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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 27, 2026 has been entered.
Response to Amendment
This correspondence is in response to arguments filed on April 27, 2026. Claims 1, 4, 5, 7, 10, 12, 15, 16, and 18 are amended. Claims 2-3, 6, 8-9, 11, 13-14, 17, and 19-20 are filed as previously and originally presented. Amendments to claim 10 obviate the claim objection set forth in the previous rejection and as such this objection is withdrawn. Applicant’s arguments are addressed below.
Response to Arguments
Applicant argues that Kiriya, Donald, Iida, Hara, and “Google Gesture Video” do not teach the amended features (see Remarks Pages 9-12). Applicant’s arguments with respect to the amended claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wu et al. (US 2023/0331259 A1; hereinafter “Wu”).
Regarding claim 1, Wu discloses a detection system (An intelligent vehicle including various subsystems as described in [0087] will be considered as a detection system since a primary function of the intelligent vehicle is to detect gestures of a user to perform designated intelligent functions as described in rejection below.) comprising:
a memory storing instructions that, when executed by a processor (“A fourth aspect of this application provides an intelligent vehicle, where the intelligent vehicle includes a processor, the processor is coupled to a memory, the memory stores program instructions, and when the program instructions stored in the memory are executed by the processor, the method described in any one of the first aspect or the possible implementations of the first aspect is implemented” [0039]. Thus, there is a memory storing instructions that when executed by the processor perform any of the disclosed methods.), cause the processor to:
detect a gesture command for an automated action from a user outside of a vehicle using sensor data, wherein the detection system authenticates the user prior to accepting the gesture command (Fig. 4 shows a method flowchart which indicates that a person in the first range around the vehicle is detected and authenticated as a target person prior to accepting a gesture command which is detected to control the vehicle to travel to a first location, i.e., gesture command for an automated action.);
predict an obstacle for an incomplete task of the automated action from a vehicle state using the sensor data subsequent to the authentication and notify the user (“In a possible manner, if a passing condition of an ambient environment of the vehicle allows (for example, no obstacle), in response to the body movement of the target person, the ego vehicle may control, in a timely manner, the ego vehicle to travel out of the parking place; or if a passing condition of an ambient environment of the vehicle does not allow, in response to the body movement, the ego vehicle may suspend control of the ego vehicle to travel out of the parking place, or send prompt information (for example, voice or light blinking) to notify the driver or the passenger that it is not suitable to travel out of the parking place in a current scenario” [0147]. Thus, there is a prediction of a passing condition of an ambient environment which does not allow the vehicle to travel out of the parking space, i.e., an obstacle for an incomplete task of the automated action from the vehicle state using sensor data, and notifies the user via voice or light blinking. Given that the user is authenticated as a target person prior to performing a gesture command, this prediction must occur subsequent to the authentication.), wherein the obstacle causes a halt of the automated action according to global positioning information about the vehicle (“When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and then performs a stop action” [0147]. “The sensor system 104 may include several sensors that sense information about an ambient environment of the intelligent vehicle 100. For example, the sensor system 104 may include a global positioning system 122 (the positioning system may be a global positioning system GPS, or may be a BeiDou system or another positioning system), an inertial measurement unit (inertial measurement unit, IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130” [0090]. Thus, the obstacle causes the vehicle to perform a stop action, i.e., halt the automated action. The detection of the obstacle is based on a detection of the ambient environment which is based on the sensor system inclusive of a global positioning system.); and
upon a corrective response subsequent to the halt that is an automated vehicle command different than the gesture command satisfying a parameter to automatically avoid the obstacle, execute the incomplete task for the automated action by an automated driving system (ADS) moving the vehicle from a stop position, wherein the corrective response is associated with a gesture of a user hand that is different than the gesture command (“In a possible implementation, description is further provided based on the scenario shown in FIG. 8. When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and the vehicle may further continuously monitor the body movement of the target person, to wait for a next operation of the target person. When an instruction corresponding to a new body movement is not received, the vehicle switches from a stop state to an execution state, to continue to execute an instruction corresponding to a previous body movement until the action is completed. If an instruction corresponding to a new body movement is received, based on a setting made in advance, the vehicle may choose to execute the instruction corresponding to the new body movement, or may choose to continue to execute an instruction corresponding to a previous body movement until the action is completed and then execute the instruction corresponding to the new body movement” [0147]. Thus, subsequent to the halt, a new body movement, i.e., corrective response associated with a gesture of a user hand that is different from the original gesture command, results in a new automated vehicle command which satisfies the current condition of the ambient environment, i.e., satisfies a parameter to automatically avoid the obstacle, and is received to execute the incomplete task for the automated action by moving the vehicle from a stop position. The control system 106 described in [0092] will be considered as the automated driving system for moving the vehicle.).
