DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is reply to the Application Number 18/937,634 filed on 08/31/2026
Claims 1 – 13 and 15 are currently pending and have been examined. Claims 1, 5, 7, 8, 12 and 15 have been amended. Claim 14 is cancelled.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/31/2026 has been entered.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 1, 4, 5, 7, 8, 11, 12 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) further in view of Akella et al. (US 20200406894 A1) and Jalaliyazdi et al. (US 12304482 B2).
Kang teaches a method of controlling an autonomous vehicle, the method comprising: (Kang: Abstract: “A method for controlling autonomous driving for an autonomous driving vehicle,”)
acquiring, by a processor, a signal indicative of connection between the autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
activating, by the processor, an autonomous driving function configured to perform autonomous driving based on the acquired signal; (Kang: Col. 4, lines 23 – 31: “the present disclosure provides a method and an apparatus for controlling autonomous driving, capable of improving longitudinal control tracking performance by generating a longitudinal control requirement value, the error of which is corrected based on surrounding environment information and vehicle body information which are obtained through various sensors provided in an autonomous driving vehicle to transmit the longitudinal control requirement value to a lower controller.”,
Supplemental Note: the vehicle is able to operate autonomously with a trailer attached by adjusting its various control parameters)
obtaining, by the processor, acceleration response data of the autonomous vehicle during autonomous driving and generating an acceleration response pattern based on the acceleration response data; (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”: Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regard to acceleration. This is interpreted as the acceleration response data)
… determining, by the processor, whether to maintain activation of the autonomous driving function based on a result of the determination on the overloading condition, (Kang: Col. 2, line 62 – Col. 3, line 4: “the sensing information on the autonomous driving may include at least one of positioning information, precision map information, camera capturing information, sensing information by a Radar or or Lidar, weather information, driving speed information, information on a recognized driver gaze, sensing information on vehicle internal failure, information on a button input to release the autonomous driving, sensing information on an operation of a steering wheel, or sensing information on an operation of the acceleration or deceleration pedal.”; Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”; Col. 7, lines 55 – 62: “the required command value may include a longitudinal control requirement value and a lateral control requirement value. The longitudinal control requirement value and the lateral control requirement value are transmitted to the acceleration or deceleration controller 235 and the steering controller 231 to perform behavior control in a longitudinal direction and behavior control in a lateral direction.”; Col. 7, lines 63 – 67: “The controller 223 may determine whether control needs to be transferred from the system to the driver, based on internal and external states of the vehicle depending on the cognition result of the cognition part 222, and whether the driver inputs a button to release autonomous driving.”).
In sum, Kang teaches a method of controlling an autonomous vehicle, the method comprising: acquiring, by a processor, a signal indicative of connection between the autonomous vehicle and a trailer; activating, by the processor, an autonomous driving function configured to perform autonomous driving based on the acquired signal; obtaining, by the processor, acceleration response data of the autonomous vehicle during autonomous driving and generating an acceleration response pattern based on the acceleration response data; determining, by the processor, whether to maintain activation of the autonomous driving function based on a result of the determination on the overloading condition. Kang however does not teach determining, by the processor, whether an overloading condition exists by comparing the acceleration response pattern with a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition; and wherein determining whether the overloading condition exists.
