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 .
This office action is in response to the Request for Continued Examination and Information Disclosure Statement filed on 16 June 2026 and Applicant’s previously submitted amendment filed on 18 May 2026.
This office action is made Non Final.
Claims 1, 8, and 15 have been amended.
Claims 21-23 have been cancelled.
All rejections as presented in the previous office action have been withdrawn as neccessited by the amendment.
Claims 1-20 are pending. Claims 1, 8, and 15 are independent claims.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/18/26 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 6/16/26 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) 1-4, 7-11, 14-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al (“Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications”, IEEE SENSORS JOURNAL, 2020, p4901-4913)(Disclosed in IDS filed on 6/24/24) in further view of Ferguson et al (US20180032078, 2028) (Disclosed in IDS filed on 2/3/26)
As per independent claim 1, Zhao discloses a method comprising:
generating one or more data points from one or more sensors coupled to a vehicle (p4903; Section III: 3D point clouds generated by lidar which represented by Cartesian coordinates p = [px , py , pz, pI], which contain a large number of points; p4908; IV. A: dataset consists of a synchronized stereo camera images and a 3D LIDAR frames captured from an autonomous vehicle; p4909: )
wherein: the one or more sensors comprise: a Light Detection and Ranging (LiDAR) sensor and a camera, (p4903: Section III)
the one or more data points comprise: a LiDAR point cloud generated by the LiDAR sensor; and an image captured by the camera (p4903; Section III; p4908; IV. A: dataset consists of a synchronized stereo camera images and a 3D LIDAR frames captured from an autonomous vehicle)
processor (p4808: Section IV)
detecting one or more obstacles within the LiDAR point cloud (p4903: lidar point cloud to extract object-region proposals (obstacles); clustering of non-ground obstacles, calculation of the 3D bounding boxes (BBs) of the clustered obstacles; Section III A: Object-Region Proposal Generation Using 3D LIDAR Data)
performing a factor query on the image to query at least one factor feature for each of the one or more detected obstacles; (Abstract: regions of interest (ROI) of the proposals are selected and input to a convolutional neural network (CNN) for further object recognition and discloses the functionality to precisely identify the sizes of all the objects; form of query at least one factor feature; p4903: extract the features from the corresponding image region and identify the object in the region)
for each obstacle of the one or more detected obstacles, based on the at least factor feature, and a first label indicating an object type of a plurality of object types. (p4907-4908, p4910: determine object-region proposal/ROI and classifying/categorizing ROI based on stored training labels.)
However, Zhao et al fails to specifically disclose labeling each said obstacle with a second label being assigned a confidence value indicative of whether the obstacle is approved for collision, wherein the second label indicates whether the obstacle is: a thing capable of being approved for collision by the vehicle, when the confidence value is above a threshold value; or a thing that is not capable of being approved for collision by the vehicle, when the confidence value is below the threshold value; comparing the confidence value to the threshold value; planning a trajectory of the vehicle by accepting one or more plans that would collide with the one or more obstacles, when the confidence value is above the threshold value; and planning a trajectory of the vehicle by not accepting one or more plans that would collide with the one or more obstacles, when the confidence value is below the threshold value.. (It is noted that the term “confidence value” is not defined in the claim or in applicant’s specification. In addition, the language does not explain how the confidence value is “indicative of whether the obstacle is approved for collision” other than using a threshold value to indicate the obstacle approval status for colliding. Therefore, the broadest reasonable interpretation is applied)
However, Ferguson et al discloses identifying objects within a future/planned path of a vehicle using lidar. (0017, 0020). Ferguson discloses labeling each said obstacle with a second label being assigned a confidence value indicative of whether the obstacle is approved for collision, wherein the second label indicates whether the obstacle is: a thing capable of being approved for collision by the vehicle, when the confidence value is above a threshold value; or a thing that is not capable of being approved for collision by the vehicle, when the confidence value is below the threshold value. Ferguson disclose classifying whether the object is drivable safe for the vehicle to drive over or not (drivability) in real time which includes associating a confidence value with the object. Ferguson discloses confidence value indicative of the likelihood that the object is safe for the vehicle to drive over or not. (Abstract, 0021-0022) 0030 discloses a confidence value provides an accuracy estimate for the actual classification. In other words, each object is classified/labeled with a confidence value by a classifier which indicates the likelihood that the object is safe for the vehicle to drive over or not is a form of likelihood that the object is safe for the vehicle is approved for collision or not (Drive over is a form of a collision). In addition, Ferguson discloses the confidence value may be compared with a threshold value to determine whether the object is to be classified as drivable or not drivable or rather, whether the vehicle can safely drive over the object (0021, 0060). In other words, threshold values used by the computing device make determinations regarding the drivability of objects (0025) Thus, if the confidence value (e.g. 0.95) is greater than the threshold value (e.g. 0.9), the object is drivable (form of approved for collision) (0061) If the confidence value (0.05) is not greater (below) than the threshold value (0.9), the object is not drivable (form of not approved for collision)( 0061). Furthermore, Ferguson discloses if the detected object in the car’s (future) path is determined/labeled as drivable based on the obtained confidence score, (as determined in 0061), then the vehicle will proceed to drive over the object (not altering the expected future path) (0069). Since the vehicle continues the expected future path as is, then is a form of planning a trajectory of the vehicle by accepting one or more plans that would collide with the one or more obstacles, when the confidence value is above the threshold value. On the other hand, Ferguson discloses if the detected object in the car’s (future) path is determined/labeled as not drivable based on the obtained confidence score, the vehicle to come to a complete stop or maneuver the vehicle around the object (altering the expected future path of the vehicle to avoid driving over the object).(0022; 0065, claim 4) . Since the expected future path is altered, then is a form of planning a trajectory of the vehicle by not accepting one or more plans that would collide with the one or more obstacles, when the confidence value is above the threshold value.