Regarding claim 2, Wu discloses the detection system of claim 1, wherein the instructions for the corrective response to the obstacle satisfying the parameter further include instructions to:
move by the vehicle automatically a door position and a mirror position associated with the vehicle that avoids the obstacle (See Fig. 2 and Figs. 2a-f which move automatically a door and mirror position associated with the vehicle to avoid collisions with a surrounding vehicle, i.e., obstacle.), the parameter is a safety area around the vehicle and factors the global positioning information (“…the first range may alternatively be understood as a range in which a sensor system of the vehicle can perform sensing” [0006]. Thus, the condition of the ambient environment, i.e., parameter, is a first range in which a sensor system can perform sensing, i.e., a safety area around the vehicle and factors the global positioning information.).
Regarding claim 3, Wu discloses the detection system of claim 2, wherein the instructions to execute the incomplete task for the automated action further include instructions to:
navigate the vehicle within a parking area using commands from the ADS, wherein the gesture command is associated with one of parking and unparking the vehicle (The disclosure describes the specific application of automatically navigating the vehicle within a parking area via gesture commands associated with unparking the vehicle.).
Regarding claim 4, Wu discloses the detection system of claim 3, wherein the instructions for the corrective response to the obstacle satisfying the parameter further include instructions to:
delay the incomplete task until the vehicle state changes (“After the vehicle enters a stop state, the vehicle continuously monitors the ambient environment. The vehicle determines, through environment sensing, that an obstacle blocking state in the ambient environment of the vehicle is removed, and the vehicle switches from a stop state to an execution state, and continues to execute an instruction corresponding to the body movement until the action is completed” [0110]. Thus, the vehicle control is suspended in a stop state, i.e., the incomplete task is delayed, until the blocking state is removed, i.e., the vehicle state changes.); and
exit a parking spot within the parking area automatically by the vehicle for clearance, and the clearance allows opening a door of the vehicle and changes the vehicle state as the corrective response (In the examples of Figs. 2a-c the vehicle exits the parking spot automatically for clearance such that the door of the vehicle may be open and collision is avoided, i.e., change the vehicle state as the corrective response.).
Regarding claim 5, Wu discloses the detection system of claim 2, wherein the instructions to notify the user further include instructions to:
generate an alert associated with the obstacle, the alert is one of flashing headlights, honking, a verbal alarm, a signal for a wireless device of the user, and a picture for the wireless device (“In a possible implementation, the target person is further notified, through light blinking or sound alarm, that the vehicle is in a stop state” [0147]. Thus, the notification includes one of flashing headlights and a verbal alarm.).
