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
Claims 1-11 are presented for examination.
Claims 1-11 are rejected.
Response to Arguments
Applicant’s arguments, see Pages 1-5, filed 12/04/2025, with respect to claims 1-11 have been fully considered and are persuasive. The Final rejection of 09/04/2025 has been withdrawn.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claim(s) 1-10 is/are rejected under 35 U.S.C. 103 as -being unpatentable over LEE et al. (US PUB. No.: 2021/0171030 A1: hereinafter “LEE”) in view of Craig et al. (US PUB. No.: 2024/0190457 A1: hereinafter “Craig”).
Consider claims 1, 5:
LEE teaches a vehicle automatic control system (Figs. 2-3, 8-9 elements 1-130), a vehicle automatic control method for a driver's driving tendency (See LEE, e.g., “…A vehicle travel control system for a vehicle may include: a launch profile generator to generate a target torque profile or a target speed profile based on monitored driving information of a host vehicle…control a speed or an acceleration of the host vehicle based on the generated profile…analyzes an intention of a driver of the host vehicle when the driver intervenes at least one of the speed, acceleration or deceleration of the host vehicle being controlled…revises the target torque profile or the target speed profile when the analyzed intention represents preferences of the driver such that the controller controls the host vehicle based on the revised profile…” of Abstract, ¶ [0007]-¶ [0017], Figs. 2-3, 8-9 elements 1-130, Figs. 4-7, Figs, 14-15 steps S100-S228), comprising: analyzing driver's braking and driving acceleration tendencies of a vehicle (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…control the speed and acceleration of the host vehicle during “the launch phase” to comply with the driver's driving pattern…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0056]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228); learning the analyzed driver's braking and driving acceleration tendencies (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
LEE further teaches and controlling braking and driving accelerations of the vehicle by applying a predetermined mode-specific map (e.g., “…When the driver input analyzer 130 determines that the action of intervention by the driver repeats under a certain situation and thus is due to the driver's preference, the driver input analyzer 130 sends the pedal inputs received from the acceleration pedal sensor 50 (or the brake pedal position sensor 48) to the profile select/calculator 110. And then, the profile select/calculator 110 may revise at least one of the generated target torque profile, target speed profile, or braking pressure profile based on the received pedal inputs so as to apply it to the next event of the similar driving condition such that the controlled driving characteristics are customized according to the deriver's driving style…”, of Figs, 14-15 steps S100-S228) based on the learned driver's braking driving and acceleration tendencies (See LEE, e.g., “…steps S110, the controller checks if a brake pedal action signal is received through the launch profile generator 10…This monitoring process of the driver's operation (e.g., braking or accelerating operation) may continue regardless of operation of the ACC function to be used for the driver's preference…the controller 2 has the launch profile generator 10 to compare the current speed…with the previously defined speed profile or torque profile at S123 to determine whether the speed deviation between the current speed profile and the predefined speed profile…is within a predetermined allowance range or not (S125). If the speed deviation…is greater than the predetermined allowance range…revises the previously defined speed profile…at S127 so that the revised speed or torque profile is stored and used for next launch driving of the host vehicle…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228); wherein the predetermined mode-specific map includes a brake map set to a deceleration level corresponding to a brake pedal travel (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…a memory 16 may also store an algorithm for calculating the target torque profile, braking pressure profile, and/or target speed profile, and a profile calculator 110 executes the algorithm…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228), and an accelerator map set to an acceleration level corresponding to acceleration pedal (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…the launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
LEE teaches “…detect steering angles of a steering wheel of the host vehicle 3; a brake pedal position sensor 48 to detect a degree of a brake pedal operation; and an acceleration pedal sensor 50 to detect a degree of an acceleration pedal operation….’, as disclosed in ¶ [0056]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], and exhibited in Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228. However, LEE does not explicitly teach and steering angle change rate.
