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 19 and 20 are objected to because of the following informalities: Claims 19 and 20 depend on claim 8, it should read claim 18. Appropriate correction is required.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The independent claim 1 (exemplary claim for claim 12) recites:
A method of inferencing performed by a computer vision system, comprising:
receiving an input image; - Pre/post activity
mapping a plurality of grid cells to the input image so that each grid cell of the plurality of grid cells includes a respective portion of the input image; - Mental process
performing an inferencing operation on the portion of the input image included in a first grid cell of the plurality of grid cells, the inferencing operation being associated with an object detection model; - Mental process
and
performing the inferencing operation on a second grid cell of the plurality of grid cells based at least in part on a result of the inferencing operation performed on the portion of the input image included in the first grid cell. – Mental process
101 Analysis:
Step Analysis
1: Statutory Category?
Yes. The claim recites a series of steps and, therefore, is a process.
2A - Prong 1: Judicial Exception Recited?
Yes. The claim recites the limitations of mapping grid cells and performing inferencing operation on grids on an image, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “a processing system” as claimed in claim 12, nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “a processing system” language, the claim 12 encompasses the user manually checking for an object in grid(s).
This limitation is a mental process.
2A - Prong 2: Integrated into a Practical Application?
No. The claim recites one additional element that a computer (claim 1) and a processing system (claim 12) is used to perform steps. The computer and processing system in steps is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of processing data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to the abstract idea.
2B: Claim provides an Inventive Concept?
No. As discussed with respect to Step 2A Prong Two, the additional element in the claim amounts to no more than mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
The claim is ineligible.
Dependent claim 2-11 and 13-20 fail to include additional elements that are sufficient to amount to significantly more than the judicial exception therefore, they are rejected as well.
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 1-7 and 11-17 are rejected under 35 U.S.C. 103 as being unpatentable over Adaptive object detection using adjacency and zoom prediction, by Lu et al.
With respect to claim 1, Lu discloses A method of inferencing performed by a computer vision system, comprising: receiving an input image, (see section 3.1, wherein … starting from the entire image…); mapping a plurality of grid cells to the input image so that each grid cell of the plurality of grid cells includes a respective portion of the input image, (see figure 1 right hand side under Our AZ-Net, the entire image has square i.e. mapping plurality of grids); performing an inferencing operation on the portion of the input image included in a first grid cell of the plurality of grid cells, the inferencing operation being associated with an object detection model, (see section 3.1, wherein … For any region “a first grid” encountered in the search procedure, the algorithm extracts features from this region to compute the zoom indicator and the adjacency predictions…); and performing the inferencing operation on a second grid cell of the plurality of grid cells [based at least in part on a result of the inferencing operation performed on the portion of the input image included in the first grid cell], (see section 3.1, wherein … the current region “a first grid” is divided into sub-regions in the manner shown in Figure 2. These sub-regions “a second grid” is then recursively processed in the same manner as its parent region…), as claimed.
Lu fails to explicitly disclose performing the inferencing operation on a second grid cell of the plurality of grid cells based at least in part on a result of the inferencing operation performed on the portion of the input image included in the first grid cell, (emphasis added) as claimed.
But, Lu in section 3.1 details that “To detect these embedded small objects, the current region is divided into sub-regions in the manner shown in Figure 2…” this teaches that the “the inferencing operation performed on the portion of the input image included in the first grid cell”, (emphasis added), as claimed.
Therefore, it would have been obvious to one ordinary skilled in the art at the effective date of invention to utilize the teaching of subdividing the regions of the image in order to detect an object can be simply called as another grid to perform similar process for object detection will yield the predictable results.
With respect to claim 2, Lu further discloses assigning a respective priority value to each of the plurality of grid cells, the inferencing operation being performed on the portion of the input image included in the first grid cell based on the priority value assigned to the first grid cell; and updating the priority value assigned to the first grid cell based on the result of the inferencing operation performed on the portion of the input image included in the first grid cell, (see figure 1, right hand side under Our AZ-Net, where the 3 grid is further divided and section 3.1, wherein … The adjacency predictions with confidence scores above a threshold are included in the set of output region proposals. If the zoom indicator is above a threshold, this indicates that the current region is likely to contain small objects…), as claimed.
With respect to claim 3, Lu further discloses wherein the result of the inferencing operation indicates whether an object of interest is detected in the portion of the input image included in the first grid cell, (see section 3.1, wherein …If the zoom indicator is above a threshold, this indicates that the current region is likely to contain small objects…), as claimed.
With respect to claim 4, Lu further discloses wherein the priority value assigned to the first grid cell is updated to a first value if the result of the inferencing operation indicates that no objects of interest are detected and is updated to a second value if the result of the inferencing operation indicates that an object of interest is detected in the portion of the input image included in the first grid cell, the first value being lower than the second value, (see section 3.3.1, wherein … The label is 1 if there exists an object with 50% of its area inside the region and the area is at most 25% of the size of the region…), as claimed.
With respect to claim 5, Lu further discloses updating the priority value assigned to the second grid cell based on the result of the inferencing operation performed on the portion of the input image included in the first grid cell, (section 3.1, wherein … If the zoom indicator is above a threshold, this indicates that the current region is likely to contain small objects. To detect these embedded small objects, the current region is divided into sub-regions in the manner shown in Figure 2. Each of these sub-regions is then recursively processed in the same manner as its parent region, until either its area or its zoom indicator is too small. Figure 1 illustrates this procedure…“updating the priority value assigned to the second grid cell based on the result of the inferencing operation”), as claimed.