Regarding claim 6, Wu discloses the detection system of claim 5, wherein the instructions for the corrective response to the obstacle satisfying the parameter further include instructions to:
receive a vehicle command from the user according to the vehicle state and the alert, the vehicle command is different than the gesture command (“Before completing execution of the first action, the vehicle keeps monitoring the gesture of the target person and an obstacle in the ambient environment. In a process of executing the first action, when the vehicle receives a second gesture (which is not a “stop” gesture, where if the second gesture is a stop gesture, the vehicle switches to a stop state) made by the target person, the vehicle stops executing the action corresponding to the first gesture of the target person, keeps an execution state unchanged, and executes an action corresponding to the second gesture of the target person. In addition, the vehicle keeps monitoring the gesture of the target person and the obstacle in the ambient environment. Before the target person sends a new gesture instruction, the vehicle always executes the action corresponding to the second gesture until execution of the action is completed. By analogy, the vehicle always and continuously updates, based on a newest gesture instruction sent by the target person, an action currently executed by the vehicle. An application scenario of the mode is as follows: In the mode, when the target person moves along a specific path and continuously sends a “come-over” gesture instruction to the vehicle, if a passing condition of an ambient environment of the vehicle (no obstacle), the vehicle can keep following the target person (including traveling straight, turning, and the like) until all instructions of the target person are executed, and the vehicle is parked in the preset range around the target person, and switches from an execution state to a stop state” [0147]. Thus, the vehicle state is continually updated according to the new gesture received from the target person to monitor the ambient environment and complete the unparking function by avoiding obstacles, i.e., changing a vehicle state. In this case, the second gesture will be considered as a vehicle command different from the gesture command which provides the initial unparking instruction.).
Regarding claim 7, Wu discloses the detection system of claim 5, wherein the instructions to detect the gesture command for the automated action further include instructions to:
infer by a learning model a feature of the gesture command using the sensor data, the learning model is trained with data about the user and the vehicle state using object perception and semantic labeling, the learning model is a vision model that recognizes a user-specific body gesture (“The target person may be understood as a user who establishes a binding relationship with the vehicle in advance. The vehicle may obtain identifier information of at least one target person in advance. The identifier information includes but is not limited to facial information, figure information, and fingerprint information. The vehicle may locally store the identifier information of the at least one target person, or may obtain the identifier information of the at least one target person from a cloud. In this application, a user whose identifier information is obtained by the vehicle is referred to as the target person, and the vehicle can respond only to a body instruction of the target person and perform an action. A user who does not establish a binding relationship with the vehicle in advance becomes another user, and the vehicle does not respond to a body instruction of the another person” [0123]. “For example, a facial recognition method is used as an example. For the facial recognition method, one or more of the following methods may be used: a feature-based recognition algorithm (feature-based recognition algorithm, FRA), an appearance-based recognition algorithm (appearance-based recognition algorithm, ARA), a template-based recognition algorithm (template-based recognition algorithm, TRA), and a recognition algorithm using neural network (recognition algorithm using neural network, RAUNN). The facial recognition information may include facial feature information, facial curve information, and the like” [0125]. Thus, to identify a target person, the identity of a person is inferred using a learned model. Such a model is trained with data about a user and vehicle state data using object perception and semantic labelling (features of the specific facial recognition disclosed above) such that when a user detected within the ambient environment, i.e., vehicle state in which sensing may occur, is determined to have a “binding relationship” based on the facial recognition information, the vehicle is trained to infer the a feature of this user’s gesture. Such user authenticated gestures are recognized by a vision model as described above.); and
authenticate the user by the detection system utilizing one of a token from a key fob, a two- factor verification through a mobile application, and facial recognition using the sensor data (“In a possible implementation, when a similarity between the obtained facial information and the identifier information that is of the target person and that is prestored in the vehicle exceeds a threshold, it is determined that the matching succeeds; or when a similarity between the obtained facial information and the identifier information that is of the target person and that is prestored in the vehicle does not exceed a threshold, it is determined that the matching fails. A person skilled in the art may set, based on a requirement, a specific image collection manner and a specific manner of performing identity authentication based on a collected image. Details are not described in this application again. For example, a facial recognition method is used as an example” [0125]. Thus, the user is authenticated by the detection system by using facial recognition based on sensor data.);
wherein the user-specific body gesture is one of a cutting gesture through a hand motion that turns off a system of the vehicle when the user is outside the vehicle and ignored inside the vehicle, a slashing gesture that turns off the system of the vehicle when the user is outside the vehicle and ignored inside the vehicle, and a keying motion that controls the system (“Optionally, with reference to the second aspect or the first possible implementation of the second aspect to the seventh possible implementation of the second aspect, in an eighth possible implementation, the sensor is further configured to obtain a start instruction when the vehicle is in a parking state; and the sensor is configured to: when learning that the start instruction matches a start instruction prestored in the vehicle, obtain the body movement of the target person” [0026]. The start instruction which is prestored is required in order to obtain the body movement instruction of the target person. Such a start instruction will be considered as a keying motion because it allows the user to commence an instruction for unparking a vehicle. In one regard, Examiner considers the start instruction motion as the keying motion as it provides a user to gain control of the vehicle navigation (see Merriam Webster definition for key). In another regard, Examiner considers the start instruction motion as the keying motion because it performs the same type of instruction as would be considered for inserting the key to start the car in that this step is required before the vehicle commences driving. Thus, the user-specific body gesture for the start instruction is one of a keying motion that controls the system. Note that Applicant merely provides an example as to what a “keying motion” is and as such the description of such an example in the specification is not limiting with respect to Examiner’s interpretation.).
