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 .
This Office Action is in response to Applicant Amendment and Arguments filed on 2/10/2026.
Claim(s) 1-4, 6-9, 12-20, 22-25, and 27-35 are pending for examination.
Claim(s) 10-11 and 26 were canceled.
This Action is made NON-FINAL.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/9/2026 has been entered.
Response to Arguments
With regards to claim(s) 1 -4, 6-9, 12-20, 22-25, and 27- 32 previously rejected under 35 U.S.C. 103, Applicant's arguments have been fully considered, but are deemed moot in view of new grounds of rejection necessitated by Applicant's amendment.
It should be noted that the previously cited Hatano in view of Wu taught the amended independent claims. However as the scope of the independent claims has changed, Ono et al. can now be considered a better fitting prior art and the 103 rejection of the independent claims is now made over Hatano in view of Ono.
Applicant also states The Office Action provides a Notice of References Cited. Under the policy of "compact" prosecution, Applicant submits that the Notice of References Cited serves as an acknowledgement that the Office conducted a full search and examination regarding the application and claims, and that no further search or examination of the claims is required in this application. See MPEP § 904.03 (instructing Examiners to search "all subject matter which the examiner reasonably anticipates might be incorporated into applicant's amendment") and MPEP § 2103 (instructing Examiners to review Applicant's "complete specification" for searching purposes).
Applicant’s interpretation of the MPEP that no further search and examination is required when the scope of the claimed amendment has changed is incorrect. It is not reasonable to search all potential amendments that could be made from the specification as examiner is not given unlimited time for search. Additionally “The examiner is not called upon to cite all references that may be available, but only the "best."” See MPEP § 904.03. The best reference may change based on the scope of the claimed invention. Applicant’s amendment has changed the scope of the claims. The change in scope of the amendment constituted a necessity for further search and new grounds of rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are a “means for obtaining data relating to driver interaction with a vehicle;” as recited in claim 30, a “means for estimating one or more driver interaction parameters based on the data relating to the driver interaction with the vehicle;” as recited in claim 30, “means for updating one or more tuning parameters associated with an automated driving or driver assistance feature based on the one or more driver interaction parameters, resulting in one or more updated tuning parameters;” as recited in claim 30, and a “means for causing the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters” as recited in claim 30.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Regarding the means for obtaining data…., means for estimating….., means for updating….., and means for causing …. the specification states in para [0048] “device 300 may include means for obtaining data relating to driver interaction with the vehicle; means for estimating one or more driver interaction parameters based on the data relating to the driver interaction with the vehicle; means for updating one or more tuning parameters associated with an automated driving or driver assistance feature based on the one or more driver interaction parameters, resulting in one or more updated tuning parameters; and/or means for causing the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters. In some aspects, the means for device 300 to perform processes and/or operations described herein may include one or more components of device 300 described in connection with Fig. 3, such as bus 305, processor 310, memory 315, input component 320, output component 325, communication component 330, and/or estimation component 335. Additionally, or alternatively, the means for device 300 to perform processes and/or operations described herein may include one or more components of the on-board system 200 described in connection with Fig. 2, such as the sensor subsystem 204, the control subsystem 206, and/or the on-board device 208, among other examples.”
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recites sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 15, 17, 29, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Hatano et al. (US 20170329330 A1, hereinafter known as Hatano) in view of Ono et al. (US 20160031321 A1, hereinafter known as Ono).
Hatano was cited in a previous office action.
Regarding claim 1, Hatano teaches A device associated with a vehicle, the device comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to cause the device to:
{Para [0073] “Explanation follows regarding the vehicle control system 100. The vehicle control system 100 is, for example, implemented by one or more processors, or by hardware having equivalent functionality such as circuitry. The vehicle control system 100 may be configured by a combination of a processor such as a CPU, a storage device, and an ECU (electronic control unit) in which a communication interface is connected by an internal bus, or a micro-processing unit (MPU) or the like.”
}
obtain data relating to driver interaction with the vehicle;
{Para [0125] “However, in cases in which the driving mode to be executed is the manual driving mode, the vehicle information collection section 105 collects information from the various sensors (step S102). Next, the vehicle information collection section 105 stores the collected sensor information and an image of the occupant captured by the in-cabin camera 92 in association with each other in the storage section 180 as the operation history information 182 (step S104).”
Para [0104] “The driving characteristics derivation section 155 derives the driving characteristics for each vehicle occupant based on the operation history information 182”
}
estimate one or more driver interaction parameters based on the data relating to the driver interaction with the vehicle;
{Para [0127] “However, in cases in which the collection count N of the sensor information has reached the specific count, the driving characteristics derivation section 155 derives the driving characteristics for each vehicle occupant based on the operation history information 182 (step S108). Next, the driving characteristics derivation section 155 stores the derived driving characteristics in the storage section 180 in association with each occupant and in association with the road of each driving segment as the occupant-specific driving characteristic information 186 (step S110).”
