DETAILED ACTION
Notice to Applicant
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-20 are pending.
Response to Arguments
3. Regarding Applicant’s concern as to whether Kim et al. (US 2022/0281345) is a valid 102(a)(2) reference, Examiner has provided a new reference in place of the Kim reference. Therefore, the following Office Action is a new Non-Final Office Action.
Claim Rejections - 35 USC § 102
4. 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.
5. Claims 1-2, 4-5, 12-14, 16-17, and 19 are rejected under 102(a)(1) as being anticipated by Holme (US 2020/0164763).
Per claim 1, Holme teaches a method comprising:
obtaining, by a processor, battery characterization data based on sensor data from one or more sensors onboard a vehicle (A BMS 210 is configured to obtain battery characterization data, such as the temperature, voltage, and current of a battery 208, via sensors 209 residing in a vehicle (Fig. 3A; ¶96-97));
providing, by the processor, the battery characterization data as input to a trained model to generate a model output (The BMS 210 is configured to provide the battery characterization data to a device 10. The device 10 includes a prediction module 30 having a trained model 32 that receives the battery characterization data as input parameters and outputs a prediction of the state of the battery (Fig. 3A; ¶91-93 and 97)); and
altering a vehicle operating parameter based on the model output (Based on the predicted state of the battery, a vehicle ECU may reduce the available max power to the vehicle drive train (¶87)).
Per claim 2, Holme teaches a method of claim 1, further comprising identifying the battery characterization data with a driver type profile (Other data 50, including driver data and driving preferences, are provided to the device 10 (Fig. 3A; ¶15-23 and 99)).
Per claim 4, Holme teaches the method of claim 1, wherein the one or more sensors include battery sensors that identify physical characteristics of a battery of the vehicle (A temperature of the battery 208 is sensed (¶97)).
Per claim 5, Holme teaches the method of claim 1, wherein the one or more sensors include non-battery sensors that identify physical characteristics of components of the vehicle other than a battery (Vehicle data relating to the state of the vehicle powertrain or regenerative braking system may be sensed (¶99)).
Per claim 12, Holme teaches the method of claim 1, wherein the vehicle operating parameter is altered responsive to the model output indicating that a battery has a particular health profile (The predicted state of the battery, which may be a battery state of health, is used by a vehicle ECU to reduce the available max power to the vehicle drive train (¶87 and 133)).
Per claim 13, Holme teaches a system comprising: one or more processors (Fig. 3A; processor 16; ¶96-97); and one or more memory devices (Fig. 3A; memory 12; ¶96-97) coupled to the one or more processors, the one or more memory devices storing instructions that are executable to cause the one or more processors to perform operations comprising:
obtaining battery characterization data based on sensor data from one or more sensors onboard a vehicle (A BMS 210 is configured to obtain battery characterization data, such as the temperature, voltage, and current of a battery 208, via sensors 209 residing in a vehicle (Fig. 3A; ¶96-97));
providing the battery characterization data as input to a trained model to generate a model output (The BMS 210 is configured to provide the battery characterization data to a device 10. The device 10 includes a prediction module 30 having a trained model 32 that receives the battery characterization data as input parameters and outputs a prediction of the state of the battery (Fig. 3A; ¶91-93 and 97)); and
altering a vehicle operating parameter based on the model output (Based on the predicted state of the battery, a vehicle ECU may reduce the available max power to the vehicle drive train (¶87)).
Per claim 14, Holme teaches the system of claim 13, further comprising identifying the battery characterization data with a driver type profile (Other data 50, including driver data and driving preferences, are provided to the device 10 (Fig. 3A; ¶15-23 and 99)).
Per claim 16, Holme teaches the system of claim 13, wherein the one or more sensors include battery sensors that identify physical characteristics of a battery of the vehicle (A temperature of the battery 208 is sensed (¶97)).
Per claim 17, Holme teaches the system of claim 13, wherein the one or more sensors include non-battery sensors that identify physical characteristics of components of the vehicle other than a battery (Vehicle data relating to the state of the vehicle powertrain or regenerative braking system may be sensed (¶99)).
