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 § 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.
Claim(s) 1-7, 32-33, and 38-39 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee KR20170064909 in view of Bar EP2431218 in view of Hyundai KR20150045.
Regarding claim 1, Lee discloses a passenger state modulation system in a passenger vehicle, comprising:
an active seat for supporting a given passenger in the passenger vehicle (P 1, 16, 62, and Figs. 1-4 disclose a nuisance mitigation system in a vehicle comprising a controllable vehicle seat for mitigating a motion sickness of a passenger by adjusting an angle of a seat back of a seat via a vehicle controller and the vehicle control unit for controlling a body control module (BCM), an air conditioning system, and an AVN system according to a nuisance sensing device which senses the motion of the passenger);
a prediction algorithm executed by a computer processor and operable to predict a state of the given passenger and motions of the passenger vehicle (P36, 56, Figs. 1-4 disclose a sensing control unit using a biometric information of the passenger collected by a biometric information collecting unit with a vibration information of the vehicle collected by a vibration information collecting unit, detects whether the occupant gets motion-sick based on a learning data stored in a learning data storing unit, wherein the vibration information collecting unit includes an IMU, a G-sensor for sensing an acceleration and a direction, a three-axis acceleration sensor for detecting acceleration and vibration, and a relative position detecting unit).
Lee does not, however, teach that the predicted motions include acceleration of the passenger vehicle; and
a command generation algorithm executed by the computer processor and configured to receive the predicted state of the given passenger and the predicted motions of the passenger vehicle from the predicted algorithm, wherein the command generation algorithm determines a preemptive command to tilt the active seat and issues the preemptive command to the active seat, where the active seat is tilted in same direction as the acceleration of the passenger vehicle.
However, Bar teaches:
that the predicted motions include acceleration of the passenger vehicle (P5, Figs. 1-4 teach a method for controlling actuators that influence a rolling movement of a vehicle, the vehicle being equipped with a function for a predictive longitudinal and lateral guidance with a look-ahead period, initially a variable influencing a future lateral acceleration is determined from the function for anticipatory longitudinal and lateral guidance; and
a command generation algorithm executed by the computer processor and configured to receive the predicted state of the given passenger and the predicted motions of the passenger vehicle from the predicted algorithm, wherein the command generation algorithm determines a preemptive command to tilt the active seat and issues the preemptive command to the active seat, where the active seat is tilted in same direction as the acceleration of the passenger vehicle (P1, 13, 17, and Figs. 1, 4 teach a method for controlling the actuator comprises the step of tilting the seat of the motor vehicle by a roll angle and/or a pitch angle and an associated motor vehicle, wherein the tilting by the pitch angle to compensate for a longitudinal acceleration curve can be particularly useful on the passenger seat where the roll axis and the pitch axis of the actuator is changed and it influences a comfort or a motion sickness risk.
Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify Lee with Bar to increase the comfort and reduce the risk of motion sickness to the passenger.
Regarding the new limitation which states wherein the preemptive command is received by the prediction algorithm and is used in the prediction of the state of the specific passenger, this is not taught by either of Lee or Bar, but was discussed in the prior rejection in claim 8 using Hyundai.
Specifically, P31-32 and Figs. 1-2 disclose a nuisance preventing unit which is a seatbelt formed on a seat of a passenger and a control unit that generates a tension on the seatbelt work by the passenger, wherein when a predicted running operation occurrence time arrives, the control unit generates a control signal for controlling the nuisance preventing unit so as to correspond to an estimated degree of a driving operation. The bolded part shows that the preemptive command is received by a prediction algorithm that then executes the command based on the predicted running operation occurrence time.
Therefore, it would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the claimed invention to modify Lee to include the teachings of Hyundai to further reduce the impacts of motion sickness on the passenger.
Regarding claim 2, Lee discloses in P 11 and 32 periodically collecting and learning the biometric information of the passenger and input information such as presence or absence of motion sickness.
Regarding claim 3, See rejection to claim 2.
Regarding claim 4, Bar discloses in P5, 16, and Figs. 1-4 a future course of the lateral acceleration based on a vehicle model is determined for the predictive period of time, and when it comes to a road inclination and its future course is determined, the semi-automatic or fully automatic vehicle systems are traffic jam assistants).
Regarding claim 5, as in claim 1, both Lee and Bar disclose a prediction algorithm for predicting a state of the passenger.
Regarding claim 6, as in claim 1, both Lee and Bar discloses that the state is comfort and motion sickness.
Regarding claim 7, as in claim 1, Bar discloses tilting the seat.
Regarding claims 32, Lee, Bar, and Hyundai disclose the passenger state modulation system of claim 1, wherein the prediction algorithm is operable to predict a state of the active seat, the prediction algorithm uses the preemptive command in the prediction of the state of the active seat, the prediction uses the predicted state of the active seat in the prediction of the state of the specific passenger, and the command generation algorithm is configured to receive the predicted state of the active seat (Hyunday P31-32 and Figs. 1-2 disclose a nuisance preventing unit which is a seatbelt formed on a seat of a passenger and a control unit that generates a tension on the seatbelt work by the passenger, wherein when a predicted running operation occurrence time arrives, the control unit generates a control signal for controlling the nuisance preventing unit so as to correspond to an estimated degree of a driving operation. The bolded part shows that the preemptive command is received by a prediction algorithm that then executes the command based on the predicted running operation occurrence time).