Regarding claim 8, Wu discloses the detection system of claim 1, wherein:
the obstacle is one of a wall and a person within a boundary area around the vehicle (“A typical application scenario of the solutions provided in this application is as follows: A vehicle parks in a narrow area. When a driver or a passenger expects to enter the vehicle, due to the narrow space, a vehicle door easily collides with a surrounding obstacle (for example, a wall or a vehicle) if the door is forcibly opened” [0104]. Thus, the obstacle may be a wall within the boundary area around the vehicle which the door may collide with. See also Fig. 8 in which the obstacle blocking the vehicle task is a person within the boundary area around the vehicle.); and
the obstacle is proximate to one of a door and a tailgate associated with the vehicle (As described throughout, the obstacle is proximate to a door associated with the vehicle.).
Regarding claim 9, Wu discloses the detection system of claim 1,
wherein the vehicle state is one of an open window, objects left in the vehicle, a person occupying the vehicle, an operator walking away from the vehicle, an authorized person outside the vehicle, and a weather forecast (The vehicle state may be an authorized person outside the vehicle (throughout the specification, the vehicle performs the unparking action based on the position of the authorized person) or a person occupying the vehicle (In [0149], a person inside of the vehicle prohibits the identification of any person around the vehicle such that the person is not frightened by an unexpected movement of the vehicle).), and
the parameter includes factoring a change from the vehicle state (The disclosure factors changes from the vehicle state such as a proximity to an authenticated user or the detection of no such person occupying the vehicle in order to determine completion of a parking maneuver.).
Regarding claim 10, Wu discloses non-transitory computer-readable medium comprising: instructions that when executed by a processor (“Some or all functions of the intelligent vehicle 100 are controlled by the computer system 112. The computer system 112 may include at least one processor 113, and the processor 113 executes instructions 115 stored in a non-transient computer-readable medium such as a memory 114” [0096]. Thus, there is a non-transient computer-readable medium storing instructions that when executed by the processor perform any of the disclosed methods of the intelligent vehicle.) cause the processor to:
detect a gesture command for an automated action from a user outside of a vehicle using sensor data, wherein the user is authenticated prior to accepting the gesture command (Fig. 4 shows a method flowchart which indicates that a person in the first range around the vehicle is detected and authenticated as a target person prior to accepting a gesture command which is detected to control the vehicle to travel to a first location, i.e., gesture command for an automated action.);
predict an obstacle for an incomplete task of the automated action from a vehicle state using the sensor data subsequent to the authentication and notify the user (“In a possible manner, if a passing condition of an ambient environment of the vehicle allows (for example, no obstacle), in response to the body movement of the target person, the ego vehicle may control, in a timely manner, the ego vehicle to travel out of the parking place; or if a passing condition of an ambient environment of the vehicle does not allow, in response to the body movement, the ego vehicle may suspend control of the ego vehicle to travel out of the parking place, or send prompt information (for example, voice or light blinking) to notify the driver or the passenger that it is not suitable to travel out of the parking place in a current scenario” [0147]. Thus, there is a prediction of a passing condition of an ambient environment which does not allow the vehicle to travel out of the parking space, i.e., an obstacle for an incomplete task of the automated action from the vehicle state using sensor data, and notifies the user via voice or light blinking. Given that the user is authenticated as a target person prior to performing a gesture command, this prediction must occur subsequent to the authentication.), wherein the obstacle causes a halt of the automated action according to global positioning information about the vehicle (“When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and then performs a stop action” [0147]. “The sensor system 104 may include several sensors that sense information about an ambient environment of the intelligent vehicle 100. For example, the sensor system 104 may include a global positioning system 122 (the positioning system may be a global positioning system GPS, or may be a BeiDou system or another positioning system), an inertial measurement unit (inertial measurement unit, IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130” [0090]. Thus, the obstacle causes the vehicle to perform a stop action, i.e., halt the automated action. The detection of the obstacle is based on a detection of the ambient environment which is based on the sensor system inclusive of a global positioning system.); and
