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 .
Claims 1, 3-5, 7-11, 13-15 and 17-20 have been examined.
Claims 2, 6, 12 and 16 have been canceled.
P = paragraph e.g. P[0001] = paragraph[0001]
Response to Arguments
Applicant's arguments filed 02/27/2026 have been considered but are moot in view of the new ground(s) of rejection. However, arguments that are relevant to the new grounds of rejection are addressed below.
Applicant's arguments filed 02/27/2026 have been fully considered but they are not persuasive.
The Applicant argues
“However, it is respectfully submitted that while Aragon may disclose a configuration for determining the longitudinal distance between the first and second vehicles, Aragon does not teach or remotely suggests a configuration for geometrically calculating the lateral distance based on the pixel horizontal coordinates of the bounding box, the longitudinal distance from the moving object, and the focal length of the camera, and comparing the calculated lateral distance with a predetermined safety margin distance to determine the collision target”.
The arguments are not persuasive. This argument does not address the combination of references used to reject the limitations argued by the Applicant, therefore, the argument therefore does not address the rejection as written and is moot. For example, the argument “and comparing the calculated lateral distance with a predetermined safety margin distance to determine the collision target” appears to be directed to the amended Claim 1 limitation “by comparing the calculated lateral distance with a predetermined safety margin distance”, however, this limitation is in view of Aragon et al. in combination with Lee et al., not only Aragon et al. as implied by this argument. Therefore, the argument is not directed to the rejection as written and does not clearly address the combination of prior art.
Also, the argument is a conclusory statement that does not provide a clear rebuttal to the rejection of specific claim limitations that are rejected in view of only of Aragon et al. For example, referring to amended Claim 1, Aragon et al. teaches calculating a lateral distance between the object and the moving body (Aragon et al.; “…a lateral distance should also be determined”, see P[0060]) using a pixel horizontal coordinate of the bounding box of the object (Aragon et al.; “…the dynamic distance estimation output platform 102 may use usual object detectors based on deep learning to determine a bounding box that covers the visible dimensions of the leader vehicle such as height and width. In one or more instances, in determining the bounding boxes, the dynamic distance estimation output platform 102 may determine bounding boxes with heights that extend from the roof of the leader vehicle to the point of contact of the wheels with the road (e.g., ground plane)”, see P[0057]), the calculated relative distance (Aragon et al.; “…the lateral distance estimation process and it shows the extension of the leader vehicle's backside plane with dashed lines”, see P[0105]), and a focal length of the image capturing device (Aragon et al.; “…F is the camera's focal length…”, see P[0083] and P[0082], also see P[0039] and P[0057]), and the Applicant provides no rebuttal to each of these citations of Aragon et al. as applied to each specific claim limitation as seen above. Therefore, the arguments are not persuasive.
The Applicant further argues
“However, it is respectfully submitted that while Lee may discloses a configuration for selecting the predicted lateral position of the moving object predicted to collide with the vehicle based on the lateral movement of the moving object, Lee does not teach or remotely suggests a configuration for geometrically calculating the lateral distance based on the pixel horizontal coordinates of the bounding box, the longitudinal distance from the moving object, and the focal length of the camera, and comparing the calculated lateral distance with a predetermined safety margin distance to determine the collision target. In particular, it is respectfully submitted that the claimed lateral distance is the actual lateral distance geometrically calculated at the time of longitudinal distance calculation, not the predicted location. Furthermore, the claimed safety margin distance considers not only the physical collision but also the clearance, which differs from the physical vehicle width in Lee”.
The arguments are not persuasive. Specifically, the argument “Lee does not teach or remotely suggests a configuration for geometrically calculating the lateral distance based on the pixel horizontal coordinates of the bounding box, the longitudinal distance from the moving object, and the focal length of the camera” is directed to claim limitations that are rejected under Aragon et al., not Lee et al., therefore, this argument is not directed to the rejection as written.
Regarding the argument “In particular, it is respectfully submitted that the claimed lateral distance is the actual lateral distance geometrically calculated at the time of longitudinal distance calculation, not the predicted location”, this argument is not directed to the claims as written, as there are no claim limitations that recite that the claimed “lateral distance” is “the actual lateral distance geometrically calculated at the time of longitudinal distance calculation, not the predicted location”. Therefore, this argument is moot as it is not directed to the claims as written.
