DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-16 filed and preliminary amended on 12/13/2024 are pending and being examined. Claims 1, 12, and 16 are independent form.
Priority
3. This application is a CON of PCT/EP2023/066419 filed on 06/19/2023, whether the benefit of foreign priority was further claimed.
Claim Rejections - 35 USC § 103
4. 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 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.
5. 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 of this title, 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.
6. Claim 1-11 are rejected under 35 U.S.C. 103 as being obvious over Chen et al (“WODIS: Water Obstacle Detection Network Based on Image Segmentation for Autonomous Surface Vehicles in Maritime Environments”, 2021, hereinafter “Chen”) in view of Pally et al (“Application of image processing and convolutional neural networks for flood image classification and semantic segmentation”, 2021, hereinafter ‘Pally”).
Regarding claim 1, Chen discloses a method of determining a vessel-water interface between at least one target vessel and a water surface of a waterbody, on which the target vessel sails, from an image showing the target vessel on the waterbody, the method (the WODIS method; see the title, the abstract, and fig.3) comprising:
receiving image data of the image (see, e.g., the “input image” shown in the left col. of fig.6);
determining interface values of pixels of the image, the interface values representative of each of the corresponding pixels showing the vessel-water interface or not (see, e.g., the segmentation mask image shown in the middle col. of fig.6, wherein each of the pixels of the segmentation mask represents one of three classes, that is, sea, objects/ including ships, or sky.);
determining bounding box data of a bounding box surrounding the target vessel within the image from the image data (see, e.g., the detected object boxes (bounding boxes) shown in the right col. of fig.6); and
determining vessel-water interface data, the vessel-water interface data representative of a position and extension of the vessel-water interface within the image, based on the determined interface values and the bounding box data (see, e.g., the detected object boxes (bounding boxes) shown in the right col. of fig.6, wherein each of the bounding boxes represents an object including a ship on the sea surface.).
As explained above, the mere difference is, Chen does not explicitly state that the segmentation mask is obtained by determining a probability of each pixel showing whether it is water, ship, or none of them as recited by the claim. However, the same field of endeavor, that is, in the field of water object detection using a neural network, Pally teaches this. See the “Softmax probabilities” calculation in fig.2, see Page.4, in the left column, paragraph 2, lines 6-10: “[t]here are two output layers out of which one is responsible for producing SoftMax probabilities for each of the categories and the second output layer is responsible for defining the bounding boxes with four real-valued numbers which represent the edges of bounding box.” It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Pally into the teachings of Chen and replace the neural network taught by Chen with the Fast R-CNN taught by Pally. Suggestion or motivation for doing so would have been to train all networks’ layers in a single step using a multi-task loss as taught by Pally, see the Section of Fast R-CNN. Therefore, the claim is unpatentable over Chen in view of Pally.
Regarding claim 2, the combination of Chen and Pally discloses the method in accordance with claim 1, wherein the interface values are interpreted as costs or rewards and the vessel-water interface data are determined by minimizing the costs or, respectively, by maximizing the rewards (Chen, see minimizing the loss functions defined by Eqs(5)-(7); Pally, see minimizing the loss functions defined by Eqs (1)-(5)).
Regarding claim 3, the combination of Chen and Pally discloses the method in accordance with claim 2, wherein: the interface values form an array of interface values, with positions of the interface values within the array corresponding to positions of the corresponding pixels within the image, and the costs and the rewards each refer to a sum or product of the interface values of all pixels which are necessary to pixel-wisely go from one lateral side of the array corresponding to one lateral side of the bounding box to another lateral side of the array corresponding to another lateral side of the bounding box (Pally, see minimizing the loss function Lloc (.) between the bounding box (bb) tu and the bb v as defined by Eq (1)).
Regarding claim 4, the combination of Chen and Pally discloses the method in accordance with claim 1, wherein the bounding box data are determined by an object detection algorithm configured to detect vessels in images or by instance segmentation (Chen, see the objects bbs shown by fig.13).