Akella teaches determining, by the processor, whether an overloading condition exists by comparing the acceleration response pattern with a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition; and (Akella: Paragraph 0023: “FIG. 2 illustrates an example of a system 200 that generates a machine-learned model for determining a desired speed based on human driving data, in an example. A training computer system 206 uses a set of driving training data 202 to produce a machine-learned model that generates a desired speed for a vehicle given a set of driving-environment characteristics that correspond to a set of driving-environment characteristics 204 that are present in the driving training data 202… In some examples, the driving training data 202 includes vehicle status information such as traction data, vehicle type, horsepower, gross weight, towing data, engine temperature, transmission temperature, time of day, day of week, month of year, battery level, and battery temperature. For each set of driving-environment characteristics in the driving training data 202, an associated vehicle speed is present.”; Paragraph 0026: “In an example, once the training computer system 206 has finished training the machine-learned mode 208, the machine-learned model 208 is capable of taking driving-environment characteristics and producing a speed that would likely be chosen by a human operator. The speed suggested by the machine-learned model 208 may be used as the desired speed for the vehicle or may be processed in combination with other data to produce a final desired speed for the vehicle”; Paragraph 0043: “In another example, the machine-learned model produces a linear or angular acceleration limit for the vehicle. In yet another example, the machine-learned model produces a plurality of outputs that may include one or more of desired speed, acceleration limit, turning limit, or other control limits… As a non-limiting example, if such a model outputs a speed limit of 30 mph in an area which is designated as having a speed limit of 40 mph, all actuations may be capped (e.g., have a threshold not to meet or exceed) of 75% of their nominal threshold (e.g., 75% of max acceleration, steering angle rate, etc.)… the desired speed provided by the machine-learned model is used as a limitation imposed on the heuristic speed. A heuristic speed may, in some examples, be calculated based on the sensor acuity of the autonomous vehicle, the capabilities of the vehicle being controlled, and the accuracy of the control system. In some examples, the target vehicle speed may be the lower of the desired speed produced by the machine-learned model, a heuristic speed, and a statutory speed limit.”; Paragraph 0047: “At decision block 608, the autonomous-vehicle control system determines the speed capability of the vehicle being controlled. In some examples, the speed capability is based on the horsepower of the vehicle, the weight of the vehicle, the weight of any load carried by the vehicle, the weight of any load towed by the vehicle, the ambient air temperature, and temperatures of internal vehicle components such as battery temperature, motor temperature, engine temperature, coolant temperature, transmission temperature. In some examples, brake temperature and brake capacity are factors that limit the vehicle speed capability. If the vehicle speed capability is less than the target speed, execution advances to block 610 and the target speed is set to the vehicle speed capability. Execution then advances to decision block 612.”,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, in which the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
… wherein determining whether the overloading condition exists (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, in which the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. One of ordinary skill in the art would find it obvious to try to implement the method of Akella of being able to determine the speed capabilities of a vehicle by using a machine learning model with the vehicle of Kang. The model is used to autonomously drive the vehicle under different conditions by evaluating multiple environmental and vehicle parameters. From the learned driving conditions of the driver operating the vehicle at a previous time, the model is able to determine a target speed for vehicle. The model then further gathers data about the vehicle status, such as the weight of any load the vehicle is carrying to then update the speed capability of the vehicle. The target speed is compared with the speed capability in which the target speed may be reduced if it is too high. Making this determination is equivalent to the overloading condition as if the learned target speed is higher than the speed capability speed, the vehicle will have adverse effects if it keeps operating in this manner. This determination from the model is obvious to try to combine with Kang as Kang also teaches an autonomous vehicle with the ability of determining it’s vehicle parameters and performing longitudinal driving control. Combining the model of Akella allows for the system to further determine the optimal speed to autonomously drive the vehicle while also determining whether or not to maintain the autonomous driving function. This ability increases the vehicle’s life as it mitigates situations wherein the vehicle autonomous drives faster than what the vehicle is capable of. Kang in view of Akella however still do not teach comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition.
Jalaliyazdi teaches comprises comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition (Jalaliyazdi: Abstract: “Methods and systems are provided for estimating vehicle parameters of a vehicle. A maximum desired vehicle acceleration and a minimum excitation acceleration in a first direction are determined.”; Col. 13, lines 26 – 37: “Referring to FIG. 6, an exemplary illustration associated with generation of an estimated vehicle mass of a vehicle 10 by a vehicle parameter estimation system 100 in accordance with at least one embodiment is shown. The ADAS 214 of the vehicle 10 was previously provided with a default vehicle mass m.sub.0. The vehicle 10 has been attached to a trailer 600 so that the actual vehicle mass m* as defined by the combination of the mass of the vehicle 10 and the mass of the trailer 600 has changed. The graph 602 represents the speed of the vehicle 10 as a function of time. The graph 604 represents the generation of the estimated vehicle mass as a function of time”; Col. 13, lines 39 – 46: “The relationship 606 illustrates an increase in the speed of the vehicle 10 as a result of an application of a first acceleration to the vehicle 10. The relationship 608 illustrates the convergence of the estimated vehicle mass to a first estimated vehicle mass value 610 as generated by the vehicle mass estimation observation model based on the application of the first acceleration.”: Col. 10, lines 14 – 19: “In various embodiments, the minimum excitation acceleration module 208 is configured to adapt the minimum excitation acceleration based on the one or more vehicle states. Examples of vehicle states include, but are not limited to, current vehicle speed in a longitudinal direction and current vehicle load.”; Col. 13, lines 46 – 51: “The relationship 612 illustrates the increase in the speed of the vehicle 10 from the initial speed v.sub.0 to the second speed v* based on an application of a temporary excitation acceleration to the vehicle 10 in accordance with at least one embodiment.”,
Supplemental Note: as seen in Figure A, the velocity and time graph shows the differences within the normal acceleration pattern and the one with temporary excitation. Excitation is taught to be dependent upon the current vehicle load, thus is interpreted as the vehicle towing a trailer, also as shown in the Figure A).