}
wherein the data relating to driver interaction with the vehicle includes data relating to driver interaction with a steering wheel of the vehicle, and
{Para [0079] “FIG. 5 is diagram of an example of driving operation history. In the figure, the driving operation history sensor information, namely, speed, forward acceleration and lateral acceleration, jerk, and steering angle, are associated which each collection count N at each specific period. The associated sensor information may be peak (maximum value) information, may be information such as a histogram associating detected values with frequency, or may be a result of statistical processing such as an average value, a median value, or a modal value. Forward acceleration is acceleration in the direction of progress of the vehicle M, and lateral acceleration is acceleration received in the vehicle width direction of the vehicle M with respect to the direction of progress of the vehicle M. Moreover, the jerk may be the amount of change with time in the forward acceleration, or may be the amount of change with time in lateral acceleration. The steering angle may be based on the operation angle of the steering wheel 78 detected by the steering angle sensor 79, or may be indirectly derived from information such as the forward acceleration and the lateral acceleration.”
}
wherein the one or more driver interaction parameters include: one or more driver steering wheel torque values associated with
one or more driver steering wheel torque values associated with
{Para [0125] “However, in cases in which the driving mode to be executed is the manual driving mode, the vehicle information collection section 105 collects information from the various sensors (step S102). Next, the vehicle information collection section 105 stores the collected sensor information and an image of the occupant captured by the in-cabin camera 92 in association with each other in the storage section 180 as the operation history information 182 (step S104).”
Para [0061] “The steering wheel 78 is an operation element for receiving turning instructions from the vehicle occupant. The steering angle sensor 79 detects the operation angle of the steering wheel 78 and outputs a steering angle signal indicating the detection result to the vehicle control system 100. The steering torque sensor 80 detects the torque placed on the steering wheel 78 and outputs a steering torque signal indicating the detection result to the vehicle control system 100.”
It is implied that the steering torque values are being collected across the entire range of steering by the driver, and thus values would be collected whether there is active or passive steering occurring.
}
update one or more tuning parameters associated with an automated driving or driver assistance feature based on the one or more driver interaction parameters, resulting in one or more updated tuning parameters;
and cause the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters.
{Para [0108-0109] “The driving characteristics derivation section 155 associates the driving characteristics modeled by a representative value or a function such as an approximation, with each occupant and with each road of the driving segments. The associated items of information are each stored in the storage section 180 as the occupant-specific driving characteristic information 186. FIG. 16 is a diagram illustrating an example of the occupant-specific driving characteristic information 186. In the figure, similarly to in the operation history information 182, the occupant-specific driving characteristic information 186 associates driving characteristics modeled for each actual type of road in the driving segment that was being traveled on with identification information (an occupant ID) for identifying the freely determined occupant using an image captured by the in-cabin camera 92 (person identification images). For example, in the case of a general road corresponding to the low speed region or the medium speed region, the forward acceleration is expressed and stored as a representative value out of α1 and α2 (one value), or as a function representing a straight line or curved line passing through freely selected points. Moreover, in the case of an expressway corresponding to the high speed region, the forward acceleration is expressed and stored as a representative value, this being α3, (one value) or as a function representing a straight line or curved line passing through freely selected points.
When the automated driving mode is executed by the automated driving controller 120, the traction controller 160 controls the traction drive force output device 200, the steering device 210, and the brake device 220 such that such that the vehicle M passes through the course generated by the course generation section 146 at the expected timings. In this event, the traction controller 160 references the occupant-specific driving characteristic information 186 and controls using the automated driving mode in which the driving characteristics of the manual driving mode are reflected. Explanation follows regarding the automated driving mode in which the driving characteristics of the manual driving mode are reflected, and this is specifically referred to as a “learned mode”.”
}
Hatano does not explicitly teach, wherein the one or more driver interaction parameters include: one or more driver steering wheel torque values associated with active steering by a driver of the vehicle, and
one or more driver steering wheel torque values associated with passive interaction with the steering wheel of the vehicle by the driver of the vehicle;
However, Ono teaches wherein the one or more driver interaction parameters include: one or more driver steering wheel torque values associated with active steering by a driver of the vehicle, and
one or more driver steering wheel torque values associated with passive interaction with the steering wheel of the vehicle by the driver of the vehicle;
{Para [0142] “The torque determination unit 333 determines whether or not the driver has performed active corrective steering, based on the torque detected by the torque sensor 318. The torque determination unit 333 can determine whether or not the driver performed active corrective steering from torque imparted to the steering wheel. Namely, a threshold value exists in the characteristics of steering wheel steering performed by human inclination, and the torque determination unit 333 can determine that active corrective steering has been performed when torque exceeding this threshold is imparted. Specifically, the torque determination unit 333 determines whether or not the driver performed active corrective steering based on whether or not torque detected by the torque sensor 318 has exceeded a predetermined threshold value. In the third exemplary embodiment, the threshold value is, for example, set to from 1 Nm to 1.5 Nm, and the torque determination unit 333 determines that corrective steering was performed when this threshold value is exceeded.”
