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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/20/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
Status of Claims
This Office action is in reply to filing by applicant on 06/20/2025.
Claims 1 - 20 are currently pending and have been examined.
This action is made non-final.
Claim Rejections – 35 USC 103
In the event the determination of the status of the application as subject to AIA 35 USC 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 USC 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 USC 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, 3, 8, 10, 15, 17 are rejected pursuant to 35 USC 103 as being unpatentable over Zeng (US12172657B2) in view of Palanisamy (US20190278282A1).
Regarding claims 1, 8, and 15:
Zeng discloses:
A system comprising
a memory having an instruction module that includes instructions that, when executed by a processor, causes the processor to (“The vehicle processor 44 may be a custom made or commercially available processor, a central processing unit (CPU), a graphics processing unit (GPU), an auxiliary processor among several processors associated with the vehicle controller 34, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, a combination thereof, or generally a device for executing instructions. The vehicle computer readable storage device or media 46 may include volatile and nonvolatile storage in read-only memory (ROM), random-access memory (RAM), and keep-alive memory (KAM),
control a vehicle (“The present disclosure relates to systems and methods for vehicle motion control. More particularly, the present disclosure describes machine learning-based predictive systems and methods for vehicle motion control.
using a control action sequence generated by a model predictive controller (“The present disclosure describes a method for vehicle motion control. In an aspect of the present disclosure, the method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.”,
that uses an enhanced predicted vehicle state based on a predicted vehicle state and (“At block 1220, the vehicle controller 34 commands the host vehicle 10 to move autonomously in accordance with a trajectory determined using the vehicle states predicted using the self-supervised RNN 400 and the actuator commands determined using the neural network 700.
Zeng does not expressly disclose, but Palanisamy teaches:
a residual generated by a last-layer Bayesian meta-learning model. In light of the Specification, a “residual” vis a vis a Bayesian model is simply a corrective amount used to account for discrepancies between the predicted vehicle state derived by the vehicle dynamics model and the actual vehicle behavior, see Specification herein at [017], that said, the “residual” created by and through the Bayesian Model represents a difference and/or deviation between actual and predicted vehicle states, … (“If the difference between the sensor prediction Z′t and the actual sensor data Zt. (that is, Zt−Z′t) is reasonably close to zero, then X′t is considered to be the new state estimate. If Zt−Z′t is reasonably larger than zero, the K(Zt−Z′t) factor is added to yield a new state estimate.”, [052]) and (“In some embodiments, the localization and mapping module 40 maintains an estimate of the vehicle's global position by incorporating data from multiple sources as discussed above in an Extended Kalman Filter (EKF) framework. Kalman filters are linear filters based on Recursive Bayesian Filters. Recursive Bayesian Filters, also referred to as Recursive Bayesian Estimation, essentially substitute the posterior of an estimation into the prior position to calculate a new posterior on a new estimation iteration … .”, [044]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application to have modified Zeng to incorporate the teachings of Palanisamy because Zeng would be more efficient and versatile should it employ Bayesian Models in its predictive vehicle analytics, thereby allowing comparisons between actual (i.e., measured, see above) and predicted (see below) vehicle states to be possible, as was done in Palanisamy ("Bayesian models may be used in some embodiments to predict driver or pedestrian intent based on semantic information, previous trajectory, and instantaneous pose, where pose is the combination of the position and orientation of an object.”, see Palanisamy (" at [055]).
Regarding claims 3, 10, and 17:
The combination of Zeng and Palanisamy contain the limitations of claims 1, 8, and 15, respectively:
Zeng further teaches:
The system of claim 1, wherein the residual represents a difference between the predicted vehicle state and a true state of the vehicle. … (“If the difference between the sensor prediction Z′t and the actual sensor data Zt. (that is, Zt−Z′t) is reasonably close to zero, then X′t is considered to be the new state estimate. If Zt−Z′t is reasonably larger than zero, the K(Zt−Z′t) factor is added to yield a new state estimate.”, [052]) and (“In some embodiments, the localization and mapping module 40 maintains an estimate of the vehicle's global position by incorporating data from multiple sources as discussed above in an Extended Kalman Filter (EKF) framework. Kalman filters are linear filters based on Recursive Bayesian Filters. Recursive Bayesian Filters, also referred to as Recursive Bayesian Estimation, essentially substitute the posterior of an estimation into the prior position to calculate a new posterior on a new estimation iteration … .”, [044]).
Allowable Subject Matter
Claims 2, 4 - 7, 9, 11 – 14, 16, and 18 – 20 would be allowable if rewritten or amended in independent claim form. The following is a statement of reasons for the indication of allowable subject matter: Independently, while the claims' limitations most recently set forth herein may individually be disclosed by the prior art, the claims as a whole are not obvious because the examiner would have to improperly use their separate limitations as a road map to combine them.
CONCLUSION
The following prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see attached form 892.
Vuorio (US20240119363A1) - A processor-implemented method includes observing an environment via one or more sensors associated with a robotic device. The processor-implemented method also includes generating, via an inference model, a belief of the environment based on data associated with prior actions of the robotic device in the environment. The processor-implemented method further includes controlling the robotic device to perform an action in the environment based on generating the belief.
Allmaras (US20190138886A1) – A method which includes steps of providing a state space model of behaviour of a physical system, the model including covariances for state transition and measurement errors, providing a data based regression model for prediction of state variables of the physical system, observing a state vector comprising state variables of the physical system, determining a prediction vector of state variables based on the state vector, using the regression model, and combining information from the state space model with predictions from the regression model through a Bayesian filter, is provided.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW COBB whose telephone number is (571) 272-3850. The examiner can normally be reached 9 - 5, M - F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to call examiner Cobb as above, or to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter Nolan, can be reached at (571) 270-7016. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/MATTHEW COBB/Examiner, Art Unit 3661
/PETER D NOLAN/Supervisory Patent Examiner, Art Unit 3661
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