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 .
Status of Claims
This Office Action is in response to the Applicants’ filing on 01/06/2026. Claims 1-20 were previously pending, of which claims 11-20 have been withdrawn in response to the restriction requirement. Accordingly, claims 1-20 are currently pending and claims 1-10 are being examined below.
Election/Restrictions of Species
This Office Action is in response to Applicant’s election without traverse of Species I in the reply filed on 01/06/2026 is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 06/27/2024 and 09/04/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Beaurepaire et al. (US 2020/0079396 A1), hereinafter Beau, in view of Bae et al. (US 2018/0194365 A1), hereinafter Bae.
With respect to claim 1, Beau discloses a method for controlling a vehicle, comprising: generating a driver profile of a driver of the vehicle, the driver profile including driving style data of the driver; (see at least [0044] “creating the passenger profile based on the collected driving behavior and user reaction data”)
estimating a cognitive state of the driver of the vehicle; (see at least [0043] “the system 100 detects that the user has a negative reaction or discomfort level with a driving behavior”)
Beau discloses the generation of a driver profile to determine appropriate control of a vehicle based on the state of the driver, but does not explicitly disclose the actual adjustment of the vehicle.
However, Bae teaches adjusting one or more actuator controls of an advanced driver-assistance system (ADAS) based on the estimated cognitive state of the driver, the driver profile of the driver, and route/traffic info of the vehicle. (see at least [0153] “The controller 170 may control at least one function of the ADAS 200 in stages according to at least one of the selected driver type, the sensed driver's condition, the conditions around the vehicle”)
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the profile generation of Beau to include the implemented ADAS control disclosed in Bae, with reasonable expectation of success. The motivation for doing so would have been to integrate the driver profile to control an ADAS to increase convenience and safety, see Bae [0105].
With respect to claim 2, Beau discloses the driving style data includes at least: a braking style; an acceleration style; a steering style; and one or more preferred cruising speeds of the driver. (see at least [0043] “collected information to specify the driving behavior or elements of the driving behavior (e.g., acceleration patterns, braking patterns, distance to neighboring vehicles, speed in curves, etc.)”)
With respect to claim 3, Beau discloses estimating the cognitive state of the driver includes estimating one or more of the cognitive state of the driver and a physiological state of the driver, based on at least one of: an output of one or more in-cabin sensors; (see at least [0051-0053] “the passenger reaction module 303 can use any combination of one or more of the sensors to determine a user reaction to a corresponding driving behavior.”)
an output of a driver monitoring system (DMS) of the vehicle, the output indicating at least one of: a cognitive load of the driver; and an estimated level of stress of the driver. (see at least [0043] “If the system 100 detects that the user has a negative reaction or discomfort level with a driving behavior” Note: It is unclear how one detects a cognitive load of the driver, it will be assumed that a negative reaction to the driving denotes a high cognitive load and stress.)
Beau discloses the generation of a driver profile to determine appropriate control of a vehicle based on the state of the driver, but does not explicitly disclose the state being drowsiness or distraction.
However, Bae teaches a level of drowsiness of the driver; a level of distraction of the driver; (see at least [0231] “the controller 170 may determine that the driver's condition is drowsiness or inattentiveness.”)
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the driver reaction of Beau to include the drowsiness and inattentiveness disclosed in Bae, with reasonable expectation of success. The motivation for doing so would have been to provide control in alignment with the driver’s condition over the profile preferences for safety, see Bae [0011, 0163-0164].
With respect to claim 4, Beau discloses the one or more in-cabin sensors includes at least one of: an in-cabin camera of the vehicle; and a passenger seat sensor of the vehicle. (see at least [0050] “the one or more sensors… a camera sensor 601 configured to detect a facial movement, an eye movement” [0052] “Touch sensors 605 located on various buttons, controls, seats”)
With respect to claim 5, Beau discloses the driver profile is retrieved from a cloud-based server based on a driver ID. (see at least [0097] “the passenger profile platform 111 may be a platform with multiple interconnected components, and may include multiple servers” [0075] “The passenger profile platform 111 can us any means to detect the identity of user… entering user credentials”)
With respect to claim 6, Beau discloses the route/traffic info is retrieved from at least one of: a navigational system of the vehicle; and external sensors of the vehicle. (see at least [0048] “These sensors can include but are not limited to location sensors 501 (e.g., GPS or other satellite-based receivers), LiDAR sensors 503, radar sensors 505”)
With respect to claim 7, Beau discloses based on: estimated cognitive states of one or more passengers of the vehicle; and driver profiles of the one or more passengers of the vehicle. (see at least [0077] “If the vehicle configuration module 307 detects that there are multiple passengers in the vehicle in step 1203, the vehicle configuration module 307 proceeds to step 1207 to initiate multiple passenger processing by retrieving passenger profiles… a common denominator driving behavior is a driving behavior that is comfortable to the multiple passengers based on their respective passenger profiles.”)
