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 .
Response to Amendment
The amendment filed on 06/16/2026 has been entered. Claims 1-6, 8-16 and 18-20 are pending in the application, with claims 1 and 11 being the independent claims. Claims 7 and 17 have been canceled without prejudice or disclaimer. Applicant’s amendments to the claims have overcome the previous rejections under 35 USC 112 set forth in the Non-Final Office Action mailed 03/16/2026.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 4 has been amended to recite “obtain a frequency of a characteristic squeal noise”. There is insufficient antecedent basis for this limitation in the claim as it is unclear if this is the same frequency and characteristic as referred to previously in the claim.
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.
Claims 1-2, 5, 9, 11-12, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Steffen (WO 2023237274 A1) in view of Seneger et al. (US 20230097755 A1), Wang et al. (NPL 'Prediction of frictional braking noise based on brake dynamometer test and artificial intelligent algorithms'), and Cho et al. (US 20220363227 A1).
Regarding claim 1, Seneger teaches an apparatus for predicting squeal noise, the apparatus comprising:
a memory configured to store computer-executable instructions ([0061]); and
at least one processor operably connected to the memory and configured to access the memory and execute the instructions ([0060]), wherein the at least one processor is configured to:
identify vehicle state data including at least one of first input data extracted from a braking device of a vehicle, second input data corresponding to a wheel of the vehicle, third input data measured from an external sensor of the vehicle, or a combination thereof ([0043], information about the brake pressure of a braking operation, speed and steering angle, and air temperature are the first, second, and third input data respectively);
and obtain first output data on a probability that the squeal noise is expected to be generated in the braking device, second output data on a frequency of the squeal noise, and third output data on an amplitude of the squeal noise by applying the vehicle state data to a squeal noise prediction model ([0044] and [0056], “output information about the probability of brake squealing occurring”; [0013], [0042], and [0051], brake noise is determined and saved, where the determination is made and the audio recording is saved based on the metadata, including frequency and volume, i.e. amplitude).
Steffen teaches that the model is trained based on parameters that describe the state of the vehicle, which include speed and brake temperature, and based on noises, i.e. first input and second input data ([0037]). However, it does not explicitly teach obtaining the vehicle state data by binding the first torque estimation data and the second torque estimation data with the first input data and the second input data.
It is noted that machine learning models operate by receiving input variables, and executing algorithms and decisions to produce an output value predicted from these input variables. This operation is definitionally tied to the relationships between the inputted values. Therefore, it is considered implicit that the first and second torque estimation data and first and second input data are bound together in some form before being inputted into the machine learning model as they are analyzed via the machine learning model at the same instance. A machine learning model is rendered nonfunctional if unrelated or unconnected input values are fed into it as the prediction will not be accurate for the intended instance of operation, but rather arbitrary for whichever values are entered.
Steffen teaches a system for detecting, predicting, and storing brake squealing as audio waveforms, but does not take any further action with these waveforms. It does not teach that the system if configured to output target noise and, by use of the target noise, cancel out the squeal noise that corresponds to the second output data and the third output data and is generated from the braking device based on the squeal noise expected to be generated from the braking device through the first output data.
In the field of outputting target audio to cancel out vehicle noises, Seneger teaches a system configured to:
output target noise and, by use of the target noise, cancel out the squeal noise that corresponds to the second output data and the third output data and is generated from the braking device ([0037-0038] and [0041-0045], target noise is determined based on the amplitude and frequency of determined vehicular noise as seen in Fig. 4, vehicular noise from vehicle hardware is stored as waveforms).
One of ordinary skill in the art would have recognized that brake squealing is a noise from vehicle hardware that is beneficial to reduce or cancel out, and thus would been able to combine these references so that the audio waveform output and stored by Steffen based on the squeal noise expected to be generated from the braking device through the first output data is then used to determine and output target noise based on the stored audio waveforms as taught by Seneger. It would have been obvious to one of ordinary skill in the art at the effective date of filing to combine these inventions based on a reasonable expectation of success and motivation, as taught by Seneger, of using personalized target audio outputs to cancel the experienced and expected audio noises of occupants in a vehicle ([0002-0005]).
