DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
• This action is in reply to the Application Number 19/010,704 filed on 01/06/2025.
• Claims 1-8 are currently pending and have been examined.
• This action is made NON-FINAL.
• The examiner would like to note that this application is now being handled by examiner Kai Wang.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/06/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Martínez, “Implementation of a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments”, Sensors (Basel), 2019 Jul 28, in view of Capito, "Optical Flow based Visual Potential Field for Autonomous Driving", 2020 IEEE Intelligent Vehicles Symposium (IV), 2020.
Regarding claims 1, 7 and 8:
Martínez teaches:
A vehicle control device comprising: processing circuitry to acquire sensor signals from a plurality of sensors detecting a physical quantity related to a surrounding environment of a vehicle; (Martínez, page 7, “The hardware architecture of the proposed system consists of five layers: Vehicle, automation, control”, page 13, “The tests to verify operation in real environments have been performed using an instrumented vehicle equipped with GPS, IMU and a computer vision system”)
to acquire image data indicating images of surroundings of the vehicle from at least one camera capturing the images; (Martínez, page 9, “processing the computer vision system. Using the images that it receives, it provides information about the road lines (position, curvature, etc.), about possible obstacles on the road (pedestrians or other vehicles) and about the different traffic signals (mainly, speed limit signs).”)
to use a potential-risk prediction model learned in advance to predict a potential risk from a characteristic quantity of the surrounding environment of a target vehicle, (Martínez, Abstract, “This paper presents a path planning algorithm based on potential fields. Potential models are adjusted so that their behavior is appropriate to the environment and the dynamics of the vehicle and they can face almost any unexpected scenarios”, page 7, “risk of collisions”)
to calculate a physical repulsive potential based on the physical quantity and the images, the physical repulsive potential being a repulsive potential caused by the surrounding environment of the vehicle; (Martínez, Figure 1 and page 4, “In the path-planning technique based on potential models, each element of the environment (lanes, other vehicles, obstacles, etc.) produces a potential field (attraction or repulsion) in an analogue way to what magnetic charges do (depending on whether their mathematical sign is positive or negative). That is, the vehicle will react to the presence of the different potential fields (Figure 1) with an appropriate and proportional behavior. Depending on the different potentials considered each time, the vehicle modifies its movement by acting on the steering wheel, the throttle or the brake pedals”, page 9, “processing the computer vision system. Using the images that it receives, it provides information about the road lines (position, curvature, etc.), about possible obstacles on the road (pedestrians or other vehicles) and about the different traffic signals (mainly, speed limit signs).”)
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Figure 1.
Martínez does not explicitly teach, but Capito teaches:
to use a human visual model calculated in advance, to calculate a visibility repulsive potential, the visibility repulsive potential being a repulsive potential affected by vision of a human recognizing the images;( Capito, Abstract, “This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow, which is used together with a gradient tracking sliding mode controller to navigate the vehicle to destination without collision with obstacles”, and page 885, “optical flow has been used as a mean to model human behavior” )
to calculate an integrated repulsive potential obtained by correcting the physical repulsive potential with the visibility repulsive potential; ( Capito, page 885, “We propose to compute an Artificial Potential Field APF (Visual Potential Field) from the optical flow vector field information, that contains both the information from the obstacles as the restrictions of the road”, and page 887,” We compute separately the potential fields for the target, the obstacles, and for the road, and then sum them up to get the total potential field ”)
to generate a target path to cause the vehicle to travel from a current position of the vehicle to a target point in accordance with a gradient calculated from the integrated repulsive potential; ( Capito, page 885, “Since there is no preset trajectory, the gradient of the visual potential field is used to obtain an orientation reference”, page 890, “we compute a feasible path from the starting point to the end point using waypoints over the road, and use a longitudinal and lateral PID controller for the path tracking”)
and to control the vehicle to cause the vehicle to travel along the target path. ( Capito, page 890, “we compute a feasible path from the starting point to the end point using waypoints over the road, and use a longitudinal and lateral PID controller for the path tracking”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing
date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to use a human visual model calculated in advance, to calculate a visibility repulsive potential, the visibility repulsive potential being a repulsive potential affected by vision of a human recognizing the images; to calculate an integrated repulsive potential obtained by correcting the physical repulsive potential with the visibility repulsive potential; to generate a target path to cause the vehicle to travel from a current position of the vehicle to a target point in accordance with a gradient calculated from the integrated repulsive potential; and to control the vehicle to cause the vehicle to travel along the target path. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Regarding claim 2:
