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 .
Status of Claims
This communication is in response to Application 19/040,070 filed on 01/29/2025. Claims 1-10 are currently pending and examined below.
Priority
Acknowledgment is made of applicant’s claim for foreign priority for Application No. JP2024024657, filed on 02/21/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/29/2025 and 08/12/2025 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 and 6-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Warren van Nus, US 20230322094A1, hereinafter referred to as van Nus.
Regarding claims 1 and 8, van Nus discloses a battery electric vehicle comprising (The EV (i.e. battery electric vehicle) can be used in combination with one or more other examples described elsewhere herein – See at least ¶60):
an electric motor as a drive source (The EV can provide the output to a powertrain. In some implementations, the powertrain comprises one or more electric motors and associated power electronics and control circuitry – See at least ¶66);
a driving operation member used for driving of the battery electric vehicle (The EV includes an accelerator pedal (i.e. a driving operation member) that a driver can depress or release so that it assumes any of a range of positions schematically indicated by an arrow 404 (e.g., the position represented as a percentage of a fully depressed position). In some implementations, the EV emulates the acceleration of one or more ICE vehicle types based at least in part of the present position of the accelerator pedal – See at least ¶60 and FIG.4);
a storage device configured to store a database (In this example, the computing device also includes a system memory, and a system bus that couples various system components including the system memory to the processing device – See at least ¶106),
the database being configured to manage a plurality of vehicle models obtained by modeling a plurality of virtual vehicles having different acceleration characteristics in response to a driving operation of a driver (The EV includes an emulation component that allows the EV driver to choose between and select any of multiple vehicle types having ICEs, and causes the EV to emulate at least the acceleration of the selected vehicle type. The emulation component can receive an input generated by a user to make the selection (e.g., using the emulation controller in FIG. 1). An input to the emulation component can be generated based on the present position of the accelerator pedal. The emulation component can apply the input to a model that corresponds to the vehicle type selected by way of the input. The emulation component can output an acceleration value based on the model and the input. The acceleration value represents the acceleration that the model specifies for the EV in the present situation. For example, the acceleration value can be essentially any value of the graph (pony car) or graph (roadster) in FIG. – See at least ¶61 and FIG. 3); and
a processing circuit configured to (processing device – See at least ¶106)
read out a target vehicle model corresponding to a target virtual vehicle from the database, the target virtual vehicle being selected by the driver from the virtual vehicles (The EV includes an emulation component that allows the EV driver to choose between and select any of multiple vehicle types having ICEs, and causes the EV to emulate at least the acceleration of the selected vehicle type – See at least 61. An EV can include a selection control (e.g., the emulation controller in FIG. 1) to receive a user selection of a first vehicle type from among multiple vehicle types each having an ICE. The user selection is made with regard to an emulation operation mode of the EV (e.g., activated by the input in FIG. 4). The EV can include an accelerator pedal to generate, with the first vehicle type chosen for the emulation operation mode, a first input made by a driver of the EV – See at least ¶68 and FIG. 4),
calculate a virtual acceleration of the target virtual vehicle in response to an operation of the driving operation member using the target vehicle model based on an operation state of the driving operation member and a traveling state of the battery electric vehicle (The EV includes an emulation component that allows the EV driver to choose between and select any of multiple vehicle types having ICEs, and causes the EV to emulate at least the acceleration of the selected vehicle type. The emulation component can receive an input generated by a user to make the selection (e.g., using the emulation controller in FIG. 1). An input to the emulation component can be generated based on the present position of the accelerator pedal. The emulation component can apply the input to a model that corresponds to the vehicle type selected by way of the input – See at least ¶61),
execute adjustment processing of calculating an adjusted virtual acceleration by multiplying the virtual acceleration by a coefficient of 1 or less according to the acceleration characteristic of the target virtual vehicle (the model applies scaling algorithm to scale down the acceleration graph representing the acceleration capacity of the emulated vehicle and scales the powertrain torque output to fit the capabilities of the EV; for example, the model provides an emulated acceleration of 0.4g in place of the EV’s native acceleration of 0.7g, corresponding to a scaling factor of approximately 0.57 – See at least ¶24, 62 and 102-103), and
control the electric motor such that an acceleration of the battery electric vehicle is the adjusted virtual acceleration (The EV can provide the output to a powertrain. In some implementations, the powertrain comprises one or more electric motors (e.g., based on induction or permanent magnets) and associated power electronics and control circuitry. The powertrain will generate an acceleration of the EV based on the output – See at least ¶66).