Para [0144] “When the torque determination unit 333 has determined that the driver performed active corrective steering, the marker presentation position calculation unit 336 calculates the yaw angular velocity r of the vehicle. Moreover, the marker presentation position calculation unit 336 determines the position to present the marker on the windshield of the vehicle based on the calculated yaw angular velocity r. Based on Equation (33) to Equation (35) below, the marker presentation position calculation unit 336 predicts the position of the vehicle after the forward gaze time, using the current position of the vehicle as a reference. Moreover, based on the predicted position of the vehicle after the forward gaze time, the predetermined driver's viewpoint position, and the predetermined position coordinates of the windshield of the vehicle, the marker presentation position calculation unit 336 determines the marker presentation position as the intersection point between a line segment connecting the eye point that is the driver's viewpoint position and the predicted arrival point, and the plane of the windshield. Moreover, the marker presentation position calculation unit 336 outputs the determined marker presentation position to the output device 92.”
Para [0146] “Moreover, when the torque determination unit 333 has determined that the driver performed active corrective steering, the steering management unit 340 does not regulate the steering device 90.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano to incorporate the teachings of Ono to determine torque values and determine whether active of passive steering is being performed and then use that determination alter vehicle control because it “improves a sense of unity between the vehicle and driver” as discussed in para [0040] of Ono
Regarding claim 15, Hatano in view of Ono teaches The device of claim 1. Hatano further teaches wherein the one or more updated tuning parameters include one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits associated with detecting active or passive interaction with the vehicle by a driver of the vehicle.
{Para [0120] “for example, as the learned mode, when the occupant seated in the driver seat performs relatively sporty driving with a driving characteristic value greater than a reference value, the traction controller 160 controls the traveling drive force output device 200, the steering device 210, and the brake device 220 such that restrictions on the behavior of the vehicle M are relaxed compared to cases in which the driving characteristics of manual driving mode are not reflected. The reference value may, for example, be derived by averaging the driving characteristics of multiple occupants. For example, the traction controller 160 widens the inter-course point distances D.sub.K(i)−K(i+1) to be acquired and increases the cornering angles φi. A wide permissible range of behavior is accordingly set for the vehicle M, and sudden acceleration/deceleration and steering are permitted to some extent. As a result, the behavior of the vehicle M is more agile than in an automated driving mode that does not reflect the driving characteristics of the manual driving mode. In such cases, although the amount of consumption of energy, such as power from gasoline or a secondary battery, tends to increase, lane changes, overtaking, and the like are more easily executed and the destination may be arrived at more quickly.”
}
Regarding claim 17, it recites A method having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Regarding claim 29, it recites A non-transitory computer-readable medium having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Additionally Hatano teaches A non-transitory computer-readable medium storing a set of instructions
{Para [0076] “The storage section 180 stores information such as high precision map information 181, operation history information 182, target lane information 183, action plan information 184, mode-specific operation permission information 185, and occupant-specific driving characteristic information 186. The storage section 180 is implemented by read only memory (ROM) or random access memory (RAM), a hard disk drive (HDD), flash memory, or the like. The program executed by the processor may be pre-stored in the storage section 180, or may be downloaded from an external device via an onboard internet setup or the like. Moreover, the program may be installed in the storage section 180 by loading a portable storage medium storing the program into a drive device, not illustrated in the drawings. Moreover, the vehicle control system 100 may be configured distributed across plural computer devices.”
}
Regarding claim 30, it recites An apparatus having limitations similar to those of claim 1 and therefore is rejected on the same basis.
Additionally Hatano teaches An apparatus for wireless communication
{Para [0052] “FIG. 2 is a functional configuration diagram focusing on the vehicle control system 100 according to the first embodiment. Detection devices DD that include the finders 20, the radars 30, the camera 40, and the like; the navigation device 50; a communication device 55; vehicle sensors 60; a human machine interface (HMI) 70; the vehicle control system 100; a traction drive force output device 200; a steering device 210; and a brake device 220 are installed in the vehicle M. These devices and apparatuses are connected to one another by a multiplex communication line such as a controller area network (CAN) communication line, or by a wireless communication network, a serial communication line, or the like. Note that the vehicle control system within the scope of the claims does not indicate only the “vehicle control system 100” and may include configuration other than that of the vehicle control system 100 (such as the detection devices DD and a HMI 70).”
}
Claim(s) 2-7 and 18-23 are rejected under 35 U.S.C. 103 as being unpatentable over Hatano et al. (US 20170329330 A1, hereinafter known as Hatano) in view of Ono et al. (US 20160031321 A1, hereinafter known as Ono) and Schneider et al. (US 20240132115 A1, hereinafter known as Schneider).
Schneider was cited in a previous office action.
Regarding Claim 2, Hatano in view of Ono teaches The device of claim 1. Hatano further teaches wherein the one or more processors, to cause the device to obtain the data relating to the driver interaction with the vehicle, are configured to cause the device to: obtain the data relating to the driver interaction with the vehicle
{Para [0125] “However, in cases in which the driving mode to be executed is the manual driving mode, the vehicle information collection section 105 collects information from the various sensors (step S102). Next, the vehicle information collection section 105 stores the collected sensor information and an image of the occupant captured by the in-cabin camera 92 in association with each other in the storage section 180 as the operation history information 182 (step S104).”