Regarding the argument “Furthermore, the claimed safety margin distance considers not only the physical collision but also the clearance, which differs from the physical vehicle width in Lee”, there are no claim limitations that recite any “clearance”, and the specification also does not recite the word “clearance”, therefore, this argument is moot as it is not directed to the claims as written.
The Examiner notes that regarding the amended Claim 1 limitation “by comparing the calculated lateral distance with a predetermined safety margin distance” and the Applicant’s argument “it is respectfully submitted that the claimed lateral distance is the actual lateral distance geometrically calculated at the time of longitudinal distance calculation, not the predicted location”, there are no limitations in Claim 1 that exclude the lateral prediction position of Lee et al.
Furthermore, the Examiner notes that Lee et al. teaches determining if a predicted lateral position is within a “safety margin distance” such as a vehicle width as seen in FIG. 4 of Lee et al., and additionally, this prediction of a lateral position is performed using a velocity of an object (Lee et al.; see P[0033]), and as seen in Equation 2 of Lee et al. in P[0035], if the velocity of the object is zero, the predicted lateral position “dLat2” would then be equivalent to the current lateral position “dLat1”, therefore, Lee et al. renders obvious comparing even a non-predicted lateral distance to a “predetermined safety margin distance”, as a non-moving object lateral prediction position would be the same as the current lateral position of the non-moving object. Therefore, the combination of Aragon et al. and Lee et al. renders obvious comparing both a current and a predicted lateral position to a “predetermined safety margin distance”, and the arguments are not persuasive.
The Applicant further argues
“However, it is respectfully submitted that while Graves may discloses a configuration that variably determines a safety margin distance based on the type of moving object, its driving speed, etc., Graves does not teach or remotely suggests a configuration that geometrically calculates a lateral distance based on the pixel horizontal coordinates of the bounding box, the longitudinal distance from the moving object, and the focal length of the camera, and compares the calculated lateral distance with a predetermined safety margin distance to determine a collision target”
However, this argument does not address the specific claim limitations that are rejected in view of Graves, and instead appears to argue Claim 1 limitations that are not rejected in view of Graves, therefore, this argument is moot as it is not directed to the rejection as written.
All claims are rejected. See the new grounds of rejection.
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, 3, 5, 7-11, 13, 15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aragon et al. (2020/0238991) in view of Lee (2016/0016561).
Regarding Claim 1, Aragon et al. teaches the claimed collision avoidance method of a moving body collision avoidance device, the collision avoidance method comprising:
acquiring a driving image captured by an image capturing device, wherein the driving image includes at least one of an object (“…the camera 104 (mounted on the ego-vehicle) may capture images…”, see P[0043]);
recognizing the object in the acquired driving image using a neural network model (“…the neural network may work as a classifier that provides a one if a vehicle back-side is detected and a zero if no back-side is detected”, see P[0080] and “The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists…”, see P[0105]) and outputting a bounding box of the recognized object (“The dynamic distance estimation output platform 102 may use the neural network trained on the vehicle back-side appearance within the bounding box by extracting an image window from it. In the description below, it may be assumed that the vehicle that was detected, by the dynamic distance estimation output platform 102 using the deep learning bounding box…”, see P[0081]); [[and]]
calculating a relative distance between a moving body and the object (“In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105]);
calculating a lateral distance between the object and the moving body (“…a lateral distance should also be determined”, see P[0060]) using a pixel horizontal coordinate of the bounding box of the object (“…the dynamic distance estimation output platform 102 may use usual object detectors based on deep learning to determine a bounding box that covers the visible dimensions of the leader vehicle such as height and width. In one or more instances, in determining the bounding boxes, the dynamic distance estimation output platform 102 may determine bounding boxes with heights that extend from the roof of the leader vehicle to the point of contact of the wheels with the road (e.g., ground plane)”, see P[0057]), the calculated relative distance (“…the lateral distance estimation process and it shows the extension of the leader vehicle's backside plane with dashed lines”, see P[0105]), and a focal length of the image capturing device (“…F is the camera's focal length…”, see P[0083] and P[0082], also see P[0039] and P[0057]); and
determining whether the object is a collision target …(“The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists, then the dynamic distance estimation output platform 102 may can exploit the methodologies described herein to provide the information on the trajectory. In one or more instances, the bounding box that contains the bicyclist image may be used by the dynamic distance estimation output platform 102 to determine his or her position. In these instances, the dynamic distance estimation output platform 102 may use the bottom of the bounding box in the same way as described above for the vehicles to determine position with respect to the ego-vehicle's camera (e.g., camera 104). In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105] and “This detection by the dynamic distance estimation output platform 102 may include static objects that might not move but that may be on the current trajectory of the ego-vehicle, which may trigger a collision warning for the ego-vehicle…The interval of 0.5 seconds may be reduced when the absolute of the ego-vehicle increases so that the detection of moving objects may be performed by the dynamic distance estimation output platform 102 fast enough so that warnings of possible future collisions may be provided with enough anticipation”, see P[0112], where determination of an object that may collide with the ego-vehicle and that results in triggering a collision warning is equivalent to “determining whether the object is a collision target”).