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Figure A: Jalaliyazdi: Fig. 6
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Jalaliyazdi with a reasonable expectation of success. Kang teaches the ability of determining the critical error between the required acceleration and the output acceleration in which the vehicle system corrects the performance of the vehicle to be within the range of the critical error. This is visually shown within the graphs of Jalaliyazdi as it compares the normal acceleration pattern with one which includes a vehicle load. Both Kang and Jalaliyazdi are determining the acceleration requirements of the vehicle based on the vehicle parameters, Jalaliyazdi is merely showing this concept within a time-speed graph as shown in Figure A. One of ordinary skill in the art would’ve recognized that the results of the combination were predictable and thus results in combining prior art elements according to known methods to yield predictable results. For example, with the use of the graph of Jalailyazdi, a critical error can still be identified Kang teaches comparing the acceleration patterns whereas Jalaliyazdi visually shows the comparison on a graph.
Regarding claim 4, Kang, as modified, teaches a second normal pattern, and (Kang: Col. 3, lines 62 – 67: “According to an embodiment, the corrected control value may be calculated by applying at least one of the controller correcting parameter, the trailer correcting parameter, or the driving context correcting parameter to the initial longitudinal control value.”: Col. 7, lines 29 – 32: “In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”: Col. 8, lines 47 – 63: “The sensing part 310 may include various sensors for autonomous driving and may generate sensing information on the autonomous driving, based on sensing information collected from the sensors. The sensing information on the autonomous driving may include positioning information, precision map information, camera capturing information, the sensing information by the Radar or Lidar, weather information, driving speed information, information on the recognized driver gaze, sensing information on vehicle internal failure, information on a button input to release the autonomous driving, sensing information on the operation of the steering wheel, or the sensing information on the operation of the acceleration or deceleration pedal, but is not limited thereto. The longitudinal control value generator 320 may generate an initial longitudinal control value “a_raw” based on the sensing information on the autonomous driving.”,
Supplemental Note: the preset normal patterns are interpreted as the initial acceleration patterns detected by the sensing part. This can include a vehicle with or without a trailer which both have an “a_raw” value, thus two normal patterns which are set per the initial conditions acquired by the sensing part).
In sum, Kang teaches a second normal pattern. Kang however does not teach wherein the preset normal response pattern comprises a first normal pattern wherein determining whether the overloading condition exists comprises determining whether the overloading condition exists based on the first normal pattern when the autonomous vehicle and the trailer are determined as being connected based on the signal.
Akella teaches wherein the preset normal response pattern comprises a first normal pattern and (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. A speed capability is also determined by the weight of any load carried by the vehicle, in which the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
… wherein determining whether the overloading condition exists comprises determining whether the overloading condition exists based on the first normal pattern when the autonomous vehicle and the trailer are determined as being connected based on the signal (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, which is the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 5, Kang, as modified, teaches wherein determining whether the overloading condition exists further comprises determining whether a loading amount of the autonomous vehicle is excessive based on the second normal pattern when the autonomous vehicle and the trailer are determined as not being connected based on the signal (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected such as the trailer not being attached and no need for a P.sub.trail correction parameter).