Although Steffen discloses a squeal noise prediction model ([0044]), it does not teach that the squeal noise prediction model is a mixed effect reflection machine learning model comprising a regression methodology configured to output the first output data based on the vehicle state data, the regression methodology considering distribution-based random effects.
In the same field of endeavor, Wang discloses a brake squeal model that is:
a mixed effect reflection machine learning model comprising a regression methodology configured to output the first output data based on the vehicle state data, the regression methodology considering distribution-based random effects (Page 2684, Col. 1, Section 'Principle of the LSTM Model', Page 2685, Col. 1, Section 'Principle of the XGBoost model', and Page 2686, Col. 1, Paras. 5-6, an XGBoost model is configured to build a strong regression model to predict the likelihood of frictional brake noise). The XGBoost model disclosed by Wang is considered to teach the model as claimed as applicant’s disclosure lists an XGBoost model as “an exemplary embodiment of the present disclosure” as claimed ([0065] of applicant’s disclosure).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the model of Steffen with the teachings of Wang based on a reasonable expectation of success and motivation of incorporating the many advantages of XGBoost models disclosed by Wang, such as different types of learners, optimization methods, cutting processing, and more advantages that enable the XGBoost to “combine a series of different weak learners to build a strong regression model with better performance than other base learners” (Page 2685, Col. 1, Section 'Principle of the XGBoost model' and Page 2686, Col. 1, Paras. 5-6).
Wang discloses how the XGBoost uses a coefficient of friction (COF) produced from a LTSM Model to predict noise as the frictional noise is very common and is correlated to a COF (Page 2684, Col. 1, Section 'Principle of the LSTM Model'). However, this LTSM model operates from generated test data as opposed to live vehicle data, and Wang does not teach generating first torque estimation data based on wheel speed data and a change in the wheel speed data at a subsequent time point, and generating second torque estimation data by multiplying the first torque estimation data by disk temperature data.
In the same field of endeavor, Cho teaches processes for detecting a COF comprising:
generating first torque estimation data based on wheel speed data and a change in the wheel speed data at a subsequent time point ([0046], [0050], [0054], and [0081], “the driving information of the vehicle, a wheel speed (i.e., the rotation speed of the brake disc)” is used such that its average and change in speed may be used as parameters), and
generating second torque estimation data by multiplying the first torque estimation data by disk temperature data ([0054-0055], [0085], and [0101], “correlation between the estimated torque value and the temperature… is acquired by multiplying the estimated torque value using the deceleration by the temperature of the brake disc”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Wang with the teachings of Cho to determine the COF for determining brake noise based on a reasonable expectation of success and motivation of ensuring that the COF can be determined accurately while the vehicle is in normal operation and for other advantages considered by Cho, including performance and accuracy increases ([0103]), a reduction of errors and generation of abnormal behaviors ([0104]), and allowing various improvements to brake systems and vehicles to be facilitated ([0105]).
Regarding claim 2, Steffen teaches:
identifying first noise data measured from a first external device provided in the vehicle ([0014]); and
including at least one of the first noise data, the second noise data, or a combination thereof in the vehicle state data ([0014]).
Seneger further teaches:
identifying second noise data measured from a second external device of a user operating the vehicle ([0034] and Fig. 6, audio is received from an external device in the vehicle cabin of a user operating the vehicle).
Regarding claim 5, Steffen teaches:
determining squeal noise prediction sub-models corresponding to sub-areas ([0014] and [0057], squeal noise is predicted independently using sub-models for sub-areas corresponding to each of the four brakes); and
obtaining the first output data, the second output data, and the third output data corresponding to each of the sub-areas to apply the vehicle state data to each of the squeal noise prediction sub-models ([0014] and [0057], audio output is determined and predicted for each brake, i.e. sub-area).
