DETAILED ACTION
This is a first action on the merits. Claims 1-20 are pending. Claims dated 11/10/2025 are being examined.
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 .
Allowable Subject Matter
Claims 2-3, 6-9, 12-13, and 16-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, the prior arts on record do not teach, describe, and/or suggest all the limitations as presented in the claim as a whole – specifically wherein the factors are selected based on a predetermined condition of correlation with an average interior temperature of the vehicle. Claim 3 is allowed based on its dependency on claim 2. Claims 12-13 are allowed for the same reasons, directed at a method claim. The closest prior art Ma does not average temperatures and while prior arts may teach including additional factors, there would be no reason to modify how the factors are selected in Ma, as Ma uses a preset formula.
Regarding claim 6, the prior arts on record do not teach, describe, and/or suggest all the limitations as presented in the claim as a whole – wherein the prediction device is configured to predict the interior temperature of the vehicle for each of a plurality of zones from the parameters of the plurality of first and second factors. Claims 7-9 are allowed based on their dependency on claim 6. Claims 16-19 are allowed for the same reasons, directed at a method claim.
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 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.
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.
Claims 1, 4, 10-11, 14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al. (CN-118238576-A), in view of Ma et al. (Collaborative thermal management of power battery and passenger cabin for energy efficiency optimization, Energy Conversion and Management, Volume 293, 2023, https://doi.org/10.1016/j.enconman.2023.117514) and herein after will be referred to as Ma and Ma 2.
Regarding claim 1, Ma teaches a battery and air conditioning control system comprising (Abstract: …a thermal model comprising a passenger cabin loop, a battery pack liquid cooling loop and an air conditioning loop is established; page 7: … the invention provides a network-connected electric automobile passenger cabin and battery heat management cooling optimization method):
a prediction device configured to: receive parameters of a plurality of first factors related to a battery thermal management system (BTMS) configured to manage a battery temperature of a vehicle (page 8: …establishing a thermal model of the battery pack; Equation 10-11) and parameters of a plurality of second factors related to an air conditioning system configured to manage interior air conditioning of the vehicle, and (page 9: …establishing a control-oriented air conditioning loop and a cabin loop temperature discrete model; Equation 20)
input the received parameters of the plurality of first and second factors to a temperature prediction model to predict an interior temperature of the vehicle; and (page 12: Equation 33: Tcab(k+1)＝Tcab(k)+f(Tcab(k), Tin(k), Tshell(k), Wbl(k))·ΔT)
an integrated controller configured to control the BTMS and the air conditioning system in an integrated manner based on the predicted interior temperature and one or more set target interior temperatures (page 12: constructing a double-layer model predictive control framework […] Selecting two state quantities of a thermal management system of a passenger cabin and a battery […] Tcab(k+s|k) is a prediction of the cabin temperature at time k+s; page 13: Tcab_ref(k+r|k) is the prediction of the desired cabin temperature at time k+r at time k […] the cabin temperature reaching the desired temperature level of the cabin; page 14: …seventh, obtaining the optimal control quantity and completing the optimization […] the optimal state output is substituted into the lower layer objective function as the target values of the cabin temperature and the battery pack temperature to perform temperature tracking).
As shown in Ma Equation 33 while the predicted interior temperature of the vehicle is based on a plurality of second factors, Ma does not explicitly teach: input the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle.
However, Ma 2 teaches input the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle (FIG. 2 predict temperature; page 6: The nonlinear model predictive controller takes target temperatures of battery and passenger cabin as inputs. It predicts the thermal management system's temperature at time k using different control variable sequences based on vehicle speed information from the predictor and feedback from the controlled system (battery SOC, ambient temperature, and temperatures).)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the temperature prediction model to incorporate the teachings of Ma 2 to include inputting the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle, with a reasonable expectation of success since doing so would have achieved the benefit of accounting for battery SOC and temperature.
Regarding claim 4, Ma, as modified, teaches the battery and air conditioning control system of claim 1.
