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 8 and 18 are objected to because of the following informalities:
Claim 8 recites the limitation " determining the shaded region candidates in, among the segments, segments in which the starting points are included" in line 7. The limitation is grammatically unclear. For the purposes of examination, the limitation will be read as “determining the shaded region candidates, among the segments in which the starting points are included.”
Claim 18 recites the limitation "determine the shaded region candidates in, among the segments, segments in which the starting points are included." in line 7. The limitation is grammatically unclear. For the purposes of examination, the limitation will be read as “determine the shaded region candidates, among the segments in which the starting points are included”
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 8-10 and 18-19 are 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.
Claims 8 and 18 recite the limitation " determining starting points based on either one or both of a change in elevation of the points and an interval between the points; " in line 7. It is unclear and indefinite what the phrase “starting points” is referring to. Starting points could be the points at which analysis begins or could be the start of the shaded region. For the purposes of examination, starting points will be viewed as points in the shaded region on the boundary between the shaded region and the not shaded region.
Claims that depend on the above rejected claims are also rejected under 35 U.S.C. 112(b) or 35
U.S.C. 112 (pre-AIA ), second paragraph.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
With respect to claim 1,
Step 1:
The claim is directed to a process as it is a method for object recognition.
Step 2A Prong One:
the following bold limitations are considered abstract:
“A processor-implemented method with object recognition, the method comprising: obtaining sensor data comprising points representing a surrounding environment of a sensor;
detecting, from the sensor data, a shaded region in which the points are not generated due to occlusion by a surrounding object;
generating feature data using the shaded region;
and performing object recognition for the surrounding environment of the sensor based on the feature data.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. Detecting a shaded region is a mathematical concept as seen in Para(s). [0062-0065] of the specification. Generating feature data is a mathematical concept as seen in Para. [0077-0078] of the specification. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Recognizing an object from data can be done in the human mind using observation, judgment, and opinion as it amounts to observing data and passing judgment based on said data.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“A processor-implemented method with object recognition, the method comprising: obtaining sensor data comprising points representing a surrounding environment of a sensor;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic sensor data.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “A processor-implemented method with object recognition, the method comprising: obtaining sensor data comprising points representing a surrounding environment of a sensor;” amounts to using a computer as a tool as the abstract idea is just processor implemented and mere data gathering as data is just obtained from a generic sensor.
Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by Yu (US 20210303912 A1) and Goswami (US 20240412531 A1).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “A processor-implemented method with object recognition, the method comprising: obtaining sensor data comprising points representing a surrounding environment of a sensor;” just tie the claim to generic data regarding the environment of the sensor.
With respect to claim 12,
Step 1:
The claim is directed to a machine as it is directed to an electrical device.
Step 2A Prong One:
the following bold limitations are considered abstract:
“An electronic device comprising: one or more processors configured to: obtain sensor data comprising points representing a surrounding environment of a sensor;
detect, from the sensor data, a shaded region in which the points are not generated due to occlusion by a surrounding object;
generate feature data using the shaded region;
and perform object recognition for the surrounding environment of the sensor based on the feature data.”
The above bolded limitations are directed to abstract ideas and would fall within the “Mathematical Concept” and “Mental Process” groupings of abstract ideas. Detecting a shaded region is a mathematical concept as seen in Para(s). [0062-0065] of the specification. Generating feature data is a mathematical concept as seen in Para. [0077-0078] of the specification. According to MPEP 2106.04(C) “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Recognizing an object from data can be done in the human mind using observation, judgment, and opinion as it amounts to observing data and passing judgment based on said data.
Step 2A Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements –
“An electronic device comprising: one or more processors configured to: obtain sensor data comprising points representing a surrounding environment of a sensor;”
Examiner views these limitations amount to generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
As such Examiner does NOT view that the claims
-Improve the functioning of a computer, or to any other technology or technical field
-Apply the judicial exception with, or by use of, a particular machine - see MPEP
2106.05(b)
-Effect a transformation or reduction of a particular article to a different state or thing -
see MPEP 2106.05(c)
-Apply or use the judicial exception in some other meaningful way beyond generally
linking the use of the judicial exception to a particular technological environment, such that the
claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP
2106.05(e) and Vanda Memo.
Moreover, Examiner views the claims to be merely generally linking the use of the judicial exception to a computer system and generic sensor data.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “An electronic device comprising: one or more processors configured to: obtain sensor data comprising points representing a surrounding environment of a sensor;” amounts to using a computer as a tool as the abstract idea is just processor implemented and mere data gathering as data is just obtained from a generic sensor.