Steffen teaches that the brake squeal is determined at sub areas defined by each of the four brakes for a prediction of the break squeal for each brake. It doesn't teach that the sub areas are determined by dividing an area of the vehicle by a predetermined number based on a center portion of the vehicle.
Seneger does teach that sub-areas are determined by:
dividing an area of the vehicle by a predetermined number based on a center portion of the vehicle ([0062-0063] and Fig. 7, the location of each passenger has a sub-model that allows for customized audio noise attenuation for each passenger).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Steffen to have the sub-areas be based on the passengers based on a reasonable expectation of success and motivation of allowing the targeted noise cancelation for each passenger as taught by Seneger ([0041]). This allows the target noise based on the brake squealing that is outputted to each passenger to also be based on what each passenger is expected to hear as Seneger already performs with other forms of audio disturbances.
Regarding claim 9, Steffen teaches:
determining whether the squeal noise of the braking device occurs based on a comparison of the first output data and a predetermined threshold value ([0056], squeal noise is predicted if the probability of brake squealing is higher than a threshold).
Seneger further teaches:
output the target noise through a sound actuator included in the vehicle based on the squeal noise expected to be generated in the braking device ([0020] and [0038]).
Regarding claim 11, Steffen teaches a method of predicting squeal noise, the method comprising:
identifying, by a processor, vehicle state data including at least one of first input data extracted from a braking device of a vehicle, second input data corresponding to a wheel of the vehicle, third input data measured from an external sensor of the vehicle, or a combination thereof ([0043], information about the brake pressure of a braking operation, speed and steering angle, and air temperature are the first, second, and third input data respectively);
obtaining, by the processor, first output data on a probability that the squeal noise is expected to be generated in the braking device, second output data on a frequency of the squeal noise, and third output data on an amplitude of the squeal noise by applying the vehicle state data to a squeal noise prediction model ([0044] and [0056], “output information about the probability of brake squealing occurring”; [0013], [0042], and [0051], brake noise is determined and saved, where the determination is made and the audio recording is saved based on the metadata, including frequency and volume, i.e. amplitude).
Steffen teaches that the model is trained based on parameters that describe the state of the vehicle, which include speed and brake temperature, and based on noises, i.e. first input and second input data ([0037]). However, it does not explicitly teach obtaining the vehicle state data by binding the first torque estimation data and the second torque estimation data with the first input data and the second input data.
It is noted that machine learning models operate by receiving input variables, and executing algorithms and decisions to produce an output value predicted from these input variables. This operation is definitionally tied to the relationships between the inputted values. Therefore, it is considered implicit that the first and second torque estimation data and first and second input data are bound together in some form before being inputted into the machine learning model as they are analyzed via the machine learning model at the same instance. A machine learning model is rendered nonfunctional if unrelated or unconnected input values are fed into it as the prediction will not be accurate for the intended instance of operation, but rather arbitrary for whichever values are entered.
Steffen teaches detecting, predicting, and storing brake squealing as audio waveforms, but does not take any further action with these waveforms. It does not teach outputting, by the processor, target noise and, by use of the target noise, cancel out the squeal noise that corresponds to the second output data and the third output data and is generated from the braking device based on the squeal noise expected to be generated from the braking device through the first output data.
In the field of outputting target audio to cancel out vehicle noises, Seneger teaches a system configured to:
outputting, by the processor, target noise and, by use of the target noise, cancel out the squeal noise that corresponds to the second output data and the third output data and is generated from the braking device based on the squeal noise expected to be generated from the braking device through the first output data ([0037-0038] and [0041-0045], target noise is determined based on the amplitude and frequency of determined vehicular noise as seen in Fig. 4, vehicular noise from vehicle hardware is stored as waveforms).
