Detailed Description
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 .
Specification
The abstract of the disclosure is objected to because it is not limited to a single paragraph within the range of 50 to 150 words in length. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Objections
Claims 1, 7, and 8 are objected to because of the following informalities:
Claim 1, lines 12-13, "wherein the target object; appears the region in said each image of the plurality of images" should read “wherein the target object[[;]] appears in the region in said each image of the plurality of images”. Similar corrections should be applied to claim 7 and 8.
Appropriate correction is required.
Applicant is advised that should claim 16 be found allowable, claim 17 will be objected to under 37 CFR 1.75 as being a substantial duplicate thereof. When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim. See MPEP § 608.01(m).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1, line 16, recites "each image of the images" which lacks antecedent basis. It is unclear if “the images” is meant to refer to the previously recited “the plurality of images” or another set of images. For examination purposes, the limitation will be interpreted as “each image of the plurality of images”.
Claims 2, 11, and 14 similarly recites “each image of the images”. Therefore, claims 2, 11, and 14 are rejected for the same reason and will be interpreted similarly.
Claim 2, lines 7 and 10, recites “the image” which lacks antecedent basis. It is unclear if the limitation is meant to refer to one of the previously recited “each image of the plurality of images” or if it is meant to refer to a different image. For examination purposes, the limitation will be interpreted as corresponding to any image out of the plurality of images.
Claims 3-4, and 9-17 similarly recites “the image”. Therefore, claims 3-4, and 9-17 are rejected for the same reason and will be interpreted similarly.
Claim 2, lines 9-11, recites “the second information excludes the image determined as having a low possibility that the target object appears from targets to be processed” which is indefinite. The claim describes the second information as specifying a position relationship with the point cloud and including position information and an imaging direction. However, it is unclear how “the second information” excludes images. Particularly, the claim does not clearly specify whether the second information itself performs an exclusion operation, whether the second information is merely data that is used by another component to exclude images, or whether the limitation is describing images that were previously excluded from the second information. For examination purposes, the limitation will be interpreted as the second information does not include images which were previously determined as having a low possibility.
Claim 3, line 4, recites “the position” which lacks antecedent basis. It is unclear if the limitation Is meant to refer to the previously recited “a position of the target object” in claim 1, “an approximate position of the target object” in claim 2, or if it is meant to refer to a new element. Claim 3 specifies “the position of the target object that is acquired from the map information”, however, the map information introduced in claim 2 does not contain any recitation of such a position. For examination purposes, the limitation will be interpreted as if introducing a new position which is included in the map information.
Claims 5-6 are rejected as being dependent from a rejected base claim.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract
idea (mental process) of evaluating and correlating images and corresponding 3D data to determine an objects position, without significantly more.
The claim recites: “A position detection device for recognizing a presence position of a target object in a three-dimensional space, the position detection device comprising a processor configured to execute operations comprising: acquiring three-dimensional point cloud information of the space; acquiring a plurality of images of an area including surroundings of an object in the space according to views from different imaging points; determining, based on the plurality of images, whether the target object appears in the plurality of images; detecting a region of the object in each image of the plurality of images, wherein the target object; appears the region in said each image of the plurality of images; specifying a region of a point cloud corresponding to the target object based on the three-dimensional point cloud information and the region of the object detected in said each image of the images; and specifying a position of the target object in the space by recognizing points corresponding to the target object from the three-dimensional point cloud information of the specified region.”
The limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the human mind. A person can mentally observe multiple images, recognize that the same object appears across those images, and identify a region of the object in each image. The person could then mentally correlate or map 3D point data to the observed image regions and infer the object’s position within a 3D space.
The judicial exception is not integrated into a practical application. For example, the claim recites the additional elements, “acquiring three-dimensional point cloud information of the space” and “acquiring a plurality of images of an area including surroundings of an object in the space according to views from different imaging points”. These additional elements can reasonably be interpreted as merely a data gathering step in conjunction with the abstract idea. For example, the three-dimensional point cloud information and plurality of images are acquired for image recognition and object position detection; therefore, the additional element does not add a meaningful limitation to the method as it is an insignificant extra-solution activity.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial expectation. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements are recited at a high-level of generality and are well- understood, routine, and conventional in the field. It is therefore a judicial exception that is not integrated into a practical application, and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This claim is not patent eligible.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation of the same abstract idea identified in the analysis of claims 1. For example, the person can mentally evaluate each image and associated mapping, position, and imaging information, to determine if objects appear in the images. This claim is not patent eligible.