Para [0104] “The driving characteristics derivation section 155 derives the driving characteristics for each vehicle occupant based on the operation history information 182”
}
Hatano in view of Ono does not teach, obtain the data relating to the driver interaction with the vehicle via online data collection while the vehicle is being driven.
However, Schneider teaches obtain the data relating to the driver interaction with the vehicle via online data collection while the vehicle is being driven.
{Para [0140] “For example, the following data are stored in the case of a conflict situation: active inputs of the driver to control the vehicle, such as steering torque, brake pressure by the brake pedal, actuation of the gas pedal, voice volume of the driver in the case of acoustic control, gesture behavior of the driver in the case of gesture control. In addition, the following passive driver inputs, i.e. a state of the driver, may be recorded and stored during a conflict situation: driver's body position or deviation of the body position from an average driver's driving position, driver's blinking behavior. The data may be stored in the data storage 4 of the vehicle or in an external data storage such as a cloud database.”
Para [0154] “For example, a steering torque, a brake pressure by the driver, an actuation of the gas pedal by the driver, a volume of the driver's voice, a gesture by the driver may be recorded and stored as active or passive inputs of the driver. As passive driver inputs, for example, a driver's posture, particularly relative to a predetermined sitting position, or a frequency of eye blinking (closing and opening of the eyelids) that indicates fatigue, may be recorded. The data regarding the state of the vehicle and/or regarding the state of the driver may be stored in the data memory of the vehicle and/or outside the vehicle, for example in a cloud.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Schneider to obtain the data relating to the driver interaction with the vehicle via online data collection while the vehicle is being driven because it improves access to data (data in the cloud can be accessed by most internet connected devices) meaning data can be processed remotely for increased efficiency.
Regarding claim 3, Hatano in view of Ono and Schneider teaches The device of claim 2,
Schneider further teaches wherein the one or more processors, to cause the device to obtain the data relating to the driver interaction with the vehicle via online data collection while the vehicle is being driven, are configured to cause the device to: obtain the data relating to the driver interaction with the vehicle via online data collection while the vehicle is being driven and the automated driving or driver assistance feature is not activated.
{Para [0118-0119] “FIG. 5 shows a schematic representation of a method for predicting a conflict. This method is executed by the prediction module 304. Upon the start at program point 700, the vehicle is in a manual mode, at which the vehicle is controlled by the driver by corresponding inputs in the longitudinal and/or lateral guidance, or in an at least partially automated mode, in which the longitudinal and/or lateral guidance of the vehicle is controlled by the control program 400.
At a subsequent program point 701, the position of the vehicle and/or the driving mode and/or the state of the driver and/or other environmental information is compared with conflict data in the database 302. For example, in step 701, a pattern of values (e.g., geolocation of the vehicle, current driver status, etc.) is compared with existing patterns in the pattern database 302 that represent a conflict situation. This pattern database may be evaluated in terms of the frequency of, e.g., control takeovers (based on previously recognized and recorded conflicts). For example: the current geolocation is compared with known geolocations associated with conflicts in the database. If the vehicle approaches the known geolocation of a conflict, further measures may be taken. If, at a subsequent program point 702, this check reveals that maintaining the manual driving mode involves a high probability of conflict with the driver's desire, the program branches to program point 703.”
Para [0140] “For example, the following data are stored in the case of a conflict situation: active inputs of the driver to control the vehicle, such as steering torque, brake pressure by the brake pedal, actuation of the gas pedal, voice volume of the driver in the case of acoustic control, gesture behavior of the driver in the case of gesture control. In addition, the following passive driver inputs, i.e. a state of the driver, may be recorded and stored during a conflict situation: driver's body position or deviation of the body position from an average driver's driving position, driver's blinking behavior. The data may be stored in the data storage 4 of the vehicle or in an external data storage such as a cloud database.”
}
Regarding claim 4, Hatano in view of Ono and Schneider teaches The device of claim 2,
Hanato further teaches wherein the data relating to the driver interaction with the vehicle includes at least one of: driver steering data, brake input data, accelerator input data, or driver condition monitoring data.
{Para [0057] “As configuration of the driving operation system, the HMI 70 includes, for example, an accelerator pedal 71, an accelerator opening sensor 72 and an accelerator pedal reaction force output device 73, a brake pedal 74 and a brake depression amount sensor (or a master pressure sensor or the like) 75, a shift lever 76 and a shift position sensor 77, a steering wheel 78, a steering angle sensor 79 and a steering torque sensor 80, and other driving operation devices 81.”
Para [0125] “However, in cases in which the driving mode to be executed is the manual driving mode, the vehicle information collection section 105 collects information from the various sensors (step S102). Next, the vehicle information collection section 105 stores the collected sensor information and an image of the occupant captured by the in-cabin camera 92 in association with each other in the storage section 180 as the operation history information 182 (step S104).”
