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 .
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 10-12 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Taghavi et al. (Patent No.: US 11, 410,388) .
Regarding claim 1, Taghavi discloses an object detection apparatus, comprising: a processor (Fig. 2, “202 processor Device”) configured to obtain point cloud data (col. 1, lines 20-23, “Light Detection And Ranging (LiDAR, also referred to a “Lidar” or “LIDAR” herein) sensor generates point cloud data representing a three-dimensional (3D) environment (also called a “scene”) scanned by the LIDAR sensor”)., select a source object and a target object based on characteristics of multiple objects included in the point cloud data and geometry information between the multiple objects (See Fig. 1, different bounding boxes (122) have different geometry information for representing “Car”, Bicyclist” and “Pedestrian”), select a target partition to apply data augmentation based on geometry information between the source object and the target object col. 2, lines 2-10, “Points in a point cloud frame must be clustered, segmented, or grouped (e.g., using object detection, semantic segmentation, instance segmentation, or panoptic segmentation) such that a collection of points in the point cloud frame may be labeled with an object class (e.g., “pedestrian” or “motorcycle”) or an instance of an object class (e.g. “pedestrian #3”), with these labeled point cloud frames being used to train models for predictions tasks, such as object detection or various types of segmentation”), perform data augmentation of the point cloud data by applying an augmentation technique to the target partition to generate augmented point cloud data, and output the augmented point cloud data (col. 19, lines 33-41, “For each point cloud frame being considered for data augmentation, a sequence of operations is performed to apply the primary policy 222 to identify a target point cloud frame 226 for data augmentation. First, the target object sub-policy m is consulted to determine which deficient object classes are identified by the primary policy 222. The following operations of step 422 are then performed for each deficient object class c, with respect to the current frame under consideration”).
Regarding claim 2, Taghavi discloses that the object detection apparatus of claim 1, wherein the processor is configured to: select the source object and the target object based on occlusion attributes of the multiple objects; and determine effectiveness of selecting the source object and the target object based on the geometry information between the source object and the target object (col. 4, lines 65-67, “Frustum dropout: Deleting a region of the visible surface of a point cloud object instance, e.g. to simulate partial occlusion”).
Regarding claim 10, Taghavi discloses the object detection apparatus of claim 1, wherein the processor is configured to: train an object detection model using the augmented point cloud data (see Fig. 2, “224 Machine learned model”) .
Regarding claim 11, it is a method claim with the same scope as apparatus claim 1. Thus, Claim 11 is rejected for the same reason as claim 1 above.
Regarding claim 12, it is a method claim with the same scope as apparatus claim 2. Thus, Claim 12 is rejected for the same reason as claim 2 above.
Regarding claim 20, it is a method claim with the same scope of apparatus claim 10. Thus, claim 20 is rejected for the same reason as claim 10 above.
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.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Taghavi et al. (Patent No.: US 11, 410,388) in view Noh et al. (Pub, No.: US 2022/0179076).
Regarding claim 3, Taghavi fails to disclose the object detection apparatus of claim 2, wherein the processor is configured to: determine that it is valid to select the source object and the target object, when the source object and the target object are located in a same quadrant with respect to a vehicle and there are a heading of the source object and a heading of the target object at a same quadrantal angle. Noh is cited to teach an apparatus for tracking an object using a Lidar Sensor which is similar to Taghavi. Noh further teaches “determine that it is valid to select the source object and the target object, when the source object and the target object are located in a same quadrant with respect to a vehicle and there are a heading of the source object and a heading of the target object at a same quadrantal angle” (see para [0170]: “When two target objects 740 and 742, which are separated from each other in a first frame t as shown in FIG. 22(a), are clustered into one object in a second frame t+1 subsequent to the first frame t as shown in FIG. 22(b), since the comparative example does not provide a reference on which to distinguish the target objects 740 and 742, the two objects 740 and 742 may be combined. In contrast, according to the embodiment, since the shape frame (or the shape flag) is assigned to at least one of the two objects 740 and 742, even if the two objects 740 and 742 are combined in the second frame t+1, it is possible to individually output information about the two objects 740 and 742, as shown in FIG. 22(b).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Taghavi with the features of two object are located in the same quadrant as to taught by Noh so as to provide individual output information about the two objects for augmentation.
Regarding claim 13, it is a method claim with the same scope of apparatus claim 3. Thus, claim 13 is rejected for the same reason as claim 3 above.
Allowable Subject Matter
Claims 4-9 and 16-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art Taghavi et al. (US 11,410,388) teach a systems for adaptive scene augmentation of a point cloud frame for inclusion in a labeled point cloud dataset used for training a machine learned model for a prediction task for point cloud frames, such as object detection or segmentation. However, Taghavi fails to teach the limitation of “segment each of the source object and the target object into a plurality of segmented partitions; extract foreground-partitions of the source object among the segmented partitions of the source object; randomly select a foreground-partition from among the foreground-partitions; determine whether there is a point in a partition of the target object, the partition corresponding to the foreground-partition; and determine that it is valid to select the foreground-partition based on determining that there is the point in the partition of the target object” as recited in claim 4. Thus, claim 4 and it’s dependent claim 5 are allowable. The method claims 14 and 15 have the same scope as apparatus claims 4 and 5. Thus, claims 14 and 15 are also allowable.
The closest prior art Taghavi et al. (US 11,410,388) teach a systems for adaptive scene augmentation of a point cloud frame for inclusion in a labeled point cloud dataset used for training a machine learned model for a prediction task for point cloud frames, such as object detection or segmentation. However, Taghavi fails to teach the limitation of “wherein the processor is configured to: translate the source object and the target object to an origin; determine data augmentation intensity based on occlusion attributes of the source object and the target object; apply a first augmentation technique based on the data augmentation intensity to perform the data augmentation; retranslate the source object and the target object augmented by the first augmentation technique to original positions; apply a second augmentation technique based on the data augmentation intensity to perform the data augmentation; and retranslate the source object and the target object augmented by the second augmentation technique to the original positions” as recited in claim 6. Thus, claim 6 and it’s dependent claim 7-9 are allowable. Claims 16-19 are method claims similar to the apparatus claim 6-9. Thus claims 17-19 are allowable for the same reason.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mellado Bataller et al. (Patent No.: US 10,902,551) is cited to teach transplanting the transformed object into a background image so as to produce an augmented image and augmenting an initial set of images with the augmented image so as to produce an augmented set of images for training a predictive model.
Morikawa et al. (EP 3,588 466) is cited to teach point cloud colorization with occlusion detection.
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/XIAO M WU/Supervisory Patent Examiner, Art Unit 2613
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