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 .
Priority
Examiner acknowledges Applicant’s claim for priority to European Patent Application No. EP24182808.6 filed under 35 U.S.C. 119 and receipt of the priority document filed on 06/18/2024.
Information Disclosure Statement
The information disclosure statement(s) (IDS)(s) submitted on 06/17/2025 has/have been received, considered, and is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS(s) has/have been considered by the Examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4 and 8-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US. 20210341920 A1 to Singh et al. (Singh).
Regarding claim 1, Singh discloses a computer system (see Figs. 1-2; vehicle controller 112; vehicle control system 113; [0022] (vehicle controller 112 may receive data collected by the sensor system 111 and analyze it to provide one or more vehicle control instructions to the vehicle control system 113)) comprising processing circuitry configured to:
determine a set of vehicle control parameters, using predictive optimization of a vehicle model with a first prediction horizon, the predictive optimization being constrained by a set of allowed vehicle states at the first prediction horizon (Singh discloses vehicle control parameters using predictive optimization and a first prediction horizon ([0006] (methods may include obtaining a prediction model trained to predict future trajectories of objects…over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory…methods…include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data); [0035] (raw data may include data corresponding to motion and/or state of objects captured in different seasons, weather conditions, locations, times of day…scenarios can be represented…by an occupancy grid, a collection of vehicle states on a map, or a graphical representation…top-down image of one or more areas of interest…the raw data also includes data corresponding to the motion and/or status of the objects in different scenarios and different object actions, behaviors, and intentions); see Fig. 7; [0062] (At 702, the system receives location and current state data corresponding to an autonomous vehicle…location data can include sensor data from sensors…mounted on or used in connection with the autonomous vehicle))); and
control an operation of a vehicle using the determined set of vehicle control parameters (Singh discloses controller 112 providing vehicle control instruction to vehicle control system 113 to control a vehicle 101 (See Figs. 1-2; [0020] (autonomous vehicle 101 may include a sensor system 111, a vehicle controller 112, a vehicle control system 113, a communications interface 114, and a user interface 115…vehicle 101 may further include…as, an engine, wheels, steering wheel, transmission…which may be controlled by vehicle control system 112 using a variety of communication signals and/or commands, such as…acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands); [0022] (vehicle controller 112 may receive data collected by the sensor system 111 and analyze it to provide one or more vehicle control instructions to the vehicle control system 113…vehicle controller 112 may include…a location subsystem 121, a perception subsystem 122, a forecasting and prediction subsystem 123, and a motion planning subsystem 124))), wherein:
the set of allowed vehicle states at the first prediction horizon is a set of initial vehicle states estimated to result in a set of safe vehicle states at a second prediction horizon longer than the first prediction horizon (Singh discloses a second prediction horizon longer than the first prediction horizon ([0006] (The prediction model may be trained over…a second prediction horizon selected to encode behavioral constraints in the predicted trajectory…the methods may…include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data); [0008] (the second prediction horizon may be longer than the first prediction horizon…the first prediction horizon may be less than 1 second…additionally and/or alternatively, the second prediction horizon may be greater than about 2 seconds))).
Regarding claim 2, Singh discloses the computer system of claim 1,
wherein the processing circuitry is configured to determine the set of allowed vehicle states in an offline process using predictive optimization of the vehicle model with the second prediction horizon (Singh discloses wherein the processing circuitry is configured to operate in an offline process using predictive optimization ([0028] (neural network 123(a) can be implemented in…an offline training phase…the training phase is used to train and configure the parameters of the neural network 123(a) and/or any other components of the prediction and forecasting subsystem 123 implemented with a machine learning system or neural network))).
Regarding claim 3, Singh discloses the computer system of claim 2,
wherein the processing circuitry is configured to determine the set of allowed vehicle states using machine learning (Singh discloses the processing circuitry configured to used machine learning ([0028] (The current disclosure describes systems and methods for using neural networks for improving the predictions performed by the prediction and forecasting subsystem 123…the neural network 123(a) may be included in the prediction and forecasting subsystem 123…the training phase is used to train and configure the parameters of the neural network 123(a) and/or any other components of the prediction and forecasting subsystem 123 implemented with a machine learning system or neural network); [0062] (The system may then employ the trained RNN model (or any other machine learning components) to generate (708) trajectory predictions for each object relative to the autonomous vehicle))).