One of ordinary skill in the art would have recognized that brake squealing is a noise from vehicle hardware that is beneficial to reduce or cancel out, and thus would been able to combine these references so that the audio waveform output and stored by Steffen based on the squeal noise expected to be generated from the braking device through the first output data is then used to determine and output target noise based on the stored audio waveforms as taught by Seneger. It would have been obvious to one of ordinary skill in the art at the effective date of filing to combine these inventions based on a reasonable expectation of success and motivation, as taught by Seneger, of using personalized target audio outputs to cancel the experienced and expected audio noises of occupants in a vehicle ([0002-0005]).
Although Steffen discloses a squeal noise prediction model ([0044]), it does not teach that the squeal noise prediction model is a mixed effect reflection machine learning model comprising a regression methodology configured to output the first output data based on the vehicle state data, the regression methodology considering distribution-based random effects.
In the same field of endeavor, Wang discloses a brake squeal model that is:
a mixed effect reflection machine learning model comprising a regression methodology configured to output the first output data based on the vehicle state data, the regression methodology considering distribution-based random effects (Page 2684, Col. 1, Section 'Principle of the LSTM Model', Page 2685, Col. 1, Section 'Principle of the XGBoost model', and Page 2686, Col. 1, Paras. 5-6, an XGBoost model is configured to build a strong regression model to predict the likelihood of frictional brake noise). The XGBoost model disclosed by Wang is considered to teach the model as claimed as applicant’s disclosure lists an XGBoost model as “an exemplary embodiment of the present disclosure” as claimed ([0065] of applicant’s disclosure).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the model of Steffen with the teachings of Wang based on a reasonable expectation of success and motivation of incorporating the many advantages of XGBoost models disclosed by Wang, such as different types of learners, optimization methods, cutting processing, and more advantages that enable the XGBoost to “combine a series of different weak learners to build a strong regression model with better performance than other base learners” (Page 2685, Col. 1, Section 'Principle of the XGBoost model' and Page 2686, Col. 1, Paras. 5-6).
Wang discloses how the XGBoost uses a coefficient of friction (COF) produced from a LTSM Model to predict noise as the frictional noise is very common and is correlated to a COF (Page 2684, Col. 1, Section 'Principle of the LSTM Model'). However, this LTSM model operates from generated test data as opposed to live vehicle data, and Wang does not teach generating first torque estimation data based on wheel speed data and a change in the wheel speed data at a subsequent time point, and generating second torque estimation data by multiplying the first torque estimation data by disk temperature data.
In the same field of endeavor, Cho teaches processes for detecting a COF comprising:
generating first torque estimation data based on wheel speed data and a change in the wheel speed data at a subsequent time point ([0046], [0050], [0054], and [0081], “the driving information of the vehicle, a wheel speed (i.e., the rotation speed of the brake disc)” is used such that its average and change in speed may be used as parameters), and
generating second torque estimation data by multiplying the first torque estimation data by disk temperature data ([0054-0055], [0085], and [0101], “correlation between the estimated torque value and the temperature… is acquired by multiplying the estimated torque value using the deceleration by the temperature of the brake disc”).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Wang with the teachings of Cho to determine the COF for determining brake noise based on a reasonable expectation of success and motivation of ensuring that the COF can be determined accurately while the vehicle is in normal operation and for other advantages considered by Cho, including performance and accuracy increases ([0103]), a reduction of errors and generation of abnormal behaviors ([0104]), and allowing various improvements to brake systems and vehicles to be facilitated ([0105]).
Regarding claim 12, Steffen teaches:
identifying first noise data measured from a first external device provided in the vehicle ([0014]); and
including at least one of the first noise data, the second noise data, or a combination thereof in the vehicle state data ([0014]).
Seneger further teaches:
identifying second noise data measured from a second external device of a user operating the vehicle ([0034] and Fig. 6, audio is received from an external device in the vehicle cabin of a user operating the vehicle).
