel position/steering wheel angle sensor that is configured to generate data indicative of a steering wheel angle”)
Menon, as modified by Jammoussi, fail to specifically disclose estimating the initial rotation value based at least on analyzing images of at least a portion of the control apparatus captured during operation of the autonomous machine.
In related art, Herman discloses estimating the initial rotation value based at least on analyzing images of at least a portion of the control apparatus captured during operation of the autonomous machine. (Herman: ¶34 “The system may further determine an “estimated” steering wheel location in the driver image based on the steering wheel rotation angle, camera 118 pitch, roll and yaw information, and the steering wheel 120 geometry (that may be pre-stored in the vehicle memory).”)
Therefore, it would have been obvious to for one of ordinary skill in the art before the effective filing date to incorporate utilizing an image to determine the rotation angle of the steering disclosed by Herman into the method of wheel alignment and calibration disclosed by Menon, as modified by to aid in determining the position of the steering wheel such that the angle of the steering wheel can be determined.
Regarding claim 19, Menon, as modified by Jammoussi and Herman, disclose the images are captured using one or more interior cameras of the autonomous machine, and wherein at least a portion of the control apparatus falls within a field of view of the one or more interior cameras. (Herman: ¶65 “When the driver starts to rotate the steering wheel 120, a steering wheel 120 view may get included in the driver image, as show in images 302b and 302c”)
Allowable Subject Matter
Claim 4-5, 11-12, 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Bosse (US 20230001915 A1) discloses techniques for using a set of non-steering variables to estimate an angle of a wheel are described. For example, a yaw rate, a linear velocity of a wheel, and vehicle dimensions (e.g., offset between the wheel and a turn-center reference line), can be used to estimate the angle of the wheel. Among other things, estimating angles based on non-steering variables may provide redundancy (e.g., when determined in parallel with steering-based command angles or other commanded angles) and/or may be used to validate commanded angles based on steering components.
Al Assad (US 11685431 B2) discloses techniques are for steering angle calibration. An autonomous vehicle receives a steering angle measurement and a yaw rate measurement, and estimates a steering angle offset using the steering angle measurement, the yaw rate measurement, and a wheel base of the autonomous vehicle. An estimated yaw rate is determined based on a yaw rate model, the steering angle measurement and the estimated steering angle offset. The yaw rate measurement and the estimated yaw rate are compared and an action is initiated on the autonomous vehicle in response to the comparing.
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/MICHAEL KIM MAIDEN/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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