Beau discloses the generation of a driver profile to determine appropriate control of a vehicle based on the state of the driver, but does not explicitly disclose the actual adjustment of the vehicle.
However, Bae teaches adjusting the one or more actuator controls of the ADAS further includes adjusting the one or more actuator controls of the ADAS (see at least [0157] “if a fellow passenger is sensed, the controller 170 may control the function of the ADAS 200… may selectively control the function of the ADAS 200 to give a comfortable ride.”)
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the profile generation of Beau to include the implemented ADAS control disclosed in Bae, with reasonable expectation of success. The motivation for doing so would have been to integrate the driver profile to control an ADAS to increase convenience and safety, see Bae [0105].
With respect to claim 8, Beau discloses the vehicle is an autonomous vehicle and the driver is an operator of the autonomous vehicle. (see at least [0001] “a humanized driving experience refers to configuring autonomous or HAD vehicles to operate… in the driving style or behavior preferred by a user when the user is driving”)
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Beau in view of Bae as applied to claim 1 above, and further in view of Telpaz et al. (US 2020/0225676 A1), hereinafter Telpaz.
With respect to claim 9, Beau discloses adjusting one or more actuator controls of the ADAS based on the estimated cognitive state of the driver, the driver profile of the driver, and route/traffic info of the vehicle further includes inputting at least the estimated cognitive state, the driver profile, and the route/traffic info into an ADAS intervention model, (see at least [0063] “the passenger profile platform 111 can use machine models to train a predictive model based on the detected driving behaviors scenarios, user reactions, and/or other contextual data to determine which driving behaviors/scenarios are preferred by a user when riding as a passenger.”)
Beau discloses using a machine learning model to determine appropriate driving for the given user, but does not explicitly disclose the model output working within limits of ADAS control.
However, Telpaz teaches adjusting the one or more actuator controls based on an output of the ADAS intervention model, the ADAS intervention model including flexible logic configured within a pre-defined range of possible actuator control customizations. (see at least [0047] “a Random Forest modeling technique method 500 for selecting the optimal driving profile… relating to passenger discomfort based on passenger feedback about the pleasantness/comfort of human driving styles… compares, for example, the forward acceleration and the longitudinal jerk of the vehicle 10 with respective threshold… the controller 34 emulates the performances of the classifier (Random Forest output).”)
As both are in the same field of endeavor, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the machine learning model of Beau to include the control limits disclosed in Telpaz, with reasonable expectation of success. The motivation for doing so would have been to ensure the control parameters remain within a threshold to keep the passenger comfortable, see Telpaz [0005, 0047].
With respect to claim 10, Beau discloses the ADAS intervention model includes at least one of: a rules-based model; a statistical model; and a machine learning model. (see at least [0072] “The user sensor data can be analyzed (e.g., using a trained machine learning model) to predict… that the user is comfortable with the detected driving behavior (e.g., the detected acceleration rate) with a 0.8 confidence (e.g., confidence output by the machine learning model).”)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chan et al. (US 2021/0133808 A1) discloses received telematics data analyzed to identify one or more driving behaviors of the driver during the time period, a driver profile is generated or modified, and a suggested vehicle type is identified.
Sicconi et al. (US 2019/0213429 A1) discloses a driver's attention and emotional state is determined to evaluate risks associated to moving vehicles and the driver's ability to deal with any projected risks.
Lei et al. (US 2022/0176986 A1) discloses determines at least one driving style, reflecting driving behavior for a driver of a vehicle and provides guidance as to how to maneuver the vehicle to reach the vehicles driven by the one or more drivers exhibiting comparable behavior.
Kapuria et al. (US 2018/0257561 A1) discloses providing feedback for an upcoming critical situation, the ADAS fetches a historical combination situation similar to the current situation combination. The intensity of the feedback is varied as per the driver reaction to the feedback provided to the driver at such historical combination situation.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHELLEY MARIE OSTERHOUT whose telephone number is (703)756-1595. The examiner can normally be reached Mon to Fri 8:30 AM - 5:30 PM.
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/S.M.O./Examiner, Art Unit 3669
/NAVID Z. MEHDIZADEH/ Supervisory Patent Examiner, Art Unit 3669