Regarding claim 15, Steffen teaches:
determining squeal noise prediction sub-models corresponding to sub-areas ([0014] and [0057], squeal noise is predicted independently using sub-models for sub-areas corresponding to each of the four brakes); and
obtaining the first output data, the second output data, and the third output data corresponding to each of the sub-areas to apply the vehicle state data to each of the squeal noise prediction sub-models ([0014] and [0057], audio output is determined and predicted for each brake, i.e. sub-area).
Steffen teaches that the brake squeal is determined at sub areas defined by each of the four brakes for a prediction of the break squeal for each brake. It doesn't teach that the sub areas are determined by dividing an area of the vehicle by a predetermined number based on a center portion of the vehicle.
Seneger does teach that sub-areas are determined by:
dividing an area of the vehicle by a predetermined number based on a center portion of the vehicle ([0062-0063] and Fig. 7, the location of each passenger has a sub-model that allows for customized audio noise attenuation for each passenger).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Steffen to have the sub-areas be based on the passengers based on a reasonable expectation of success and motivation of allowing the targeted noise cancelation for each passenger as taught by Seneger ([0041]). This allows the target noise based on the brake squealing that is outputted to each passenger to also be based on what each passenger is expected to hear as Seneger already performs with other forms of audio disturbances.
Regarding claim 19, Steffen teaches:
determining whether the squeal noise of the braking device occurs based on a comparison of the first output data and a predetermined threshold value ([0056], squeal noise is predicted if the probability of brake squealing is higher than a threshold).
Seneger further teaches:
outputting the target noise through a sound actuator included in the vehicle based on the squeal noise expected to be generated in the braking device ([0020] and [0038]).
Claims 3-4, 10, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Steffen in view of Seneger, Wang, and Cho as applied to claims 1 and 11 above, and in further view of Kamiya et al. (US 20040174067 A1).
Regarding claim 3, Steffen teaches:
identifying an outside air temperature measured from the external sensor based on the squeal noise expected to be generated in the braking device through the first output data ([0015]).
Seneger further teaches:
outputting the target noise ([0058]).
The prior combination does not teach identifying soaking time of the vehicle, identifying a first sub-condition comparing the outside air temperature with a first threshold value, and a second sub-condition comparing the soaking time with a second threshold value; and that the outputted noise is based on the first sub-condition and the second sub-condition.
Note that soaking time has been interpreted as “the driving time of the vehicle” as defined in [0100] of applicant’s disclosure.
In the field of predicting and controlling brake squealing, Kamiya teaches:
identifying soaking time of the vehicle ([0040-0041], travel time, i.e. soaking time is determined);
and identifying a first sub-condition comparing the outside air temperature with a first threshold value, and a second sub-condition comparing the soaking time with a second threshold value ([0040-0041], outer air temperature and travel time are compared to predetermined values to determine “in-the-cold” and “first-in-the-morning” conditions).
One of ordinary skill in the art would have been able to modify the prior combination to factor in these “in-the-cold” and “first-in-the-morning” conditions and output a target noise based on the first sub-condition and the second sub-condition as these “in-the-cold” and “first-in-the-morning” conditions are analyzed to predict if brake squealing will occur. It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to use these sub-conditions in the determination of brake squealing based on a reasonable expectation of success and motivation of blocking break squeal noise, as Kamiya teaches that such in-the-cold” and “first-in-the-morning” conditions tend to produce brake noise ([0014]).
Regarding claim 4, Steffen teaches:
obtaining the frequency of the squeal noise and the amplitude of the squeal noise ([0013] and [0042]).
The prior combination does not teach obtaining the squeal noise characteristics at the temperature lower than the predetermined temperature or the soaking, in response that the outside air temperature is less than the first threshold value and the soaking time is less than the second threshold value.
