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 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.
Claims 1-5, 8, and 9-13 are rejected under 35 U.S.C. 103 as being unpatentable over Kollmann et al (US Pub. 2017/0323154) in view of Suresh et al (US Pub. 2020/0050893).
With respect to claim 1, Kollmann discloses A method for generating a segmentation image, the method comprising: obtaining a target maritime image generated from a camera, wherein the camera is installed on a port or a vessel, and the target maritime image represents a target object, (see figure 4A, camera 28 installed on a vessel taking a maritime image of object 46); and generating a target segmentation image using the target maritime image and a neural network, wherein the neural network is trained to output a segmentation image in response to an input image, wherein, in the segmentation image, pixels in an area corresponding to an object represented in the input image are assigned a value corresponding to the object, wherein the value is selected from identification values including range, wherein the first distance range is closer than the second distance range” the probability can be 50%, 55%, 60%, etc. i.e. the distance range closer or farthest from the vessel] that there is a person within a particular field of view or area of interest within the field of view…), as claimed.
However, Kollmann fails to explicitly disclose including at least a first value, a second value, and a third value, wherein the first value corresponds to a water surface, (emphasis added), as claimed.
Suresh teaches including at least a first value, a second value, and a third value, wherein the first value corresponds to a water surface, (emphasis added, see paragraph 0071, wherein … method may further include identifying objects… The objects can include, for example, a seashore, a watercraft, an iceberg, a canal entrance, a lock gate, a bridge, a static far object, a moving far object, plain sea “the first value corresponds to a water surface”, or other objects.…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of object detection in sea. The teaching of Suresh to detect multiple objects in the maritime can be incorporate in to the system of Kollmann system as suggested (see paragraph 0024, wherein embodiment of an object detection system 1 on a marine vessel 2), for suggestion, and modifying the system yields a controlling maritime vessels (see Suresh paragraph 0002), for motivation.
With respect to claim 2, combination of Kollmann and Suresh further discloses wherein the identification values include at least a fourth value and a fifth value, wherein the fourth value is corresponding to a second object at the first distance range and the fifth value corresponding to the second object at the second distance range, and wherein the second object is of a different type than the first object, (see Suresh figure 3 for “different type of objects” and paragraph 0070 wherein …identifying objects in the plurality of images. Identification 102 may be performed in an offline mode. The objects can include, for example, a seashore, a watercraft, an iceberg, a canal entrance, a lock gate, a bridge, a static far object, a moving far object, plain sea, or other objects [this is read as second, third, etc objects], and see Kollmann paragraph 0047, wherein …an ANN trained to detect the presence of a person 46 in a marine environment may output detection information 56 as a percentage, or probability [this is read as “identification values” and they can be 1,2,3,4,….n], that there is a person in the frame, or image, captured by the image sensor 28; and paragraph 0049, wherein …detection information 56 indicating that a person is in the water near the marine vessel 2, such as a 95% probability [this is read as “wherein the sixth value is corresponding to the first object at a third distance range, and wherein the third distance range is farther than the first distance range and the second distance range” the probability can be 50%, 55%, 60%, etc. i.e. the distance range closer or farthest from the vessel]), as claimed.
With respect to claim 3, combination of Kollmann and Suresh further discloses wherein the first object is a first type vessel, and wherein the second object is a second type vessel, (see Suresh figure 2, sailing yacht, cargo ship, cruse ship coast guard boat “the first object is a first type vessel, and wherein the second object is a second type vessel”), as claimed.
With respect to claim 4, combination of Kollmann and Suresh further discloses wherein the method comprises: determining a type of the target object based on the value corresponding to the target object in the generated segmentation image, (see Suresh figure 3 different types of object as segmented), as claimed.
With respect to claim 5, combination of Kollmann and Suresh further discloses wherein the identification values include at least a sixth value, wherein the sixth value is corresponding to the first object at a third distance range, and wherein the third distance range is farther than the first distance range and the second distance range, (see Kollmann paragraph 0047, wherein …an ANN trained to detect the presence of a person 46 in a marine environment may output detection information 56 as a percentage, or probability [this is read as “identification values” and they can be 1,2,3,4,….n], that there is a person in the frame, or image, captured by the image sensor 28; and paragraph 0049, wherein …detection information 56 indicating that a person is in the water near the marine vessel 2, such as a 95% probability [this is read as “wherein the sixth value is corresponding to the first object at a third distance range, and wherein the third distance range is farther than the first distance range and the second distance range” the probability can be 50%, 55%, 60%, etc. i.e. the distance range closer or farthest from the vessel]), as claimed.
Claim 8 is rejected for the same reasons as set forth in the rejections for claim 1, because claim 8 is claiming subject matter of similar scope as claimed in claim 1.
Claims 9-13 are rejected for the same reasons as set forth in the rejections for claims 1-5, because claims 9-13 are claiming subject matter of similar scope as claimed in claims 1-5.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kollmann et al (US Pub. 2017/0323154) in view of Suresh et al (US Pub. 2020/0050893) as applied to claim 1 above, and further in view of Damage detection of offshore platform mooring line using artificial neural network, by Sidarta et al.
With respect to claim 6, combination of Kollmann and Suresh discloses as claimed and as rejected in claim 1 above. However, they fail to explicitly disclose wherein the identification values include at least a seventh value, wherein the seventh value corresponding to a rope, wherein the target maritime image represents a target rope, and wherein the method comprises: determining whether the target object is moored based on a value corresponding to the target rope in the obtained segmentation image, as claimed.
Sidarta teaches wherein the identification values include at least a seventh value, wherein the seventh value corresponding to a rope, wherein the target maritime image represents a target rope, and wherein the method comprises: determining whether the target object is moored based on a value corresponding to the target rope in the obtained segmentation image, (see page 3 left hand column last paragraph wherein … this study includes three conditions of mooring lines “rope”: intact mooring lines, a damaged line 9, and a damaged line 10. Lines 9 and 10 are adjacent lines with a five degree azimuth separation. This is a good test case to check whether the trained ANN model can detect and identify a damaged line that is five-degree apart to its adjacent line.), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object detection using image analysis. The teaching of Sidarta detect mooring lines the maritime can be incorporate into the system of Kollmann system as suggested (see paragraph 0024, wherein embodiment of an object detection system 1 on a marine vessel 2), for suggestion, and modifying the system yields indication of mooring failure (see Sidarta Abstract), for motivation.
Claim 14 is rejected for the same reasons as set forth in the rejections for claim 6, because claim 14 is claiming subject matter of similar scope as claimed in claim 6.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Kollmann et al (US Pub. 2017/0323154) in view of Suresh et al (US Pub. 2020/0050893) as applied to claim 1 above, and further in view of Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems, by Rodin et al.
With respect to claim 7, combination of Kollmann and Suresh discloses as claimed and as rejected in claim 1 above. However, they fail to explicitly disclose wherein the identification values include at least an eighth value, and wherein the eighth value corresponding to a buoy, as claimed.
Rodin teaches wherein the identification values include at least an eighth value, and wherein the eighth value corresponding to a buoy, (see section I. Introduction, last paragraph wherein …In this paper, a CNN is trained to classify boats, buoys, people and pallets in images captured…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object detection using image analysis. The teaching of Rodin detect mooring lines the maritime can be incorporate into the system of Kollmann system as suggested (see paragraph 0024, wherein embodiment of an object detection system 1 on a marine vessel 2), for suggestion, and modifying the system yields to classify various objects (see Rodin Abstract), for motivation.
Claim 15 is rejected for the same reasons as set forth in the rejections for claim 7, because claim 15 is claiming subject matter of similar scope as claimed in claim 7.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663