}
Regarding claim 6, Hatano in view of Ono and Schneider teaches The device of claim 1,
Hanato further teaches wherein the one or more processors, are further configured to cause the device to: categorize the data relating to the driver interaction with the vehicle into a plurality of data sets associated with different respective driving scenarios, wherein the data relating to the driver interaction with the vehicle into the plurality of data sets based on at least one of: different road geometries, different speed ranges, different acceleration states of the vehicle, or different road or environment conditions.
{Fig. 19 and Para [0117] “FIG. 19 is a diagram illustrating contents of the learned mode according to the type of road. As illustrated, when the driving characteristics are “A” on a general road, the automated driving mode reflecting the driving characteristics A is executed as the learned mode on the general road. Moreover, when the driving characteristics are “B” on an expressway, an automated driving mode reflecting the driving characteristics B are executed as the learned mode on the expressway.”
}
Regarding claim 7, Hatano in view of Ono and Schneider teaches The device of claim 6,
Hanato further teaches wherein the one or more processors, to cause the device to estimate one or more driver interaction parameters based on the data relating to the driver interaction with the vehicle, are configured to cause the device to: estimate respective driver interaction parameters for one or more data sets of the plurality of data sets.
{Para [0127] “However, in cases in which the collection count N of the sensor information has reached the specific count, the driving characteristics derivation section 155 derives the driving characteristics for each vehicle occupant based on the operation history information 182 (step S108). Next, the driving characteristics derivation section 155 stores the derived driving characteristics in the storage section 180 in association with each occupant and in association with the road of each driving segment as the occupant-specific driving characteristic information 186 (step S110).”
}
Regarding claim 18, it recites A method having limitations similar to those of claim 2 and therefore is rejected on the same basis.
Regarding claim 19, it recites A method having limitations similar to those of claim 3 and therefore is rejected on the same basis.
Regarding claim 20, it recites A method having limitations similar to those of claim 4 and therefore is rejected on the same basis.
Regarding claim 21, it recites A method having limitations similar to those of claim 5 and therefore is rejected on the same basis.
Regarding claim 22, it recites A method having limitations similar to those of claim 6 and therefore is rejected on the same basis.
Regarding claim 23, it recites A method having limitations similar to those of claim 7 and therefore is rejected on the same basis.
Claim(s) 8 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Hatano et al. (US 20170329330 A1, hereinafter known as Hatano) in view of Ono et al. (US 20160031321 A1, hereinafter known as Ono) and Prucka et al. (US 12304485 B2, hereinafter known as Prucka).
Prucka was cited in a previous office action.
Regarding Claim 8, Hatano in view of Ono teaches The device of claim 1. Hatano further teaches wherein the one or more processors, to cause the device to obtain the data relating to the driver interaction with the vehicle, are configured to cause the device to: obtain the data relating to the driver interaction with the vehicle via a vehicle-driver interaction sequence
{Para [0125] “However, in cases in which the driving mode to be executed is the manual driving mode, the vehicle information collection section 105 collects information from the various sensors (step S102). Next, the vehicle information collection section 105 stores the collected sensor information and an image of the occupant captured by the in-cabin camera 92 in association with each other in the storage section 180 as the operation history information 182 (step S104).”
Para [0104] “The driving characteristics derivation section 155 derives the driving characteristics for each vehicle occupant based on the operation history information 182”
}
Hatano in view of Ono does not teach, obtain the data relating to the driver interaction with the vehicle via a vehicle-driver interaction sequence while the vehicle is stationary.
However, Prucka teaches obtain the data relating to the driver interaction with the vehicle via a vehicle-driver interaction sequence while the vehicle is stationary.
{Column 2 “According to another example aspect of the invention, a launch control method for a vehicle having a powertrain is presented. In one exemplary implementation, the launch control method comprises providing a driver interface configured to display information to and input from a driver of the vehicle, the driver interface comprising a brake pedal, an accelerator pedal, and two paddle shifters, and a controller in communication with the driver interface, obtaining, by the controller, a desired launch torque curve and a desired launch speed, performing, by the controller, a pre-staging procedure including generating a torque reserve for the powertrain, in response to (i) the brake pedal being fully engaged, (ii) the paddle shifters being a first configuration, and (iii) the accelerator pedal being at wide-open throttle (WOT), after the pre-staging procedure is complete, controlling, by the controller, vehicle positioning/movement in response to driver manipulation of the brake pedal, performing, by the controller, a staging procedure including (a) increasing the torque reserve, (b) activating a trans brake feature to hold the vehicle stationary, and (c) increasing a speed of the powertrain to the desired launch speed, in response to (i) the paddle shifters in a different second configuration and (ii) fully releasing the brake pedal, and after the staging procedure and in response to the paddle shifters being in a different third configuration, executing, by the controller, a vehicle launch procedure by depleting the torque reserve according to the desired launch torque curve.”