In the field of predicting and controlling brake squealing, Kamiya teaches:
predicting that brake squealing is occurring at the temperature lower than the predetermined temperature or the soaking, in response that the outside air temperature is less than the first threshold value and the soaking time is less than the second threshold value ([0040-0041], when the outer air temperature and driving time are less than predetermined values, "in-the-cold" and "first-in-the-morning" conditions are met, which predictably results in brake squealing).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to determine the squeal noise characteristics when such outside-air temperature and soaking time conditions are fulfilled based on a reasonable expectation of success and motivation of blocking break squeal noise, as Kamiya teaches that such in-the-cold” and “first-in-the-morning” conditions tend to produce brake noise ([0014]).
Regarding claim 10, the prior combination does not teach applying the first output data, the second output data and the third output data to the braking device or a regenerative braking motor to adjust hydraulic braking of the braking device, or regenerative braking of the regenerative braking motor.
In the field of predicting and controlling brake squealing, Kamiya teaches:
applying the first output data, the second output data and the third output data to the braking device to adjust hydraulic braking of the braking device ([0045], [0049], and [0057], the hydraulic braking of the braking device is adjusted when squeal noise is predicted to occur).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to apply the output data to control the hydraulic braking of a braking device based on a reasonable expectation of success and motivation, as taught by Kamiya, to control and reduce brake squealing ([0041]).
Regarding claim 13, Steffen teaches:
identifying an outside air temperature measured from the external sensor based on the squeal noise expected to be generated in the braking device through the first output data ([0015]).
Seneger further teaches:
outputting the target noise ([0058]).
The prior combination does not teach identifying soaking time of the vehicle, identifying a first sub-condition comparing the outside air temperature with a first threshold value, and a second sub-condition comparing the soaking time with a second threshold value; and that the outputted noise is based on the first sub-condition and the second sub-condition.
Note that soaking time has been interpreted as “the driving time of the vehicle” as defined in [0100] of applicant’s disclosure.
In the field of predicting and controlling brake squealing, Kamiya teaches:
identifying soaking time of the vehicle ([0040-0041], travel time, i.e. soaking time is determined);
and identifying a first sub-condition comparing the outside air temperature with a first threshold value, and a second sub-condition comparing the soaking time with a second threshold value ([0040-0041], outer air temperature and travel time are compared to predetermined values to determine “in-the-cold” and “first-in-the-morning” conditions).
One of ordinary skill in the art would have been able to modify the prior combination to factor in these “in-the-cold” and “first-in-the-morning” conditions and output a target noise based on the first sub-condition and the second sub-condition as these “in-the-cold” and “first-in-the-morning” conditions are analyzed to predict if brake squealing will occur. It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to use these sub-conditions in the determination of brake squealing based on a reasonable expectation of success and motivation of blocking break squeal noise, as Kamiya teaches that such in-the-cold” and “first-in-the-morning” conditions tend to produce brake noise ([0014]).
Regarding claim 14, Steffen teaches:
obtaining the frequency of the squeal noise and the amplitude of the squeal noise ([0013] and [0042]).
The prior combination does not teach obtaining the squeal noise characteristics at the temperature lower than the predetermined temperature or the soaking, in response that the outside air temperature is less than the first threshold value and the soaking time is less than the second threshold value.
In the field of predicting and controlling brake squealing, Kamiya teaches:
predicting that brake squealing is occurring at the temperature lower than the predetermined temperature or the soaking, in response that the outside air temperature is less than the first threshold value and the soaking time is less than the second threshold value ([0040-0041], when the outer air temperature and driving time are less than predetermined values, "in-the-cold" and "first-in-the-morning" conditions are met, which predictably results in brake squealing).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to determine the squeal noise characteristics when such outside-air temperature and soaking time conditions are fulfilled based on a reasonable expectation of success and motivation of blocking break squeal noise, as Kamiya teaches that such in-the-cold” and “first-in-the-morning” conditions tend to produce brake noise ([0014]).
Regarding claim 20, the prior combination does not teach applying the first output data, the second output data and the third output data to the braking device or a regenerative braking motor to adjust hydraulic braking of the braking device, or regenerative braking of the regenerative braking motor.