Regarding claim 5, the combination of Chen and Pally discloses the method in accordance with claim 1, further comprising, after receiving the image data and before determining the vessel-water interface data: determining vessel-water values of the pixels of the image from the image data, the vessel-water values representative of a probability of the corresponding pixels showing the target vessel, the water surface, or none of both; and determining the interface values from the vessel-water values (See the “Softmax probabilities” calculation in fig.2, see Page.4, in the left column, paragraph 2, lines 6-10).
Regarding claim 6, the combination of Chen and Pally discloses the method in accordance with claim 1, further comprising, after receiving the image data and before determining the vessel-water interface data: determining edge values of the pixels of the image, the edge values representative of the corresponding pixels showing an edge within the image or not; and determining the vessel-water interface data based on the edge values (see Sec. III-A, par.2, “low-level features” detection for the objects “such as edges, corners”).
Regarding claim 7, the combination of Chen and Pally discloses the method in accordance with claim 6, wherein the vessel-water interface data are determined based on the edge values by: pixel-wisely multiplying the interface values with the corresponding edge values and by determining the vessel-water interface data based on the corresponding products; or pixel-wisely adding the interface values to the corresponding edge values and by determining the vessel-water interface data based on the corresponding sums (Chen, see Sec. III-B, 2) “Feature fusion model”).
Regarding claim 8, the combination of Chen and Pally discloses the method in accordance with claim 1, further comprising determining all pixels, whose corresponding interface values are below a predetermined threshold, as not showing the vessel-water interface (Chen, see, e.g., the segmentation mask image shown in the middle col. of fig.6, wherein each of the pixels of the segmentation mask represents one of three classes, that is, sea, objects/ including ships, or sky).
Regarding claim 9, the combination of Chen and Pally discloses the method in accordance with claim 5, wherein the interface values and/or the vessel-water values are determined by a neural network (Chen, “the encoder-decoder” network shown by fig.3).
Regarding claim 10, the combination of Chen and Pally discloses the method in accordance with claim 1, wherein the vessel-water interface data comprise positions and/or coordinates of the pixels showing the vessel-water interface within the image (Chen, see, e.g., the segmentation mask image shown in the middle col. of fig.6, wherein each of the pixels of the segmentation mask represents one of three classes, that is, sea, objects including ships, or sky).
Regarding claim 11, the combination of Chen and Pally discloses the method in accordance with claim 1, further comprising modifying the image data based on the determined vessel-water interface data such that the vessel-water interface is illustrated within the image when the image is shown on a display (Chen, e.g., see the “object detection” in fig.6).
7. Claim 12-16 are rejected under 35 U.S.C. 103 as being obvious over Chen in view of Pally and further in view of Steccanella et al (“Waterline and obstacle detection in images from low-cost autonomous boats for environmental monitoring”, 2020, hereinafter “Steccanella”).
Regarding claim 12, the combination of Chen and Pally discloses the claimed invention except for an ego vessel comprising a camera configured to capture an image of surroundings of the ego vessel. However, in the same filed of endeavor, Steccanella teaches a method for waterline detection from images taken by cameras mounted on low-cost autonomous surface vehicles (ASVs) is a key process for obtaining a fast obstacle detection. See fig.1 and Abstract. Further, one skilled in the art would easily conceive that an ego vessel” recited by claim 12 could be one of the objects recited in claim 1. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Steccanella into the teachings of the combination of Chen and Pally and utilize an ego vessel comprising a camera configured to capture an image of surroundings of the ego vessel taught by Steccanella. Suggestion or motivation for doing so would have been to “use vision-based sensing to reach this goal, focusing on the domain of small, low-cost ASVs where sensors commonly utilized for localization purposes” as taught by Steccanella, see Sec. 1, para.3. Therefore, the claim is unpatentable over Chen in view of Pally and further in view of Steccanella.
Regarding claims 13-15, for each of them, there are no additional inventive elements, it is thus not patentable over Chen in view of Pally and further in view of Steccanella.
Regarding claim 16, claim 16 is an inherent variation of claim 12, thus it is not patentable over Chen in view of Pally and further in view of Steccanella.
Conclusion
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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