Regarding claim 7, Kang teaches a non-transitory computer-readable recording medium having a program recorded thereon, the program to direct a processor to perform acts of: (Kang: Col. 11, lines 58 – 62: “The operations of the methods or algorithms described in connection with the processor embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor.”; Col. 12, lines 1 – 9 :“The exemplary storage medium may be coupled to the processor. The processor may read out information from the storage medium and may write information in the storage medium. Alternatively, the storage medium may be integrated with the processor. The processor and storage medium may reside in an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as separate components of the terminal of the user.”)
acquiring a signal indicative of connection between an autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30; Col. 7, lines 24 – 32)
activating an autonomous driving function configured to perform autonomous driving based on the acquired signal; (Wang: Col. 4, lines 23 – 31:,
Supplemental Note: the vehicle is able to operate autonomously with a trailer attached by adjusting its various control parameters)
obtaining, by the processor, acceleration response data of the autonomous vehicle during autonomous driving and generating an acceleration response pattern based on the acceleration response data (Kang: Col. 10, lines 34 – 49; Col. 11, lines 31 – 57,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regard to acceleration. This is interpreted as the acceleration response data)
… determining whether to maintain activation of the autonomous driving function based on a result of the determination on the overloading condition (Kang: Col. 2, line 62 – Col. 3, line 4; Col. 7, lines 24 – 32; Col. 7, lines 55 – 62; Col. 7, lines 63 – 67).
In sum, Kang teaches a non-transitory computer-readable recording medium having a program recorded thereon, the program to direct a processor to perform acts of: acquiring a signal indicative of connection between an autonomous vehicle and a trailer; activating an autonomous driving function configured to perform autonomous driving based on the acquired signal; obtaining, by the processor, acceleration response data of the autonomous vehicle during autonomous driving and generating an acceleration response pattern based on the acceleration response data determining whether to maintain activation of the autonomous driving function. Kang however does not teach determining whether an overloading condition exists by comparing the acceleration response pattern with a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition.
Akella teaches determining whether an overloading condition exists by comparing the acceleration response pattern with a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition; and (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, in which the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
…wherein determining whether the overloading condition exists (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses. Kang in view of Akeela however still do not teach comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition.
Jalaliyazdi teaches comprises comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition (Jalaliyazdi: Abstract; Col. 13, lines 26 – 37; Col. 13, lines 39 – 46; Col. 10, lines 14 – 19; Col. 13, lines 46 – 51,
Supplemental Note: as seen in Figure A, the velocity and time graph shows the differences within the normal acceleration pattern and the one with temporary excitation. Excitation is taught to be dependent upon the current vehicle load, thus is interpreted as the vehicle towing a trailer, also as shown in the Figure A).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Jalaliyazdi with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 8, Kang, teaches an autonomous vehicle comprising a processor, wherein
the processor is configured to: (Kang: Abstract; Col. 11, lines 58 – 62: “The operations of the methods or algorithms described in connection with the processor embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor.”)
acquire a signal indicative of connection between the autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30; Col. 7, lines 24 – 32)
activate an autonomous driving function configured to perform autonomous driving based on the acquired signal; (Wang: Col. 4, lines 23 – 31,
Supplemental Note: the vehicle is able to operate autonomously with a trailer attached by adjusting its various control parameters)
obtain acceleration response data of the autonomous vehicle during the autonomous driving and generate an acceleration response pattern based on the acceleration response data; (Kang: Col. 10, lines 34 – 49; Col. 11, lines 31 – 57,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regard to acceleration. This is interpreted as the acceleration response data)
… determine whether to maintain activation of the autonomous driving function based on a result of the determination on the overloading condition (Kang: Col. 2, line 62 – Col. 3, line 4; Col. 7, lines 24 – 32; Col. 7, lines 55 – 62; Col. 7, lines 63 – 67).
In sum, Kang teaches an autonomous vehicle comprising a processor, wherein the processor is configured to: acquire a signal indicative of connection between the autonomous vehicle and a trailer; activate an autonomous driving function configured to perform autonomous driving based on the acquired signal; obtain acceleration response data of the autonomous vehicle during the autonomous driving and generate an acceleration response pattern based on the acceleration response data; determine whether to maintain activation of the autonomous driving function. Kang however does not teach determine whether an overloading condition exists by comparing the acceleration response pattern and a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition.