upon a corrective response subsequent to the halt that is an automated vehicle command different than the gesture command satisfying a parameter to automatically avoid the obstacle, execute the incomplete task for the automated action by an automated driving system moving the vehicle from a stop position, wherein the corrective response is associated with a gesture of a user hand that is different than the gesture command (“In a possible implementation, description is further provided based on the scenario shown in FIG. 8. When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and the vehicle may further continuously monitor the body movement of the target person, to wait for a next operation of the target person. When an instruction corresponding to a new body movement is not received, the vehicle switches from a stop state to an execution state, to continue to execute an instruction corresponding to a previous body movement until the action is completed. If an instruction corresponding to a new body movement is received, based on a setting made in advance, the vehicle may choose to execute the instruction corresponding to the new body movement, or may choose to continue to execute an instruction corresponding to a previous body movement until the action is completed and then execute the instruction corresponding to the new body movement” [0147]. Thus, subsequent to the halt, a new body movement, i.e., corrective response associated with a gesture of a user hand that is different from the original gesture command, results in a new automated vehicle command which satisfies the current condition of the ambient environment, i.e., satisfies a parameter to automatically avoid the obstacle, and is received to execute the incomplete task for the automated action by moving the vehicle from a stop position. The control system 106 described in [0092] will be considered as the automated driving system for moving the vehicle.).
Regarding claim 11, Wu teaches the non-transitory computer-readable medium of claim 10, wherein the instructions for the corrective response to the obstacle satisfying the parameter further include instructions to:
move by the vehicle automatically a door position and a mirror position associated with the vehicle that avoids the obstacle (See Fig. 2 and Figs. 2a-f which move automatically a door and mirror position associated with the vehicle to avoid collisions with a surrounding vehicle, i.e., obstacle.), the parameter is a safety area around the vehicle and factors the global positioning information (“…the first range may alternatively be understood as a range in which a sensor system of the vehicle can perform sensing” [0006]. Thus, the condition of the ambient environment, i.e., parameter, is a first range in which a sensor system can perform sensing, i.e., a safety area around the vehicle and factors the global positioning information.).
Regarding claim 12, Wu teaches a method (“With reference to the foregoing descriptions, an embodiment of this application provides a vehicle summoning method, and the method may be applied to the intelligent vehicle 100 shown in FIG. 1” [0103]. ) comprising:
detecting a gesture command for an automated action from a user outside of a vehicle using sensor data, wherein the user is authenticated prior to accepting the gesture command (Fig. 4 shows a method flowchart which indicates that a person in the first range around the vehicle is detected and authenticated as a target person prior to accepting a gesture command which is detected to control the vehicle to travel to a first location, i.e., gesture command for an automated action.);
predicting an obstacle for an incomplete task of the automated action from a vehicle state using the sensor data subsequent to the authentication and notifying the user (“In a possible manner, if a passing condition of an ambient environment of the vehicle allows (for example, no obstacle), in response to the body movement of the target person, the ego vehicle may control, in a timely manner, the ego vehicle to travel out of the parking place; or if a passing condition of an ambient environment of the vehicle does not allow, in response to the body movement, the ego vehicle may suspend control of the ego vehicle to travel out of the parking place, or send prompt information (for example, voice or light blinking) to notify the driver or the passenger that it is not suitable to travel out of the parking place in a current scenario” [0147]. Thus, there is a prediction of a passing condition of an ambient environment which does not allow the vehicle to travel out of the parking space, i.e., an obstacle for an incomplete task of the automated action from the vehicle state using sensor data, and notifies the user via voice or light blinking. Given that the user is authenticated as a target person prior to performing a gesture command, this prediction must occur subsequent to the authentication.), wherein the obstacle causes a halt of the automated action according to global positioning information about the vehicle (“When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and then performs a stop action” [0147]. “The sensor system 104 may include several sensors that sense information about an ambient environment of the intelligent vehicle 100. For example, the sensor system 104 may include a global positioning system 122 (the positioning system may be a global positioning system GPS, or may be a BeiDou system or another positioning system), an inertial measurement unit (inertial measurement unit, IMU) 124, a radar 126, a laser rangefinder 128, and a camera 130” [0090]. Thus, the obstacle causes the vehicle to perform a stop action, i.e., halt the automated action. The detection of the obstacle is based on a detection of the ambient environment which is based on the sensor system inclusive of a global positioning system.); and
