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 1-9 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Golan et al (US Pub. 2019/0034712) in view of Vincent et al (US 9,104,914).
With respect to claim 1, Golan discloses A method, comprising: receiving frames captured with a video camera; for each frame captured with the video camera, identifying, using a model, first foreground pixels in the frame, wherein the identified first foreground pixels correspond to an identified foreground object; tracking, using the model, each identified foreground object, (see figure 2, numerical 22 video cameras, figure 5, numerical 52 tracking, figure 6, numerical 64 human-in-water model, and paragraph 0059-0065, to detect the presence of human “foreground object” in a video using the model to track the identified object and finally detecting if the human is drowned or not); and identifying,
However, Golan fails to explicitly disclose identifying, without using the model, second foreground pixels in the frame, wherein the identified second foreground pixels correspond to the identified foreground object, (emphasis added), as claimed.
Vincent teaches identifying, without using the model, second foreground pixels in the frame, wherein the identified second foreground pixels correspond to the identified foreground object, (emphasis added, see col. 7, lines 35-67, wherein … Method 200 begins by detecting regions in an image that includes faces at step 202. As mentioned earlier, two face detectors may be used: a primary detector …and a secondary detector. The primary detector detects regions illustrated in a box 206, and the secondary detector detects regions illustrated in a box 204. As the primary detector has a higher recall rate, box 206 shows more regions than box 204. For example, box 206 includes a region 212 that includes a face and is not detected by the secondary detector. In another example, box 206 includes a false positive region 213 that is not detected by the secondary detector. However, in the example illustrated, both the primary and secondary detectors detect a region 210.; and col. 5 lines 45-50 wherein … data from a second object detector module that is not trained for high-recall, (e.g., a score that corresponds to a confidence that the region includes an object of the particular type or a score of a landmarking stage of second object detector module that determines the likely positions of the eyes, nose, and mouth of the face within the box)), 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 identification using image analysis. Teaching of Vincent to obtain an object in an image without using a trained detector i.e. model can be incorporated into the Golan system as suggested in paragraph 0100 of Golan that there could be more than one cameras with the multiple systems and in paragraph 0006 that one of the system as a conventional system is the underwater camera system, for suggestion, and modifying the system yields a better alarm system for pool drowning, for motivation.
With respect to claim 2, combination of Golan and Vincent further discloses tracking, using an object tracking algorithm, a foreground object, (see Golan figure 5, numerical 52, and paragraph 0061, wherein …human in water candidate may be tracked in the video frames using a visual tracking algorithm (step 52)), as claimed.
With respect to claim 3, combination of Golan and Vincent further discloses determining whether the model is tracking the foreground object, wherein: in accordance with a determination that the foreground object is not tracked using the model, the foreground object is tracked using the object tracking algorithm; and in accordance with a determination that the foreground object is tracked using the model, the foreground object continues to be tracked using the model, (see Golan paragraph 0061, for tracking the humans in water in video frames based on Kalman filter, and paragraph 0063, for humans in water detection/recognition methods may be based on machine learning algorithms that are pre-trained), as claimed.
With respect to claim 4, combination of Golan and Vincent further discloses wherein the object tracking algorithm is Kalman tracking, optical flow method, Lucas-Kanade Algorithm, Horn-Schunck method, or Black—Jepson method, (see Golan paragraph 0061, wherein …tracking method may be based for instance on Kalman filter…), as claimed.
With respect to claim 5, combination of Golan and Vincent further discloses wherein the model is a pre-trained supervised model, deep learning model, an Artificial Neural Network (ANN) model, a Random Forest (RF) model, a Convolutional Neural Network (CNN) model, a Hierarchical extreme learning machine (H-ELM) model, a Local binary patterns (LBP) model, a Scale-Invariant Feature Transform (SIFT) model, a Histogram of gradient (HOG) model, a Fastest Pedestrian Detector of the West (FPDW) model, or a Stochastic Gradient Descent (SGD) model, (see Golan paragraph 0065, wherein … the algorithm 61 outputs after the training stage a model or a plurality of models for humans-in-water. These models may be used to detect humans-in-water candidates in the underwater images. These models may comprise one or more visual representations (such as a particular distribution of pixels) that indicate the fact that a human is present in the underwater image), as claimed.