In the field of predicting and controlling brake squealing, Kamiya teaches:
applying the first output data, the second output data and the third output data to the braking device to adjust hydraulic braking of the braking device ([0045], [0049], and [0057], the hydraulic braking of the braking device is adjusted when squeal noise is predicted to occur).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify the prior combination to apply the output data to control the hydraulic braking of a braking device based on a reasonable expectation of success and motivation, as taught by Kamiya, to control and reduce brake squealing ([0041]).
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Steffen in view of Seneger, Wang, and Cho as applied to claims 1 and 11 above, and in further view of Singh et al. (US 20250222945 A1).
Regarding claim 6, Seneger teaches:
identifying the locations of the occupants boarding the vehicle ([0062]); and
determining the identified location as a location to which the target noise outputs ([0061-0062]).
Seneger uses camera systems to identify the occupant locations, and does not teach determining first location data for identifying the locations of the occupants boarding the vehicle based on a weight sensor included in the vehicle; determining second location data for identifying the locations of the occupants boarding the vehicle based on at least one of an ultrasonic sensor, a radio detection and ranging (RADAR) sensor, or a combination thereof included in the vehicle; and determining third location data for identifying the locations of the occupants boarding the vehicle based on a connection between the vehicle and portable terminals of the occupants boarding the vehicle.
In the field of vehicle occupant detection systems, Singh teaches:
determining first location data for identifying the locations of the occupants boarding the vehicle based on a weight sensor included in the vehicle ([0217]);
determining second location data for identifying the locations of the occupants boarding the vehicle based on at least one of an ultrasonic sensor, a radio detection and ranging (RADAR) sensor, or a combination thereof included in the vehicle ([0188], mmWave radar sensors); and
determining third location data for identifying the locations of the occupants boarding the vehicle based on a connection between the vehicle and portable terminals of the occupants boarding the vehicle ([0190], get occupant identification via connection with mobile devices).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Seneger with the various occupant detection systems disclosed by Singh for the motivation of allowing for occupant detection in cases where the camera systems are obscured or otherwise nonfunctional, and for the well-known technical advantages of such sensors, such as reduced power usage or quicker operations.
Regarding claim 16, Seneger teaches:
identifying the locations of the occupants boarding the vehicle ([0062]); and
determining the identified location as a location to which the target noise outputs ([0061-0062]).
Seneger uses camera systems to identify the occupant locations, and does not teach determining first location data for identifying the locations of the occupants boarding the vehicle based on a weight sensor included in the vehicle; determining second location data for identifying the locations of the occupants boarding the vehicle based on at least one of an ultrasonic sensor, a radio detection and ranging (RADAR) sensor, or a combination thereof included in the vehicle; and determining third location data for identifying the locations of the occupants boarding the vehicle based on a connection between the vehicle and portable terminals of the occupants boarding the vehicle.
In the field of vehicle occupant detection systems, Singh teaches:
determining first location data for identifying the locations of the occupants boarding the vehicle based on a weight sensor included in the vehicle ([0217]);
determining second location data for identifying the locations of the occupants boarding the vehicle based on at least one of an ultrasonic sensor, a radio detection and ranging (RADAR) sensor, or a combination thereof included in the vehicle ([0188], mmWave radar sensors); and
determining third location data for identifying the locations of the occupants boarding the vehicle based on a connection between the vehicle and portable terminals of the occupants boarding the vehicle ([0190], get occupant identification via connection with mobile devices).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Seneger with the various occupant detection systems disclosed by Singh for the motivation of allowing for occupant detection in cases where the camera systems are obscured or otherwise nonfunctional, and for the well-known technical advantages of such sensors, such as reduced power usage or quicker operations.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Steffen in view of Seneger, Wang, Cho, and Singh as applied to claims 6 and 16 above, and in further view of MacNeille et al. (US 20170213541 A1).
Regarding claim 8, Seneger teaches:
outputting the target noise for each location when there are multiple vehicle occupants detected ([0065] and Fig. 8).