Aragon et al. does not expressly recite the bolded portions of the claimed
determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance.
However, Lee (2016/0016561) teaches wherein the determining whether the object is a collision target includes calculating lateral distance of object, and determining whether the object is a collision target by comparing the calculated lateral distance and a safety margin distance (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 3, Aragon et al. does not expressly recite the claimed collision avoidance method of claim [[2]] 1,
wherein the safety margin distance is a minimum distance in [[the]] a Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction.
However, Lee (2016/0016561) teaches wherein the safety margin distance is a minimum distance in the Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the safety margin distance is a minimum distance in a Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 5, Aragon et al. teaches the claimed collision avoidance method of claim 1, wherein the neural network model recognizes the object in the driving image, and outputs [[a]] the bounding box indicating an object area in the driving image and a classification result for the object in the bounding box (“The dynamic distance estimation output platform 102 may use the neural network trained on the vehicle back-side appearance within the bounding box by extracting an image window from it. In the description below, it may be assumed that the vehicle that was detected, by the dynamic distance estimation output platform 102 using the deep learning bounding box…”, see P[0081]).
Regarding Claim 7, Aragon et al. does not expressly recite the claimed collision avoidance method of claim [[2]] 1,
wherein the determining whether the object is [[a]] the collision target includes determining the object in which the calculated lateral distance is smaller than the safety margin distance, as [[a]] the collision target of the moving body, and determining the object in which the lateral distance is greater than the safety margin distance as a non-collision target.
However, Lee (2016/0016561) teaches wherein the determining whether the object is the collision target includes determining the object in which the calculated lateral distance is smaller than the safety margin distance, as the collision target of the moving body, and determining the object in which the lateral distance is greater than the safety margin distance as a non-collision target (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the determining whether the object is the collision target includes determining the object in which the calculated lateral distance is smaller than the safety margin distance, as the collision target of the moving body, and determining the object in which the lateral distance is greater than the safety margin distance as a non-collision target, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 8, Aragon et al. does not expressly recite the claimed collision avoidance method of claim 1, further including:
calculating a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined.
However, Lee (2016/0016561) teaches calculating a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined (Lee; “…if the distance between the PCS lateral position and the own vehicle line O is less than the predetermined value, TTC (Time-To-Collision) concerning the present object is calculated by a method of, for example, dividing the distance between the object and the own vehicle by a relative speed of the object (S32)”, see P[0051]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and calculating a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 9, Aragon et al. does not expressly recite the claimed collision avoidance method of claim 8, further including:
controlling an operation of the moving body based on the calculated required collision time.
However, Lee (2016/0016561) teaches controlling an operation of the moving body based on the calculated required collision time (Lee; “…if the distance between the PCS lateral position and the own vehicle line O is less than the predetermined value, TTC (Time-To-Collision) concerning the present object is calculated by a method of, for example, dividing the distance between the object and the own vehicle by a relative speed of the object (S32)”, see P[0051] and “Next, the vehicle control section 16 compares the TTC with an operation timing T1 of the brake unit 30 (S33). If the TTC is the operation timing T1 or less, it means that the TTC has reached the operation timing T1. Hence, the vehicle control section 16 transmits a drive signal to the brake unit 30 (S34)”, see P[0052]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and controlling an operation of the moving body based on the calculated required collision time, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 10, Aragon et al. does not expressly recite the claimed collision avoidance method of claim 9,
wherein the controlling [[an]] the operation of the moving body includes providing a collision warning guidance or controlling the moving body to be braked when there is a possibility of colliding with the object.