Examiner further notes that such additional elements are viewed to be well known routine and conventional as evidenced by Yu (US 20210303912 A1) and Goswami (US 20240412531 A1).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Considering the claim as a whole, one of ordinary skill in the art would not know the practical application of the present invention since the claims do not apply or use the judicial exception in some meaningful way. As currently claimed, Examiner views that the additional elements do not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, because the claim fails to recite clearly how the judicial exception is applied in a manner that does not monopolize the exception because the limitations “An electronic device comprising: one or more processors configured to: obtain sensor data comprising points representing a surrounding environment of a sensor;” just tie the claim to generic data regarding the environment of the sensor.
Dependent claims 2-11 and 13-19 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claims are not directed to an abstract idea, as detailed below:
The dependent claims are directed to further limit the way that the data is processed to generate feature data and defining shaded regions out of data which are mathematical concepts and mental processes. Claim 11 is directed to a computer readable storage medium which is an additional element, however, it amounts to using a generic computer as a tool for computation.
Therefore, dependent claims 2-11 and 13-19 further limit the abstract idea with an abstract idea and thus the claims are still directed to an abstract idea without significantly more.
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 1-5, 7, 11-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Behind the Curtain: Learning Occluded Shapes for 3D Object Detection, 2021; as seen in the IDS dated 03/21/2024) as modified by Yu (US 20210303912 A1).
With respect to claims 1 and 12,
Xu teaches,
detecting, from the sensor data, a shaded region in which the points are not generated due to occlusion by a surrounding object; (Section 3 teaches “As illustrated in Figure 3, BtcDet first identifies the regions of occlusion ROC and signal miss RSM,”)
generating feature data using the shaded region; (“and then, let a shape occupancy network Ω estimate the probability of object shape occupancy P(OS). The training process is described in Sec. 3.1. Next, BtcDet extracts the point cloud 3D features by a backbone network Ψ. The features are sent to a Region Proposal Network (RPN) to generate 3D proposals.”)
and performing object recognition for the surrounding environment of the sensor based on the feature data. (Section 1.3 teaches “To the best of our knowledge, BtcDet is the first 3D object detector that targets the object shapes affected by occlusion. With the knowledge of shape priors, BtcDet estimates the occupancy of complete object shapes in the regions affected by occlusion and signal miss. Section 3 teaches After that, BtcDet conducts object detection conditioned on the estimated probability of occupancy P(OS).)
Xu does not explicitly teach,
A processor-implemented method with object recognition, the method comprising:
obtaining sensor data comprising points representing a surrounding environment of a sensor;
Yu teaches,
A processor-implemented method with object recognition, the method comprising: (Para. [0039] teaches “The machine (e.g., computer system) 1200 may include a hardware processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1204,”)
obtaining sensor data comprising points representing a surrounding environment of a sensor; (Para. [0024] teaches “Embodiments of the present application provides architectures to perform 3D semantic segmentation based on a point cloud data set for a time-ordered sequence of 3D frames, including a current 3D frame and one or more historical 3D frames previous to the current 3D frame. The point cloud data set for a time-ordered sequence of 3D frames may be captured by a LiDAR mounted on a vehicle.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu with a processor-implemented method with object recognition, the method comprising: obtaining sensor data comprising points representing a surrounding environment of a sensor; such as that of Yu.
One of ordinary skill would have been motivated to modify Xu, because the model of Xu was tested on a data set that included LiDAR point clouds in 3d as seen in section 4 and 4.1. Therefore, the LiDAR data must be obtained by a LiDAR sensor. Furthermore, it would be obvious to run the model of xu on a processor as it would be faster and more efficient than by doing it with any other means. Also, Xu compares their model to other computer vision models meaning that Xu must also be run on a computer.
With respect to claims 2 and 13,
Xu teaches,
The method of claim 1 and the electronic device of claim 12, wherein the feature data is generated using a reference area comprising at least a portion of the shaded region. (Section 3 teaches “For each region proposal, we construct local grids covering the proposal box. BtcDet pools the local geometric features fgeo onto the local grids, aggregates the grid features, and generates the final bounding box predictions.” The description of Fig. 4 teaches “Learning Occluded Shapes. (a) The regions of occlusion or signal miss ROC ∪ RSM can be identified after the spherical voxelization for the point cloud. (b) To label the occupancy OS (1 or 0), We place the approximated complete object shapes S (red points) in the corresponding boxes.”)