Regarding claim 6, van Nus discloses wherein the processing circuit is configured to calculate a target drive force of the battery electric vehicle to set the acceleration of the battery electric vehicle to the adjusted virtual acceleration, and change a motor torque output by the electric motor to provide the target drive force to the battery electric vehicle (The EV can include an accelerator pedal to generate, with the first vehicle type chosen for the emulation operation mode, a first input made by a driver of the EV. The first input is associated with a first acceleration of the EV (e.g., the acceleration value determined by the torque controller in FIG. 4) with the emulation operation mode inactive. The EV can include a model (e.g., the model in FIG. 4 ) to provide the EV, while the emulation operation mode is active and using a powertrain of the EV (e.g., the powertrain in FIG. 4 ), with a second acceleration (e.g., corresponding to the output in FIG. 4 ) determined based on the first vehicle type and the input. The second acceleration is capped by the first acceleration – See at least ¶62 and 68).
Regarding claim 7, van Nus discloses wherein: each of the vehicle models has one or a plurality of parameters related to the acceleration characteristic; and the processing circuit is further configured to set the one or the plurality of parameters of the target vehicle model according to the target virtual vehicle (The model can calculate the emulation of the ICE vehicle in any of multiple different ways. The model can take inputs such as accelerator pedal position gear selection, and reactive load, and output the calculated torque and resultant acceleration value – See at least ¶61 and 62).
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) 2-5 and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Warren van Nus, US 20230322094A1, hereinafter referred to as van Nus.
Regarding claims 2 and 9, van Nus fails to disclose set the coefficient to a value obtained by dividing a realizable maximum acceleration of the battery electric vehicle by a maximum acceleration in the acceleration characteristic of the target virtual vehicle when the acceleration characteristic of the target virtual vehicle exceeds an acceleration capability of the battery electric vehicle.
However, it would have been obvious to set the coefficient using the ratio of the realizable maximum acceleration of the battery electric vehicle to the maximum acceleration of the target virtual vehicle because van Nus teaches scaling down the acceleration characteristic of the emulated vehicle to fit the acceleration capability of the battery electric vehicle (See ¶102-103 of van Nus). Using the claimed ratio would have predictably scaled the maximum acceleration of the target virtual vehicle to a value realizable by the battery electric vehicle.
Regarding claims 3 and 10, van Nus fails to disclose set the coefficient to 1 when the acceleration characteristic of the target virtual vehicle does not exceed an acceleration capability of the battery electric vehicle.
However, it would have been obvious to set the coefficient to 1 under such circumstances because van Nus teaches scaling the acceleration characteristic as necessary to fit the capabilities of the battery electric (See ¶102-103 of van Nus). When the acceleration characteristic does not exceed the acceleration capability of the battery electric vehicle, no reduction is required, and setting the coefficient to 1 would have predictably maintained the virtual acceleration without adjustment.
Regarding claim 4, van Nus fails to disclose set the coefficient to 1while the virtual acceleration is realizable by the battery electric vehicle, regardless of the acceleration characteristic of the target virtual vehicle.
However, it would have been obvious to set the coefficient to 1 while the virtual acceleration is realizable by the battery electric vehicle because van Nus teaches scaling the acceleration characteristic in one or more regions to fit the capabilities of the battery electric vehicle (See ¶102-103 of van Nus). Setting the coefficient to 1 for a presently realizable virtual acceleration would have predictably preserved that acceleration without unnecessary reduction, regardless of whether another portion of the acceleration characteristic exceeds the capability of the battery electric vehicle.
Regarding claim 5, van Nus discloses wherein: the driving operation member includes an accelerator pedal (Accelerator pedal – See at least ¶6).
van Nus fails to disclose change a value of the coefficient based on an accelerator operation amount of the accelerator pedal and a vehicle speed of the battery electric vehicle when the acceleration characteristic of the target virtual vehicle exceeds an acceleration capability of the battery electric vehicle.
However, it would have been obvious to change the coefficient based on the accelerator operation amount and vehicle speed because van Nus teaches scaling the accelerator pedal input as a function of vehicle speed to determine an acceleration value and further teaches scaling the acceleration characteristic to fit the capabilities of the battery electric vehicle (See ¶62 and 102-103 of van Nus). Using the accelerator operation amount and vehicle speed would have predictably identified the applicable operating point of the acceleration characteristic and enabled the coefficient to be adjusted to provide an acceleration realizable by the battery electric vehicle.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MAHMOUD M KAZIMI whose telephone number is (571)272-3436. The examiner can normally be reached M-F 7am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin Bishop can be reached at 5712703713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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RESPECTFULLY SUBMITTED
/MAHMOUD M KAZIMI/Examiner, Art Unit 3665
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