With respect to claim 6, Lu further discloses wherein the result of the inferencing operation performed on the portion of the input image included in the first grid cell indicates that an object of interest is detected at an edge of the first grid cell overlapping the second grid cell, (see section 2, page 3 left hand side column, wherein … Our work is most related to the recent work by Ren et al. [22], which uses a set of heuristically designed 2400 overlapping anchor regions “an edge of the first grid cell overlapping the second grid cell”… we show that it is possible to detect small object instances in the scene without an excessive number of anchor regions…), as claimed.
With respect to claim 7, Lu further discloses wherein the inferencing operation is performed on the portion of the input image included in the second grid cell based on the updated priority value assigned to the second grid cell, (see section 3.1, wherein … We consider a recursive search strategy, starting from the entire image as the root region. For any region encountered in the search procedure, the algorithm extracts features from this region to compute the zoom indicator and the adjacency predictions. The adjacency predictions with confidence scores above a threshold are included in the set of output region proposals. If the zoom indicator is above a threshold, this indicates that the current region is likely to contain small objects… Each of these sub-regions is then recursively processed in the same manner as its parent region, until either its area or its zoom indicator is too small. Figure 1 illustrates this procedure…), as claimed.
With respect to claim 11, Lu further discloses refraining from performing the inferencing operation on the portion of the input image included in a third grid cell of the plurality of grid cells based on the priority value assigned to the third grid cell; and incrementing the priority value assigned to the third grid cell, (see figure 2, right side Our AZ-Net where only the third grid is further divided i.e. the other grids 1, 2 and 4 are “refrained[ing] from performing the inferencing operation”), as claimed.
Claims 12-17 are rejected for the same reasons as set forth in the rejections of claims 1, 2, 3+4, 5, 6 and 7, because claims 12-17 are claiming subject matter of similar scope as claimed in claims 1, 2, 3+4, 5, 6 and 7 respectively.
Claims 8-10 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Adaptive object detection using adjacency and zoom prediction, by Lu et, in view of Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, by Ren et al.
With respect to claim 8, Lu discloses all the limitation as claimed and as rejected in claim 1 above. However, Lu fails to explicitly disclose comparing the result of the inferencing operation performed on the portion of the input image included in the first grid cell with a result of the inferencing operation performed on the portion of the input image included in the second grid cell; and outputting one of the results based at least in part on the comparison, as claimed.
Ren teaches comparing the result of the inferencing operation performed on the portion of the input image included in the first grid cell with a result of the inferencing operation performed on the portion of the input image included in the second grid cell; and outputting one of the results based at least in part on the comparison, (see page 7, left hand column, wherein … RPN proposals highly overlap with each other. To reduce redundancy, we adopt non-maximum suppression (NMS) on the proposal regions based on their cls scores [this is read as comparing the proposals i.e. grids; and redundancy is read as outputting one of the results] …), 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 using image analysis. Teaching of Ren to use the similarity of proposals i.e. the comparison of grids can be incorporated in to Lu’s system as suggested (see page 3 of Lu, left hand side column, wherein … Our work is most related to the recent work by Ren et al. [22], which uses a set of heuristically designed 2400 overlapping anchor regions…), for suggestion, and modifying the system yields a more accurate object detection system, for motivation.
With respect to claim 9, combination of Lu and Ren further discloses wherein the outputting of one of the results comprises: detecting one or more redundancies based on comparing the result of the inferencing operation performed on the portion of the input image included in the first grid cell with the result of the inferencing operation performed on the portion of the input image included in the second grid cell, the one or more redundancies representing inferences associated with the result of the inferencing operation performed on the portion of the input image included in the first grid cell; and filtering the one or more redundancies from the output, (see page 7 left hand side column, wherein … RPN proposals highly overlap with each other. To reduce redundancy, we adopt non-maximum suppression (NMS) on the proposal regions based on their cls scores. We fix the IoU threshold for NMS at 0.7, which leaves us about 2000 proposal regions per image. As we will show, NMS does not harm the ultimate detection accuracy, but substantially reduces the number of proposals…), as claimed.
With respect to claim 10, combination of Lu and Ren further discloses wherein the outputting of one of the results comprises: identifying one or more duplicate detections based on comparing the result of the inferencing operation performed on the portion of the input image included in the first grid cell with the result of the inferencing operation performed on the portion of the input image included in the second grid cell, the one or more duplicate detections representing inferences associated with the result of the inferencing operation performed on the portion of the input image included in the first grid cell that overlap inferences associated with the result of the inferencing operation performed on the portion of the input image included in the second grid cell; and filtering the one or more duplicate detections from the output, (see page 7 left hand side column, wherein … RPN proposals highly overlap with each other. To reduce redundancy, we adopt non-maximum suppression (NMS) on the proposal regions based on their cls scores. We fix the IoU threshold for NMS at 0.7, which leaves us about 2000 proposal regions per image. As we will show, NMS does not harm the ultimate detection accuracy, but substantially reduces the number of proposals…), as claimed.
Claims 18-20 are rejected for the same reasons as set forth in the rejections of claim 8-10, because claims 18-20 are claiming subject matter of similar scope as claimed in claims 8-10.
Conclusion
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/VIKKRAM BALI/Primary Examiner, Art Unit 2663