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 the Claims
This Office Action is in response to the Applicants’ filing on 04/27/2026. Claims 1-20 were previously pending with claims 11-20 withdrawn due a restriction requirement, of which claims 1-4 and 9-10 have been amended, and no claims have been cancelled or newly added. Accordingly, claims 1-20 are currently pending and claims 1-10 are being examined below.
Response to Arguments
With respect to Applicant's remarks, see pages 9-15, filed 04/27/2026; Applicant’s “Amendment and Remarks” have been fully considered. With respect to the claim rejections under 35 U.S.C. § 103 are persuasive. The prior art of record does not appear to fully disclose all of the limitations, as amended in claim 1. However, due to the nature of the applicant’s amendments, the scope of the applicant’s invention has changed and thus requires new analysis and new application of prior art. Further search found that Wilson in view of Chan did disclose this limitation as mapped in the final office action below.
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 1-10 are rejected under 35 U.S.C. 103 as being unpatentable over Wilson (US 2015/0266455 A1), hereinafter Wilson, in view of Chan et al. (US 2021/0133808 A1), hereinafter Chan.
With respect to claim 1, Wilson discloses a method for controlling a vehicle, comprising:
retrieving a driver style model of a driver of the vehicle from a driver profile of the driver, the driver style model based on a braking style of the driver, an acceleration style of the driver, a steering style of the driver, and one or more preferred cruising speeds of the driver; (see at least [0089] “The driver model may be accessed by, for example, model generation device 120 by querying road/driver/vehicle model storage device 130” [0092] “The accessed driver model may be associated with, for example, a particular individual” [0074-0075] “A driver model may describe an individual vehicle's deviation from the path of the road model in any of multiple dimensions when driven by a particular driver.” [0081] “other parameters that may be included within W include the point at which a driver begins to decelerate… accelerates… begins to turn… how fast the driver typically accelerates or decelerates… rates of lateral acceleration, and maximum tilt angles.”)
inputting (see at least [0089] “The driver model may be accessed by, for example, model generation device 120 by querying road/driver/vehicle model storage device 130… based on, for example, a characteristic of the driver, a driver preference, an environmental condition, a characteristic of the path, and/or some combination thereof.”)
the ADAS intervention model including flexible logic configured within a pre-defined range of possible actuator control customizations; (see at least [0074] “A driver model may describe an individual vehicle's deviation from the path of the road model … represented as a set of parameters collectively identified as W.” [0077] “W may also be used by the vehicle industry to establish certain driving parameters appropriate for a given driver and may perform an action when an inappropriate parameter presents itself (see description of step 430 and 445, provided below).” [0033] “The road, driver, and/or vehicle models are built using vehicle and driver behavior data directly thus; these models do not need to be inferred from geometry or other factors present in a traditional map. Instead, the road, driver, and/or vehicle models contain ranges of normal driving behaviors in any given situation.” [0062-0063])
receiving an ADAS strategy for intervening into driver control of the vehicle as an output of the ADAS intervention model; (see at least [0078] “W may further be used to guide the operation of autonomous, semi-autonomous and driver assistance systems (in conjunction with, for example, real-time sensor data) to determine when to intervene or the target behaviors the vehicle seeks to adjust and/or emulate.”)
adjusting one or more actuator controls of the ADAS based on the ADAS strategy; (see at least [0009] “adjusting the manner in which the vehicle is driven include… modifying an operation of actuator systems resident in the vehicle… Adjusting the manner in which the vehicle is driven may be responsive to, for example, the road model, the characteristics of the driver, driver behavior, and a driver preference.” [0078] “W may further be used to guide… driver assistance systems (in conjunction with, for example, real-time sensor data) to determine when to intervene or the target behaviors the vehicle seeks to adjust and/or emulate.”)
wherein adjusting the one or more actuator controls based on the ADAS strategy includes adjusting an intervention of the ADAS in a manner consistent with one or more of the braking style of the driver, the acceleration style of the driver, and the steering style of the driver, (see at least [0076] “W offers a concise description of driver style” [0078] “W may further be used to guide… driver assistance systems” [0081] “other parameters that may be included within W include the point at which a driver begins to decelerate… accelerates… begins to turn… how fast the driver typically accelerates or decelerates… rates of lateral acceleration, and maximum tilt angles.” [0009] “Adjusting the manner in which the vehicle is driven may be responsive to, for example, the road model, the characteristics of the driver, driver behavior, and a driver preference.”)
and further, adjusting an intervention of the ADAS in a manner intentionally inconsistent with one or more of the braking style of the driver, the acceleration style of the driver, and the steering style of the driver. (see at least [0076] “W offers a concise description of driver style” [0077] “W may also be used by the vehicle industry to establish certain driving parameters appropriate for a given driver and may perform an action when an inappropriate parameter presents itself (see description of step 430 and 445, provided below).” [0100] “When driver behavior is not consistent with the road model and/or driver model, a predetermined action may be executed (step 430).”)