Claims 11 and 14 contain elements found analogous to claim 4. Therefore, claim 11 and 14 are similarly rejected under 35 U.S.C. 101.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation of the same abstract idea identified in the analysis of claims 1. For example, the person can mentally evaluate an imaging point on an image to compare with a predetermined range to determine target object positions. The person can further calculate a ratio of the portion of the range which falls in view of the image. This claim is not patent eligible.
Claims 12 and 15 contain elements found analogous to claim 4. Therefore, claim 12 and 15 are similarly rejected under 35 U.S.C. 101.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation of the same abstract idea identified in the analysis of claims 1. For example, the person can mentally evaluate images to determine whether target objects appear based on known positions. The person can further mentally correlate or map 3D point data to the observed image regions by grouping regions of the same object. This claim is not patent eligible.
Claims 9, 10, 13, 16, and 17 contain elements found analogous to claim 4. Therefore, claim 9, 10, 13, 16, and 17 are similarly rejected under 35 U.S.C. 101.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to a further limitation of the same abstract idea identified in the analysis of claims 1. For example, the person can mentally set scores for each target object region, apply thresholds, and group object regions accordingly. This claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claim recites additional elements recited at a high
level of generality such that they amount to merely applying machine learning for image recognition. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. This claim is not patent eligible.
Claim 7 contains elements found analogous to claim 1. Therefore, claim 7 is similarly rejected under 35
U.S.C. 101.
Claim 8 contains elements found analogous to claim 1, with the addition of “a computer-readable non-transitory recording medium”. The additional element is recited at a high level of generality such that they amount to merely using a computer as a tool to implement the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, claim 8 is similarly rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4, 7-8, 13, and 16-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ozaki et al. (JP 2009053059 A), (hereinafter Ozaki).
Regarding claim 1, Ozaki teaches a position detection device (Ozaki, “The present invention relates to an object specifying device, an object specifying method, and an object specifying program for measuring the position of a road sign using, for example, a three-dimensional point cloud model generated based on data measured by a laser radar and a camera image”, pg. 2, lines 3-6) for recognizing a presence position of a target object in a three-dimensional space, the position detection device comprising a processor configured to execute operations comprising:
acquiring three-dimensional point cloud information of the space (Ozaki, “The measuring vehicle 200 (MMS) is fixedly installed on a top plate 201 (base) provided with a GPS receiver 210, a three-axis gyro 220, an odometer 230, a camera 240, and an LRF 250 (Laser Range Finder) on a roof portion”, pg. 4, lines 19-21, “The LRF 250 irradiates the side of the traveling measuring vehicle 200 with a laser, observes the laser reflected back to the feature, and locates the feature based on the observation values at each observation point where the laser was observed. This is a laser radar that generates a plurality of point data (point group data) indicating a relative distance and a relative direction with respect to the LRF 250. The LRF 250 stores the generated point cloud data in the distance bearing point cloud storage unit 293 as distance bearing point cloud data representing the feature.”, pg. 5, lines 23-29);
acquiring a plurality of images of an area including surroundings of an object in the space according to views from different imaging points (Ozaki, “The camera 240 images the front (traveling direction) of the traveling measuring vehicle 200 at different times, and stores the image data of each captured image in the image data storage unit 292. That is, the camera 240 stores road image data captured at different points in the image data storage unit 292. Hereinafter, the image data measurement vehicle 200 is in the storage unit 292 the image data of the road by the camera 240 is captured when passing through the P 0 point 15 (hereinafter referred to as captured image A) and the measurement vehicle 200 has passed the P 1 point Assume that road image data (hereinafter referred to as captured image B) captured by the camera 240 is stored. Further, it is assumed that the same road sign is reflected in the captured image A and the captured image B.”, pg. 5, lines 10-19, A vehicle collects multiple images of its surroundings from different positions, each containing the same object.);
determining, based on the plurality of images, whether the target object appears in the plurality of images; detecting a region of the object in each image of the plurality of images, wherein the target object; appears the region in said each image of the plurality of images (Ozaki, “Next, the road sign image recognizing unit 120 specifies the sign image pickup range Aa and the sign image pickup range Ba in which the same road sign is captured from the captured image A and the captured image B. At this time, the road sign image recognition unit 120 inputs the captured image A and the captured image B from the image data storage unit 292, and performs arbitrary image recognition processing for recognizing the same road sign for the captured image A and the captured image B. The road sign image recognition unit 120, based on the image 20 recognition processing result, to identify the labeled imaging range Aa in which the road sign is captured on the captured image A, is the road sign on the captured image B identifying a is reflected labeled imaging range Ba.”, pg. 15, lines 14-20, Image recognition is performed for each captured image to identify target objects, such as road signs, and respective imaging ranges for those objects in each of the images.);