However, Aragon et al. does teach providing a warning if a collision may occur based on a distance estimation with respect to an object (“This detection by the dynamic distance estimation output platform 102 may include static objects that might not move but that may be on the current trajectory of the ego-vehicle, which may trigger a collision warning for the ego-vehicle”, see P[0112]).
Furthermore, Lee (2016/0016561) teaches wherein the controlling the operation of the moving body includes providing a collision warning guidance or controlling the moving body to be braked when there is a possibility of colliding with the object (Lee; “…if the distance between the PCS lateral position and the own vehicle line O is less than the predetermined value, TTC (Time-To-Collision) concerning the present object is calculated by a method of, for example, dividing the distance between the object and the own vehicle by a relative speed of the object (S32)”, see P[0051] and “Next, the vehicle control section 16 compares the TTC with an operation timing T1 of the brake unit 30 (S33). If the TTC is the operation timing T1 or less, it means that the TTC has reached the operation timing T1. Hence, the vehicle control section 16 transmits a drive signal to the brake unit 30 (S34)”, see P[0052]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the controlling the operation of the moving body includes providing a collision warning guidance or controlling the moving body to be braked when there is a possibility of colliding with the object, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 11, Aragon et al. teaches the claimed moving body collision avoidance device comprising:
an image acquiring unit acquiring a driving image captured by an image capturing device, wherein the driving image includes at least one of an object (“…the camera 104 (mounted on the ego-vehicle) may capture images…”, see P[0043]);
an object recognizing unit recognizing the object in the acquired driving image using a neural network model (“…the neural network may work as a classifier that provides a one if a vehicle back-side is detected and a zero if no back-side is detected”, see P[0080] and “The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists…”, see P[0105]) and outputting a bounding box of the recognized object (“The dynamic distance estimation output platform 102 may use the neural network trained on the vehicle back-side appearance within the bounding box by extracting an image window from it. In the description below, it may be assumed that the vehicle that was detected, by the dynamic distance estimation output platform 102 using the deep learning bounding box…”, see P[0081]);
a calculating unit calculating a relative distance between a moving body and the object (“In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105]), calculating a lateral distance between the object and the moving body (“…a lateral distance should also be determined”, see P[0060]) using a pixel horizontal coordinate of the bounding box of the object (“…the dynamic distance estimation output platform 102 may use usual object detectors based on deep learning to determine a bounding box that covers the visible dimensions of the leader vehicle such as height and width. In one or more instances, in determining the bounding boxes, the dynamic distance estimation output platform 102 may determine bounding boxes with heights that extend from the roof of the leader vehicle to the point of contact of the wheels with the road (e.g., ground plane)”, see P[0057]), the calculated relative distance (“…the lateral distance estimation process and it shows the extension of the leader vehicle's backside plane with dashed lines”, see P[0105]), and a focal length of the image capturing device (“…F is the camera's focal length…”, see P[0083] and P[0082], also see P[0039] and P[0057])[[;]], and …(“The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists, then the dynamic distance estimation output platform 102 may can exploit the methodologies described herein to provide the information on the trajectory. In one or more instances, the bounding box that contains the bicyclist image may be used by the dynamic distance estimation output platform 102 to determine his or her position. In these instances, the dynamic distance estimation output platform 102 may use the bottom of the bounding box in the same way as described above for the vehicles to determine position with respect to the ego-vehicle's camera (e.g., camera 104). In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105] and “This detection by the dynamic distance estimation output platform 102 may include static objects that might not move but that may be on the current trajectory of the ego-vehicle, which may trigger a collision warning for the ego-vehicle…The interval of 0.5 seconds may be reduced when the absolute of the ego-vehicle increases so that the detection of moving objects may be performed by the dynamic distance estimation output platform 102 fast enough so that warnings of possible future collisions may be provided with enough anticipation”, see P[0112], where determination of an object that may collide with the ego-vehicle and that results in triggering a collision warning is equivalent to “determining whether the object is a collision target”).
Aragon et al. does not expressly recite the bolded portions of the claimed
determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance.
However, Lee (2016/0016561) teaches determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 13, Aragon et al. does not expressly recite the claimed collision avoidance method device of claim [[12]] 11,
wherein the safety margin distance is a minimum distance in [[the]] a Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction.
