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
Claim 17 is objected to because of the following informalities:
A typographical error in claim 17 reading “a processing unit for object detection of the objected based the measurement data”, should read “a processing unit for object detection of the object based on the measurement data”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “processing unit” and “sensor” in claims 17 and 18.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 11, 15, and 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Luo et al. of US 2019/0145765 A1 (hereinafter referred to as Luo).
Regarding claim 11, Luo discloses a method for object detection of an object based on measurement data from at least one point-based sensor capturing the object, the method comprising the following: (see Luo par. [0006] “An example aspect of the present disclosure is directed to a computer-implemented method of object detection”… “object detection can include receiving, … sensor data that can include information based at least in part on sensor output associated with one or more three-dimensional representations including one or more objects detected by one or more sensors. Each of the one or more three-dimensional representations can include a plurality of points”) processing the measurement data, which are based on a point cloud having a plurality of points and associated features, including: (see Luo par. [0027] “the sensor data can include LIDAR data associated with the three-dimensional positions or locations of objects detected by a LIDAR system (e.g., LIDAR point cloud data)”) in a point-based first processing step having at least one processing level, realizing input-side features of the point cloud as learned features and enriching the input-side features at least by information about relationships between the points; (see Luo par. [0052] “when the object detection computing system generates one or more segments, each of which includes a set of the plurality of points associated with one or more representations associated with the sensor output, the object detection computing system can use the position, shape, and orientation of each segment to determine or estimate the position, shape, and/or orientation of the associated object.” “Learned features” indicates features that have been classified, which this prior art indicates being done.) and in a grid-based second processing step having at least one processing level, transferring the learned features onto a model grid having a plurality of grid cells, and generating cell-related output data (see Luo par. [0148] “In some embodiments, a two-dimensional representation of a scene in bird's eye view (BEV) can be used. This two-dimensional representation can be suitable as it is memory efficient and objects such as vehicles do not overlap. This can simplify the detection process when compared to other representations such as range view which projects the points to be seen from the observer's perspective.”).
Claims 17 and 19 are rejected under the same analysis as claim 11 above.
Regarding claim 15, Luo discloses wherein the first processing step applies a trained (see Luo par. [0151] “the machine-learned model can be trained to receive an input including data (e.g., the sensor data) and, responsive to receiving the input, generate an output including one or more detected object predictions.”) artificial neural network (see Luo par. [0152] “the machine-learned model can use one or more classification processes or classification techniques based at least in part on a neural network (e.g., deep neural network, convolutional neural network)”).
Regarding claim 18, Luo discloses wherein the point-based sensor is configured to output at least one point cloud as measurement data (see Luo par. [0124] “the sensor data received by the computing system 108 can include LIDAR point cloud data associated with a plurality of points (e.g., three-dimensional points) corresponding to the surfaces of objects detected within sensor data obtained by the one or more LIDAR sensors of the vehicle 104”).
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo in view of R. Q. Charles, H. Su, M. Kaichun and L. J. Guibas, "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 77-85, doi: 10.1109/CVPR.2017.16 (hereinafter referred to as Qi).
Regarding claim 12, Luo fails to disclose wherein the input-side features are included in an input-side feature vector associated with an individual point and the learned features are included in a latent feature vector associated with the individual point.
However, Qi discloses wherein the input-side features are included in an input-side feature vector associated with an individual point and the learned features are included in a latent feature vector associated with the individual point (see Qi pg. 1 “The basic architecture of our network is surprisingly simple… each point is represented by just its three coordinates (x, y, z)” and pg. 4 “After computing the global point cloud feature vector, we feed it back to per point features by concatenating the global feature with each of the point features. Then we extract new per point features based on the combined point features - this time the per point feature is aware of both the local and global information” and Fig. 2 on pg. 3).
It would have been obvious for a person having ordinary skill in the art to combine the object detection of Luo with the input feature vectors of Qi because it is predictable that doing so assigns values to the features that the points represent, which is common practice for point clouds.
Regarding claim 13, Luo fails to disclose wherein the input-side feature vector has a different dimension compared to the latent feature vector.
However, Qi discloses wherein the input-side feature vector has a different dimension compared to the latent feature vector (see Qi pg. 1 “Additional dimensions may be added by computing normal and other local or global features” Indicating dimensions are added when local and global features are computed, i.e. after the fact. Also see Figure 5 caption and Figure 2, indicating the “mlp” with more dimensional layers than the input vector.).
It would have been obvious for a person having ordinary skill in the art to combine the object detection of Luo with the different dimensions of Qi because it is predictable that doing so allows the output feature vectors to contain more information than the input feature vectors, hence assigning them object information in line with a typical object detection or point cloud feature detection program.
Regarding claim 14, Luo fails to disclose wherein the input-side features of the individual point include information about a spatial position of the individual point and/or properties of the individual point and/or adjacent points of the individual point.
However, Qi discloses wherein the input-side features of the individual point include information about a spatial position of the individual point and/or properties of the individual point and/or adjacent points of the individual point (see Qi pg. 2 “A point cloud is represented as a set of 3D points {Pi|i=1, …, n}, where each point Pi is a vector of its (x, y, z) coordinate plus extra feature channels such as color, normal etc.”).
It would have been obvious for a person having ordinary skill in the art to combine the object detection of Luo with the point cloud features of Qi because it is predictable that doing so would add the extra features such as position or color to the feature vector as is common in point cloud practice to aid the software in determining features and therefore objects from the point cloud data, therefore increasing accuracy and efficiency.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo in view of Ali, W., Abdelkarim, S., Zidan, M., Zahran, M., Sallab, A.E. “YOLO3D: End-to-End Real-Time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud.” 2018 Computer Vision – ECCV 2018 Workshops. Lecture Notes in Computer Science, pp. 716-728, vol 11131. Springer, Cham. https://doi.org/10.1007/978-3-030-11015-4_54 (hereinafter referred to as Ali).
Regarding claim 16, Luo fails to disclose wherein object-related output data for calculating an oriented bounding box of the object are formed from the cell- related output data via at least one further processing step.
However, Ali discloses wherein object-related output data (see Ali pg. 2 “The predictions include 8 regression outputs + classes (versus 5 regressors + classes in case of YOLOV2): the OBB center in 3D (x, y, z), the 3D dimensions (length, width and height)”) for calculating an oriented bounding box of the object (see Ali pg. 1 “to generate oriented 3D object bounding boxes from LiDAR point cloud”) are formed from the cell- related output data via at least one further processing step (see Ali pg. 2 “In the input phase, we feed the bird-view of the 3D PCL to the input convolution channels.”).
It would have been obvious for a person having ordinary skill in the art to combine the object detection of Luo with the bounding box of Ali because it is predictable that doing so would allow the object detection to separate the object from the background and improve object detection accuracy, therefore improving system performance and efficiency.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARIO A. RODIN whose telephone number is (571)272-8003. The examiner can normally be reached M-F 8:00-5:00.
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/MARIO ANTHONY RODIN/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
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