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 .
Response to Amendment
Applicants’ submission filed on 13 July 2026 has been entered. Claims 14 and 15 have been amended. Claims 1-15 are currently pending and have been considered below.
Response to Arguments
Applicants’ arguments filed 13 July 2026 have been fully and carefully considered but they are not persuasive.
Applicant argues on pages 8-9 of the Remarks that the combination of references fails to disclose the limitation, “a learning part which uses the learning data set to generate a learning model that outputs a composition parameter for compositing the plurality of filtered images for each corresponding section.”
The Examiner respectfully disagrees.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Wilbee discloses at page 49, 3.3 Edge Detection Filter Creation, “The proposed output of a system built from the proposed framework are edge filters ... The training set for the input is created by taking the training images and extracting the features from them. The training set for the output is created by taking the images and determining the best filters or methodologies in the chosen search space for that image ... The set of all filters in the training set are the training filters” and at page 56, 3.4.1 Linear CA Neural Network, “For a stand alone Linear CA filter, the only information needed to distinguish between filters is which pixels in its neighborhood are to be used to determine its next state. In this system, the next state determines if the pixel of interest is an edge pixel ... The input to this network will be the features gathered ... The end result of this training will be a neural network which takes in a new image and then outputs a new filter based on the features of the image, Figure 3.5.”
Kobayashi discloses in paragraph [0018], “generates conversion images by reducing a size of a captured image in different magnifications and performs edge detection on the individual conversion images. By reducing a size of an image in a plurality of magnifications, optimum edges are detected relative to the image deterioration in one of the conversion images. Since degrees of image deterioration are uneven in an image, optimum edges are detected in different conversion images. The information processing apparatus 200 computes reliability of results of the edge detection based on sharpness of a luminance gradient value ... The information processing apparatus 200 selects an edge having a highest reliability in the edges detected in the plurality of conversion images as an optimum edge for each region” and in paragraph [0026], “An image conversion unit 202 converts the input captured image 310 by image processing to generate a plurality of conversion images 311a to 311d. The image conversion unit 202 transmits the generated conversion images 311a to 311d to an edge detection unit 204.”
Kobayashi further discloses in paragraph [0027], “The edge detection unit 204 performs an edge detection process on the conversion images 311a to 311d. The edge detection unit 204 generates a detection edge list 320 and a luminance gradient value map to be transmitted to a reliability computation unit 205” and in paragraph [0029], “The edge selection unit 206 obtains the detection edge reliability 334 from the detection edge list 320 and selects one of the edges in the conversion images 311a to 311d which has a highest reliability to generate a selection edge list 340.”
Kobayashi additionally discloses in paragraph [0037], “The edge detection unit 204 performs the edge detection by applying the Canny filter to the conversion images 311a to 311d. The edge detection unit 204 generates a luminance gradient value map storing luminance gradient values of individual pixels and a luminance gradient direction map storing luminance gradient directions of the individual pixels for each of the conversion images 311a to 311d while performing the edge detection using the Canny filter ... “Fx” and “Fy” indicate kernels in horizontal and vertical directions of the Prewitt filter;” and in paragraph [0041], “The edge detection unit 204 generates the detection edge lists 320 for the individual conversion images 311a to 311d.”
Further, Kobayashi discloses in paragraphs [0043] - [0046], “FIG. 5 is a concept diagram illustrating content of the process performed by the reliability computation unit 205 in step S1040. Partial images 312a to 312d are obtained by extracting regions in the vicinity of a specific edge detected in the conversion images 311a to 311d … The edge selection unit 206 obtains the detection edge lists 320 and selects edges of high reliability to generate the selection edge list 340” and in paragraph [0069], “Different regions may be obtained for different portions of the targets 301.”
Wilbee discloses learning or training data to generate a model (3.3 Edge Detection Filter Creation, training set comprises input images with extracted features and all of the training filters which are used to determine the best filters); the model (3.4.1 Linear CA Neural Network, the neural network) outputs a composition parameter (3.4.1 Linear CA Neural Network, output of a system … are edge filters; the neural network which takes in a new image and outputs new filters specifically created from the set of all training filters) for the plurality of filtered images for each corresponding section (3.3 Edge Detection Filter Creation, determine the best filter in the chosen search space for that image; See Figure 3.5 on page 57 where each section of the image has a separate labeled filter).