However, Lee (2016/0016561) teaches wherein the safety margin distance is a minimum distance in a Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the safety margin distance is a minimum distance in a Y-axis direction at which the moving body and the object do not collide with each other when the moving body moves in a traveling direction, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 15, Aragon et al. teaches the claimed collision avoidance device of claim 11,
wherein the neural network model recognizes the object in the driving image, and outputs [[a]] the bounding box indicating an object area in the driving image and a classification result for the object in the bounding box (“The dynamic distance estimation output platform 102 may use the neural network trained on the vehicle back-side appearance within the bounding box by extracting an image window from it. In the description below, it may be assumed that the vehicle that was detected, by the dynamic distance estimation output platform 102 using the deep learning bounding box…”, see P[0081]).
Regarding Claim 17, Aragon et al. does not expressly recite the claimed collision avoidance device of claim [[12]] 11,
wherein the calculating unit determines the object in which the calculated lateral distance is smaller than the safety margin distance, as [[a]] the collision target of the moving body, and determines the object in which the lateral distance is greater than the safety margin distance as a non-collision target.
However, Lee (2016/0016561) teaches wherein the calculating unit determines the object in which the calculated lateral distance is smaller than the safety margin distance, as the collision target of the moving body, and determines the object in which the lateral distance is greater than the safety margin distance as a non-collision target (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the calculating unit determines the object in which the calculated lateral distance is smaller than the safety margin distance, as the collision target of the moving body, and determines the object in which the lateral distance is greater than the safety margin distance as a non-collision target, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 18, Aragon et al. does not expressly recite the claimed collision avoidance device of claim 11,
wherein the calculating unit calculates a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined.
However, Lee (2016/0016561) teaches calculating a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined (Lee; “…if the distance between the PCS lateral position and the own vehicle line O is less than the predetermined value, TTC (Time-To-Collision) concerning the present object is calculated by a method of, for example, dividing the distance between the object and the own vehicle by a relative speed of the object (S32)”, see P[0051]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and wherein the calculating unit calculates a required collision time between the object and the moving body based on the calculated relative distance when a collision target object is determined, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 19, Aragon et al. does not expressly recite the claimed collision avoidance device of claim 18, further including:
a controller controlling an operation of the moving body based on the calculated required collision time.
However, Lee (2016/0016561) teaches controlling an operation of the moving body based on the calculated required collision time (Lee; “…if the distance between the PCS lateral position and the own vehicle line O is less than the predetermined value, TTC (Time-To-Collision) concerning the present object is calculated by a method of, for example, dividing the distance between the object and the own vehicle by a relative speed of the object (S32)”, see P[0051] and “Next, the vehicle control section 16 compares the TTC with an operation timing T1 of the brake unit 30 (S33). If the TTC is the operation timing T1 or less, it means that the TTC has reached the operation timing T1. Hence, the vehicle control section 16 transmits a drive signal to the brake unit 30 (S34)”, see P[0052]).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and the collision avoidance device of claim 18, further including a controller controlling an operation of the moving body based on the calculated required collision time, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Regarding Claim 20, Aragon et al. teaches the claimed electronic device for a moving body, the electronic device comprising:
an image capturing unit capturing a driving image of the moving body (“…the camera 104 (mounted on the ego-vehicle) may capture images…”, see P[0043]);
an image acquiring unit acquiring [[a]] the driving image captured by the image capturing unit (see P[0038] and “Referring to FIG. 2B, at step 206, the camera 104 may send a video output based on the captured video to the dynamic distance estimation output platform 102”, see P[0044]);
a recognizing unit recognizing an object in the acquired driving image using a neural network model (“…the dynamic distance estimation output platform 102 may use a neural network…”, see P[0079] and “…the neural network may work as a classifier that provides a one if a vehicle back-side is detected and a zero if no back-side is detected”, see P[0080] and “The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists…”, see P[0105]) and outputting a bounding box of the recognized object (“The dynamic distance estimation output platform 102 may use the neural network trained on the vehicle back-side appearance within the bounding box by extracting an image window from it. In the description below, it may be assumed that the vehicle that was detected, by the dynamic distance estimation output platform 102 using the deep learning bounding box…”, see P[0081]);