specifying a region of a point cloud corresponding to the target object based on the three-dimensional point cloud information and the region of the object detected in said each image of the images (Ozaki, “The road sign position measuring apparatus 100 according to the first embodiment recognizes a road recognized on a captured image in order to identify which point group represents a road sign among a plurality of points constituting the three-dimensional point cloud model. Identify the point cloud corresponding to the sign. Therefore, after the preprocessing (S100), the road sign position measuring apparatus 100 projects each point constituting the three-dimensional point cloud model onto the captured image A and the captured image B (S140, S160), and the road among the projected points. Each point located in the marker imaging range Aa and the marker imaging range Ba in which the marker is reflected is extracted (S151, S171), the extracted points are grouped (S152), and the marker imaging range of each group point group A group point group common to Aa and the sign imaging range Ba is specified as a group point group representing a road sign (S180).”, pg. 18, lines 9-23, A 3D point cloud model is generated from the vehicles laser range-finder data. This point cloud is projected into each image to extract and group 3D points which fall inside the objects imaging range. This allows the system to identify point groups in vehicles point cloud model which corresponds to target objects.); and
specifying a position of the target object in the space by recognizing points corresponding to the target object from the three-dimensional point cloud information of the specified region (Ozaki, “Then, the road sign position measuring apparatus 100 calculates the three-dimensional coordinates of the road sign based on the three-dimensional coordinates indicated by the points constituting the group point group representing the specified road sign (S190). “, pg. 18, lines 20-23, “In FIG. 6, the sign position calculation unit 170 calculates the position of the road sign based on the sign three-dimensional point group. At this time, the marker position calculation unit 170 acquires the marker three-dimensional point group from the marker three-dimensional point group storage unit 196. 10 Then, the sign position calculation unit 170 calculates the three-dimensional coordinates of the center point (center of gravity) of the road sign as the position of the road sign based on the three-dimensional coordinates of each point indicated by the sign three-dimensional point group.”, pg. 27, lines 7-14, Once defined, the point groups are used to calculate the precise 3D position, such as center points, of target objects.).
Regarding claim 4, Ozaki teaches the position detection device according to claim 1, wherein the determining further comprises determining whether or not the target object corresponds to the same object based on an imaging position of the image or image recognition on the detected object region (Ozaki, “The road sign image recognition unit 120, based on the image recognition processing result, to identify the labeled imaging range Aa in which the road sign is captured on the captured image A, is the road sign on the captured image B identifying a is reflected labeled imaging range Ba. For example, the road sign image recognition unit 120 identifies the sign image pickup range from the picked-up image by pattern matching with the feature information (shape, color, pattern, etc.) of each road sign stored in advance in the storage device.”, pg. 15, lines 20-26), and,
the specifying a region of a point cloud further comprises specifying a region of the point cloud for the target object by calculating and integrating object regions of the target object for each of target objects which are determined as the same object (Ozaki, “Therefore, after the preprocessing (S100), the road sign position measuring apparatus 100 projects each point constituting the three-dimensional point cloud model onto the captured image A and the captured image B (S140, S160), and the road among the projected points. Each point located in the marker imaging range Aa and the marker imaging range Ba in which the marker is reflected is extracted (S151, S171), the extracted points are grouped (S152), and the marker imaging range of each group point group A group point group common to Aa and the sign imaging range Ba is specified as a group point group representing a road sign (S180).”, pg. 18, lines 13-23, The point group representing the road sign is calculated by grouping 3D data points which fall within the target object ranges defined for the captured images of the same target object.).
Claim 7 corresponds to claim 1, reciting a position detection method for performing the functions according to claim 1. Ozaki teaches a position detection method (Ozaki, “A road sign position measurement process (target identification method) executed by the road sign position measurement apparatus 100 according to the first embodiment will be described below with reference to FIGS. 6 and 7.”, pg. 13, lines 12-14) for performing the functions according to claim 1. As indicated in the analysis of claim 1, Ozaki teaches all the limitation according to claim 1. Therefore, claim 7 is rejected for the same reason as claim 1.
