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 .
Claim Objections
Claims 1, 11-13, and 23 are objected to because of the following informalities:
Claim 1 and 13: “6D (degrees of freedom)” should be --6DoF (six degrees of freedom)--.
Claims 11 and 23: “6D” should be --6DoF”--.
Claim 12: “the fixed scale image processing module configured to” should be --the fixed scale image processing module is configured to--.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 3-4, 9-10, 12, 15-16, 21-22, and 24 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
A broad range or limitation together with a narrow range or limitation that falls within the broad range or limitation (in the same claim) may be considered indefinite if the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. See MPEP § 2173.05(c). In the present instance, claims 12 and 24 recite the broad recitation "the localization plugins exploiting prior class information to detect errors", and the claim also recites “optionally, wherein such errors include an object's bounding box being poorly located or the object being misclassified" which is the narrower statement of the range/limitation. The claim(s) are considered indefinite because there is a question or doubt as to whether the feature introduced by such narrower language is (a) merely exemplary of the remainder of the claim, and therefore not required, or (b) a required feature of the claims.
Claims 3 and 15 recite the limitation “the real-world object size”. There is insufficient antecedent basis for this limitation in the claims.
Claims 4 and 16 recite the limitation “the real-world”. There is insufficient antecedent basis for this limitation in the claims.
Claims 9 and 21 recite the limitation "the forkable object". There is insufficient antecedent basis for this limitation in the claims.
Claims 10 and 22 recite the limitation “the forkable object”. There is insufficient antecedent basis for this limitation in the claims.
Claims 12 and 24 recite the limitation “the localization plugins”. There is insufficient antecedent basis for this limitation in the claims.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 18 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends.
Claim 18 recites “The AMR of claim 17”. The claim fails to include all the limitations of the claim upon which it depends. Claim 18 should recite --The method of claim 17--.
Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim(s) 1, 5-6, 8-9, 13, 17-18, and 20-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (CN-117115240-A), and further in view of Sun et al. (CN-113393503-A).
Regarding claim 1, Deng teaches:
An autonomous mobile robot (AMR) (“intelligent forklifts need to know the placement and orientation of pallets when picking up and placing goods, and the current mainstream pallet recognition methods all use 3D cameras to acquire point cloud information of the scene,” Para [n0004]), comprising:
at least one processor in communication with at least one computer memory device (“The storage unit is used to store a program including the steps of the general stack 3D pose positioning method as described above, so that the control unit, image recognition unit, texture recognition unit, and processing unit can retrieve and execute it as needed,” Para [n0030]);
at least one 3D sensor configured to collect 3D point cloud data of an object (“Collect point cloud data and texture images in the scene, and establish a mapping relationship A between the 3D point coordinates in the point cloud data and the pixel coordinates of the texture image,” Para [n0008]);
and a fixed scale image processing module configured to:
receive the 3D point cloud data (“Extract the point cloud near the stack plane,” Para [n0011]);
transform the 3D point cloud data into at least one fixed scale (FS) image (“project it onto the stack plane to obtain a projection image,” Para [n0011]);
and detect, identify, and localize the object in image space of the at least one FS image (“After the image recognition unit determines the stack type, the 3D point coordinates corresponding to the center pixel of the projection image are used as the recognition position. The unit also calculates the angle between the stack plane and the Z-axis of the camera coordinate system to obtain the stack pose,” Para [n0033]).
Deng is not relied upon to teach the following limitations. Sun, however, further teaches:
select a plugin associated with an object type of the object (“Template matching methods match templates with observed images or depth maps using manual or deep learning feature descriptors,” Para [n0003]);
and apply the plugin for 6D (degrees of freedom) pose estimation of the object in real- world space to localize the object (“and use the 6D pose annotations corresponding to the templates as the estimation results,” Para [n0003]).
Sun is considered to be analogous to the claimed invention because they are both in the field of robot environment sensing. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Sun into Deng for the benefit of more accurate pallet pose identification and detection.
Regarding claim 5, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, and Deng further teaches:
wherein the AMR further comprises: a load engagement apparatus configured to engage the object (“In practice, intelligent forklifts need to know the placement and orientation of pallets when picking up and placing goods,” Para [n0004]).
Regarding claim 6, the rejection of claim 5 is incorporated herein. Deng in view of Sun teach the AMR of claim 5, and Deng further teaches:
wherein the load engagement apparatus comprises at least one fork (“intelligent forklifts need to know the placement and orientation of pallets when picking up and placing goods,” Para [n0004]).
Regarding claim 8, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, and Deng further teaches:
wherein the object is a forkable object (“intelligent forklifts need to know the placement and orientation of pallets when picking up and placing goods, and the current mainstream pallet recognition methods all use 3D cameras to acquire point cloud information of the scene,” Para [n0004]).
Regarding claim 9, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, and Deng further teaches:
wherein the forkable object is a pallet (“intelligent forklifts need to know the placement and orientation of pallets when picking up and placing goods, and the current mainstream pallet recognition methods all use 3D cameras to acquire point cloud information of the scene,” Para [n0004]).
Regarding claims 13, 17-18, and 20-21, the rejection of claims 1, 5-6, and 8-9 apply, mutatis mutandis, to these claims.