upon a corrective response subsequent to the halt that is an automated vehicle command different than the gesture command satisfying a parameter to automatically avoid the obstacle, executing the incomplete task for the automated action by an automated driving system (ADS) moving the vehicle from a stop position, wherein the corrective response is associated with a gesture of a user hand that is different than the gesture command (“In a possible implementation, description is further provided based on the scenario shown in FIG. 8. When the vehicle is blocked by an obstacle and cannot travel forward, the vehicle enters a stop state, and the vehicle may further continuously monitor the body movement of the target person, to wait for a next operation of the target person. When an instruction corresponding to a new body movement is not received, the vehicle switches from a stop state to an execution state, to continue to execute an instruction corresponding to a previous body movement until the action is completed. If an instruction corresponding to a new body movement is received, based on a setting made in advance, the vehicle may choose to execute the instruction corresponding to the new body movement, or may choose to continue to execute an instruction corresponding to a previous body movement until the action is completed and then execute the instruction corresponding to the new body movement” [0147]. Thus, subsequent to the halt, a new body movement, i.e., corrective response associated with a gesture of a user hand that is different from the original gesture command, results in a new automated vehicle command which satisfies the current condition of the ambient environment, i.e., satisfies a parameter to automatically avoid the obstacle, and is received to execute the incomplete task for the automated action by moving the vehicle from a stop position. The control system 106 described in [0092] will be considered as the automated driving system for moving the vehicle.).
Regarding claim 13, Wu discloses the method of claim 12, wherein the corrective response to the obstacle satisfying the parameter further includes:
moving by the vehicle automatically a door position and a mirror position associated with the vehicle that avoids the obstacle (See Fig. 2 and Figs. 2a-f which move automatically a door and mirror position associated with the vehicle to avoid collisions with a surrounding vehicle, i.e., obstacle.), the parameter is a safety area around the vehicle and factors the global positioning information (“…the first range may alternatively be understood as a range in which a sensor system of the vehicle can perform sensing” [0006]. Thus, the condition of the ambient environment, i.e., parameter, is a first range in which a sensor system can perform sensing, i.e., a safety area around the vehicle and factors the global positioning information.).
Regarding claim 14, Wu discloses the method of claim 13, wherein executing the incomplete task for the automated action further includes:
navigating the vehicle within a parking area using commands from the ADS, wherein the gesture command is associated with one of parking and unparking the vehicle (The disclosure describes the specific application of automatically navigating the vehicle within a parking area via gesture commands associated with unparking the vehicle.).
Regarding claim 15, Wu discloses the method of claim 14, wherein the corrective response to the obstacle satisfying the parameter further includes:
delaying the incomplete task until the vehicle state changes (“After the vehicle enters a stop state, the vehicle continuously monitors the ambient environment. The vehicle determines, through environment sensing, that an obstacle blocking state in the ambient environment of the vehicle is removed, and the vehicle switches from a stop state to an execution state, and continues to execute an instruction corresponding to the body movement until the action is completed” [0110]. Thus, the vehicle control is suspended in a stop state, i.e., the incomplete task is delayed, until the blocking state is removed, i.e., the vehicle state changes.); and
exiting a parking spot within the parking area automatically by the vehicle for clearance, and the clearance allows opening a door of the vehicle and changes the vehicle state as the corrective response (In the examples of Figs. 2a-c the vehicle exits the parking spot automatically for clearance such that the door of the vehicle may be open and collision is avoided, i.e., change the vehicle state as the corrective response.).
