DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is in response to claims in application 18/612,226 filed on 3/21/2024.
The instant application claims benefit to provisional application #63/589,440 with a priority date of 10/11/2023.
The Pre-Grant publication # 2017/0282014 is published on 10/5/2017.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-5,9,11-17, 20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by US 20040167761 A1 to Sizov.
Claim 1. Sizov teaches a system, comprising:
a processor ( Para 0024 notebook computer having built-in 3D graphics processor ) ; and
a memory storing (Para 0024 notebook computer having memory) :
an automated motion module including instructions that, when executed by the processor, cause the processor to cause a vehicle to perform, in an automated manner, a first iteration of a maneuver (Para 0025-0027 portable computer is controlled by a Simulation Engine Software processing the real-time state data from various vehicle controls as an automated motion module);
an instruction module including instructions that, when executed by the processor, cause the processor to cause an instruction to be provided to an operator of the vehicle during a second iteration of the maneuver (Para 0029 The method further comprises providing computing means 22 reading the real-time data of the state of the vehicle's controls to simulate a Virtual Driving Environment, VDE ); and
a communications module including instructions that, when executed by the processor (Para 0027 communication to instructions to driver), cause the processor to:
receive, during the second iteration, a query from the operator about a performance of the second iteration (Para 028 driver use of optional motion feedback as a query for operator in a second iteration); and
communicate a response to the query to train the operator to perform the second iteration or a third iteration of the maneuver in a manner that mimics the first iteration (Para 0029 communication of response to computer second iteration generating a graphical representation as for example at the current orientation of the driver's head mimicking or displaying the graphical representation by the portable audio and visual means to the driver).
Claim 2. Sizov teaches the system of claim 1, wherein: the vehicle is configured to be switchable: from a first mode to a second mode, and from the second mode to the first mode, the first mode is a mode in which car controls, required to perform the maneuver, are configured to operate under a control of the operator, and the second mode is a mode in which the car controls, required to perform the maneuver, are configured to operate automatically (Fig.3 use of switch sensors for change from one mode to another, for example switching between brake pedal and gas pedal sensors) .
Claim 3. Sizov teaches the system of claim 1, wherein: the vehicle is configured to be switchable: from a first mode to a second mode, and from the second mode to the first mode, the first mode is a mode in which car controls, required to perform the maneuver, are configured to operate under a control of the operator (Para 0020 turntables allow the driver/trainee to operate the steering wheel of the vehicle without applying excessive force, which would have been necessary on the actual road due to the friction between the steered wheels and the surface of the road while the vehicle is immobile as a first mode), and the second mode is a mode in which the vehicle is configured to operate autonomously ( Para 0024 virtual driving environment configured for autonomous operation as second mode).
Claim 4. Sizov teaches the system of claim 1, wherein the instructions to communicate the response to the query include instructions to communicate, to an external communications device, the response to the query (Para 0030 Computer Based Training CBD can include short fragments of a video presented to the student driver, followed by a commentary and a series of questions).
Claim 5. Sizov teaches the system of claim 1, wherein at least one of the instruction provided to the operator or the response to the query is based on a result of a comparison of a measurement of a performance of the maneuver by the operator and a measurement of a performance of the maneuver in the automated manner (Para 0031 assessing driver's skills in actual immobilized vehicle compared to simulated driving experience by driving through the Virtual Driving Environment (VDE) and can be performed by using the actual vehicle, such as the operator's own vehicle; measurement of a performance of the maneuver by the operator and in automated manner could be compared).
Claim 9. Sizov teaches the system of claim 8, wherein the optimal control system is a model predictive control system (Para 0024 control systems with predictive plurality of actuators).
Claim 11. Sizov teaches the system of claim 10, wherein a significant constraint of the objective function is at least one of a duration of time to complete the maneuver or a measurement of a degree of safety associated with the performance of the maneuver (Para 0020 real-time feedback effect).
Claim 12. Sizov teaches the system of claim 1, wherein the communications module further includes instructions to: receive, during the first iteration, a query from the operator about a performance of the first iteration; and communicate a response to the query from the operator about the performance of the first iteration (Para 0030 performance, question and operator feedback).
Claim 13. Sizov teaches the system of claim 1, wherein the communications module further includes instructions to receive a signal from the operator, wherein the signal is different from the query (Para 0025 processes the data from the head tracking sensor acting like query information) .
Claim 14. Sizov teaches the system of claim 13, wherein the instructions to receive the signal from the operator include instructions to receive the signal from the operator via at least one of a steering operator interface, a brake operator interface, an acceleration operator interface, or a transmission operator interface (Para 0029 brake operations).
Claim 15. Sizov teaches the system of claim 1, wherein the communications module further includes instructions to cause, after the instruction has been provided, a message to be provided to the operator to train the operator to perform the second iteration or the third iteration, wherein the message is different from the response to the query (Para 0029 relieve of friction enabling the actual vehicle's steering to be operated differently by a user without using excessive force) .