With respect to claims 3 and 14,
Xu further teaches,
The method of claim 1 and the electronic device of claim 12, wherein the generating of the feature data comprises: determining a reference area comprising at least a portion of the shaded region; (Section 3 teaches “For each region proposal, we construct local grids covering the proposal box. BtcDet pools the local geometric features fgeo onto the local grids, aggregates the grid features, and generates the final bounding box predictions.” The description of Fig. 4 teaches “Learning Occluded Shapes. (a) The regions of occlusion or signal miss ROC ∪ RSM can be identified after the spherical voxelization for the point cloud. (b) To label the occupancy OS (1 or 0), We place the approximated complete object shapes S (red points) in the corresponding boxes.”)
generating second sub-feature data corresponding to the reference area using a deep learning model; (Section 3.2 teaches “Ψ, a sparse 3D convolutional network that extracts detection features in the Cartesian coordinate.” (i.e. a convolutional neural network is a type of deep learning model.)
and generating the feature data by merging the second sub-feature data with first sub-feature data corresponding to the points of the sensor data. (Section 3.2 further teaches “where fin Ψi, fout Ψi−1, and maxpool denote the input features of Ψ’s ith layer, the output features of Ψ’s i−1th layer, and applying stride-2 maxpooling i − 1 times, respectively.” Section 3.3 teaches “We pool the nearby features fgeo onto the nearby grids through trilinear interpolation (see Figure 3) and aggregates them by sparse 3D convolutions. After that, the refinement module predicts an IoU-related class confidence score and the residues between the 3D proposal boxes and the ground truth bounding boxes” (i.e. pooling is viewed as merging the data)
With respect to claim 4 and 15,
Xu further teaches,
The method of claim 3 and the electronic device of claim 14, wherein the generating of the second sub-feature data comprises: determining a target point in the reference area; (Section 3.1 teaches “A heuristic H(A, B) is created to evaluate if a source object B covers most parts of a target object A and provides points that can fill A’s shape miss. To approximate A’s complete shape, we select the top 3 source objects B1, B2, B3 with the best scores. The final approximation S consists of A’s original points and the points of B1, B2, B3 that fill A’ shape miss.” And “Create training targets. In ROC ∪ RSM, we predict the probability P(OS) for voxels if they contain points of S. As illustrated in 4(b), S are placed at the locations of the corresponding objects.”
generating reference data based on a geometric relationship between reference points in the reference area among the points and the target point; (Section 3 teaches “Finally, BtcDet applies the proposal refinement. The local geometric features fgeo are composed of P(OS) and the multi-scale features from Ψ”)
and generating the second sub-feature data by executing the deep learning model based on the reference data. (Section 3.2 teaches “Trained with the customized supervision, Ω learns the shape priors of partially observed objects and generates P(OS). To benefit detection, P(OS) is transformed from the spherical coordinate to the Cartesian coordinate and fused with Ψ, a sparse 3D convolutional network that extracts detection features in the Cartesian coordinate.”)
With respect to claim 5,
Xu further teaches,
The method of claim 4, wherein the generating of the reference data comprises: generating the reference data based on relative coordinates between the reference points and the target point. (Section D teaches “The feature maps of the second, the third, and the final layer are further integrated with P(OS)⊥ to form a local geometric feature fgeo, which supports the proposal refinement.” Section G teaches “The blue points are the points of the target object and the red points are the points of the source objects. The assembled object faithfully covers the originally partially observed parts of the target objects and provides reasonable recovery points in the shape miss regions of the target objects.” (i.e. recovery points are generated using points of the target object which are relative to the target points)
With respect to claims 7 and 17,
Xu further teaches,
The method of claim 1 and the electronic device of claim 12, wherein the detecting of the shaded region comprises: dividing a virtual space corresponding to the surrounding environment of the sensor into segments; determining shaded region candidates having a possibility to be the shaded region based on the segments; and determining the shaded region based on the shaded region candidates. (Section 3.1 teaches “We propose to voxelize the point cloud using an evenly spaced spherical grid so that the occluded regions can be accurately formed by the spherical voxels behind any LiDAR point. (i.e. grid is viewed as dividing the virtual space and the voxels are viewed as segments.”)
With respect to claim 11,
Xu further teaches,
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1.
Yu further teaches,
A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1. (Para. [0038] teaches “In an example, the whole or part of one or more hardware processors may be configured by firmware or software (e.g., instructions, an application portion, or an application) as an engine that operates to perform specified operations. In an example, the software may reside on a tangible machine-readable storage medium.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, configure the one or more processors to perform the method of claim 1 such as that of Yu.