Martínez in view of Capito, as shown in the rejection above, discloses the limitations of claim 1. Martínez teaches:
The vehicle control device according to claim 1, wherein the processing circuitry uses a lateral inhibition model mathematically modeling human lateral inhibition, to calculate a psychological repulsive potential affecting an ability of discovering an obstacle by human lateral inhibition… (Martínez, Figure 4 and page 6, “Figure 4 shows how the lateral potential field varies according to the distance to each of the limits of the lane.”, and page 9, “Using the images that it receives, it provides information about the road lines (position, curvature, etc.), about possible obstacles on the road (pedestrians or other vehicles) and about the different traffic signals (mainly, speed limit signs). Then, the potential planning node is responsible for calculating the potential fields. With all these data, the tasks of lateral planning … are carried out”)
Martínez does not explicitly teach, but Capito teaches:
…as at least a portion of the visibility repulsive potential. ( Capito, Abstract, “This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to include wherein the processing circuitry uses a lateral inhibition model mathematically modeling human lateral inhibition, to calculate a psychological repulsive potential affecting an ability of discovering an obstacle by human lateral inhibition as at least a portion of the visibility repulsive potential. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Regarding claim 3:
Martínez in view of Capito, as shown in the rejection above, discloses the limitations of claim 1. Martínez teaches:
The vehicle control device according to claim 1, wherein the processing circuitry uses a motion perception model mathematically modeling human motion perception, to calculate a motion-perception repulsive potential affecting an ability of discovering an obstacle by human motion perception ... (Martínez, Fig.7 and page 9, “Using the images that it receives, it provides information about the road lines (position, curvature, etc.), about possible obstacles on the road (pedestrians or other vehicles) and about the different traffic signals (mainly, speed limit signs). Then, the potential planning node is responsible for calculating the potential fields”)
Martínez does not explicitly teach, but Capito teaches:
…as at least a portion of the visibility repulsive potential. ( Capito, Abstract, “This work proposes to generate an artificial potential field, i.e. visual potential field, from a sequence of images using sparse optical flow)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to include wherein the processing circuitry uses a motion perception model mathematically modeling human motion perception, to calculate a motion-perception repulsive potential affecting an ability of discovering an obstacle by human motion perception as at least a portion of the visibility repulsive potential. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Regarding claim 4:
Martínez in view of Capito, as shown in the rejection above, discloses the limitations of claim 1. Martínez does not explicitly teach, but Capito teaches:
The vehicle control device according to claim 1, wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. ( Capito, page 887,” We compute separately the potential fields for the target, the obstacles, and for the road, and then sum them up to get the total potential field ”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to include wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Regarding claim 5 :
Martínez in view of Capito, as shown in the rejection above, discloses the limitations of claim 2. Martínez does not explicitly teach, but Capito teaches:
The vehicle control device according to claim 2, wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. ( Capito, page 887,” We compute separately the potential fields for the target, the obstacles, and for the road, and then sum them up to get the total potential field ”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to include wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Regarding claim 6 :
Martínez in view of Capito, as shown in the rejection above, discloses the limitations of claim 3. Martínez does not explicitly teach, but Capito teaches:
The vehicle control device according to claim 3, wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. ( Capito, page 887,” We compute separately the potential fields for the target, the obstacles, and for the road, and then sum them up to get the total potential field ”)
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a Potential Field-Based Decision-Making Algorithm on Autonomous Vehicles for Driving in Complex Environments from Martínez to include these above teachings from Capito in order to include wherein the processing circuitry adds or multiplies the physical repulsive potential to or by the visibility repulsive potential to correct the physical repulsive potential. One of ordinary skill in the art would have been motivated to make this modification as “This visual based navigation method is less computationally expensive than learning based techniques, but at the same time, it allows to capture the features of dynamically changing environment.” (Capito, Description).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chavez (US12478237B1) teaches potential field algorithms to enable an autonomous operation of SCARA arm.
PEARSON (US 20240395154 A1) teaches a vertical take-off and landing (VTOL) aerial vehicle with at least one processor determines the repulsion potential field model.
Park (US 20220203967 A1 ) teaches a route search system and method for autonomous parking based on a cognitive sensor which the space with obstacles may be recognized by generating a principal component analysis force potential field at the start point location of the principal component analysis.
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/KAI NMN WANG/Examiner, Art Unit 3664