In an analogous field of endeavor, Craig teaches and steering angle change rate (See Craig, e.g., “…the resulting longitudinal and lateral travel plans that are ultimately output by the motion planning module comply with as many of the user's ride preferences as possible while optimizing the cost variable and avoiding collisions by varying one or more of the vehicle's velocity, acceleration/deceleration…steering angle, and steering angle rate of change...” of ¶ [0041], and Fig. 1 elements 10-100).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine “…A vehicle travel control system for a vehicle may include: a launch profile generator to generate a target torque profile or a target speed profile based on monitored driving information of a host vehicle…control a speed or an acceleration of the host vehicle based on the generated profile…analyzes an intention of a driver of the host vehicle when the driver intervenes at least one of the speed, acceleration or deceleration of the host vehicle being controlled…revises the target torque profile or the target speed profile when the analyzed intention represents preferences of the driver such that the controller controls the host vehicle based on the revised profile…” disclosed in LEE with “and steering angle change rate”, as taught in Craig with a reasonable expectation of success to yield “desirable to provide automated driving systems adapted for autonomous driving using turn-by-turn navigation or other route-based navigation provided by a navigation system or service independent of the supplier or format of the navigation information”, as disclosed, ¶ [0005].
Consider claims 2, 6:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claims 1, 5. In addition, LEE teaches further comprising: a storage module storing (“…a memory 16 may also store an algorithm for calculating the target torque profile, braking pressure profile, and/or target speed profile, and a profile calculator 110 executes the algorithm…”, of Figs. 2-3, 8-9 elements 1-130) driver's braking and driving acceleration tendency information analyzed by the driving analyzer and the driver's braking and driving acceleration tendency information learned by the driver tendency learning module (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…control the speed and acceleration of the host vehicle during “the launch phase” to comply with the driver's driving pattern…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0056]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
Consider claims 3, 7:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claims 1, 5. In addition, LEE teaches wherein the driving analyzer analyzes braking, an acceleration pedal of the vehicle (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…control the speed and acceleration of the host vehicle during “the launch phase” to comply with the driver's driving pattern…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228). Craig teaches and a steering angle change rate of the vehicle (See Craig, e.g., “…the resulting longitudinal and lateral travel plans that are ultimately output by the motion planning module comply with as many of the user's ride preferences as possible while optimizing the cost variable and avoiding collisions by varying one or more of the vehicle's velocity, acceleration/deceleration…steering angle, and steering angle rate of change...” of ¶ [0041], and Fig. 1 elements 10-100). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify LEE with the teachings of Craig so as to, with a reasonable expectation of success, yield “desirable to provide automated driving systems adapted for autonomous driving using turn-by-turn navigation or other route-based navigation provided by a navigation system or service independent of the supplier or format of the navigation information”, as disclosed, ¶ [0005].
Consider claims 4, 8:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claims 1, 5. In addition, LEE teaches wherein the brake map includes target pressure values for each vehicle speed range (low, medium, and high) based on the depth of brake pedal depression (pedal stroke amount) (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…a memory 16 may also store an algorithm for calculating the target torque profile, braking pressure profile, and/or target speed profile, and a profile calculator 110 executes the algorithm…receives a brake pressure profile or a target speed profile from the braking profile generator…depending on the brake control system configuration…calculate either one of the following commands for decelerating and stopping the vehicle 3: i) brake pedal position commands for the brake pedal commander 22 that converts the brake pedal position commands into desired brake pressures, or ii) desired pressure values for the brake controller 24…” of ¶ [0055]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
Consider claim 9:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claim 8. In addition, LEE teaches wherein in the controlling (e.g., “…When the driver input analyzer 130 determines that the action of intervention by the driver repeats under a certain situation and thus is due to the driver's preference, the driver input analyzer 130 sends the pedal inputs received from the acceleration pedal sensor 50 (or the brake pedal position sensor 48) to the profile select/calculator 110. And then, the profile select/calculator 110 may revise at least one of the generated target torque profile, target speed profile, or braking pressure profile based on the received pedal inputs so as to apply it to the next event of the similar driving condition such that the controlled driving characteristics are customized according to the deriver's driving style…”, of Figs, 14-15 steps S100-S228), the braking and driving accelerations of the vehicle are controlled in response to the driver's braking tendency at a deceleration level of a brake map in a predetermined braking mode based on a driver's vehicle braking tendency (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…control the speed and acceleration of the host vehicle during “the launch phase” to comply with the driver's driving pattern…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0056]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