Wilbee discloses compositing plural sections of a same image but does not explicitly disclose “compositing the plurality of filtered images.”
Kobayashi was used for this limitation. Kobayashi discloses compositing the plurality of filtered images for each corresponding section (Kobayashi, paragraph [0041], The edge detection unit 204 generates the detection edge lists 320 for the individual conversion images 311a to 311d; Kobayashi, paragraph [0069], Different regions may be obtained for different portions of the targets 301).
Therefore, the combination of references discloses the limitation, “a learning part which uses the learning data set to generate a learning model that outputs a composition parameter for compositing the plurality of filtered images for each corresponding section.
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 14 and 15 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 14 recites the limitation "the composite ratio for each predetermined section" in lines 11-12 of the claim. While lines 6-7 of the claim recite, “a composite ratio for each corresponding section of the plurality of filtered images” and lines 8-9 recite, “specifying a number of sections in which the plurality of filtered images are composited according to separate composite ratios,” it is unclear if “each predetermined section” is referring to a / the “corresponding section” or a / the “specified number of sections” or if “each predetermined section” is referring to something else.
Claim 14 recites the limitation "the composite image" in line 14 of the claim. While lines 5-7 of the claim recite, “outputting a feature extraction image of the workpiece by compositing the plurality filtered images based on a composite ratio,” it is unclear if “the composite image” is referring to the feature extraction image or some other image. If the limitation is referring to the feature extraction image, the claim should recite, i.e., “the composite feature extraction image.”
Claim 14 recites the limitation "extracting a feature of the workpiece” in line 15 of the claim. It is unclear if this limitation is referring to “extracting a feature of the workpiece” as recited in line 1 of the claim or if this limitation is referring to a different “extracting a feature of the workpiece.”
Claim 14 recites the limitation "a plurality of filtered images" in line 15 of the claim. It is unclear if this limitation is referring to a / the “plurality of filtered images” as recited in at least line 4, line 6 and line 7 of the claim or if this limitation is referring to a different plurality of filtered images.
Claim 15 recites the limitation "the set" in line 10 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the composite ratio for each predetermined section" in lines 11-12 of the claim. While lines 6-7 of the claim recite, “a composite ratio for each corresponding section of the plurality of filtered images” and lines 8-9 recite, “specifying a number of sections in which the plurality of filtered images are composited according to separate composite ratios,” it is unclear if “each predetermined section” is referring to a / the “corresponding section” or a / the “specified number of sections” or if “each predetermined section” is referring to something else.
Claim 15 recites the limitation “extracting a feature of the workpiece” in line 15 of the claim. It is unclear if this limitation is referring to “extracting a feature of the workpiece” as recited in line 7 of the claim or if this limitation is referring to a different “extracting a feature of the workpiece.
Claim 15 recites the limitation "a plurality of filtered images" in line 15 of the claim. It is unclear if this limitation is referring to a / the “plurality of filtered images” as recited in at least line 5, lines5- 6, lines 6-7 and lines 8-9 of the claim or if this limitation is referring to a different plurality of filtered images
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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wilbee, A. J. A Framework For Learning Scene Independent Edge Detection. Rochester Institute of Technology, 2015, hereinafter, “Wilbee”, and further in view of Kobayashi et al., U.S. Publication No. 2019/0370974, hereinafter, “Kobayashi”.