Per claim 19, Holme teaches a computer-readable storage device (Fig. 3A; memory 12; ¶96-97) storing instructions that, when executed by one or more processors (Fig. 3A; processor 16; ¶96-97), cause the processors to:
obtain battery characterization data based on sensor data from one or more sensors onboard a vehicle (A BMS 210 is configured to obtain battery characterization data, such as the temperature, voltage, and current of a battery 208, via sensors 209 residing in a vehicle (Fig. 3A; ¶96-97));
provide the battery characterization data as input to a trained model to generate a model output (The BMS 210 is configured to provide the battery characterization data to a device 10. The device 10 includes a prediction module 30 having a trained model 32 that receives the battery characterization data as input parameters and outputs a prediction of the state of the battery (Fig. 3A; ¶91-93 and 97)); and
alter a vehicle operating parameter based on the model output (Based on the predicted state of the battery, a vehicle ECU may reduce the available max power to the vehicle drive train (¶87)).
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 3 and 15 are rejected under 35 U.S.C. 103 as being obvious in view of Holme and Vaidya et al. (US 2014/0232411 – hereinafter “Vaidya”).
Per claim 3, Holme does not explicitly teach the method of claim 1, wherein the sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle.
In contrast, Vaidya teaches a system for battery monitoring and states that, since SOH is a slow moving parameter and multiple charging and discharging cycles are involved for the value to change appreciably, an average of degradations obtained over multiple cycles to obtain and accurate value of SOH.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle. One of ordinary skill would make such a modification for the purpose of determining an accurate SOH value (Vaidya; ¶51).
Per claim 15, Holme does not explicitly teach the system of claim 13, wherein the sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle.
In contrast, Vaidya teaches a system for battery monitoring and states that, since SOH is a slow moving parameter and multiple charging and discharging cycles are involved for the value to change appreciably, an average of degradations obtained over multiple cycles to obtain and accurate value of SOH.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Holme such that the sensor data is captured during a time period that includes multiple discharging operations and multiple recharging operations of a battery of the vehicle. One of ordinary skill would make such a modification for the purpose of determining an accurate SOH value (Vaidya; ¶51).
8. Claims 6, 18, and 20 are rejected under 35 U.S.C. 103 as being obvious in view of Holme and Monfort (US 2023/0139353).
Per claim 6, Holme does not explicitly teach the method of claim 1, wherein the vehicle operating parameter includes a throttle responsiveness parameter.
In contrast, Monfort teaches a battery management system wherein a battery management controller processor 510 is configured to limit throttle of a vehicle powered by battery cells 230 when the battery cells 230 are unable to output full power (¶67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the vehicle operating parameter includes a throttle responsiveness parameter. One of ordinary skill would make such a modification for the purpose of limiting throttle of a vehicle based on a health of a battery (Monfort; ¶67).
Per claim 18, Holme does not explicitly teach the system of claim 13, wherein the vehicle operating parameter includes a throttle responsiveness parameter.
In contrast, Monfort teaches a battery management system wherein a battery management controller processor 510 is configured to limit throttle of a vehicle powered by battery cells 230 when the battery cells 230 are unable to output full power (¶67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Holme such that the vehicle operating parameter includes a throttle responsiveness parameter. One of ordinary skill would make such a modification for the purpose of limiting throttle of a vehicle based on a health of a battery (Monfort; ¶67).
Per claim 20, Holme does not explicitly teach the computer-readable storage device of claim 19, wherein the vehicle operating parameter includes a throttle responsiveness parameter.
In contrast, Monfort teaches a battery management system wherein a battery management controller processor 510 is configured to limit throttle of a vehicle powered by battery cells 230 when the battery cells 230 are unable to output full power (¶67).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Holme such that the vehicle operating parameter includes a throttle responsiveness parameter. One of ordinary skill would make such a modification for the purpose of limiting throttle of a vehicle based on a health of a battery (Monfort; ¶67).