Claim 8 corresponds to claim 1, additionally reciting a computer-readable non-transitory recording medium storing a computer-executable program for performing the functions according o claim 1. Ozaki teaches a computer-readable non-transitory recording medium storing a computer-executable program (Ozaki, “the road sign position measurement device 100 and the measurement vehicle 200 are also referred to as a CPU 911 (Central Processing Unit, central processing unit, 15 processing unit, arithmetic unit, microprocessor, microcomputer, processor for executing a program. ). The CPU 911 is connected to the ROM 913, the RAM 914, the communication board 915, and the magnetic disk device 920 via the bus 912, and controls these hardware devices.”, pg. 9, lines 1-19) for performing the functions according of claim 1. As indicated in the analysis of claim 1, Ozaki teaches all the limitation according to claim 1. Therefore, claim 8 is rejected for the same reason as claim 1.
Claim 13 corresponds to claim 4, reciting a position detection method for performing the functions according to claim 4. Ozaki teaches a position detection method (see analysis of claim 7) for performing the functions according to claim 4. As indicated in the analysis of claim 4, Ozaki teaches all the limitation according to claim 4. Therefore, claim 13 is rejected for the same reason as claim 4.
Claim 16 corresponds to claim 4, additionally reciting a computer-readable non-transitory recording medium storing a computer-executable program for performing the functions according to claim 4. Ozaki teaches a computer-readable non-transitory recording medium storing a computer-executable program (see analysis of claim 8) for performing the functions according of claim 4. As indicated in the analysis of claim 4, Ozaki teaches all the limitation according to claim 4. Therefore, claim 16 is rejected for the same reason as claim 4.
Claim 17 corresponds to claim 4, additionally reciting a computer-readable non-transitory recording medium storing a computer-executable program for performing the functions according to claim 4. Ozaki teaches a computer-readable non-transitory recording medium storing a computer-executable program (see analysis of claim 8) for performing the functions according of claim 4. As indicated in the analysis of claim 4, Ozaki teaches all the limitation according to claim 4. Therefore, claim 17 is rejected for the same reason as claim 4.
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 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ozaki et al. (JP 2009053059 A) in view of Hayasaka et al. (US 20100067740 A1), (hereinafter Hayasaka).
Regarding claim 5, Ozaki teaches the position detection device according to claim 4, wherein the specifying a region of a point cloud further comprises:
Assigning a score based on an image recognition result to a respective detected region of the object in said each image of the plurality of images and corresponding to a region of the same target object (Ozaki, “At this time, the road sign image recognition unit 120 inputs the captured image A and the captured image B from the image data storage unit 292, and performs arbitrary image recognition processing for recognizing the same road sign for the captured image A and the captured image B…the road sign image recognition unit 120 identifies the sign image pickup range from the picked-up image by pattern matching with the feature information (shape, color, pattern, etc.) of each road sign stored in advance in the storage device.”, pg. 15, lines 17-26, “Therefore, after the preprocessing (S100), the road sign position measuring apparatus 100 projects each point constituting the three-dimensional point cloud model onto the captured image A and the captured image B (S140, S160), and the road among the projected points. Each point located in the marker imaging range Aa and the marker imaging range Ba in which the marker is reflected is extracted (S151, S171), the extracted points are grouped (S152), and the marker imaging range of each group point group A group point group common to Aa and the sign imaging range Ba is specified as a group point group representing a road sign (S180).”, pg. 18, lines 13-20, Pattern matching identifies an imaging range for each image which corresponds to the same object across multiple images. These ranges would include scores for feature matching. A point cloud model can then be projected into each image and points located within the respective regions are grouped to specify a region in the point cloud.).
Ozaki does not teach integrating the object regions having a score equal to or higher than a threshold value.
However, Hayasaka teaches integrating the object regions having a score equal to or higher than a threshold value (Hayasaka, “The pedestrian candidate extraction portion 21 extracts a pedestrian candidate region from near-infrared images. The method for this extraction is not particularly limited, but various methods may be applied. Examples of the extraction method include a method in which a template of a pedestrian is prepared, and is used for pattern matching. Concretely, as shown in FIG.3, rectangular regions of a predetermined size are sequentially cut out of a near-infrared image, and the degree of matching of each cut-out rectangular region image with the image of the template is found. If the degree of matching is greater than or equal to a threshold value, the rectangular region is determined as a pedestrian candidate region. The thus-extracted pedestrian candidate region (here inafter, referred to as "pedestrian candidate') is output to the determination portion 22.”, pgs. 2 and 3, paragraph 0034).