Regarding claim 4, Singh discloses the computer system of claim 3,
wherein the processing circuitry is configured to determine the set of allowed vehicle states using a machine learning model trained with training sets, each being annotated based on a corresponding set of vehicle states resulting from predictive optimization of the vehicle model with the second prediction horizon, starting from the training set (in claim(s) 1-3, e.g. Singh).
Regarding claim 8, Singh discloses the computer system of claim 1, wherein the computer system comprises:
first processing circuitry configured to:
determine the set of allowed vehicle states in an offline process using predictive optimization of the vehicle model with the second prediction horizon (in claim(s) 1 & 2, e.g. Singh); and
second processing circuitry configured to:
receive the set of allowed vehicle states from the first processing circuitry (in claim(s) 1 & 3; e.g. Singh);
determine the set of vehicle control parameters (in claim(s) 1 & 5, e.g. Singh); and
control the vehicle using the determined set of vehicle control parameters (in claim(s) 1 & 5-6, e.g. Singh).
Regarding claim 9, Singh discloses the computer system of claim 8, wherein:
the first processing circuitry is configured to determine a plurality of sets of allowed vehicle states, each being adapted to a corresponding vehicle configuration (in claim(s) 1 & 4, e.g. Singh);
the second processing circuitry is comprised in a vehicle (in claim(s) 1 & 2, e.g. Singh); and
the second processing circuitry is configured to receive a set of allowed vehicle states adapted to a vehicle configuration of the vehicle comprising the second processing circuitry (in claim(s) 1 & 4, e.g. Singh).
Regarding claim 10, Singh discloses a vehicle comprising the computer system of claim 1 (in claim 1, e.g. Singh).
Regarding claim 11, Singh discloses a vehicle comprising the second processing circuitry of the computer system of claim 8 (in claim 8, e.g. Singh).
Regarding claim 12, Singh discloses a computer-implemented method (in claim 1, e.g. Singh) comprising:
determining a set of vehicle control parameters, using predictive optimization of a vehicle model with a first prediction horizon, the predictive optimization being constrained by a set of allowed vehicle states at the first prediction horizon (in claim 1, e.g. Singh); and
controlling an operation of a vehicle using the determined set of vehicle control parameters (in claim 1, e.g. Singh), wherein:
the set of allowed vehicle states at the first prediction horizon is a set of initial vehicle states estimated to result in a set of safe vehicle states at a second prediction horizon longer than the first prediction horizon (in claim 1, e.g. Singh).
Regarding claim 13, Singh discloses the method of claim 12, wherein the method comprises:
determining the set of allowed vehicle states in an offline process using predictive optimization of the vehicle model with the second prediction horizon (in claim(s) 1 & 2, e.g. Singh).
Regarding claim 14, Singh discloses the method of claim 13, wherein the method comprises:
determining the set of allowed vehicle states using machine learning (in claim 3, e.g. Singh).
Regarding claim 15, Singh discloses a computer program product comprising program code for performing, when executed by the processing circuitry comprised in the computer system of claim 1, a computer-implemented method comprising:
determining a set of vehicle control parameters, using predictive optimization of a vehicle model with a first prediction horizon, the predictive optimization being constrained by a set of allowed vehicle states at the first prediction horizon (in claim 1, e.g. Singh); and
controlling an operation of a vehicle using the determined set of vehicle control parameters (in claim(s) 1 & 5-6, e.g. Singh), wherein:
the set of allowed vehicle states at the first prediction horizon is a set of initial vehicle states estimated to result in a set of safe vehicle states at a second prediction horizon longer than the first prediction horizon (in claim 1, e.g. Singh).
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.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over US. 20210341920 A1 to Singh in view of U.S. 12606131 B2 to Ito.
Regarding claim 5, Singh discloses the computer system of claim 1, wherein the processing circuitry is configured to:
determine that the predictive optimization of the vehicle model with the first prediction horizon is unable to result in a vehicle state within the set of allowed vehicle states at the first prediction horizon (Singh discloses constraints within the recurrent neural network (RNN) model that affect the vehicle state on at least a first prediction horizon(s) ([0056] (the RNN model is used to predict trajectories of objects for shorter time horizons…and is configured as an inertial constraints RNN…this single step rollout functionally initializes the RNN to produce constant velocity predictions…the RNN is encoded for taking into short-timescale inertial constraints…inherent in performing trajectory predictions and forecasting))).
