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 .
Response to Arguments
Applicant’s arguments, see page 8, filed 05/19/2025, with respect to the rejection of claims 3, 4, 10, 11, 16, and 17 under 35 USC 112(b) have been fully considered and are persuasive in light of the amended claims. The rejection of 02/19/2025 has been withdrawn.
Applicant's arguments filed 05/19/2025 with respect to the rejection under 35 USC 103 have been fully considered but they are not persuasive. The applicant argues that Xu et al. fails to teach approximating a trajectory of a trailer using a model, instead merely determining a kinematic relationship between the trailer and a vehicle to which it is connected. However, paragraphs [0084-0102] of Xu et al. disclose the calculations by which a trajectory of the trailer and a potential collision point are determined. In particular, paragraphs [0095-0100] disclose specifically the calculations for determining an inner trailer boundary line and whether or not its travel path falls within a virtual circle indicating that a sideswipe may occur. The rejection below has been clarified to this effect, showing how the model is based on position.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 2, 5, 8, 9, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. (US 20210070362, previously cited) in view of Yue et al. (CN 107628134, previously cited).
Claim 1.
Xu et al. teaches:
a computer processor associated with the [[robot]]
(Xu – [0059]) “the controller 28 is configured with a microprocessor 84”
track movement of a [[robot]] moving forward
(Xu – [0104]) “when the object 15 is determined to be in the travel path of the vehicle 14 as the vehicle 14 is turning to the right”
[Examiner Note: In order to determine if an object is in the travel path, the travel path of the vehicle must be tracked.]
approximate, using a model, a trajectory of at least one trailer attached to a back of the [[robot]] that is different from the path of the [[robot]] to which the at least one trailer is attached, wherein the trajectory is based on the movement of the [[robot]] and the model is based on a function of the [[robot’s]] positions
(Xu – [0002]) “A trailer being towed by a vehicle does not follow the exact path of the vehicle as the vehicle turns.”
(Xu – [0061]) “determine a kinematic relationship between the vehicle 14 and the trailer 12 for use in the trailer sideswipe avoidance routine 98.”
(Xu – [0095]) “the sideswipe avoidance system 10 may utilize an inner trailer boundary line 90 extending between point A and point B, where point A is an intersection between an inner side 13 of the trailer 12 and a line extending outward along the axis of a trailer and a line extending outward along the axis of a trailer axle, and point B is a point displaced from point A a distance equal to the length of trailer wheel base D in the trailer forward direction substantially parallel to a longitudinal centerline of the trailer 12. … The location of point A(xA, yA) may be determined with the position of the trailer (xt, yt), the trailer yaw rate
ω
2
, the trailer width Tw, and the trailer yaw angle
β
.”
identify an obstacle
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
determine a distance of the trailer from the obstacle based on the estimated trajectory
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
determine that the trailer will collide with the obstacle along the estimated trajectory even when the robot will not collide with the obstacle, when the distance fails to satisfy a threshold distance from the obstacle
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
move the trajectory of the [[robot]] away from the obstacle wherein the robot moves along the moved trajectory on the surface to prevent collision of the trailer with the obstacle
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
However, Xu et al. does not teach a robot. Yue teaches:
a robot
(Yue – [0008]) “The robot is connected to the trailer robot through an articulation method with springs”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the edge avoidance system of Xu et al. with the robot with trailer of Yue. One would have been motivated to do this to take advantage of the ability of the system disclosed by Xu et al. to issue autonomous steering inputs (Xu et al. – [0054]).
Claim 2.
The combination of Xu et al. and Yue teaches all the limitations of claim 1, as discussed above. Xu et al. further teaches:
wherein the moved trajectory of the [[robot]] results in a change of the trajectory
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
However, Xu et al. does not teach a robot. Yue teaches:
robot
(Yue – [0008]) “The robot is connected to the trailer robot through an articulation method with springs”
It would have been obvious to one possessing ordinary skill in the art to combine these teachings for the same reasoning discussed in claim 1.
Claim 5.
The combination of Xu et al. and Yue teaches all the limitations of claim 1, as discussed above. Xu et al. further teaches:
wherein the distance is determined by transforming for the trailer, a vertex comprising a center of one or more trailer segments relative to a position of the [[robot]] and comparing the vertex to a location of the obstacle
(Xu – [0018]) “determining a dynamic trailer turning radius and a trailer turn center, detecting an object with a sensor system of the vehicle, determining a distance from the trailer turn center to the object, determining whether an inner trailer boundary line intersects a virtual circle having a center point at the trailer turn center and a radius equal to the distance from the trailer turn center to the object, and executing a sideswipe avoidance measure”
However, Xu et al. does not teach a robot. Yue teaches:
robot
(Yue – [0008]) “The robot is connected to the trailer robot through an articulation method with springs”
It would have been obvious to one possessing ordinary skill in the art to combine these teachings for the same reasoning discussed in claim 1.
Claim 8.
Rejected by the same reasoning as claim 1.
Claim 9.
Rejected by the same reasoning as claim 2.
Claim 12.
Rejected by the same reasoning as claim 5.
Claim(s) 3, 4, 6, 7, 10, 11, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. and Yue as applied to claims 1, 2, 4, 5, 8-9, 12, and 14 above, and further in view of Koohi et al. (US 20210108935, previously cited).
Claim 3.
The combination of Xu et al. and Yue teaches all the limitations of claim 1, as discussed above. However, neither Xu et al. nor Yue teach calculating a movement magnitude or comparing to a threshold angle. Koohi teaches:
calculating cross products of positions of the robot during the movement
(Koohi – [0129]) “Based on the result of θ, the driving pattern can be identified… To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product of the vectors”
[Examiner Note: The vectors here are defined as the vector from the starting position to a midpoint of the distance travelled and the vector from the midpoint to the end position, respectively.]
calculating an average of the cross products
(Koohi – [0042]) “the information regarding the route 102 may account for either the full route 102 (with all stops) or only an average of the subset of stops generally required."