Regarding claim 16, Wu discloses the method of claim 13, wherein notifying the user further includes:
generating an alert associated with the obstacle, the alert is one of flashing headlights, honking, a verbal alarm, a signal for a wireless device of the user, and a picture for the wireless device (“In a possible implementation, the target person is further notified, through light blinking or sound alarm, that the vehicle is in a stop state” [0147]. Thus, the notification includes one of flashing headlights and a verbal alarm.).
Regarding claim 17, Wu discloses the method of claim 16, wherein the corrective response to the obstacle satisfying the parameter further includes:
receiving a vehicle command from the user according to the vehicle state and the alert, the vehicle command is different than the gesture command (“Before completing execution of the first action, the vehicle keeps monitoring the gesture of the target person and an obstacle in the ambient environment. In a process of executing the first action, when the vehicle receives a second gesture (which is not a “stop” gesture, where if the second gesture is a stop gesture, the vehicle switches to a stop state) made by the target person, the vehicle stops executing the action corresponding to the first gesture of the target person, keeps an execution state unchanged, and executes an action corresponding to the second gesture of the target person. In addition, the vehicle keeps monitoring the gesture of the target person and the obstacle in the ambient environment. Before the target person sends a new gesture instruction, the vehicle always executes the action corresponding to the second gesture until execution of the action is completed. By analogy, the vehicle always and continuously updates, based on a newest gesture instruction sent by the target person, an action currently executed by the vehicle. An application scenario of the mode is as follows: In the mode, when the target person moves along a specific path and continuously sends a “come-over” gesture instruction to the vehicle, if a passing condition of an ambient environment of the vehicle (no obstacle), the vehicle can keep following the target person (including traveling straight, turning, and the like) until all instructions of the target person are executed, and the vehicle is parked in the preset range around the target person, and switches from an execution state to a stop state” [0147]. Thus, the vehicle state is continually updated according to the new gesture received from the target person to monitor the ambient environment and complete the unparking function by avoiding obstacles, i.e., changing a vehicle state. In this case, the second gesture will be considered as a vehicle command different from the gesture command which provides the initial unparking instruction.).
Regarding claim 18, Wu discloses the method of claim 16, wherein detecting the gesture command for the automated action further includes:
inferring by a learning model a feature of the gesture command using the sensor data, the learning model is trained with data about the user and the vehicle state using object perception and semantic labeling, the learning model is a vision model that recognizes a user-specific body gesture (“The target person may be understood as a user who establishes a binding relationship with the vehicle in advance. The vehicle may obtain identifier information of at least one target person in advance. The identifier information includes but is not limited to facial information, figure information, and fingerprint information. The vehicle may locally store the identifier information of the at least one target person, or may obtain the identifier information of the at least one target person from a cloud. In this application, a user whose identifier information is obtained by the vehicle is referred to as the target person, and the vehicle can respond only to a body instruction of the target person and perform an action. A user who does not establish a binding relationship with the vehicle in advance becomes another user, and the vehicle does not respond to a body instruction of the another person” [0123]. “For example, a facial recognition method is used as an example. For the facial recognition method, one or more of the following methods may be used: a feature-based recognition algorithm (feature-based recognition algorithm, FRA), an appearance-based recognition algorithm (appearance-based recognition algorithm, ARA), a template-based recognition algorithm (template-based recognition algorithm, TRA), and a recognition algorithm using neural network (recognition algorithm using neural network, RAUNN). The facial recognition information may include facial feature information, facial curve information, and the like” [0125]. Thus, to identify a target person, the identity of a person is inferred using a learned model. Such a model is trained with data about a user and vehicle state data using object perception and semantic labelling (features of the specific facial recognition disclosed above) such that when a user detected within the ambient environment, i.e., vehicle state in which sensing may occur, is determined to have a “binding relationship” based on the facial recognition information, the vehicle is trained to infer the a feature of this user’s gesture. Such user authenticated gestures are recognized by a vision model as described above.); and
authenticating the user utilizing one of a token from a key fob, a two-factor verification through a mobile application, or facial recognition using the sensor data (“In a possible implementation, when a similarity between the obtained facial information and the identifier information that is of the target person and that is prestored in the vehicle exceeds a threshold, it is determined that the matching succeeds; or when a similarity between the obtained facial information and the identifier information that is of the target person and that is prestored in the vehicle does not exceed a threshold, it is determined that the matching fails. A person skilled in the art may set, based on a requirement, a specific image collection manner and a specific manner of performing identity authentication based on a collected image. Details are not described in this application again. For example, a facial recognition method is used as an example” [0125]. Thus, the user is authenticated by the detection system by using facial recognition based on sensor data.)