One of ordinary skill would have been motivated to modify Xu, because it would be obvious to run the model of Xu on a computing system with a processor and memory as it would be faster and more efficient than by doing it with any other means. Also, Xu compares their model to other computer vision models meaning that Xu must also be run on a computer.
With respect to claim 20,
Xu teaches,
detect, from the sensor data, a shaded region in which the points are not generated due to occlusion by a surrounding object; (Section 3 teaches “As illustrated in Figure 3, BtcDet first identifies the regions of occlusion ROC and signal miss RSM,”)
generate feature data using the shaded region; (“and then, let a shape occupancy network Ω estimate the probability of object shape occupancy P(OS). The training process is described in Sec. 3.1. Next, BtcDet extracts the point cloud 3D features by a backbone network Ψ. The features are sent to a Region Proposal Network (RPN) to generate 3D proposals.”)
and perform object recognition for the surrounding environment of the sensor based on the feature data; (Section 1.3 teaches “To the best of our knowledge, BtcDet is the first 3D object detector that targets the object shapes affected by occlusion. With the knowledge of shape priors, BtcDet estimates the occupancy of complete object shapes in the regions affected by occlusion and signal miss. Section 3 teaches After that, BtcDet conducts object detection conditioned on the estimated probability of occupancy P(OS).)
Xu does not explicitly teach,
A vehicle comprising: a sensor configured to generate sensor data comprising points representing a surrounding environment of a sensor; one or more processors, and a control system configured to control the vehicle based on a result of the object recognition.
Yu teaches,
A vehicle comprising: a sensor configured to generate sensor data comprising points representing a surrounding environment of a sensor; (Para. [0024] teaches “Embodiments of the present application provides architectures to perform 3D semantic segmentation based on a point cloud data set for a time-ordered sequence of 3D frames, including a current 3D frame and one or more historical 3D frames previous to the current 3D frame. The point cloud data set for a time-ordered sequence of 3D frames may be captured by a LiDAR mounted on a vehicle.”)
one or more processors, (Para. [0039] teaches “The machine (e.g., computer system) 1200 may include a hardware processor 1202 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 1204,”)
and a control system configured to control the vehicle based on a result of the object recognition. (Para. [0040] teaches “In an embodiment, the system 300 may be mounted on a vehicle having a LiDAR, as shown in FIG. 4. The system 300 may be used to provide 3D semantic segmentation results of surroundings along a route of the vehicle, for use with an autonomous vehicle control system. “Para. [0047] teaches “The outcome of the 3D semantic segmentation may be used by an autonomous vehicle control system to make a decision on a driving strategy.” Para. [0003] teaches “semantic segmentation may be used to provide information about other vehicles, pedestrians and other objects on a road, as well as information about lane markers, curbs, and other relevant items.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Xu with a vehicle comprising: a sensor configured to generate sensor data comprising points representing a surrounding environment of a sensor; one or more processors, and a control system configured to control the vehicle based on a result of the object recognition such as that of Yu.
One of ordinary skill would have been motivated to modify Xu, because the model of Xu was tested on a data set that included LiDAR point clouds in 3d as seen in section 4 and 4.1. Xu further teaches vehicle detection in Table 3. Therefore, the LiDAR data must be obtained by a LiDAR sensor and it would be obvious for the LiDAR sensor to be attached to a vehicle. Furthermore, it would be obvious to run the model of Xu on a processor as it would be faster and more efficient than by doing it with any other means. Lastly, controlling the vehicle based on the detected objects would prevent accidents from occurring.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Behind the Curtain: Learning Occluded Shapes for 3D Object Detection, 2021; as seen in the IDS dated 03/21/2024) and Yu (US 20210303912 A1) as applied to claims 1 and 12 above, and further in view of Koivisto (US 20240192320 A1).
With respect to claims 6 and 16,
Xu does not explicitly teach,
The method of claim 1 and the electronic device of claim 12, wherein the detecting of the shaded region comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; and detecting the shaded region based on either one or both of a change in elevation of the points and an interval between the points.
Koivisto teaches,
wherein the detecting of the shaded region comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; (Para. [0086] teaches “The depth data 314 may be representative of a depth value that corresponds to a distance or depth (or 3D location) of the detected object from the sensor(s), such as from a camera(s). The depth value may be provided using a ReLU activation function, by way of example.”)
and detecting the shaded region based on either one or both of a change in elevation of the points and an interval between the points. (Para(s). [0087-0088] teaches “The bottom or height visibility data 320 may be representative of a value that indicates whether a bottom or height of the detected object is completely visible or partially occluded (or truncated by the image). In other examples, the value may indicate an amount of the bottom of height of the detected object that is visible, occluded, or truncated. The value may be provided using a sigmoid activation function, as an example.” (I.e. height is viewed as elevation)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Xu and Yu wherein the detecting of the shaded region comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; and detecting the shaded region based on either one or both of a change in elevation of the points and an interval between the points such as that of Koivisto.