Consider claim 10:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claim 8. In addition, LEE teaches wherein in the controlling (e.g., “…When the driver input analyzer 130 determines that the action of intervention by the driver repeats under a certain situation and thus is due to the driver's preference, the driver input analyzer 130 sends the pedal inputs received from the acceleration pedal sensor 50 (or the brake pedal position sensor 48) to the profile select/calculator 110. And then, the profile select/calculator 110 may revise at least one of the generated target torque profile, target speed profile, or braking pressure profile based on the received pedal inputs so as to apply it to the next event of the similar driving condition such that the controlled driving characteristics are customized according to the deriver's driving style…”, of Figs, 14-15 steps S100-S228), the braking and driving accelerations of the vehicle are controlled in response to the driver's driving acceleration tendency at the acceleration level of the accelerator map in a predetermined acceleration mode based on a driver's vehicle driving acceleration tendency (See LEE, e.g., “…learning the records of acceleration and braking of the vehicle such that the vehicle provides customized profiles according to the driver's preferences in driving the vehicle…control the speed and acceleration of the host vehicle during “the launch phase” to comply with the driver's driving pattern…launch profile generator includes a driver input analyzer configured to receive a pedal input corresponding to a degree of an acceleration operated by the driver and configured to analyze the intention of the driver based on the received pedal input…” of ¶ [0056]-¶ [0059], ¶ [0062], ¶ [0066]-¶ [0069], ¶ [0071]-¶ [0079], ¶ [0082]-¶ [0083], Figs. 2-3, 8-9 elements 1-130, Figs, 14-15 steps S100-S228).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as -being unpatentable over LEE in view of Craig, and in further view of Oh et al. (US PUB. No.: 2021/0039661 A1: hereinafter “Oh”).
Consider claim 11:
The combination of LEE, Craig teaches everything claimed as implemented above in the rejection of claim 10. However, the combination of LEE, Craig does not explicitly teach wherein the acceleration mode includes the normal mode and the sport mode, and controls the braking and driving accelerations by applying the target spin/yaw rate of the accelerator map according to the driver's set mode.
In an analogous field of endeavor, Oh teaches wherein the acceleration mode includes the normal mode and the sport mode (See Oh, e.g., “…when a corresponding vehicle is a vehicle having various driving modes such as eco-friendly, normal, and sports modes, it is preferable that the driving characteristics or the like may be displayed based on the mode that is the most fundamental and has driving characteristics at an intermediate level, for example, the normal mode (that is, the normal mode is the default mode)...” of ¶ [0042]-¶ [0049], and Figs. 5-8 elements U-Ia), and controls the braking and driving accelerations by applying the target spin/yaw rate of the accelerator map according to the driver's set mode (See Oh, e.g., “…a parameter value may be a value related to driving characteristics (a sense of driving) and traveling characteristics when a vehicle is controlled, and may refer to a setup value for each item that affects or changes the traveling characteristics, a sense of driving, and a sense of traveling of the vehicle using control logic when the driver sets or changes a parameter value for each predetermined parameter item…select and use a setup mode for allowing the driver to set and change a parameter value related to the driving characteristics and traveling characteristics of the vehicle...” of ¶ [0042]-¶ [0049], and Figs. 5-8 elements U-Ia).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of LEE, Craig with “wherein the acceleration mode includes the normal mode and the sport mode, and controls the braking and driving accelerations by applying the target spin/yaw rate of the accelerator map according to the driver's set mode.”, as taught in Oh with a reasonable expectation of success to yield a system, and a method to provide drivers with enhanced, seamless, and robust control of autonomous vehicles.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
OKAMOTO et al. (US Pub. No.: 2018/0148066 A1) teaches “When the number of pieces of traveling data belonging to the same category as a category of the current traveling data is equal to or greater than a first number being predetermined, a driving characteristic measurement apparatus selects the traveling data, and, when the number of pieces of the traveling data is less than the first number, selects traveling data whose number of pieces is not less than the first number, from among traveling data belonging to a category similar to the category of the current traveling data. The driving characteristic measurement apparatus measures driving characteristics on a currently traveling road using the selected traveling data.”
LI (US Pub. No.: 2017/0297586 A1) teaches “The driver preferences system can determine driver habits and preferences based on output from a plurality of sensors. Utilizing the output from the plurality of sensors, an autonomous vehicle can operate according to the learning habits and preferences of the driver. The operator of the driver preferences system can finely adjust any habits or preferences via a driver preferences interface, as well as select preset modes including an aggressive driving mode or a cautious driving mode. Additionally, one or more driver profiles can be stored and selected via the driver preferences interface so that more than one driver can have an autonomous vehicle operator according to their personal driving habits and/or preferences.”
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/BABAR SARWAR/Primary Examiner, Art Unit 3667