Ma also teaches wherein the plurality of first and second factors includes two or more of a compressor temperature, an evaporator temperature, a battery cell temperature, a battery consumption voltage, a valve angle, an electric fan rotation speed, a refrigerant pressure, a warmer temperature, an inverter consumption current, an inverter temperature, a battery cooling sequence, an interior air temperature, an exterior air temperature, or a state of charge (SOC) change amount (page 8: …establishing a thermal model of the battery pack; Equation 10-11; page 9: …establishing a control-oriented air conditioning loop and a cabin loop temperature discrete model; Equation 20).
Regarding claim 10, Ma, as modified, teaches the battery and air conditioning control system of claim 1.
Ma also teaches wherein the integrated controller is configured to: determine one or more target actuators to be controlled among a plurality of actuators included in the BTMS and the air conditioning system based on the predicted interior temperature and the one or more set target interior temperatures; generate, for each of the one or more target actuators, a control value for controlling the target actuator; and output the generated control value to a system, among the BTMS and the air conditioning system, that includes the target actuator, wherein the system that includes the target actuator is configured to control operation of the target actuator based on the received control value (page 12: optimal control amounts are calculated; supported by AI synopsis page 3: the temperature of the battery pack and the cabin is reduced rapidly by controlling the rotation speed of the water pump, the rotation speed of the variable speed fan and the air mass flow of the air blower, and energy consumption is reduced).
Regarding claim 11, Ma teaches a battery and air conditioning control method comprising:
receiving, by a prediction device, parameters of a plurality of first factors related to a battery thermal management system (BTMS) configured to manage a battery temperature of a vehicle (page 8: …establishing a thermal model of the battery pack; Equation 10-11) and parameters of a plurality of second factors related to an air conditioning system configured to manage interior air conditioning of the vehicle (page 9: …establishing a control-oriented air conditioning loop and a cabin loop temperature discrete model; Equation 20);
inputting, by the prediction device, the parameters of the plurality of first and second factors to a temperature prediction model and predicting, by the prediction device, using the temperature prediction model, an interior temperature of the vehicle based on the parameters of the plurality of first and second factors; and (page 12: Equation 33: Tcab(k+1)＝Tcab(k)+f(Tcab(k), Tin(k), Tshell(k), Wbl(k))·ΔT);
controlling, by an integrated controller, the BTMS and the air conditioning system in an integrated manner based on the predicted interior temperature and one or more set target interior temperatures (page 12: constructing a double-layer model predictive control framework […] Selecting two state quantities of a thermal management system of a passenger cabin and a battery […] Tcab(k+s|k) is a prediction of the cabin temperature at time k+s; page 13: Tcab_ref(k+r|k) is the prediction of the desired cabin temperature at time k+r at time k […] the cabin temperature reaching the desired temperature level of the cabin; page 14: …seventh, obtaining the optimal control quantity and completing the optimization […] the optimal state output is substituted into the lower layer objective function as the target values of the cabin temperature and the battery pack temperature to perform temperature tracking).
As shown in Ma Equation 33 while the predicted interior temperature of the vehicle is based on a plurality of second factors, Ma does not explicitly teach: inputting the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle.
However, Ma 2 teaches inputting the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle (FIG. 2 predict temperature; page 6: The nonlinear model predictive controller takes target temperatures of battery and passenger cabin as inputs. It predicts the thermal management system's temperature at time k using different control variable sequences based on vehicle speed information from the predictor and feedback from the controlled system (battery SOC, ambient temperature, and temperatures).)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify the temperature prediction model to incorporate the teachings of Ma 2 to include inputting the received parameters of the plurality of “first factors” to a temperature prediction model to predict an interior temperature of the vehicle, with a reasonable expectation of success since doing so would have achieved the benefit of accounting for battery SOC and temperature.
Regarding claim 20, Ma, as modified, teaches the battery and air conditioning control method of claim 11.