9. Claim 7 is rejected under 35 U.S.C. 103 as being obvious in view of Holme and Ozeki et al. (US 2004/0192494 – hereinafter “Ozeki”).
Per claim 7, Holme does not explicitly teach the method of claim 1, wherein the vehicle operating parameter includes a length of time that gears are held.
In contrast, Ozeki teaches a control device for a hybrid vehicle wherein a shift point to decide the gear shift in the transmission of the vehicle is changed to a relatively low speed side when a running control of a prime mover is restricted by an allowable output amount or acceptance capacity of an energy accumulator (¶24).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the EV is a hybrid vehicle wherein the vehicle operating parameter includes a length of time that gears are held. One of ordinary skill would make such a modification for the purpose of setting a shift point in accordance with an allowable output amount of an energy accumulator (Ozeki; ¶24).
10. Claim 8 is rejected under 35 U.S.C. 103 as being obvious in view of Holme and Prokhorov (US 2009/0112395).
Per claim 8, Holme does not explicitly teach the method of claim 1, wherein the vehicle operating parameter includes an engine rotations per minute parameter.
In contrast, Prokhorov teaches a system for detecting battery malfunction in a hybrid electric vehicle wherein a speed of an engine is increased to increase the state of charge of a battery when the battery is faulty (¶8 and 12).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the EV is a hybrid vehicle wherein the vehicle operating parameter includes an engine rotations per minute parameter. One of ordinary skill would make such a modification for the purpose of increasing the engine speed to charge a battery having poor health (Prokhorov; ¶8 and 12).
11. Claim 9 is rejected under 35 U.S.C. 103 as being obvious in view of Holme and Grand et al. (US 2003/0132664 – hereinafter “Grand”).
Per claim 9, Holme does not explicitly teach the method of claim 1, wherein the vehicle operating parameter includes a traction control system parameter.
In contrast, Grand teaches a traction controller 16 for an EV 10 that is configured to calculate a maximum regenerative limit 46 for regenerative braking based on the voltage of a high-voltage battery (Abstract; ¶22).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the vehicle operating parameter includes a traction control system parameter. One of ordinary skill would make such a modification for the purpose of controlling a braking system of an EV based on a state of a battery (Grand; ¶22).
12. Claim 10 is rejected under 35 U.S.C. 103 as being obvious in view of Holme and Vickery et al. (US 2019/0207267 – hereinafter “Vickery”).
Per claim 10, Holme does not explicitly teach the method of claim 1, further comprising, prior to altering the vehicle operating parameter, determining a current operation of the vehicle, wherein the vehicle operating parameter is not changed responsive to determining that the vehicle is in motion.
In contrast, Vickery teaches a battery management system wherein measured battery information is transmitted to a server via a battery charging station (¶33).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that, prior to altering the vehicle operating parameter, determining a current operation of the vehicle, wherein the vehicle operating parameter is not changed responsive to determining that the vehicle is in motion. One of ordinary skill would make such a modification to perform analysis on battery information when the vehicle is at a charging station (Vickery; ¶33).
13. Claim 11 is rejected under 35 U.S.C. 103 as being obvious in view of Holme and Feldman et al. (US 2022/0055500 – hereinafter “Feldman”).
Per claim 11, Holme does not explicitly teach the method of claim 1, wherein the vehicle operating parameter is altered responsive to the model output indicating that a battery has a particular charge.
In contrast, Feldman teaches a battery system wherein a vehicle state of charge is estimated by a trained model and an alert for a route modification is generated based on the estimated SOC (¶41 and 47).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Holme such that the vehicle operating parameter is altered responsive to the model output indicating that a battery has a particular charge. One of ordinary skill would make such a modification for the purpose of changing the route of a vehicle or alerting to battery degradation when an estimated vehicle SOC is low (Feldman; ¶47).
Conclusion
14. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAS A. SANGHERA whose telephone number is (571)272-4787. The examiner can normally be reached M-Th, alt. Fri, 8-5 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, WALTER LINDSAY can be reached at (571) 272-1674. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAS A SANGHERA/Primary Examiner, Art Unit 2852