Wilson discloses using a machine learning model to determine appropriate driving for the given user, but does not explicitly disclose the use of a driver profile containing an estimated driver state.
However, Chan teaches using a driver profile for estimating a driver status of the driver of the vehicle; (see at least [0064] “The profile information categories 150 may include driving behavior information 152… driver state information 158, and/or other information 160.” [0077] “Driver state information 158 may include driver attentiveness and/or driver emotional state… determine a driver's emotional state and/or level of attentiveness (e.g., calm, angry, distracted, etc.) by using image recognition”)
As both pertain to monitoring and assessing the driving of a driver of a vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the modeled assistance control disclosed in Wilson with the driver state of Chan, with reasonable expectation of success. The motivation for doing so would have been to monitor the driver to determine if they have an appropriate level of attention required to safely drive the path and adjust as needed, see Wilson [0120].
With respect to claim 2, Wilson discloses adjusting the one or more actuator controls based on the ADAS strategy further comprises adjusting the one or more actuator controls within a personalization envelope of the driver, the personalization envelope defining a range of possible customizations of actuator control patterns and parameters related to driving.(see at least [0041] “driving data… may be received by, for example, a model generation device such as model generation device 120… the received data may include information that may impact driving behaviors, such as… driver information… skill level… driving history” [0059-0060] “The path may be built as a multidimensional space consisting of potentially all of the kinematic variables… In addition to a path, a road model may include indications of a typical range of vehicle behaviors when driving the path… Deviations may also be represented by… for example, the 90th percentile spatial envelope.” [0127] “road models can provide targets for various automated processes. Exemplary targets include vehicle speed, lateral position within the lane… an individual preference, as may be the case when the vehicle behavior is matched to a driver's behavior (W).”)
With respect to claim 3, Wilson using a machine learning model to determine appropriate driving for the given user, but does not explicitly disclose using sensors to estimate the state of the driver.
However, Chan teaches estimating the cognitive state of the driver includes estimating one or more of the driver status 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 [0077] “determine a driver's emotional state and/or level of attentiveness (e.g., calm, angry, distracted, etc.) by using image recognition and/or other image processing techniques to process driver image data from sensor data 104.” [0120])
an output of a driver monitoring system (DMS) of the vehicle, the output indicating at least one of: a level of drowsiness of the driver; a level of distraction of the driver; a cognitive load of the driver; and an estimated level of stress of the driver. (see at least [0077] “driving behaviors identification unit 80 or another unit of data analysis unit 74 may determine a driver's emotional state and/or level of attentiveness (e.g., calm, angry, distracted, etc.)”)
As both pertain to monitoring and assessing the driving of a driver of a vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the modeled assistance control disclosed in Wilson with the driver state of Chan, with reasonable expectation of success. The motivation for doing so would have been to monitor the driver to determine if they have an appropriate level of attention required to safely drive the path and adjust as needed, see Wilson [0120].
With respect to claim 3, Wilson using a machine learning model to determine appropriate driving for the given user, but does not explicitly disclose using sensors to estimate the state of the driver.
However, Chan teaches 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 [0029] “internal sensor(s) 38 may include inward-facing digital cameras… and/or one or more seat sensors configured to detect the presence of the driver and/or passengers in the respective seat(s).”)
As both pertain to monitoring and assessing the driving of a driver of a vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the modeled assistance control disclosed in Wilson with the driver state of Chan, with reasonable expectation of success. The motivation for doing so would have been to monitor the driver to determine if they have an appropriate level of attention required to safely drive the path and adjust as needed, see Wilson [0120].
With respect to claim 4, Wilson discloses the range of possible customizations of the personalization envelope is defined based on one or more of a speed limit of a road the vehicle is travelling on, driving conditions of the vehicle, a minimum established following distance behind a lead vehicle based on a speed of the vehicle, a measured traction of the vehicle under current road conditions via an anti-blocking system, weather conditions, and lighting conditions. (see at least [0127] “When this invention is used in conjunction with automated and semi-automated driving systems, road models can provide targets for various automated processes. Exemplary targets include vehicle speed, lateral position within the lane, acceleration, and deceleration. These targets may be designed to be appropriate for the conditions (e.g., weather, light glare, vehicle weight, etc.), driving style of the driver… when the vehicle behavior is matched to a driver's behavior (W).” [0041] “the received data may include information that may impact driving behaviors, such as… weather information (rainy, icy, fog, temperature, fair, lighting and sun angle), roadway information (speed limit, road type, traffic congestion conditions, maintenance and incident information) and other information… such as traffic conditions or vehicle speed.”)
