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 .
Information Disclosure Statement
Acknowledgment is made of the Information Disclosure Statement dated 02/06/2024, 11/25/2024, 02/26/2025 and 03/11/2026. All of the cited references have been considered except where lined through.
Drawings
The drawings have been received on 02/02/2024. These drawings are accepted.
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 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1,
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 1 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“producing a semantic segmentation, [by a semantic segmentation network,] corresponding with the label; and”
“identifying objects of a scene, wherein a first object of the scene has partially occluded edges and a second object of the scene does not have an occluded edge, based on the label and the semantic segmentation.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., producing, identifying). The above limitations in the context of this claim encompass, inter alia, producing a semantic segmentation, identifying objects of a scene (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“[producing a semantic segmentation,] by a semantic segmentation network, [corresponding with the label; and]”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a semantic segmentation network (e.g., by using these elements as tools).
The limitations:
“receiving a sequence of training images, by a convolutional neural network (CNN), to identify an object by a label;”
As drafted, amount to insignificant extra-solution activities, which do not integrate a judicial exception into a practical application. For example, the additional elements of "receiving a sequence of training images" amount to mere data gathering and data storage, respectively, which are insignificant extra-solution activities that do not integrate a judicial exception into a practical application. See MPEP 2106.05(g).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The limitations:
“[producing a semantic segmentation,] by a semantic segmentation network, [corresponding with the label; and]”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a semantic segmentation network (e.g., by using these elements as tools).
As discussed above with respect to integration of the abstract idea into a practical application, all of the additional elements are insignificant extra-solution activities or mere instructions to apply an exception. (i.e., the additional element describes a unit for applying the abstract ideas). Insignificant extra-solution activities and mere instructions to apply an exception cannot provide an inventive concept. Moreover, receiving, communicating, and storing data are insignificant extra-solution activities that are well-understood, routine, and conventional. See MPEP 2106.05(d)(II) ("The courts have recognized the following computer functions as well-understood, routine, and conventional functions ... i. Receiving or transmitting data over a network") (citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015)).
The claim is not patent eligible.
Regarding Claim 2,
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 2 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the identifying objects of the scene comprises generating a displayable image with the identified objects of the scene.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., identifying). The above limitations in the context of this claim encompass, inter alia, identifying (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 3,
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 3 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the scene is part of a dataset comprising pixels.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., identifying). The above limitations in the context of this claim encompass, inter alia, identifying (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 4,
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 4 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the scene comprises a plurality of the objects and a background image.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., identifying). The above limitations in the context of this claim encompass, inter alia, identifying (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 5,
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 5 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“wherein the scene comprises a cluttered scene.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., identifying). The above limitations in the context of this claim encompass, inter alia, identifying (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 6,
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 6 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: The limitations:
“labeling edges of the scene pixel-wise.”
As drafted, under their broadest reasonable interpretation, cover concepts performed in human mind (including an observation, evaluation, judgement, or opinion, e.g., labeling). The above limitations in the context of this claim encompass, inter alia, labeling edges (corresponding to mental processes which can be done mentally or by pen and paper).
Step 2A Prong Two Analysis: Please see the corresponding analysis of Claim 1.
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Regarding Claim 7,
Claim 7 recites a method for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 8,
Claim 8 recites a method for performing steps similar of claim 2 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 9,
Claim 9 recites a method for performing steps similar of claim 3 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 10,
Claim 10 recites a method for performing steps similar of claim 4 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 11,
Claim 11 recites a method for performing steps similar of claim 5 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 12,
Claim 12 recites a method for performing steps similar of claim 6 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 13,
Claim 13 recites an apparatus for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis, in view of the following additional elements, considered individually and as an ordered combination with the additional elements identified above, failing to integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
“a memory to store instructions and”
“a graphics processing unit (GPU) comprising one or more processors, that based on execution of the instructions, are to:”
This is a recitation of generic computing components to be used in performing the abstract idea, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).
Regarding Claim 14,
Claim 14 recites an apparatus for performing steps similar of claim 2 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 15,
Claim 15 recites an apparatus for performing steps similar of claim 3 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 16,
Claim 16 recites an apparatus for performing steps similar of claim 4 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 17,
Claim 17 recites an apparatus for performing steps similar of claim 5 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 18,
Claim 18 recites an apparatus for performing steps similar of claim 6 and is rejected with the same rationale, mutatis mutandis.
Regarding Claim 19,
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claim 19 is directed to a non-transitory computer-readable medium, i.e., a machine, one of the statutory categories.