With respect to claim 6, combination of Golan and Vincent further discloses wherein the frames comprise a view of an area, the method further comprising defining a virtual boundary, wherein the virtual boundary surrounds the area, (see Golan paragraph 0049, wherein … According to some embodiments, the system's cameras may enable a full visual coverage of the pool “a view of an area”. According to some embodiments, each camera covers a different part of the pool), as claimed.
With respect to claim 7, combination of Golan and Vincent further discloses wherein the frames comprise a view of a swimming pool, (see Golan paragraph 0049, wherein … According to some embodiments, the system's cameras may enable a full visual coverage of the pool. According to some embodiments, each camera covers a different part of the pool), as claimed.
With respect to claim 8, combination of Golan and Vincent further wherein the each foreground object is a swimmer, the method further comprising, based on the identified foreground pixels, tagging the swimmer in the frame with a respective identifier, (see Golan paragraph 0065, wherein … According to some embodiments, the algorithm 61 outputs after the training stage a model or a plurality of models for humans-in-water “swimmer in the frame” . These models may be used to detect humans-in-water candidates “tagging the swimmer” in the underwater images. These models may comprise one or more visual representations (such as a particular distribution of pixels “foreground pixels”) that indicate the fact that a human is present in the underwater image), as claimed.
With respect to claim 9, combination of Golan and Vincent further discloses determining whether a criterion is met for a foreground object; in accordance with a determination that the criterion is met for the foreground object, generating a detection signal indicating an event occurrence associated with the foreground object; and in accordance with a determination that the criterion is not met for the foreground object, forgoing generating the detection signal, (see Golan paragraph 0061, wherein …At each time, the chance for each track to be associated with a drowning event may be estimated (step 53). According to some embodiments, the drowning risk is estimated based on non-movement detection, while the body is being fully submerged in water; and paragraph 0099, wherein …drowning detection, it is of interest to recognize situations in which a human is submerged and does not move. Therefore, the method may be interested in tracks in which the location and posture did not change over time. This may be accomplished by assigning different tracks for different postures 153. For each active track it may be possible to estimate its “amount” of movement at a certain temporal window 154, and in case of non-movement, or a movement smaller than a certain pre-defined for a pre-defined amount of time that is associated with a drowning risk, the method may output the severity of the event, and the system may output a warning, or an alarm, depending on the severity), as claimed.
Claims 13 and 14 are rejected for the same reasons as set forth in the rejections for claim 1, because claims 13 and 14 are claiming subject matter of similar scope as claimed in claim 1.
Claims 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Golan et al (US Pub. 2019/0034712) in view of Vincent et al (US 9,104,914) as applied to claim 1 above, and further in view of Yau et al (WO 02/097758, IDS document).
With respect to claim 10, combination of Golan and Vincent discloses all the elements as claimed and as rejected in claim 1 above. However, they fail to explicitly disclose updating a counter associated with the second identified object, as claimed.
Yau in the same filed teaches updating a counter associated with the second identified object, (see page 10, lines 14-15, wherein …a swimmer detection module is launched to detect and count the number of swimmers…), 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 identification using image analysis. Teaching of Yau to count the number of swimmer in the image can be incorporated into the Golan system as suggested in figure 8, numerical 81 object detection and recognition, for suggestion, and modifying the system yields a better system that will count the total number of swimmer and keep track of them for pool drowning, for motivation.
With respect to claim 11 for the same reasons of combining as mentioned in rejection of claim 10 combination of Golan, Vincent and Yau further discloses updating a counter associated with the identified object, (see Yau page 10, lines 14-15, wherein …a swimmer detection module is launched to detect and count the number of swimmers “the identified object”…), as claimed.
With respect to claim 12 for the same reasons of combining as mentioned in rejection of claim 10 combination of Golan, Vincent and Yau further discloses updating a counter associated with a non-foreground object, (see Yau page 15, lines 6-10, wherein …update the background global model “non-foreground object”), as claimed.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663