However, Singh does not appear to further expressly disclose, wherein the processing circuitry is configured to:
determine a backup set of vehicle control parameters using a set of predefined rules; and
Ito, in the same field of endeavor, further discloses, wherein the processing circuitry is configured to:
determine a backup set of vehicle control parameters using a set of predefined rules (Ito discloses backup control parameters from a backup control unit 130 to hold a vehicle SV parked via activation of an automatic hold switch 75 which is triggered based on control parameters saved in storage (¶ (25) (The automatic hold switch 75 is a momentary-type ON/OFF switch…when the vehicle SV stops in a state in which the automatic hold switch 75 is ON or when the automatic hold switch 75 is turned on in a state in which the vehicle SV is stopped, brake hold control of holding the oil pressures within the wheel cylinders of the hydraulic brake apparatus 60 is started…the driver can continuously maintain the vehicle SV in the stopped state without depressing the brake pedal…the brake hold control is cancelled when the driver turns off the automatic hold switch 75 or depresses the accelerator pedal); ¶ (43) (When the above…end condition…is satisfied…backup control section 130 executes…holding the vehicle SV in the stopped state by activating both or either of the electric parking brake apparatus 70 and the parking lock apparatus 55…the backup for holding the vehicle SV in the stopped state without fail even after the end of the brake hold control is established); ¶ (35) (When either of the following execution conditions (1) and (2) is satisfied, the brake hold control unit 120 executes brake hold control (stop hold control) of holding the oil pressures of the wheel cylinders of the hydraulic brake apparatus 60, thereby continuously holding the stopped state of the vehicle SV…the brake hold control unit 120 is one example of the stop hold release control unit of the present disclosure))); and
Ito discloses using a backup set of vehicle control parameters to hold a vehicle in a stop position using the backup vehicle control parameters (¶ (35) (When either of the following execution conditions…is satisfied, the brake hold control unit 120 executes brake hold control…of holding the oil pressures of the wheel cylinders of the hydraulic brake apparatus 60, thereby continuously holding the stopped state of the vehicle SV…brake hold control unit 120 is one example of the stop hold release control unit); ¶ (36) (Execution condition (1): When the automatic hold switch 75 is turned ON and the driver stops the vehicle SV, or when the automatic hold switch 75 is turned OFF and the driver stops the vehicle SV and then turned ON the automatic hold switch 75))).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the trajectory forecast system of Singh to incorporate the vehicle control device of Ito to include backup control parameters and using predefined instruction and using the backup vehicle control parameters to control a vehicle such by utilizing a brake hold control unit to bring the vehicle to and from a stop position, with predictable results, with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to combine Singh and Ito for the express benefit of including a backup set of vehicle control parameters, wherein the backup control parameters are used to hold a vehicle in a stop position, as explained in Ito ¶ (25), (35)-(36) and (43).
Claim(s) 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over US. 20210341920 A1 to Singh in view of U.S. 8185217 B2 to Thiele.
Regarding claim 6, Singh discloses the computer system of claim 1.
However, Singh does not appear to further expressly disclose, wherein the processing circuitry is configured to:
determine that control of the vehicle using the set of vehicle control parameters resulted in a vehicle state outside the set of allowed vehicle states; and
modify the predictive optimization.
Thiele, in the same field of endeavor, further discloses, wherein the processing circuitry is configured to:
determine that control of the vehicle using the set of vehicle control parameters resulted in a vehicle state outside the set of allowed vehicle states (Thiele discloses controlling the vehicle based on parameters of a vehicle state(s) both within and outside allowed vehicle state(s) (¶ (40) (the target control and auxiliary variables…are provided as inputs to the MPC controller 52 which…uses these target values…to determine a new set of steady state manipulated variables MV.sub.SS (over the control horizon) which drives the current control and manipulated variables…to the target values…at the end of the control horizon…MPC controller 52 changes the manipulated variables in steps in an attempt to reach the steady state values for the steady state manipulated variables MV.sub.SS which…will result in the process obtaining the target control and auxiliary variables); ¶ (41) (MPC controller 52 includes a control prediction process model 70); ¶ (42) (control prediction process model 70 then predicts a future control parameter for each of the control variables and auxiliary variables…over the control horizon based on the disturbance and manipulated variables provided to other inputs of the control prediction process model 70…control prediction process model 70 also produces the predicted steady state values of the control variables and the auxiliary variables))); and
modify the predictive optimization (Thiele discloses modifying the predictive optimization and controlling prediction error to estimate change in mismatching within the process model(s) (¶ (22) (a method that may be used in…an MPC controller unit, uses an autocorrelation function of a control error and/or a prediction error to determine an estimated magnitude of or a change in the model mismatch between the process model currently used in the MPC controller and the actual process...this estimate may be used to initiate a new adaptation/tuning cycle to update the MPC controller design and tuning parameters to thereby perform better control in the presence of the new amount of model mismatch…this method of detecting model mismatch may be used to determine when a controller is tuned in a manner that makes it more susceptible to process state changes, especially when such state changes are accompanied by a change in the process model, and can therefore be used to modify or retune an MPC controller prior to the occurrence of a process change that the currently tuned MPC controller may not be able to handle well))).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the trajectory forecast system of Singh to incorporate the adaptive model predictive controller of Thiele to include the ability to control the vehicle both outside and inside allowed vehicle state(s) and modifying the predictive optimization and prediction error in order to determine more precise outcome parameters, with predictable results, with a reasonable expectation of success. One of ordinary skill in the art would have been motivated to combine Singh and Thiele for the express benefit of including a precise method of controlling the vehicle based on parameters within and outside the allowed vehicle states and to modify the predictive optimization, control error and prediction error of estimated outcomes and manage model mismatch and mismatch between process model(s) and/or vehicle state changes, as explained in Thiele ¶ (22), (40)-(42).