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to have modified the cited art with the discussed features of Ferguson et al since it would have provided the benefit of allowing the system to differentiate between objects which the vehicle must drive around to avoid an accident versus objects which the vehicle can drive over, thereby avoiding unnecessary maneuvering or stopping for objects that are safe to drive over and improving safety overall. (0016)
As per dependent claim 2, Zhao et al discloses wherein the performing the factor query comprises one or more of: performing a color query on the image for each of the one or more detected obstacles; performing a shape query on the image for each of the one or more detected obstacles; and performing a movement query on the image for each of the one or more detected obstacles. (Abstract: discloses the functionality to precisely identify the sizes of all the objects; form of a shape query; p4907: discloses adjusting the sizes of the boundary rectangles so that the entire object is inside the rectangle)
As per dependent claim 3-4, based on the rejection of Claim 1 and the rationale along with the motivation to combine is incorporated, Ferguson et al discloses using the processor: for each of the one or more detected obstacles, based on the first label and/or second label of the obstacle, determining one or more vehicle actions for the vehicle to perform; and causing the vehicle to perform the one or more actions; wherein the one or more actions comprises one or more of: increasing a speed of the vehicle; decreasing a speed of the vehicle; stopping the vehicle; and adjusting a trajectory of the vehicle (0065; 0067: vehicle may slow down or stop; Claim 4: altering the path to avoid object in response to the obstacle being drivable or not).
As per dependent claim 7, Zhao et al discloses for each of the one or more detected obstacles, based on the factor query, determining whether the obstacle is one or more of: a piece of vegetation; a pedestrian; and a vehicle. (Abstract; FIG. 11; p4908: pedestrians, cars)
As per independent claims 8 and 15, Claims 8 and 15 recite similar limitations as in Claim 1 and are rejected under similar rationale. Furthermore, Zhao et al discloses a vehicle (p4808: Section IV A. 4.1) and a computing device with a processor and memory (p4808: Section IV: A skilled artisan would understand that the combination of an Intel (R) Core (TM) i7-4790 3.6 GHz processor, with 64 GB RAM are part of a computing device )
As per dependent claims 9-11, 14, 16-18 and 20, claims 9-11, 14, 16-18 and 20 recite similar limitations as in Claims 2-4, 7 and are rejected under similar rationale.
Claim(s) 5-6, 12-13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhao et al in further view of Ferguson et al in further view of Chen (US20210192234, 2021) in further view of Levy et al (US20240193805, EFD 12/9/2022)
As per dependent claim 5, Zhao et al discloses:
generating at least one patch in the LiDAR point cloud indicative of the one or more detected obstacles; (Abstract: p4903: clustering of non-ground obstacles, calculation of the 3D bounding boxes (BBs) of the clustered obstacles; p4907: Section III A(3))
projecting the at least one patch of the LiDAR point cloud into the image to obtain combined data, (p4903: projection of the BBs onto an image; Abstract: 3D LIDAR data to generate accurate object-region proposals. Then, these candidates are mapped onto the image space)
said at least one patch designates a region of the image that is to be analyzed for each of the one or more detected obstacles, and said at least one patch forms a bounding box on the image; (p4903: clustering of non-ground obstacles, calculation of the 3D bounding boxes (BBs) of the clustered obstacles; p4907: generate object-region proposals in an image using 3D LIDAR data; bounding box for each cluster; 2D candidate boundary rectangles are generated from the mapping area of each 3D boundary box in the image)
Furthermore, Zhao et al fails to disclose said at least one patch designates a region of the image that is to be analyzed for determining whether each of the one or more detected obstacles is a thing which is capable of being approved for collision by the vehicle. However, Chen et al discloses said at least one patch designates a region of the image that is to be analyzed for determining whether each of the one or more detected obstacles is a thing which is capable of being approved for collision by the vehicle (0036-0038, 0061, 0063-0064;FIG 10: outputs of LIDAR data is analyzed to determine objects in the path and their class (which includes contactable or not))
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to have modified the cited art with the discussed features of Chen et al since it would have provided the benefit of improving overall safety of autonomous vehicles and its passengers (0015, 0061)
However, the cited art fails to specifically cropping the region of the image within the bounding box, forming a cropped image. However, Levy et al discloses cropping the image to the size of the region of the image of the bounding box (FIG. 1; Abstract; 0021-0022)
It would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to have modified the cited art with the discussed features of Levy et al since it would have provided the benefit of achieving fast and accurate detections on small objects in images. (0002)
As per dependent claim 6, based on the rejection of Claim 5 and the rationale along with the motivation to combine is incorporated, Levy et al discloses resizing the cropped image, forming a resized image, wherein performing the factor query comprises performing the factor query on the resized image. (FIG. 1; Abstract; 0021-0022: cropping the image to the size of the region of the image of the bounding box includes resizing the image to resized image. Furthermore, 0018, 0044 discloses keypoints/features from the contents of the resized image are determined/identified. Form of performing a factor query on the resized image)
As per dependent claims 12-13 and 19, claims 12-13 and 19 recite similar limitations as in Claims 5-6 and are rejected under similar rationale.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 8, 15 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.
Conclusion
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/ADAM M QUELER/Supervisory Patent Examiner, Art Unit 2172
/D.F/Examiner, Art Unit 2172
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