(Koohi – [0128]) “a map including a route 102 and stops 104 with vectors 1502 and 1503 representing vectors u and v, respectively”
determining a magnitude of the averaged cross products
(Koohi – [0129]) “Based on the result of
θ
, the driving pattern can be identified. For example, the driving pattern may be “reversing” if
θ
is
0
°
≤
θ
≤
25
°
and may be a “turn” if
70
°
≤
θ
≤
135
°
… To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product of the vectors”
comparing the magnitude to a threshold angle
(Koohi – [0129]) “Based on the result of
θ
, the driving pattern can be identified. For example, the driving pattern may be “reversing” if
θ
is
0
°
≤
θ
≤
25
°
and may be a “turn” if
70
°
≤
θ
≤
135
°
… To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product of the vectors”
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the edge detection for a robot obtained from the combination of Xu et al. and Yue with the driving algorithm of Koohi. One would have been motivated to do this because it allows for optimization of the routes followed by the robots (Koohi – [0002]).
Claim 4.
The combination of Xu et al., Yue, and Koohi teaches all the limitations of claim 3, as discussed above. Koohi further teaches:
classify the movement as a turn when the magnitude exceeds the threshold angle
(Koohi – [0129]) “Based on the result of
θ
, the driving pattern can be identified. For example, the driving pattern may be “reversing” if
θ
is
0
°
≤
θ
≤
25
°
and may be a “turn” if
70
°
≤
θ
≤
135
°
.”
classify the movement as following a line when the magnitude is below the threshold angle
(Koohi – [0006]) “the identified state of the item carrier is one of driving, turning, and reversing”
[Examiner Note: As there exists a range of values that indicate a “turn” or a reversal, and it is explicitly recited that the state is one of these two states or “driving”, it is apparent that anything that does not reach the threshold for turning is thus in a “driving” state. Please note that, due to the way this angle is determined, a value of 180 degrees corresponds to a state in which the vehicle has not made a turn.]
It would have been obvious to one possessing ordinary skill in the art to combine these teachings for the same reasoning discussed in claim 3.
Claim 6.
The combination of Xu et al. and Yue teaches all the limitations of claim 1, as discussed above. However, neither Xu et al. nor Yue teach calculating a cross product of two or more positions. Koohi teaches:
determining positions of the robot
(Koohi – [0008]) “the location data points comprise global positioning system (GPS) location data points received from a GPS device carried by the item carrier”
calculating a cross product of two or more positions of the robot
(Koohi – [0129]) “To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product of the vectors”
determining a magnitude of a turn by the robot based on the cross product
(Koohi – [0129]) “To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product of the vectors”
It would have been obvious to one possessing ordinary skill in the art to combine these teachings for the same reasoning discussed in claim 3.
Claim 7.
The combination of Xu et al. and Yue teaches all the limitations of claim 1, as discussed above. However, neither Xu et al. nor Yue teach calculating a cross product. Koohi teaches:
identifying a left turn when the cross product is positive
(Koohi – [0129] “To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product… and identifies a z-component of the resulting three-dimensional vector to determine the type of turn. … If the value of z is positive, then the turn is a left turn.”
identifying a right turn when the cross product is negative
(Koohi – [0129] “To determine a type of turn, the system 100 applies the driving algorithm(s) to determine a cross product… and identifies a z-component of the resulting three-dimensional vector to determine the type of turn. … If the value of z is positive, then the turn is a left turn.”
[Examiner Note: If a positive z-component indicates a left turn, then logically a negative z-component indicates a right turn.]
It would have been obvious to one possessing ordinary skill in the art to combine these teachings for the same reasoning discussed in claim 3.
Claim 10.
Rejected by the same rationale as claim 3.
Claim 11.
Rejected by the same rationale as claim 4.
Claim 13.
Rejected by the same rationale as claim 6.
Claim 14
Rejected by the same rationale as claim 7.
Claim(s) 15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al. and Yue as applied to claims 1, 2, 4, 5, 7-9, 12, and 14 above, and further in view of Manesis (NPL 1, previously cited).
Claim 15.
Manesis teaches:
approximating, using a model a trajectory of at least one trailer attached to a back of the robot based on a position of each of two or more segments of the trailer with respect to the robot
(Manesis – 2) “Figure 1 shows the coordinate system of a vehicle model with a two-axle tractor and trailers with slid kingpins.”
PNG
media_image1.png
430
717
media_image1.png
Greyscale
Figure 1: Coordinates of vehicle and segments of trailer (originally Manesis Fig. 1)
The rest of the claim is rejected by the same rationale as claim 1.
It would have been obvious to one possessing ordinary skill in the art before the effective filing date to combine these teachings, modifying the sideswipe avoidance system of Xu et al. with the automata-based modeling of Manesis et al. One would have been motivated to do this as a means of dealing with the nonlinear dynamics of the system while modelling it (Manesis – 1. Introduction)
Claim 18.
Rejected by the same rationale as claim 5.
Claim(s) 16, 17, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Xu et al., Yue, and Manesis as applied to claims 1, 2, 4, 5, 7-9, 12, and 14 above, and further in view of Koohi.
Claim 16.
Rejected by the same rationale as claim 3.
Claim 17.
Rejected by the same rationale as claim 4.
Claim 19.
Rejected by the same rationale as claim 6.
Claim 20.
Rejected by the same rationale as claim 7.
Conclusion
THIS ACTION IS MADE FINAL. 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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/S.A.M./Examiner, Art Unit 3669
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
6/16/25