Where the driver is using various inputs while the vehicle is stationary to initiate launch control.
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Prucka to obtain driver inputs while the vehicle is stationary because it allows for initiation of launch control a feature that some vehicle users enjoy experiencing.
Regarding claim 24, it recites A method having limitations similar to those of claim 8 and therefore is rejected on the same basis.
Claim(s) 9, 12-14, 16, 25, and 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Hatano et al. (US 20170329330 A1, hereinafter known as Hatano) in view of Ono et al. (US 20160031321 A1, hereinafter known as Ono) and Wong et al. (US 20230166773 A1, hereinafter known as Wong).
Wong was cited in a previous office action.
Regarding Claim 9, Hatano in view of Ono teaches The device of claim 1,
Hatano in view of Ono does not teach, wherein the one or more updated tuning parameters are associated with transferring control of the vehicle between the automated driving or driver assistance feature and a driver of the vehicle.
However, Wong teaches wherein the one or more updated tuning parameters are associated with transferring control of the vehicle between the automated driving or driver assistance feature and a driver of the vehicle.
{para [0056-0057] “With reference back to FIG. 3, the override threshold determination module 106 receives the safety barrier override threshold data 120, the feature mode data 112, vehicle parameters data 122, and external disturbances data 124. The override threshold determination module 106 determines an optimal override threshold and generates override threshold data 130 using vehicle and math-based data that provides natural driver override feel and consistent performance across different vehicle applications.
For example, the calibration datastore 110 stores calibration data, as shown in FIG. 6, that characterizes steady-state driver steering torque 604 that tracks different curvatures 602 with varying vehicle parameters such as speeds (37 mph, 52 mph, and 67 mph) and disturbances (e.g., crosswind, road bank angle, road friction, etc.). The required steering torque can be derived from empirical or analytical data such as vehicle tests or engineering calculations. The override threshold determination module 106 derives an optimal override threshold by setting the override threshold to closely match the steady-state steering torque τ.sub.ss required to track the target path curvature under the measured vehicle parameters and external disturbances. This provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Wong to have the tuning parameters be associated with transfer of control because as discussed in para [0057] of Wong it “provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Regarding Claim 12, Hatano in view of Ono teaches The device of claim 1,
Hatano in view of Ono does not teach, wherein one or more driver interaction parameters include one or more steering wheel torque thresholds, integrals, derivatives, or gains associated with detecting active or passive interaction with the steering wheel of the vehicle by a driver of the vehicle.
However, Wong teaches wherein one or more driver interaction parameters include one or more steering wheel torque thresholds, integrals, derivatives, or gains associated with detecting active or passive interaction with the steering wheel of the vehicle by a driver of the vehicle.
{para [0056-0057] “With reference back to FIG. 3, the override threshold determination module 106 receives the safety barrier override threshold data 120, the feature mode data 112, vehicle parameters data 122, and external disturbances data 124. The override threshold determination module 106 determines an optimal override threshold and generates override threshold data 130 using vehicle and math-based data that provides natural driver override feel and consistent performance across different vehicle applications.
For example, the calibration datastore 110 stores calibration data, as shown in FIG. 6, that characterizes steady-state driver steering torque 604 that tracks different curvatures 602 with varying vehicle parameters such as speeds (37 mph, 52 mph, and 67 mph) and disturbances (e.g., crosswind, road bank angle, road friction, etc.). The required steering torque can be derived from empirical or analytical data such as vehicle tests or engineering calculations. The override threshold determination module 106 derives an optimal override threshold by setting the override threshold to closely match the steady-state steering torque τ.sub.ss required to track the target path curvature under the measured vehicle parameters and external disturbances. This provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Wong to have the interaction parameters be associated with transfer of control because as discussed in para [0057] of Wong it “provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Regarding Claim 13, Hatano in view of Ono teaches The device of claim 1,
Hatano in view of Ono does not teach, wherein the one or more updated tuning parameters include one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits associated with steering wheel torque based detection of active or passive interaction with the steering wheel of the vehicle by a driver of the vehicle.
However, Wong teaches wherein the one or more updated tuning parameters include one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits associated with steering wheel torque based detection of active or passive interaction with the steering wheel of the vehicle by a driver of the vehicle.
{para [0056-0057] “With reference back to FIG. 3, the override threshold determination module 106 receives the safety barrier override threshold data 120, the feature mode data 112, vehicle parameters data 122, and external disturbances data 124. The override threshold determination module 106 determines an optimal override threshold and generates override threshold data 130 using vehicle and math-based data that provides natural driver override feel and consistent performance across different vehicle applications.