Claim(s) 2-3 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Wu et al., "SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud", arXiv:1710.07368v1.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Wu, however, further teaches:
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wherein the at least one FS image (Fig. 2(B)) comprises pixels that correspond to real-world 3D positions and physical dimensions of the object (Figs. 2(A) and 2(B), Fig. 2(B) is the result of projecting 3D LiDAR points into a 2D space).
Wu is considered to be analogous to the claimed invention because they are both in the field of identifying objects in 3D using fewer computational resources. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Wu into Deng and Sun for the benefit of 3D object detection that is not computationally complex.
Regarding claim 3, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Wu, however, further teaches:
wherein the fixed scale image processor is configured to directly estimate the real-world object size from the at least one FS image (Fig. 2(B), object size can be directly estimated from the “range” values).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Wu into Deng and Sun for the benefit of 3D object detection that is not computationally complex.
Regarding claims 14-15, the rejection of claims 2-3 applies, mutatis mutandis, to claims 14-15.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Yang et al., "PIXOR: Real-time 3D Object Detection from Point Clouds", arXiv:1902.06326v3.
Regarding claim 4, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Yang, however, further teaches:
wherein a scale in the FS image is fixed such that a pixel in the FS image represents a fixed area measurement in the real-world ("We set the region of interest for the point cloud to [0,70] × [−40,40] meters and do bird’s eye view projection with a discretization resolution of 0.1 meter,” Section 4.1.1 Implementation Details).
Yang is considered to be analogous to the claimed invention because they are both in the field of 3D to 2D projections for object detection. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Yang into Deng and Sun for the benefit of more accurate object detection.
Regarding claim 16, the rejection of claim 4 applies, mutatis mutandis, to claim 16.
Claim(s) 7 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Iqbal et al. (US-20200061811-A1).
Regarding claim 7, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Iqbal, however, further teaches:
wherein the fixed scale image processing module is configured to identify and localize the object in real time or near real time (“When coupled with a real-time 6-DoF object pose estimator, at least one embodiment is capable of grasping objects from any position and orientation within the graspable workspace,” Para [0055]).
Iqbal is considered to be analogous to the claimed invention because they are both in the field of robots that interact with objects. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Iqbal into Deng and Sun for the benefit of a more efficient robot or forklift.
Regarding claim 19, the rejection of claim 7 applies, mutatis mutandis, to claim 19.
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Guo et al. (US-20220067960-A1).
Regarding claim 10, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitation. Guo, however, further teaches:
wherein the forkable object is an industrial rack, cart, or container (“The working state monitor is configured to monitor a working state of the intelligent forklift while the intelligent forklift is carrying and moving a stock container to be moved,” Para [0005]).
Guo is considered to be analogous to the claimed invention because they are both in the field of intelligent forklifts. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Guo into Deng and Sun for the benefit of a more versatile forklift.
Regarding claim 22, the rejection of claim 10 applies, mutatis mutandis, to claim 22.
Claim(s) 11 and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Cesic et al. (US-20220121837-A1).
Regarding claim 11, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Cesic, however, further teaches:
wherein the fixed scale image processing module is further configured to generate a signal indicating the object was localized or localization failed (Cesic, “Pose determination module 332 may, responsive to determining that the three-dimensional pose is indeterminable, transmit an alert that is caused to be received by operator device 110,” Para [0056]) based on the 6D pose estimation of the object (Sun, “Template matching methods match templates with observed images or depth maps using manual or deep learning feature descriptors, and use the 6D pose annotations corresponding to the templates as the estimation results,” Para [n0003]).
Cesic is considered to be analogous to the claimed invention because they are both in the field of determining poses of objects via an autonomous robot. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Cesic into Deng and Sun for the benefit of more efficient object pose estimation.
Regarding claim 23, the rejection of claim 11 applies, mutatis mutandis, to claim 11.
Claim(s) 12 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng in view of Sun as applied to claims 1 and 13 above, and further in view of Djugash (US-20140161345-A1).
Regarding claim 12, the rejection of claim 1 is incorporated herein. Deng in view of Sun teach the AMR of claim 1, but are not relied upon to teach the following limitations. Djugash, however, further teaches:
wherein the fixed scale image processing module configured to use the localization plugins to exploit prior class information to detect errors (“the detection tuner module includes a detection tuner model and the detection tuner model adjusts the at least one object detection parameter based on the pose estimation error,” Para [0044]), optionally, wherein such errors include an object's bounding box being poorly located or the object being misclassified.
Djugash is considered to be analogous to the claimed invention because they are both in the field of autonomous robots that interact with objects. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have incorporated the teachings of Djugash into Deng and Sun for the benefit of fewer robot-object interaction errors.
Note: the claim is being examined with its broader limitation.
Regarding claim 24, the rejection of claim 12 applies, mutatis mutandis, to claim 24.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fang et al. (CN-115546202-A) teaches a method for detecting and localizing a tray in the context of unmanned forklifts.
Douglas et al. (US-10614319-B2) teaches a method for detecting and localizing pallets in the context of unmanned forklifts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL A OMETZ whose telephone number is (571)272-2535. The examiner can normally be reached 8:30am-5:30pm ET Monday-Thursday, 7:30am-3:30pm ET every other Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Rachel Anne Ometz/Examiner, Art Unit 2668 Rachel.ometz@uspto.gov
9/8/26
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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