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 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mizuno et al (US PG Pub 2010/0198441) in view of Doraiswamy et al (WO 2020205703).
Claim 1
Mizuno et al. discloses a method comprising: receiving, at a processor aboard a vehicle, an optimization directive for the vehicle (optimize fuel consumption, paragraph [0013]); receiving, at the processor from at least one tire sensor while the vehicle is in transit, a tire forces signal (see load sensor apparatus 33 and paragraphs [0093] and [0094]); estimating, via the processor and based at least in part on the tire forces signal, at least one aspect of vehicle performance; and modifying, via a wheel alignment controller, a wheel alignment of the vehicle based at least in part on a desired wheel alignment signal (wheel alignment adjusted to improve fuel consumption, paragraph [0083]).
Mizuno does not disclose executing, via the processor, a machine learning model, wherein inputs to the machine learning model comprise the optimization directive and the at least one aspect of vehicle performance, and wherein outputs of the machine learning model comprise a desired wheel alignment signal
However Doraiswamy discloses a machine learning model, wherein inputs to the machine learning model comprise the optimization directive and the at least one aspect of vehicle performance, and wherein outputs of the machine learning model comprise specific vehicle operation setting (for example determining a maximum vehicle speed based on determined tire wear status, paragraphs [0062]-[0066])
Therefore it would have been obvious to modify the method disclosed by Mizuno with the use of a machine learning model as disclosed by Doraiswamy in order to more accurately control vehicle operations.
Claim 2
Mizuno/Doraiswamy disclose a method of claim 1, further comprising: receiving, at the wheel alignment controller, the tire forces signal; and calculating, at the wheel alignment controller, an error between a desired wheel alignment value associated with the desired wheel alignment signal and an actual wheel alignment value identified by the tire forces signal (see Mizuno, Fig. 7 items S35-S37 and paragraphs [0124]-[0128]).
Claim 3
Mizuno/Doraiswamy disclose a method of claim 2, further comprising: modifying the machine learning model based on the error (see Doraiswamy, neural network is trained using error feedback, paragraphs [0062]-[0064]).
Claim 4
The method of claim 1, wherein the machine learning model is a reinforcement learning algorithm (see Doraiswamy, neural network is trained using error feedback, paragraphs [0062]-[0064]).
Claim 5
Mizuno/Doraiswamy disclose a method of claim 1, wherein the at least one aspect of vehicle performance comprises at least one of: fuel economy of the vehicle while in transit, comfort level of the vehicle while in transit, traction of the vehicle while in transit, and rate of tire wear on tires of the vehicle while in transit (Mizuno optimizes for fuel economy, see at least paragraphs [0013] and [0083]).
Claim 6
Mizuno/Doraiswamy do not explicitly disclose a method of claim 1, wherein the optimization directive is provided by one of a passenger or a driver of the vehicle.
Mizuno discloses the fuel economy optimization as being automatically selected.
However, it is well known within the art to provide the option of optimization directives to the driver of the vehicle (fuel economy, driving comfort, speed etc.), and it would have been obvious to provide the driver with the option to choose an optimization directive in order to improve driver satisfaction and control.
Claim 7
Mizuno/Doraiswamy disclose a method of claim 1, wherein the optimization directive comprises instructions to maximize at least one of: fuel economy of the vehicle while in transit, comfort level of the vehicle while in transit, traction of the vehicle while in transit, and tire wear on tires of the vehicle while in transit (Mizuno optimizes for fuel economy, see at least paragraphs [0013] and [0083]; Doraiswamy optimizes for tire wear).
Claim 8
Mizuno/Doraiswamy disclose a method of claim 1, wherein the machine learning model is generated by: performing a sensitivity analysis which identifies correlations between known values of vehicle data associated with the vehicle, known values of wheel alignment components, known driving cycles, and known vehicle applications; forming, via a computing device, a neural network using the correlations; and converting, via the computing device, the neural network to computer executable code, resulting in the machine learning model.
Doraiswamy discloses performing the claimed process forming their neural network using the claimed process in regards to tire wear as opposed to wheel alignment, however using this known process of constructing a neural network in order to optimize wheel alignment as opposed to tire wear would have been obvious to one having ordinary skill in the art, and they would have been motivated to do so in order to optimize wheel alignment in order to optimize fuel economy.
Claim 9
Mizuno/Doraiswamy do not explicitly disclose a method of claim 1, wherein the tire forces signal identifies: a vertical force on at least one tire of the vehicle; a lateral force on the at least one tire of the vehicle; and a longitudinal force on the at least one tire of the vehicle (Mizuno discloses three-axis load sensors 33, see paragraph [0094])
Claim 10
A vehicle comprising: at least one wheel (2); at least one tire attached to the at least one wheel; at least one tire sensor (33) associated with the at least one wheel; a wheel alignment controller (100) configured to modify an alignment of the at least one wheel; a processor; a non-transitory computer-readable storage medium having instructions stored which, when executed by the processor, cause the processor to perform operations comprising: receiving an optimization directive for the vehicle; receiving, from the at least one tire sensor while the vehicle is in transit, a tire forces signal; estimating, based at least in part on the tire forces signal, at least one aspect of vehicle performance; and executing a machine learning model, wherein inputs to the machine learning model comprise the optimization directive and the at least one aspect of vehicle performance, and wherein outputs of the machine learning model comprise a desired wheel alignment signal; and wherein the wheel alignment controller modifies a wheel alignment of the vehicle based at least in part on the desired wheel alignment signal (see rejection of Claim 1 above).
Claims 11-20 cover the same scope as claims 1-9 and are rejected by the prior art as applied to the corresponding claims above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIZO BINDA VILAKAZI whose telephone number is (571)270-3926. The examiner can normally be reached 10am-6pm.
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/SIZO B VILAKAZI/Primary Examiner, Art Unit 3747