Regarding claim 33, Lee teaches the command generation algorithm determines optimized preemptive commands to tilt the active seat using historically aggregated information of the active seat and historically aggregated passenger information (P36, 56, Figs. 1-4 disclose a sensing control unit using a biometric information of the passenger collected by a biometric information collecting unit with a vibration information of the vehicle collected by a vibration information collecting unit, detects whether the occupant gets motion-sick based on a learning data stored in a learning data storing unit, wherein the vibration information collecting unit includes an IMU, a G-sensor for sensing an acceleration and a direction, a three-axis acceleration sensor for detecting acceleration and vibration, and a relative position detecting unit. Learning date is historically aggregated data and this is how any machine learning algorithm works).
Regarding claim 38, please see claim 1 and how this feature is addressed by Hyundai.
Regarding claim 39, please see claim 32.
Claim(s) 8-16, 34-35 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee KR20170064909 in view of Hyundai KR20150045164.
Regarding claim 8, the only differences between claim 1 and claim 8 is that claim 8 recites:
an active restraint residing in the passenger vehicle and configured to restrain a given passenger in the passenger vehicle; and
that the command generation algorithm determines a preemptive command for the active restraint and issues the preemptive command to the active restraint.
All the other limitations were discussed in claim 1 and disclosed by Lee.
Regarding the two differing limitations, these are taught by Hyundai. Specifically, P31-32 and Figs. 1-2 disclose a nuisance preventing unit which is a seatbelt formed on a seat of a passenger and a control unit that generates a tension on the seatbelt work by the passenger, wherein when a predicted running operation occurrence time arrives, the control unit generates a control signal for controlling the nuisance preventing unit so as to correspond to an estimated degree of a driving operation.
Regarding the new limitation of wherein the command generation algorithm determines optimized preemptive commands for the active restraint using historically aggregated information of the active restraint and historically aggregated passenger information (Lee teaches P36, 56, Figs. 1-4 disclose a sensing control unit using a biometric information of the passenger collected by a biometric information collecting unit with a vibration information of the vehicle collected by a vibration information collecting unit, detects whether the occupant gets motion-sick based on a learning data stored in a learning data storing unit, wherein the vibration information collecting unit includes an IMU, a G-sensor for sensing an acceleration and a direction, a three-axis acceleration sensor for detecting acceleration and vibration, and a relative position detecting unit. Learning date is historically aggregated data and this is how any machine learning algorithm works).
Claims 9, 10, 12, and 13 are addressed with claims 2, 3, 5, and 6.
Regarding claim 11, Hyundai discloses in claim 7 and Figs. 1-4b, the driving information includes a terrain information according to a position of the vehicle, an accelerator pedal operation rate, a brake operation state, a steering wheel operation, and a relative distance with respect to the other vehicle in front of the vehicle and the relative speed.
Claims 14-16 are addressed in the citations of Hyundai in claim 8.
Claim 34 is addressed in claim 1.
Claim 35 is addressed in claim 32.
Claim(s) 25-31 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee KR20170064909 in view of Bar EP2431218.
Claim 25 is addressed by claim 1 as in the non-final office action. Regarding the new limitation, please see how it is addressed in claim 8.
Claim 26 is addressed in claim 2.
Claim 27 is addressed in claim 3.
Claim 28 is a mirror of claim 5.
Regarding claim 29, as disclosed in the rejection to claim 25, Lee discloses an audio, visual, navigation system that senses the motion of the occupant. This is the same as an image device.
Regarding claim 30, this claim merely amounts to nonfunctional descriptive information because the only difference between claim 29 and claim 25 is the input data. The prediction algorithm functions in the same way regardless of what the input is, and the algorithm itself does not change the structure of the system.
Regarding claim 31, as disclosed above in Lee, the interface is a display.
Response to Arguments
Applicant’s arguments are fully considered but are deemed moot. The 35 U.S.C. 112 rejections are withdrawn due to claim amendments. Regarding the 35 U.S.C. 103 arguments, on Page 10 Applicant argues that the claims recite wherein the preemptive command is received and used in the prediction of the state of the specific passenger, but Hyundai describes a system that prevents motion sickness by predicting the driver’s operations and provides this to the passenger. The Examiner does not understand the distinction. The preemptive command is the control unit generating a control signal to mitigate motion sickness for the passenger. Moreover, Lee was used to teach preemptive commands that tilt the seat where P1, 13, 17, and Figs. 1, 4 teach a method for controlling the actuator comprises the step of tilting the seat of the motor vehicle by a roll angle and/or a pitch angle and an associated motor vehicle, wherein the tilting by the pitch angle to compensate for a longitudinal acceleration curve can be particularly useful on the passenger seat where the roll axis and the pitch axis of the actuator is changed and it influences a comfort or a motion sickness risk.
Next, for claim 8, Applicant argues that Lee does not teach using historically aggregated information. The Examiner disagrees - P36, 56, Figs. 1-4 disclose a sensing control unit using a biometric information of the passenger collected by a biometric information collecting unit with a vibration information of the vehicle collected by a vibration information collecting unit, detects whether the occupant gets motion-sick based on a learning data stored in a learning data storing unit, wherein the vibration information collecting unit includes an IMU, a G-sensor for sensing an acceleration and a direction, a three-axis acceleration sensor for detecting acceleration and vibration, and a relative position detecting unit. Learning date is historically aggregated data and this is how any machine learning algorithm works.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ARYAN E WEISENFELD whose telephone number is (571)272-6602. The examiner can normally be reached M-F 9-5.
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ARYAN E. WEISENFELD
Primary Examiner
Art Unit 3689
/ARYAN E WEISENFELD/Primary Examiner, Art Unit 3667