Ma also teaches wherein the controlling in an integrated manner includes: determining, by the integrated controller, one or more target actuators to be controlled, among a plurality of actuators included in the BTMS and the air conditioning system based on the predicted interior temperature and the one or more set target interior temperatures; generating, by the integrated controller for each of the one or more target actuators, a control value for controlling the target actuator; and outputting, by the integrated controller, the generated control value to a system, among the BTMS and the air conditioning system, that includes the target actuator, wherein the system that includes the target actuator is configured to control operation of the target actuator based on the received control value (page 12: optimal control amounts are calculated; supported by AI synopsis page 3: the temperature of the battery pack and the cabin is reduced rapidly by controlling the rotation speed of the water pump, the rotation speed of the variable speed fan and the air mass flow of the air blower, and energy consumption is reduced).
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Ma, in view of Ma 2, in view of Xia (CN-113895202-A) and herein after will be referred to as Xia.
Regarding claim 5, Ma, as modified, teaches the battery and air conditioning control system of claim 1.
Ma, as modified, does not explicitly teach wherein the prediction device is further configured to: receive parameters of third factors related to a driving state of the vehicle; and input the parameters of third factors to the temperature prediction model.
However, Xia teaches wherein the prediction device is further configured to: receive parameters of third factors related to a driving state of the vehicle; and input the parameters of third factors to the temperature prediction model ([0004] This application provides a method for predicting the temperature inside a passenger compartment, comprising: acquiring various vehicle state data; the various vehicle state data including at least: the vehicle's motion data and the operating data of the air conditioner in the vehicle; calculating the current usage state of the air conditioner based on the various vehicle state data; the current usage state including: the air conditioner's off state in a specific usage scenario, or the air conditioner's specific temperature change state when it is on; acquiring a target parameter corresponding to the current usage state; and calculating the temperature of the passenger compartment based on the target parameter and a temperature simulation model corresponding to the current usage state).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Ma, as modified, to incorporate the teachings of Xia to include wherein the prediction device is further configured to: receive parameters of third factors related to a driving state of the vehicle; and input the parameters of third factors to the temperature prediction model, with a reasonable expectation of success since doing so would have achieved the benefit of accounting for vehicle motion data.
Regarding claim 15, Ma, as modified, teaches the battery and air conditioning control method of claim 11.
Ma, as modified, does not explicitly teach further comprising: receiving parameters of third factors related to a driving state of the vehicle; and inputting the parameters of third factors to the temperature prediction model.
However, Xia teaches further comprising: receiving parameters of third factors related to a driving state of the vehicle; and inputting the parameters of third factors to the temperature prediction model ([0004] This application provides a method for predicting the temperature inside a passenger compartment, comprising: acquiring various vehicle state data; the various vehicle state data including at least: the vehicle's motion data and the operating data of the air conditioner in the vehicle; calculating the current usage state of the air conditioner based on the various vehicle state data; the current usage state including: the air conditioner's off state in a specific usage scenario, or the air conditioner's specific temperature change state when it is on; acquiring a target parameter corresponding to the current usage state; and calculating the temperature of the passenger compartment based on the target parameter and a temperature simulation model corresponding to the current usage state).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present claimed invention to modify Ma, as modified, to incorporate the teachings of Xia to include further comprising: receiving parameters of third factors related to a driving state of the vehicle; and inputting the parameters of third factors to the temperature prediction model, with a reasonable expectation of success since doing so would have achieved the benefit of accounting for vehicle motion data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
For claim 6 while Mueller (US-12686389-B1) teaches that it is known for interior vehicle temperature differs in a plurality of zones (col 3 ln. 40-45: For example, a vehicle may be configured with multiple seating locations disposed in different quadrants or zones of the vehicle. Thermal conditions at the various seating locations or zones may be customized based on individual passengers' preferences), it does not teach all of claim 6 requiring the prediction of the interior temperature of the vehicle for each of a plurality of zones from the parameters of the plurality of first and second factors.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVIN SEOL whose telephone number is (571) 272-6488. The examiner can normally be reached on Monday-Friday 9:00 a.m. to 5:00 p.m.
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 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, Jelani Smith can be reached on (571) 270-3969. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVIN SEOL/Primary Examiner, Art Unit 3662
Prosecution Insights