wherein the user-specific body gesture is one of a cutting gesture through a hand motion that turns off a system of the vehicle when the user is outside the vehicle and ignored inside the vehicle, a slashing gesture that turns off the system of the vehicle when the user is outside the vehicle and ignored inside the vehicle, or a keying motion that controls the system (“Optionally, with reference to the second aspect or the first possible implementation of the second aspect to the seventh possible implementation of the second aspect, in an eighth possible implementation, the sensor is further configured to obtain a start instruction when the vehicle is in a parking state; and the sensor is configured to: when learning that the start instruction matches a start instruction prestored in the vehicle, obtain the body movement of the target person” [0026]. The start instruction which is prestored is required in order to obtain the body movement instruction of the target person. Such a start instruction will be considered as a keying motion because it allows the user to commence an instruction for unparking a vehicle. In one regard, Examiner considers the start instruction motion as the keying motion as it provides a user to gain control of the vehicle navigation (see Merriam Webster definition for key). In another regard, Examiner considers the start instruction motion as the keying motion because it performs the same type of instruction as would be considered for inserting the key to start the car in that this step is required before the vehicle commences driving. Thus, the user-specific body gesture for the start instruction is one of a keying motion that controls the system. Note that Applicant merely provides an example as to what a “keying motion” is and as such the description of such an example in the specification is not limiting with respect to Examiner’s interpretation.).
Regarding claim 19, Wu discloses the method of claim 12, wherein:
the obstacle is one of a wall and a person within a boundary area around the vehicle (“A typical application scenario of the solutions provided in this application is as follows: A vehicle parks in a narrow area. When a driver or a passenger expects to enter the vehicle, due to the narrow space, a vehicle door easily collides with a surrounding obstacle (for example, a wall or a vehicle) if the door is forcibly opened” [0104]. Thus, the obstacle may be a wall within the boundary area around the vehicle which the door may collide with. See also Fig. 8 in which the obstacle blocking the vehicle task is a person within the boundary area around the vehicle.); and
the obstacle is proximate to one of a door and a tailgate associated with the vehicle (As described throughout, the obstacle is proximate to a door associated with the vehicle.).
Regarding claim 20, Wu discloses the method of claim 12,
wherein the vehicle state is one of an open window, objects left in the vehicle, a person occupying the vehicle, an operator walking away from the vehicle, and a weather forecast (The vehicle state may be an authorized person outside the vehicle (throughout the specification, the vehicle performs the unparking action based on the position of the authorized person) or a person occupying the vehicle (In [0149], a person inside of the vehicle prohibits the identification of any person around the vehicle such that the person is not frightened by an unexpected movement of the vehicle).), and
the parameter includes factoring a change from the vehicle state (The disclosure factors changes from the vehicle state such as a proximity to an authenticated user or the detection of no such person occupying the vehicle in order to determine completion of a parking maneuver.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2017/0200335 A1 teaches a method for detecting a gesture of a user based on proximity of a key fob.
US 2022/0350326 A1 teaches a method for parking and unparking a car in narrow parking spaces using remote user input outside of the vehicle.
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/S.L.M./Examiner, Art Unit 3656
/WADE MILES/Supervisory Patent Examiner, Art Unit 3656
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