Claim 16. A method, comprising: causing, by a processor, a vehicle to perform, in an automated manner, a first iteration of a maneuver; causing, by the processor, an instruction to be provided to an operator of the vehicle during a second iteration of the maneuver; receiving, by the processor and during the second iteration, a query from the operator about a performance of the second iteration; and communicating, by the processor, a response to the query to train the operator to perform the second iteration or a third iteration of the maneuver in a manner that mimics the first iteration (Para 0025-0027 portable computer is controlled by a Simulation Engine Software processing the real-time state data from various vehicle controls as an automated motion module; Para 0029 The method further comprises providing computing means 22 reading the real-time data of the state of the vehicle's controls to simulate a Virtual Driving Environment, VDE; Para 028 driver use of optional motion feedback as a query for operator in a second iteration; Fig.3 use of switch sensors for change from one mode to another, for example switching between brake pedal and gas pedal sensors).
Claim 17. The method of claim 16, wherein a performance of the maneuver comprises a sequence of operations of various car controls (Para 0026 sequences to design desired operations in lessons).
Claim 20. A non-transitory computer-readable medium for training an operator of a vehicle to perform a maneuver of the vehicle, the non-transitory computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to: cause a vehicle to perform, in an automated manner, a first iteration of a maneuver; cause an instruction to be provided to an operator of the vehicle during a second iteration of the maneuver; receive, during the second iteration, a query from the operator about a performance of the second iteration; and communicate a response to the query to train the operator to perform the second iteration or a third iteration of the maneuver in a manner that mimics the first iteration (Para 0029 a portable computer readable with a Simulation Engine Software.
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 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 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.
Claims 6-8,10,19 are rejected under 35 U.S.C. 103 as being unpatentable over Patent Number US 20040167761 A1 to Sizov and in view of Patent Application Publication Number to US 20230141801 A1 to US 20230141801 A1 to Schweidel et al.( Schweidel)
Claim 6. Sizov teaches the system of claim 5, wherein the instructions to communicate the response to the query include instructions to communicate the response to the query in response to the result of the comparison but without specific indication of threshold. Schweidel, however, teaches the identification of threshold (¶0016 control system receives an operator command (e.g., maneuver 15 degrees left threshold) where the predictive control generates an adjusted motion command that is similar threshold safe for a maneuver instead of other actions). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate instructions to communicate the response to the query include instructions to communicate the response to the query in response to the result of the comparison being greater than a threshold, as taught by Schweidel, into the system of Sizov, in order to provide quick control on parameters of each phases of operator training.
Claim 7. Sizov teaches the system of claim 5, but does not identify wherein: the performance of the maneuver by the operator produces an operator trajectory, the performance of the maneuver in the automated manner produces a reference trajectory, and the result of the comparison comprises a measurement of a degree of adherence of the operator trajectory to the reference trajectory. Schweidel, however, teaches the performance of the maneuver by the operator produces an operator trajectory, the performance of the maneuver in the automated manner produces a reference trajectory, and the result of the comparison comprises a measurement of a degree of adherence of the operator trajectory to the reference trajectory (¶0016 measurement of a degree of adherence). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the performance of the maneuver by the operator produces an operator trajectory, the performance of the maneuver in the automated manner produces a reference trajectory, and the result of the comparison comprises a measurement of a degree of adherence of the operator trajectory to the reference trajectory, as taught by Schweidel, into the system of Sizov, in order to good measurement outcome.
Claims 8, 10, 18 and 19 . Sizov teaches the system of claim 8, wherein at least one of the trajectory produced by the trajectory planning stage or the trajectory produced by the optimal control system is produced without optimal control system , optimizing an objective function, query to be processed using a machine learning model developed or large language model. Schweidel, however, teaches the performance of the maneuver by the operator produces an operator trajectory, the performance of the maneuver in the automated manner produces a reference trajectory, and the result of the comparison comprises without optimal control system , optimizing an objective function, query to be processed using a machine learning model developed or large language model operator trajectory to the reference trajectory (¶0016 optimize modulated machine control with LLM). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date, to incorporate the performance of the maneuver by the operator produces an operator trajectory, the performance of the maneuver in the automated manner produces a reference trajectory, optimal control system , optimizing an objective function, query to be processed using a machine learning model developed or large language model operator trajectory, as taught by Schweidel, into the system of Sizov, in order to enhance predicted outcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20250118222 A1 Yasuda; Hiroshi Feedback training
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US 12469407 B2 Yasuda; Hiroshi et al.
Systems and methods for training drivers via vehicle light control US 12272160 B2 Systems and methods for estimating grip intensity on a steering wheel
US 12539864 B2 Causing a vibration within a vehicle to change a degree of somnolence of an occupant of the vehicle.
US 20190108768 A1 Mohamed; Farzan
Driver Training System and Method
US 20230322234 A1 PERSONALIZED VEHICLE LANE CHANGE MANEUVER PREDICTION WANG; ZIRAN et al.
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/S.Z/Examiner, Art Unit 3715 March 7, 2026
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715