Akella teaches determine whether an overloading condition exists by comparing the acceleration response pattern and a preset normal response pattern, wherein the preset normal response pattern comprises the acceleration response pattern corresponding to a normal loading condition; and (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, in which the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
wherein determining whether the overloading condition exists (Akella: Paragraph 0023; Paragraph 0026:; Paragraph 0043; Paragraph 0047).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses. Kang in view of Akella however still do not teach comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition.
Jalaliyazdi teaches comprises comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition (Jalaliyazdi: Abstract; Col. 13, lines 26 – 37; Col. 13, lines 39 – 46; Col. 10, lines 14 – 19; Col. 13, lines 46 – 51,
Supplemental Note: as seen in Figure A, the velocity and time graph shows the differences within the normal acceleration pattern and the one with temporary excitation. Excitation is taught to be dependent upon the current vehicle load, thus is interpreted as the vehicle towing a trailer, also as shown in the Figure A).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Jalaliyazdi with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 11, Kang, as modified, teaches a second normal pattern, and (Kang: Col. 3, lines 62 – 67; Col. 8, lines 47 – 63,
Supplemental Note: the preset normal patterns are interpreted as the initial acceleration patterns detected by the sensing part. This can include a vehicle with or without a trailer which both have an “a_raw” value, thus two normal patterns which are set per the initial conditions acquired by the sensing part).
In sum, Kang teaches a second normal pattern. Kang however does not teach wherein the preset normal pattern comprises a first normal response pattern and wherein the processor is configured to determine whether the overloading condition exists based on the first normal pattern when the autonomous vehicle and the trailer are connected based on the signal.
Akella teaches wherein the preset normal pattern comprises a first normal response pattern and (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047)
wherein the processor is configured to determine whether the overloading condition exists based on the first normal pattern when the autonomous vehicle and the trailer are connected based on the signal (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, which is the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 12, Kang, as modified, teaches wherein the processor is further configured to determine whether a loading amount of the autonomous vehicle is excessive based on the second normal pattern when the autonomous vehicle and the trailer are not connected based on the signal (Kang: Col. 10, lines 34 – 49 ; Col. 11, lines 31 – 57,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected such as the trailer not being attached and no need for a P.sub.trail correction parameter).
Regarding claim 15, Kang, as modified, does not teach wherein the processor is configured to determine that the overloading condition exists.
Akella teaches wherein the processor is configured to determine that the overloading condition exists (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses. Kang in view of Akella however still do not teach when the acceleration response pattern lies below the preset normal response pattern in the time-speed graph.
Jalaliyazdi teaches when the acceleration response pattern lies below the preset normal response pattern formed as a reference line in the time-speed graph (Jalaliyazdi: Abstract; Col. 13, lines 26 – 37; Col. 13, lines 39 – 46; Col. 10, lines 14 – 19; Col. 13, lines 46 – 51,
Supplemental Note: as seen in Figure A, the velocity and time graph shows the differences within the normal acceleration pattern and the one with temporary excitation. Excitation is taught to be dependent upon the current vehicle load, thus is interpreted as the vehicle towing a trailer, also as shown in the Figure. The line of 612 is interpreted as the reference line corresponding to the acceleration due to the vehicle with a trailer attached).
Claim(s) 2 and 3 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2), Akella et al. (US 20200406894 A1) and Jalaliyazdi et al. (US 12304482 B2) as applied to claim 1 above, and further in view of Smalley et al. (US 20250074440 A1).
Regarding claim 2, Kang, as modified, teaches providing information related to the deactivated autonomous driving function through a display (Kang: Col. 8, lines 1 – 5: “The controller 223 may perform a control operation to output a specific warning notification for requesting for transferring the control to the driver, when the control needs to be transferred to the driver.”).
In sum, Kang teaches providing information related to the deactivated autonomous driving function through a display. Kang however does not teach further comprising: when the overloading condition exists.
Akella teaches further comprising:
when the overloading condition exists, (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, which is the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses. Kang in view of Akella however still do not teach deactivating, by the processor, the autonomous driving function.
