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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 6-8 and 16-18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims state that locations of occupants boarding the vehicle are identified “based on the squeal noise expected to be generated in the braking device”. It is unclear how exactly the claimed invention would use squeal noise that is expected to be generated in a braking device in order to identify the locations of occupants boarding the vehicle. Instead, applicant’s disclosure states that the locations of occupants in the vehicle are determined using a plurality of sensors ([0109]). Therefore, as no further description of this process is included in applicant’s disclosure that would convey to one of ordinary skill in the art that the applicant possessed a system or a method of determining the locations of occupants in a vehicle based on squeal noise, nor is such disclosure present that would enable one of ordinary skill in the art to construct such a system or perform such a method, the claims are rejected.
As no enablement of these claims is present in applicant’s disclosure, and it is not clear how or if the location of an occupant in a vehicle can be identified based on the expected squeal noise of braking devices, this rejection is the only rejection provided for claims 6 and 16. A substantive rejection will be provided for these claims once they are properly enabled.
Claims 7-8 and 17-18 are also rejected under 35 U.S.C. 112(a) based on their dependency to claims 6 and 16 respectively. However, as the matter introduced in these claims is properly enabled and described in applicant’s disclosure, a rejection over prior art of these claims will be given based on their dependency to the independent claims 1 and 11 respectively.
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).
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 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 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]).
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. 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 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 method includes 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 method for:
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 ([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 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]).
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. 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 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 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Steffen in view of Seneger as applied to claims 1 and 11 above, and in further view of Singh et al. (US 20250222945 A1).
Regarding claim 7, Seneger teaches:
identifying the locations of the occupants boarding the vehicle ([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 17, Seneger teaches:
identifying the locations of the occupants boarding the vehicle ([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 as applied to claims 1 and 11 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.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACK R. BREWER whose telephone number is (571)272-4455. The examiner can normally be reached 9AM-6PM.
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/JACK R BREWER/Examiner, Art Unit 3663
/ADAM D TISSOT/Primary Examiner, Art Unit 3663