Step 2A Prong One Analysis: Please see the corresponding analysis of Claim 1.
Step 2A Prong Two Analysis: The judicial exceptions are not integrated into a practical application.
The limitations:
“wherein the server comprises the memory and the GPU.”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a memory and a GPU (e.g., by using these elements as tools).
Step 2B Analysis: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The limitations:
“wherein the server comprises the memory and the GPU.”
As drafted, are additional elements that amount to no more than mere instructions to apply the exception for the abstract ideas. See MPEP 2106.05(f). Specifically, they amount to mere instructions to apply the exception using a memory and a GPU (e.g., by using these elements as tools).
The claim is not patent eligible.
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 1-19 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (Multi-instance Object Segmentation with Occlusion Handling); hereinafter Chen in view of Yuille et al. (Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and Discriminatively Trained Domain Transform); hereinafter Yuille
Claim 1 is rejected over Chen and Yuille.
Regarding claim 1, Chen teaches receiving a sequence of training images, by a convolutional neural network (CNN), to identify an object by a label; (Chen [page 4]: “Foreground images and cropped images are fed into Region and Box CNN respectively to jointly train the CNN network. Finally, the grouped CNN features are used to train class-specific classifiers.”)
identifying objects of a scene, wherein a first object of the scene has partially occluded edges and a second object of the scene does not have an occluded edge, based on the label and the semantic segmentation. (Chen [page 5, 3.5. Graph Cut with Occlusion Handling]: “Specifically, we infer the occluding regions (i.e., the overlap between two instances) based on segmentation proposals with top classification scores.”; and [page 1]: “Figure 1(a) shows such an example where a motorbike is occluded by the leg of a person and is split into two parts.”)
Chen does not appear to explicitly teach at least one non-transitory computer-readable medium comprising instructions stored thereon, that if executed by one or more graphics processing units (GPUs), cause the one or more GPUs to perform:
producing a semantic segmentation, by a semantic segmentation network, corresponding with the label; and
However, Yuille teaches at least one non-transitory computer-readable medium comprising instructions stored thereon, that if executed by one or more graphics processing units (GPUs), cause the one or more GPUs to perform: (Yuille [page 7]: “On a NVIDIA Tesla K40 GPU, our GPU implementation of domain transform further reduces the average computation time to 25 ms/image.“)
producing a semantic segmentation, by a semantic segmentation network, corresponding with the label; and (Yuille [page 2, 3.1. Model Overview]: “The first component that produces coarse semantic segmentation score predictions is based on the publicly available DeepLab model, [5], which modifies VGG-16 net [40] to be FCN [31]. The model is initialized from the VGG-16 ImageNet [36] pretrained model.”)
It would have been obvious before the effective filing date to combine the semantic segmentation of Chen with the GPU of Yuille to effectively reduce computation time (Yuille, [page 7]). Chen and Yuille are analogous art because they both concern semantic segmentation in images.
Claim 2 is rejected over Chen and Yuille with the incorporation of claim 1.
Regarding claim 2, Chen teaches wherein the identifying objects of the scene comprises generating a displayable image with the identified objects of the scene. (Chen [page 7, Figure 6]: “Top detection results (with respect to the ground truth) of SDS [19] and the proposed algorithm on the PASCAL VOC 2012 segmentation validation dataset. Compared with SDS, the proposed algorithm obtains favorable segmentation results for different categories. Best viewed in color.”)
Claim 3 is rejected over Chen and Yuille with the incorporation of claim 1.
Regarding claim 3, Chen teaches wherein the scene is part of a dataset comprising pixels. (Chen [page 6, 4. Experiments]: “There are 1449 images on the PASCAL VOC 2012 segmentation validation set.”; and [page 4, 3.3. Class-specific Likelihood Map]: “We use superpixel to represent an image I. For each superpixel sp covered by hk, we record the corresponding category and score.”)
Claim 4 is rejected over Chen and Yuille with the incorporation of claim 1.
Regarding claim 4, Chen teaches wherein the scene comprises a plurality of the objects and a background image. (Chen [page 5, 3.5. Graph Cut with Occlusion Handling]: “an appearance model Ai consists of two Gaussian mixture models (GMM), one for the foreground (yp = 1) and another for the background (yp = 0).”; Note: See Figures 6 and 7 of Chen to see the objects and the background)
Claim 5 is rejected over Chen and Yuille with the incorporation of claim 1.