One of ordinary skill would have been motivated to modify Xu, because aligning points according to distance from the sensor would allow the system to understand what points are possibly occluded. Furthermore, as seen in Para. [0008] of Koivisto the method therein helps increase accuracy of the final detected objects.
Claims 8-10, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (Behind the Curtain: Learning Occluded Shapes for 3D Object Detection, 2021; as seen in the IDS dated 03/21/2024) and Yu (US 20210303912 A1) as applied to claims 7 and 17 above, and further in view of Goswami (US 20240412531 A1).
With respect to claims 8 and 18,
Xu does not explicitly teach,
The method of claim 7 and the electronic device of claim 17, wherein the determining of the shaded region candidates comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; determining starting points based on either one or both of a change in elevation of the points and an interval between the points; and determining the shaded region candidates in, among the segments, segments in which the starting points are included.
Goswami teaches,
wherein the determining of the shaded region candidates comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; determining starting points based on either one or both of a change in elevation of the points and an interval between the points; (Para. [0025] teaches “To that end, the vehicular sensing system classifies each respective second pixel (x.sub.i, y.sub.i) in the second set of second pixels as occluded or non-occluded to identify the extent (if any) of an occlusion region in the respective portion of the three-dimensional point cloud. In particular, the system compares the respective range value (e.g., which may be initialized or non-initialized) for each second pixel to the range value of a corresponding first pixel. That is, the respective second pixel and the corresponding first pixel share a same respective coordinate such that the range values may be compared to determine whether the second pixel is occluded or non-occluded.” (i.e. range is distance from the sensor)
and determining the shaded region candidates in, among the segments, segments in which the starting points are included. (Para. [0028] teaches “For example, FIG. 4 shows an example query region at a particular height (e.g., denoted by the white box of the example query region) and an occlusion map that includes the classification for each pixel from the query region. Here, the white pixels in the query region of the occlusion map indicate non-occluded classifications and the black pixels in the query region of the occlusion map indicate occluded classifications.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Xu and Yu wherein the determining of the shaded region candidates comprises: aligning the points according to a distance between a sensor point corresponding to the sensor and each of the points; determining starting points based on either one or both of a change in elevation of the points and an interval between the points; and determining the shaded region candidates in, among the segments, segments in which the starting points are included such as that of Goswami.
One of ordinary skill would have been motivated to modify Xu, because aligning points according to distance from the sensor and determining where the occluded regions start would allow the system to understand what points are possibly occluded. Furthermore, as seen in Para. [0014] of Goswami identifying the occlusion regions would allow the system to plan for objects that would otherwise be unexpected.
With respect to claims 9 and 19,
Xu does not explicitly teach,
The method of claim 8 and the electronic device of claim 17, wherein the determining of the shaded region comprises: determining the shaded region by merging two or more of the shaded region candidates based on a geometric relationship between the starting points.
Goswami teaches,
wherein the determining of the shaded region comprises: determining the shaded region by merging two or more of the shaded region candidates based on a geometric relationship between the starting points. (Para. [0028] teaches “wherein the determining of the shaded region comprises: determining the shaded region by merging two or more of the shaded region candidates based on a geometric relationship between the starting points.” (i.e. the black regions are black pixels grouped together which is viewed as merging the candidates.))
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination of Xu and Yu wherein the determining of the shaded region comprises: determining the shaded region by merging two or more of the shaded region candidates based on a geometric relationship between the starting points such as that of Goswami.
One of ordinary skill would have been motivated to modify Xu, because merging the points together would allow the system to create a complete image of the occluded and non-occluded regions as seen in Fig. 4 of Goswami.
With respect to claim 10,
Xu further teaches,
The method of claim 9, further comprising determining a distance between the sensor point and a starting point of the shaded region based on an average of distances between the sensor point and starting points of the two or more of the shaded region candidates. (Section 3.1 teaches “In ROC ∪ RSM, we encode each nonempty spherical voxel with the average properties of the points inside (x,y,z,feats), then, send them to a shape occupancy network Ω.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to JOSHUA L FORRISTALL whose telephone number is 703-756-4554. The examiner
can normally be reached Monday-Friday 8:30 AM- 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use
the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on 571-272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like
assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or
571-272-1000.
/JOSHUA L FORRISTALL/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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