For example, the calibration datastore 110 stores calibration data, as shown in FIG. 6, that characterizes steady-state driver steering torque 604 that tracks different curvatures 602 with varying vehicle parameters such as speeds (37 mph, 52 mph, and 67 mph) and disturbances (e.g., crosswind, road bank angle, road friction, etc.). The required steering torque can be derived from empirical or analytical data such as vehicle tests or engineering calculations. The override threshold determination module 106 derives an optimal override threshold by setting the override threshold to closely match the steady-state steering torque τ.sub.ss required to track the target path curvature under the measured vehicle parameters and external disturbances. This provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Wong to have the tuning parameters be associated with transfer of control because as discussed in para [0057] of Wong it “provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Regarding Claim 14, Hatano in view of Ono and Wong teaches The device of claim 13,
Wong teaches wherein the one or more processors, to cause the device to cause the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters, are configured to cause the device to: transfer control of the vehicle from the automated driving or driver assistance feature to the driver of the vehicle in connection with detecting active interaction with the steering wheel of the vehicle based at least in part on the one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits; or transfer control of the vehicle to the automated driving or driver assistance feature in connection with detecting passive interaction with the steering wheel of the vehicle based at least in part on the one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits.
{para [0056-0057] “With reference back to FIG. 3, the override threshold determination module 106 receives the safety barrier override threshold data 120, the feature mode data 112, vehicle parameters data 122, and external disturbances data 124. The override threshold determination module 106 determines an optimal override threshold and generates override threshold data 130 using vehicle and math-based data that provides natural driver override feel and consistent performance across different vehicle applications.
For example, the calibration datastore 110 stores calibration data, as shown in FIG. 6, that characterizes steady-state driver steering torque 604 that tracks different curvatures 602 with varying vehicle parameters such as speeds (37 mph, 52 mph, and 67 mph) and disturbances (e.g., crosswind, road bank angle, road friction, etc.). The required steering torque can be derived from empirical or analytical data such as vehicle tests or engineering calculations. The override threshold determination module 106 derives an optimal override threshold by setting the override threshold to closely match the steady-state steering torque τ.sub.ss required to track the target path curvature under the measured vehicle parameters and external disturbances. This provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Para [0014] “In another embodiments, a system for controlling steering of an autonomous vehicle is provided. The system includes a non-transitory computer readable medium comprising computer instructions configured to perform a process; and a processor, configured to perform the process. The process includes: operating, by the processor, the autonomous vehicle in a path-based automated driving assist mode; receiving, by the processor, driver input including a driver torque; classifying, by the processor, an operation mode based on a type of the path-based automated driving assist mode; determining, by the processor, an override threshold for overriding the path-based automated driving assist mode on a first lateral side of the autonomous vehicle based on the operation mode, determining, by the processor, a driver override status based on the override threshold; and generating, by the processor, control signals to control the steering of the autonomous vehicle based on the driver override status and the driver torque.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Wong to have the tuning parameters be associated with transfer of control because as discussed in para [0057] of Wong it “provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Regarding Claim 16, Hatano in view of Ono teaches The device of claim 15,
Hatano in view of Ono does not teach, wherein the one or more processors, to cause the device to cause the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters, are configured to cause the device to: transfer control of the vehicle from the automated driving or driver assistance feature to the driver of the vehicle in connection with detecting active interaction with the vehicle based at least in part on the updated thresholds, gains, integral parameters, derivative parameters, or rate limits; or transfer control of the vehicle to the automated driving or driver assistance feature in connection with detecting passive interaction with the vehicle based at least in part on the one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits.
However, Wong teaches wherein the one or more processors, to cause the device to cause the automated driving or driver assistance feature to be applied at the vehicle in accordance with the one or more updated tuning parameters, are configured to cause the device to: transfer control of the vehicle from the automated driving or driver assistance feature to the driver of the vehicle in connection with detecting active interaction with the vehicle based at least in part on the updated thresholds, gains, integral parameters, derivative parameters, or rate limits; or transfer control of the vehicle to the automated driving or driver assistance feature in connection with detecting passive interaction with the vehicle based at least in part on the one or more updated thresholds, gains, integral parameters, derivative parameters, or rate limits.
{para [0056-0057] “With reference back to FIG. 3, the override threshold determination module 106 receives the safety barrier override threshold data 120, the feature mode data 112, vehicle parameters data 122, and external disturbances data 124. The override threshold determination module 106 determines an optimal override threshold and generates override threshold data 130 using vehicle and math-based data that provides natural driver override feel and consistent performance across different vehicle applications.
For example, the calibration datastore 110 stores calibration data, as shown in FIG. 6, that characterizes steady-state driver steering torque 604 that tracks different curvatures 602 with varying vehicle parameters such as speeds (37 mph, 52 mph, and 67 mph) and disturbances (e.g., crosswind, road bank angle, road friction, etc.). The required steering torque can be derived from empirical or analytical data such as vehicle tests or engineering calculations. The override threshold determination module 106 derives an optimal override threshold by setting the override threshold to closely match the steady-state steering torque τ.sub.ss required to track the target path curvature under the measured vehicle parameters and external disturbances. This provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Para [0014] “In another embodiments, a system for controlling steering of an autonomous vehicle is provided. The system includes a non-transitory computer readable medium comprising computer instructions configured to perform a process; and a processor, configured to perform the process. The process includes: operating, by the processor, the autonomous vehicle in a path-based automated driving assist mode; receiving, by the processor, driver input including a driver torque; classifying, by the processor, an operation mode based on a type of the path-based automated driving assist mode; determining, by the processor, an override threshold for overriding the path-based automated driving assist mode on a first lateral side of the autonomous vehicle based on the operation mode, determining, by the processor, a driver override status based on the override threshold; and generating, by the processor, control signals to control the steering of the autonomous vehicle based on the driver override status and the driver torque.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Wong to have the tuning parameters be associated with transfer of control because as discussed in para [0057] of Wong it “provides a natural driver override feel during override maneuver on curved roads in the absence of safety barriers. The safety barrier threshold acts as a modifier of the override threshold to increase the difficulty of override maneuver that would bring the vehicle into collision with safety barrier. The additional driver override effort deters the driver from inadvertently overriding the driving assist feature.”