With respect to claim 5, Wilson the driver profile is retrieved from a cloud-based server based on a driver ID. (see at least [0038] “a vehicle driver and/or an administrator of system 100 may be used to access, for example, model generation device 72, Road/driver/vehicle model storage device 130, and/or driving data storage device 135 in order to, for example, access road, driver, and or vehicle models or provide information… driver identifying information” [0144] “The one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment”)
With respect to claim 6, Wilson 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 [0034] “Driving data may be collected by a variety of sensors… lidar equipment, radar equipment, and ultrasonic sensors.” [0058] “additional data (e.g., GPS coordinate data… may also be used to build or augment the road model.”)
With respect to claim 7, Wilson discloses adjusting the one or more actuator controls of the ADAS further includes adjusting the one or more actuator controls of the ADAS based on: driver profiles of the one or more passengers of the vehicle. (see at least [0127] “When this invention is used in conjunction with automated and semi-automated driving systems, road models can provide targets for various automated processes. Exemplary targets include vehicle speed, lateral position within the lane, acceleration, and deceleration… driving style of the driver… when the vehicle behavior is matched to a driver's behavior (W).” [0009] “adjusting the manner in which the vehicle is driven include… modifying an operation of actuator systems resident in the vehicle… Adjusting the manner in which the vehicle is driven may be responsive to, for example, the road model, the characteristics of the driver, driver behavior, and a driver preference.” [0120] “The standard behavioral description of a roadway or path, R, may be used to characterize roads or paths based on their ability to be driven or the amount of attention required by a driver to safely drive the path.”)
Wilson discloses using a machine learning model to determine appropriate driving for the given user based on a stored model and attention required, but does not explicitly disclose the cognitive state being estimated.
However, Chan teaches estimated cognitive states of one or more passengers of the vehicle;(see at least [0077] “determine a driver's emotional state and/or level of attentiveness (e.g., calm, angry, distracted, etc.) by using image recognition”)
As both pertain to monitoring and assessing the driving of a driver of a vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the modeled assistance control disclosed in Wilson with the driver state of Chan, with reasonable expectation of success. The motivation for doing so would have been to monitor the driver to determine if they have an appropriate level of attention required to safely drive the path and adjust as needed, see Wilson [0120].
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”)
With respect to claim 9, Wilson discloses transmitting the data to a cloud-based server storing the driver profile; (see at least [0037] “Driving data communicated by sensors 115 a-115 n and/or 116 may be received by a driving data storage device 135 and/or model generation device 72.”)
and generating the driving style model at the cloud-based server based on the sensor data (see at least [0037] “Model generation device 72 may use they received driving data to generate one or more road models, driver models, and/or vehicle models in accordance with the processes described below.”)
Wilson discloses using a machine learning model to determine appropriate driving for the given user based on a stored model and attention required, but does not explicitly disclose the sensor data including brake, accelerator, and steering monitoring.
However, Chan teaches collecting sensor data from a brake pedal position sensor, an accelerator pedal position sensor, and a steering wheel angle sensor of the vehicle; (see at least [0030] “On-board system 14 may also include hardware, firmware and/or software subsystems that monitor… how the brakes of vehicle 12 are applied… depression of a gas pedal… how the vehicle 12 is being steered” [0021] “vehicle telematics data” may include any suitable type or types of data provided by the vehicle (e.g., one or more sensors and/or subsystems of the vehicle)”)
As both pertain to monitoring and assessing the driving of a driver of a vehicle, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the driving data disclosed in Wilson with the telematics data of Chan, with reasonable expectation of success. The motivation for doing so would have been to monitor the driver’s behavior to determine driving patterns and compliance that may increase the likelihood of the driver causing damage to the vehicle, see Chan [0065] and Wilson [0076].
With respect to claim 10, Wilson 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 [0062] “execution of a machine learning process with the received driving data, categorized data, and identified/determined variables may be used to build the road model by way of an iterative process. In later iterations or stages of the road building process, a supervised learning algorithm” [0059] “the road model may be generated using various statistical analyses of the data”)
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 extension fee 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 date of this final action.
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
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