Regarding claim 7, the combination of Singh and Thiele discloses the computer system of claim 6 in for example the obviousness to combine in the rejection of corresponding parts of claim(s) 1-2 and 6 above incorporated herein by reference, wherein the processing circuitry is configured to:
determine the set of allowed vehicle states using the modified predictive optimization of the vehicle model with the second prediction horizon (in claim(s) 1-2 & 6, e.g. Singh & Thiele).
It would have been obvious to combine for the reasons set forth in the rejection of corresponding parts of claim(s) 1-2 & 6 above incorporated herein by reference.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure as teaching the state of the art of determining vehicle control parameters using predictive optimization with enhanced constraint(s), at the time of filing. For example:
US 20210064888 A1 to Harda; Peter teaches, inter alia LANE KEEPING FOR AUTONOMOUS VEHICLES in for example the ABSTRACT, Figures and/or Paragraphs below:
“A system for a lane keeping feature of a vehicle is provided. The lane keeping feature has a predefined safety requirement criterion for keeping the vehicle within bounds while the lane keeping feature is active. The system comprises a road estimation module and a trajectory planning module. The road estimation module is configured to receive sensor data comprising information about a surrounding environment of the vehicle, and to determine a drivable area based on the sensor data. The drivable area comprises a left boundary and a right boundary extending along a direction of travel of the vehicle, wherein each boundary comprises a plurality of points distributed along each boundary, each point being associated with a confidence level. The trajectory planning module is configured to receive the determined drivable area, and to determine a nominal trajectory for the vehicle based on the received drivable area.”
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US 20220067850 A1 to Bhasme; Saurabh teaches, inter alia SYSTEMS AND METHODS FOR MANAGING ENERGY STORAGE SYSTEMS in for example the ABSTRACT, Figures and/or Paragraphs below:
“Systems, methods, and at least one computer-readable medium are described. The system comprises at least one processor and at least one computer-readable storage medium having encoded thereon instructions that, when executed, program the at least one processor to for each candidate model of a plurality of candidate models, determine a reward for using the candidate model in a context, wherein the context comprises a value of a feature selected from a group consisting of, a feature relating to an environment in which an energy application is operating, a feature relating to the energy application, and a feature relating to one or more energy storage devices associated with the energy application. The at least one processor being further programmed to select a model from the plurality of candidate models, based at least in part on the respective rewards for using the candidate models in the context.”
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US 9448546 B2 to Sayyarrodsari; Bijan teaches, inter alia Deterministic Optimization Based Control System And Method For Linear And Non-linear Systems in for example the ABSTRACT, Figures and/or Paragraphs below:
“The embodiments described herein include one embodiment that provides a control method including determining a linear approximation of a pre-determined non-linear model of a process to be controlled, determining a convex approximation of the nonlinear constraint set, determining an initial stabilizing feasible control trajectory for a plurality of sample periods of a control trajectory, executing an optimization-based control algorithm to improve the initial stabilizing feasible control trajectory for a plurality of sample periods of a control trajectory, and controlling the controlled process by application.”
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/ROBERT L PINKERTON/Examiner, Art Unit 3665
/HUNTER B LONSBERRY/Supervisory Patent Examiner, Art Unit 3665
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