Ozaki teaches assigning scores to detected object regions in each image using pattern matching, and identifying target image ranges corresponding to detected objects (Ozaki, pg. 15, lines 17-26). Ozaki further teaches integrating image ranges of the same object to specify a region in the point cloud (Ozaki, pg. 18, lines 13-20). Hayasaka teaches applying thresholding to pattern matching scores determined for object regions (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the pattern matching of Ozaki to include the score thresholding as taught by Hayasaka (Hayasaka, pgs. 2 and 3, paragraph 0034), thereby selecting object imaging ranges based on the applied thresholding prior to integrating those ranges to specifying regions in the point cloud. The motivation for doing so would have been to filter out erroneous object regions and improve the reliability of object detection. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Ozaki with Hayasaka to obtain the invention as specified in claim 5.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ozaki et al. (JP 2009053059 A) in view of Cho et al. (KR 20210026412 A), (hereinafter Cho).
Regarding claim 6, Ozaki teaches the position detection device according to claim 4. Ozaki does not teach wherein the specifying a region of a point cloud further comprises integrating the object regions by performing upon a respective detected region of the object in said each image of the plurality of images, image recognition based on a convolutional neural network and image recognition based on local feature amounts and by setting, as a score to be assigned to each of the object regions, a value obtained by weighting and summing reliabilities of image recognition results.
However, Cho teaches wherein the specifying a region of a point cloud further comprises integrating the object regions by performing upon a respective detected region of the object in said each image of the plurality of images, image recognition based on a convolutional neural network and image recognition based on local feature amounts and by setting, as a score to be assigned to each of the object regions, a value obtained by weighting and summing reliabilities of image recognition results (Cho, “the present invention, by using YOLO (You Only Look Once: Real-Time Object Detection) suitable for real-time object detection, object detection is performed independently based on image data and PCD, and then each result is fused to detect undetected. We propose a YOLO-based adaptive object detection method and apparatus with improved performance. Based on PCD and image data including reflectance and distance information, object detection learning is performed for each of the three CNN-based YOLOs, and bounding boxes and reliability scores for the objects in each model are predicted. Thereafter, in order to fuse the object detection results, the final bounding box is determined through a weighted average of the coordinates of the bounding box based on the reliability score for the object. This determines the coordinates of the final bounding box close to the bounding box of the model with a higher confidence score.”, pg. 3, lines 15-20, “In step 120, a bounding box and a reliability score for an object in each object detection model learned based on the PCD and the image data are predicted. The object detection result of each learned object detection model predicts the reliability score of the probability that the object is classified by reflecting the geometric information of the bounding box and the reliability indicating the degree to which the detected object matches the actual value.”, pg. 4, lines 12-14, “In step 130, in order to fuse the object detection results, the final bounding box is determined through a weighted average of the coordinates of the bounding box based on the reliability score for the object. The average value is weighted according to the reliability scores of the bounding boxes for the object in each object detection model, and merged into one bounding box based only on the geometric information of each bounding box.”, pg. 5, lines 14-16, CNN-based object detection models predict bounding boxes and corresponding reliability scores. The scores are used as weights in calculate a weighted average of the bounding box coordinates, allowing boxes to be merged into a final bounding box.).
Ozaki teaches object detection in which object regions are scored for each image based on pattern matching to identifying target image ranges corresponding to detected objects (Ozaki, pg. 15, lines 17-26). Cho teaches object detection using a CNN in which bounding boxes and corresponding reliability scores are predicted for object in images, and a final bounding box for each object is determined by weighting and combining the predicted bounding boxes (see above). Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to have modified the object detection of Ozaki to be based on the CNN object detection as taught by Cho (Cho, pg. 3, lines 15-20, pg. 4, lines 12-14, and pg. 5, lines 14-16), thereby determining the target image ranges using CNN-generated final bounding boxes. The motivation for doing so would have been to improve generalization and increase object detection accuracy. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine the teachings of Ozaki with Cho to obtain the invention as specified in claim 6.
Examiner notes that no prior art was applied against claims 2, 3, 9-12, and 14. However, claims 2, 3, 9-12, and 14 stands rejected under 35 U.S.C. 112(b) due to indefiniteness, and cannot be considered to be allowable subject matter because the scope of the claims is not clearly defined.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONNOR LEVI HANSEN whose telephone number is (703)756-5533. The examiner can normally be reached Monday-Friday 9:00-5:00 (ET).
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/CONNOR L HANSEN/Examiner, Art Unit 2672 /GANDHI THIRUGNANAM/Primary Examiner, Art Unit 2672
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