a calculating unit calculating a relative distance between the moving body and the object (“In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105]), calculating a lateral distance between the object and the moving body (“…a lateral distance should also be determined”, see P[0060]) using a pixel horizontal coordinate of the bounding box of the object (“…the dynamic distance estimation output platform 102 may use usual object detectors based on deep learning to determine a bounding box that covers the visible dimensions of the leader vehicle such as height and width. In one or more instances, in determining the bounding boxes, the dynamic distance estimation output platform 102 may determine bounding boxes with heights that extend from the roof of the leader vehicle to the point of contact of the wheels with the road (e.g., ground plane)”, see P[0057]), the calculated relative distance (“…the lateral distance estimation process and it shows the extension of the leader vehicle's backside plane with dashed lines”, see P[0105]), and a focal length of the image capturing device (“…F is the camera's focal length…”, see P[0083] and P[0082], also see P[0039] and P[0057]) , and determining whether the object is a collision target …(“The deep learning bounding box may be used by the dynamic distance estimation output platform 102 to provide useful information for driving risk assessments (or any other driving related task) for objects other than vehicles. Accordingly, since the dynamic distance estimation output platform 102 may use deep learning to detect bicyclists, then the dynamic distance estimation output platform 102 may can exploit the methodologies described herein to provide the information on the trajectory. In one or more instances, the bounding box that contains the bicyclist image may be used by the dynamic distance estimation output platform 102 to determine his or her position. In these instances, the dynamic distance estimation output platform 102 may use the bottom of the bounding box in the same way as described above for the vehicles to determine position with respect to the ego-vehicle's camera (e.g., camera 104). In these instances, longitudinal and lateral distances to the bicyclists may be provided by the dynamic distance estimation output platform”, see P[0105] and “This detection by the dynamic distance estimation output platform 102 may include static objects that might not move but that may be on the current trajectory of the ego-vehicle, which may trigger a collision warning for the ego-vehicle…The interval of 0.5 seconds may be reduced when the absolute of the ego-vehicle increases so that the detection of moving objects may be performed by the dynamic distance estimation output platform 102 fast enough so that warnings of possible future collisions may be provided with enough anticipation”, see P[0112], where determination of an object that may collide with the ego-vehicle and that results in triggering a collision warning is equivalent to “determining whether the object is a collision target”); and
an output unit outputting information for guiding driving of the moving body based on the determined collision target (“This detection by the dynamic distance estimation output platform 102 may include static objects that might not move but that may be on the current trajectory of the ego-vehicle, which may trigger a collision warning for the ego-vehicle”, see P[0112]).
Aragon et al. does not expressly recite the bolded portions of the claimed
determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance.
However, Lee (2016/0016561) teaches wherein the determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance (Lee; “In the case of the lateral prediction position P4 of the pedestrian, since it is located outside the vehicle width, the emergency braking is not operated unlike the related art. The lateral prediction position P4′ of the pedestrian is an intermediate position between a start position P1 and a final position P4. Even though the lateral prediction position P4′ of the pedestrian is located within the warning control distance, the warning or the braking is not performed because the collision does not occur at the lateral prediction position P4 of the pedestrian”, see P[0054] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Lee, and determining whether the object is a collision target by comparing the calculated lateral distance with a predetermined safety margin distance, as rendered obvious by Lee, in order to provide “an improved emergency braking system” (Lee; see P[0010]).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Aragon et al. (2020/0238991) in view of Lee (2016/0016561) further in view of Graves (2020/0276988).
Regarding Claim 4, Aragon et al. does not expressly recite the claimed collision avoidance method of claim [[2]] 1,
wherein the determining whether the recognized object is [[a]] the collision target includes determining the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of [[an]] a user of the moving body.
However, Graves (2020/0276988) teaches determining the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of a user of the moving body (Graves; see P[0084] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Graves, and wherein the determining whether the recognized object is the collision target includes determining the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of a user of the moving body, as rendered obvious by Graves, in order to provide for “controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle” (Graves; see Abstract).
Regarding Claim 14, Aragon et al. does not expressly recite the claimed collision avoidance method device of claim [[12]] 11,
wherein the calculating unit determines the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of a user of the moving body.
However, Graves (2020/0276988) teaches determining the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of a user of the moving body (Graves; see P[0084] and FIG. 4).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Aragon et al. with the teachings of Graves, and wherein the calculating unit determines the safety margin distance variably based on at least one of a type of the moving body, a driving speed of the moving body, a driving location of the moving body and a physical condition of a user of the moving body, as rendered obvious by Graves, in order to provide for “controlling safety of both an ego vehicle and social objects in an environment of the ego vehicle” (Graves; see Abstract).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ISAAC G SMITH/ Primary Examiner, Art Unit 3662
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