Smalley teaches deactivating, by the processor, the autonomous driving function, and (Smalley: Paragraph 0066: “The stability evaluation module 213 may be configured as described herein to evaluate the results of these computations to determine if an instability condition exists in the vehicle-trailer system. The stability evaluation module 213 may also be configured as described herein to, responsive to a determination that an instability condition exists, autonomously control certain operations of the vehicle 100 to generate instability alerts to users of the vehicle and/or to deactivate certain vehicle systems to facilitate full manual control of the vehicle. These steps may help the user stabilize the motion of the vehicle-trailer system as soon as possible.”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Smalley teaches the ability to cancel the autonomous driving function if the instability condition is persistent so the driver is able to manually control a vehicle (Smalley: Paragraph 0066). Detecting instability and returning manual control to the driver mitigates conditions where the instability can cause damage the vehicle or the user. Kang already teaches the ability to gather trailer specification data and adjusting the required acceleration needed to power the vehicle with an attached trailer, thus combining the instability mitigation functions of Smalley increases the functionality of the vehicle to detect a instability while also increasing the safety of the road users. Therefore this function of Smalley would be obvious to try to combine with the vehicle of Kang.
Regarding claim 3, Kang, as modified, teaches maintaining, by the processor, the activation of the autonomous driving (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected).
In sum, Kang teaches maintaining, by the processor, the activation of the autonomous driving. Kang however does not teach further comprising: when the overloading condition does not exist.
Akella teaches further comprising:
when the overloading condition does not exist, (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047,
Supplemental Note: the target speed is determined by the machine learning model in part of what speed the driver usually drives the vehicle at along with vehicle status and environmental information. Knowing this information and sending this to the machine learning model is equivalent to a time-speed graph as the system is gathering data of the vehicle speed along a period of time. The speed also directly affects the acceleration limits as cited in the example of paragraph 0043. This target speed is interpreted as the preset normal response pattern. A speed capability is also determined by the weight of any load carried by the vehicle, which is the target speed is limited. Since the speed of the vehicle dictates the acceleration limits of the vehicle, if the target speed is above the speed capability speed, this is interpreted as an overloading condition)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Claim(s) 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2), Akella et al. (US 20200406894 A1) and Jalaliyazdi et al. (US 12304482 B2) as applied to claim 8 above, and further in view of Smalley et al. (US 20250074440 A1).
Regarding claim 9, Kang, as modified, teaches provide information about the deactivated the autonomous driving function through a display (Kang: Col. 8, lines 1 – 5).
In sum, Kang teaches provide information about the deactivated the autonomous driving function through a display. Kang however does not teach wherein the processor is further configured to: when the overloading condition exists.
Akella teaches wherein the processor is further configured to:
when the overloading condition exists, (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses. Kang in view of Akella however still do not teach deactivating, by the processor, the autonomous driving function.
Smalley teaches deactivate the autonomous driving function, and (Smalley: Paragraph 0066).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Please refer to the rejection of claim 2 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 10, Kang, as modified, teaches maintain activation of the autonomous driving function (Kang: Col. 10, lines 34 – 49 ,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected).
In sum, Kang teaches maintain activation of the autonomous driving function. Kang however does not teach wherein the processor is configured to: when the overloading condition does not exist.
Akella teaches wherein the processor is configured to:
when the overloading condition does not exist, (Akella: Paragraph 0023; Paragraph 0026; Paragraph 0043; Paragraph 0047,
Supplemental Note: an overloading condition does not is if the target speed is below or equal to the speed capability speed).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Akella with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) in view of Akella et al. (US 20200406894 A1) and Jalaliyazdi et al. (US 12304482 B2) as applied to claim 1 above, and further in view of Jang et al. (US 20230398983 A1).
Regarding claim 6, Kang, as modified, does not teach wherein the autonomous driving function comprises a smart cruise control (SCC).
Jang teaches wherein the autonomous driving function comprises a smart cruise control (SCC) (Jang: Paragraph 0053: “The autonomous driving system 100 may provide various functions to the driver. For example, the autonomous driving system 100 may provide functions such as lane departure warning (LDW), lane keeping assist (LKA), high beam assist (HBA), automatic emergency braking (AEB), traffic sign recognition (TSR), smart cruise control (SCC), and/or blind spot detection (BSD).”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Lee with a reasonable expectation of success. Autonomous driving is taught by Kang to be between automation levels 0 to 5 per the American Society of Automotive Engineers (Kang: Col. 5, lines 57 – 67), which the vehicle system is able to be configured to. SCC as taught by Jang is also an autonomous driving function, therefore the SCC would be a simple substitution with the autonomous driving function of Kang’s vehicle by one of ordinary skill in the art.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) in view of Akella et al. (US 20200406894 A1) and Jalaliyazdi et al. (US 12304482 B2) as applied to claim 8 above, and further in view of Jang et al. (US 20230398983 A1).