Seneger does not teach providing a notification to a driver of the vehicle to input a priority of a location to which the target noise outputs, based on a number of the identified locations being at least one; and outputting the target noise for each location corresponding to the priority based on inputting of the priority.
In the field of personalized audio cancelation systems, MacNeille teaches:
providing a notification to a driver of the vehicle to input a priority of a location to which the target noise outputs, based on a number of the identified locations being at least one ([0037-0040 and [0072], multiple occupants causes multiple zones to be created, and the audio for each zone is presented via the media display screen 214 to the driver; and
outputting the customized audio noise control for each location corresponding to the priority based on inputting of the priority ([0037-0040] and [0072], the priority of each selected audio is then output for each location based on the volume and selected priority, thereby allowing different noise and sound controlled to be enabled or disabled for each user).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Seneger with the audio priority selection process of MacNeille based on a reasonable expectation of success and motivation to allow for the customized audio selection for each occupant, thereby giving control over the noise control audio outputted per the desire of each occupant.
Regarding claim 18, Seneger teaches:
outputting the target noise for each location when there are multiple vehicle occupants detected ([0065] and Fig. 8).
Seneger does not teach providing a notification to a driver of the vehicle to input a priority of a location to which the target noise outputs, based on a number of the identified locations being at least one; and outputting the target noise for each location corresponding to the priority based on inputting of the priority.
In the field of personalized audio cancelation systems, MacNeille teaches:
providing a notification to a driver of the vehicle to input a priority of a location to which the target noise outputs, based on a number of the identified locations being at least one ([0037-0040 and [0072], multiple occupants causes multiple zones to be created, and the audio for each zone is presented via the media display screen 214 to the driver; and
outputting the customized audio noise control for each location corresponding to the priority based on inputting of the priority ([0037-0040] and [0072], the priority of each selected audio is then output for each location based on the volume and selected priority, thereby allowing different noise and sound controlled to be enabled or disabled for each user).
It would have been obvious to one of ordinary skill in the art at the effective date of filing to modify Seneger with the audio priority selection process of MacNeille based on a reasonable expectation of success and motivation to allow for the customized audio selection for each occupant, thereby giving control over the noise control audio outputted per the desire of each occupant.
Response to Arguments
Applicant's arguments filed 06/16/2026 have been fully considered.
Regarding the amendments to the independent claims 1 and 11, applicant argues that the limitation of “obtain the vehicle state data by binding the first torque estimation data and the second torque estimation data with the first input data and the second input data” is not taught by the combination of Steffen in view of Seneger as applied previously. This argument is unpersuasive. As considered in the claims, the binding of this first and second torque estimation data with this first and second input data is for the purpose of producing the vehicle state data, which is then input to be applied by the squeal prediction model. However, the degree of specificity in which this “binding” is claimed is considered small, and thus lacks substantial patentable significance within the claim language. To this end, as stated in the rejection above, the inputs into a machine learning model upon which a prediction is made are, to some degree, required to be bound together. For example, it is considered that the claimed squeal noise prediction model operates by predicting whether the vehicle state data produced at an instance of time would produce squeal noise at that instance of time. This vehicle state data is required to be “bound” together in that the state data must be corresponding to an instance, otherwise the model would be unable to accurately predict whether squeal noise is occurring at that instance. Therefore, as stated in the rejection, it is considered implicit that the first and second torque estimation data is bound with this first and second input data to some degree or else the squeal noise prediction model would be rendered nonfunctional and unable to accurately predict if squeal noises occurs at the point in time being considered.
Applicant is advised to amend the claim language to add specificity to how this “binding” is performed in order to go beyond what one of ordinary skill in the art would recognize as a required step for machine learning models.
The remainder of applicant’s arguments regarding the amendments to the independent claims are considered persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wang and Cho as necessitated by applicant’s amendments to the independent claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JACK R BREWER/Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663
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