Regarding claim 25, it recites A method having limitations similar to those of claim 9 and therefore is rejected on the same basis.
Regarding claim 27, it recites A method having limitations similar to those of claim 12 and therefore is rejected on the same basis.
Regarding claim 28, it recites A method having limitations similar to those of claim 14 and therefore is rejected on the same basis.
Claim(s) 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over Hatano et al. (US 20170329330 A1, hereinafter known as Hatano) in view of Ono et al. (US 20160031321 A1, hereinafter known as Ono) and Kade et al. (US 20070091173 A1, hereinafter known as Kade).
Regarding claim 33, Hatano in view of Ono teaches The device of claim 1. Ono teaches wherein the one or more driver interaction parameters include
{Para [0142] “The torque determination unit 333 determines whether or not the driver has performed active corrective steering, based on the torque detected by the torque sensor 318. The torque determination unit 333 can determine whether or not the driver performed active corrective steering from torque imparted to the steering wheel. Namely, a threshold value exists in the characteristics of steering wheel steering performed by human inclination, and the torque determination unit 333 can determine that active corrective steering has been performed when torque exceeding this threshold is imparted. Specifically, the torque determination unit 333 determines whether or not the driver performed active corrective steering based on whether or not torque detected by the torque sensor 318 has exceeded a predetermined threshold value. In the third exemplary embodiment, the threshold value is, for example, set to from 1 Nm to 1.5 Nm, and the torque determination unit 333 determines that corrective steering was performed when this threshold value is exceeded.”
Para [0144] “When the torque determination unit 333 has determined that the driver performed active corrective steering, the marker presentation position calculation unit 336 calculates the yaw angular velocity r of the vehicle. Moreover, the marker presentation position calculation unit 336 determines the position to present the marker on the windshield of the vehicle based on the calculated yaw angular velocity r. Based on Equation (33) to Equation (35) below, the marker presentation position calculation unit 336 predicts the position of the vehicle after the forward gaze time, using the current position of the vehicle as a reference. Moreover, based on the predicted position of the vehicle after the forward gaze time, the predetermined driver's viewpoint position, and the predetermined position coordinates of the windshield of the vehicle, the marker presentation position calculation unit 336 determines the marker presentation position as the intersection point between a line segment connecting the eye point that is the driver's viewpoint position and the predicted arrival point, and the plane of the windshield. Moreover, the marker presentation position calculation unit 336 outputs the determined marker presentation position to the output device 92.”
Para [0146] “Moreover, when the torque determination unit 333 has determined that the driver performed active corrective steering, the steering management unit 340 does not regulate the steering device 90.”
}
Hatano in view of Ono does not teach, wherein the one or more driver interaction parameters include rates of change of driver-input steering wheel torque signals
However, Kade teaches wherein the one or more driver interaction parameters include
{Para [0019] “If processor 104 determines that vehicular speed is less than the threshold speed, initialization routine 150 is repeated. Conversely, if vehicle 100 is traveling at a speed (S.sub.v) that is greater than the minimum threshold speed (S.sub.MIN), processor 104 utilizes data provided by steering wheel angle sensor 136 (FIG. 2) and a clock to determine whether the change in steering wheel angle over a given period of time (.DELTA..theta./.DELTA.t) exceeds a threshold value (.alpha.) as indicated in FIG. 3 at step 158. If the rate of steering wheel angle change is greater than the threshold value, it is assumed that the driver is actively steering vehicle 100 and routine 150 is repeated. If, however, the rate of angle change (.DELTA..theta./.DELTA.t) is less than or equal to the threshold value (.alpha.), it is assumed that the driver is not actively steering vehicle 100 and therefore that any lane drifting is unintentional. In this case, a lane monitoring process is performed, such as the exemplary lane monitoring process described below conjunction with FIGS. 4 and 5.”
}
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Hatano in view of Ono to incorporate the teachings of Kade to use rates of change to determine if the driver is actively steering or not because rate of change clearly shows intention even when only small torque levels are applied such as during slow speed or when only small corrections are made.
Conclusion
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/A.G.M./Examiner, Art Unit 3668
/JAMES J LEE/Supervisory Patent Examiner, Art Unit 3668
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