Regarding claim 13, Kang, as modified, does not teach wherein the autonomous driving function comprises a smart cruise control (SCC).
Jang teaches wherein the autonomous driving function comprises a smart cruise control (SCC) (Jang: Paragraph 0053: “The autonomous driving system 100 may provide various functions to the driver. For example, the autonomous driving system 100 may provide functions such as lane departure warning (LDW), lane keeping assist (LKA), high beam assist (HBA), automatic emergency braking (AEB), traffic sign recognition (TSR), smart cruise control (SCC), and/or blind spot detection (BSD).”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Lee with a reasonable expectation of success. Please refer to the rejection of claim 6 as both claim the same function and therefore rejected under the same pretenses.
Response to Arguments
Applicant’s arguments, see section Claim Objections of the REMARKS, filed 08/31/2026, with respect to the claim objections of claims 5 and 12 have been fully considered and are persuasive. The claim objections of claims 5 and 12 have been withdrawn.
Applicant’s arguments, see section Claim Rejections – 35 USC 103 of the REMARKS, filed 08/31/2026, with respect to the 35 U.S.C. 103 prior art rejections of claims 1 – 13 and 15 have been considered but are not fully persuasive.
Applicant states regarding the amendments made to independent claim 1, similarly recited in claims 7 and 8, stating: “wherein determining whether the overloading condition exists comprises comparing the acceleration response pattern formed on a time-speed graph to accelerate from a first speed to a second speed with the preset normal response pattern formed on the time-speed graph to accelerate from the first speed to the second speed corresponding to the normal loading condition”, is not taught by the previously used prior art of Kang or Baur. Regarding these amended claim limitations, Examiner agrees that neither Kang independently nor in view of Baur teach them. However, through further search and consideration, a rejection is made in view of Akella (US 20200406894 A1) and Jalaliyazdi (US 12304482 B2). Please refer to section Claim Rejections - 35 USC § 103 for the claim mappings and motivation to combine.
Applicant further states the prior art Kang does not teach the claim limitation of “determining, by the processor, whether to maintain activation of the autonomous driving function based on a result of the determination on the overloading condition,”, as stated in claim 1 and similarly stated in claims 7 and 8. Examiner respectfully disagrees. The overloading condition as specified within the specification is based on the first normal pattern (connected trailer) and second normal pattern (no connected trailer). The patterns consists of acceleration patterns of the vehicle. Kang teaches the ability of determining whether or not to transfer the vehicle control from the (autonomous) system to the driver based on internal and external states depending on the cognition results. The internal and external states include determining whether or not the trailer is attached along with the vehicles driving speed information, which are all part of the cognition results. Therefore, when combined with the prior art of Akella, the overloading condition based on the acceleration pattern taught by Akella is obvious to try to implement within the parameters of the cognition results. This combination allows the overloading condition as taught by Akella to also determine the transfer of control between the autonomous system and the driver.
Applicant further states regarding claims 4 and 5, that the combination of Kang with the prior art of Baur is improper and it is extracting features with completely different physical dimensions from each of the prior arts and individually mapping them to the uniform pattern configuration of the present invention. Examiner agrees, however with further search and consideration, Baur is no longer used to reject these limitations and is now taught by the prior art of Akella (US 20200406894 A1). Please refer to section Claim Rejections - 35 USC § 103 for the claim mappings and motivation to combine.
Applicant further states the claim limitations of claims 14 and 15 are not taught in view of the prior art of Chakraborty as it teaches the vehicle decelerating due to inertia and weight while the drive power delivery is cut off and not when the vehicle is accelerating as stated within the claim limitations. Examiner agrees, however with further search and consideration, Chakraborty is no longer used to reject these limitations and is now taught by the prior art of Jalaliyazdi (US 12304482 B2). Please refer to section Claim Rejections - 35 USC § 103 for the claim mappings and motivation to combine.
Conclusion
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665
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