Regarding claim 5, Chen teaches wherein the scene comprises a cluttered scene. (Chen [page 6, 4.1 Results of Joint Detection and Segmentation]: “Each image in the VOCoccluded dataset satisfies the following: (a) It contains at least two instances (with respect to the VOC object categories) and (b) There is an overlap between two instances in the image.”; Note: An overlapping of objects in the images shows that the scene is cluttered.)
Claim 6 is rejected over Chen and Yuille with the incorporation of claim 1.
Regarding claim 6, Chen does not appear to explicitly teach comprising instructions stored thereon, that if executed by one or more graphics processing units (GPUs), cause the one or more GPUs to perform:
labeling edges of the scene pixel-wise.
However, Yuille teaches comprising instructions stored thereon, that if executed by one or more graphics processing units (GPUs), cause the one or more GPUs to perform:
labeling edges of the scene pixel-wise. (Yuille [page 7]: “On a NVIDIA Tesla K40 GPU, our GPU implementation of domain transform further reduces the average computation time to 25 ms/image.“; and [page 2, 3.1. Model overview]: “A convolutional layer with kernel size 1x1 and one output channel is applied to yield edge prediction.”)
It would have been obvious before the effective filing date to combine the semantic segmentation of Chen with the GPU of Yuille to effectively reduce computation time (Yuille, [page 7]). Chen and Yuille are analogous art because they both concern semantic segmentation in images.
Claim 7 recites a method for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis.
Dependent claim 8 is claim 2 in the form of a method and is rejected for the same reasons as claim 2 stated above. For the rejection of the limitations specifically pertaining to the method of claim 7, see the rejection of claim 7 above.
Dependent claim 9 is claim 3 in the form of a method and is rejected for the same reasons as claim 3 stated above. For the rejection of the limitations specifically pertaining to the method of claim 7, see the rejection of claim 7 above.
Dependent claim 10 is claim 4 in the form of a method and is rejected for the same reasons as claim 4 stated above. For the rejection of the limitations specifically pertaining to the method of claim 7, see the rejection of claim 7 above.
Dependent claim 11 is claim 5 in the form of a method and is rejected for the same reasons as claim 5 stated above. For the rejection of the limitations specifically pertaining to the method of claim 7, see the rejection of claim 7 above.
Dependent claim 12 is claim 6 in the form of a method and is rejected for the same reasons as claim 6 stated above. For the rejection of the limitations specifically pertaining to the method of claim 7, see the rejection of claim 7 above.
Claim 13 recites an apparatus for performing steps similar of claim 1 and is rejected with the same rationale, mutatis mutandis.
Dependent claim 14 is claim 2 in the form of an apparatus and is rejected for the same reasons as claim 2 stated above. For the rejection of the limitations specifically pertaining to the apparatus of claim 13, see the rejection of claim 13 above.
Dependent claim 15 is claim 3 in the form of an apparatus and is rejected for the same reasons as claim 3 stated above. For the rejection of the limitations specifically pertaining to the apparatus of claim 13, see the rejection of claim 13 above.
Dependent claim 16 is claim 4 in the form of an apparatus and is rejected for the same reasons as claim 4 stated above. For the rejection of the limitations specifically pertaining to the apparatus of claim 13, see the rejection of claim 13 above.
Dependent claim 17 is claim 5 in the form of an apparatus and is rejected for the same reasons as claim 5 stated above. For the rejection of the limitations specifically pertaining to the apparatus of claim 13, see the rejection of claim 13 above.
Dependent claim 18 is claim 6 in the form of an apparatus and is rejected for the same reasons as claim 6 stated above. For the rejection of the limitations specifically pertaining to the apparatus of claim 13, see the rejection of claim 13 above.
Claim 19 is rejected over Chen and Yuille with the incorporation of claim 13.
Regarding claim 19, Chen does not appear to explicitly teach comprising a server, wherein the server comprises the memory and the GPU.
However, Yuille teaches comprising a server, wherein the server comprises the memory and the GPU. (Yuille [page 7]: “On a NVIDIA Tesla K40 GPU, our GPU implementation of domain transform further reduces the average computation time to 25 ms/image.“; Note: GPUs also have their own memory.)
It would have been obvious before the effective filing date to combine the semantic segmentation of Chen with the GPU of Yuille to effectively reduce computation time (Yuille, [page 7]). Chen and Yuille are analogous art because they both concern semantic segmentation in images.
Conclusion
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/DAVID H TRAN/Examiner, Art Unit 2147
/ERIC NILSSON/Primary Examiner, Art Unit 2151
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