As per claim 1, Wilbee discloses a machine learning device, comprising:
a learning data acquisition part which acquires, as a learning data set, data regarding a plurality of different filters applied to images in which a workpiece is captured (Wilbee, Abstract, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image is introduced. The framework has four parts: the learning stage, image feature extraction, training filter creation, and filter selection training; Wilbee, page 3, Introduction, In this thesis a framework is developed for creating a system which will match an input image with its corresponding edge filter ... the fields of: manufacturing, fault detection ... For applications in mobile robotics and photography), and
data indicating a state of each predetermined section of a plurality of filtered images processed by the plurality of filters (Wilbee, page 61-63, 4.1.1 First Filter Set for Non-Fuzzy Prototype System, Five filter sets were trained each against the same ground truth index of 400 training images ... To illustrate the outcome of the prototype system a representative test image was selected with its accompanying Canny and Sobel edge images, Figure 4.1. The ground truths which accompany this image are given by, Figure 4.2 ... Figure 4.3 gives the edge images produced by the five systems ... For each system, the filter selected was different but the edge image was very similar), and
a learning part which uses the learning data set to generate a learning model that outputs a composition parameter for the plurality of filtered images for each corresponding section (Wilbee, page 49, 3.3 Edge Detection Filter Creation, The proposed output of a system built from the proposed framework are edge filters ... The training set for the input is created by taking the training images and extracting the features from them. The training set for the output is created by taking the images and determining the best filters or methodologies in the chosen search space for that image ... The set of all filters in the training set are the training filters; Wilbee, page 56, 3.4.1 Linear CA Neural Network, For a stand alone Linear CA filter, the only information needed to distinguish between filters is which pixels in its neighborhood are to be used to determine its next state. In this system, the next state determines if the pixel of interest is an edge pixel ... The input to this network will be the features gathered ... The end result of this training will be a neural network which takes in a new image and then outputs a new filter based on the features of the image, Figure 3.5; Wilbee, page 58, 3.4.2 Fuzzy CA Neural Network, this method will select from a filter bank).
Wilbee does not explicitly disclose the following limitations as further recited however Kobayashi discloses
for compositing the plurality of filtered images for each corresponding section (Kobayashi, ¶0018, generates conversion images by reducing a size of a captured image in different magnifications and performs edge detection on the individual conversion images. By reducing a size of an image in a plurality of magnifications, optimum edges are detected relative to the image deterioration in one of the conversion images. Since degrees of image deterioration are uneven in an image, optimum edges are detected in different conversion images. The information processing apparatus 200 computes reliability of results of the edge detection based on sharpness of a luminance gradient value ... The information processing apparatus 200 selects an edge having a highest reliability in the edges detected in the plurality of conversion images as an optimum edge for each region; Kobayashi, ¶0026, An image conversion unit 202 converts the input captured image 310 by image processing to generate a plurality of conversion images 311a to 311d. The image conversion unit 202 transmits the generated conversion images 311a to 311d to an edge detection unit 204; Kobayashi, ¶0027, The edge detection unit 204 performs an edge detection process on the conversion images 311a to 311d. The edge detection unit 204 generates a detection edge list 320 and a luminance gradient value map to be transmitted to a reliability computation unit 205; Kobayashi, ¶0029, The edge selection unit 206 obtains the detection edge reliability 334 from the detection edge list 320 and selects one of the edges in the conversion images 311a to 311d which has a highest reliability to generate a selection edge list 340; Kobayashi, ¶0037, The edge detection unit 204 performs the edge detection by applying the Canny filter to the conversion images 311a to 311d. The edge detection unit 204 generates a luminance gradient value map storing luminance gradient values of individual pixels and a luminance gradient direction map storing luminance gradient directions of the individual pixels for each of the conversion images 311a to 311d while performing the edge detection using the Canny filter ... “Fx” and “Fy” indicate kernels in horizontal and vertical directions of the Prewitt filter; Kobayashi, ¶0041, The edge detection unit 204 generates the detection edge lists 320 for the individual conversion images 311a to 311d; Kobayashi, ¶0043, FIG. 5 is a concept diagram illustrating content of the process performed by the reliability computation unit 205 in step S1040. Partial images 312a to 312d are obtained by extracting regions in the vicinity of a specific edge detected in the conversion images 311a to 311d; Kobayashi, ¶0046, The edge selection unit 206 obtains the detection edge lists 320 and selects edges of high reliability to generate the selection edge list 340; Kobayashi, ¶0069, Different regions may be obtained for different portions of the targets 301; Kobayashi, ¶0073; Kobayashi, ¶0077-0078).
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to combine the teachings of Kobayashi and Wilbee because they are in the same field of endeavor. One skilled in the art would have been motivated to substitute the compositing of edge sections from multiple images as taught by Kobayashi for the compositing of multiple filters as taught by Wilbee in order to provide an alternate means to detect and determine the edges of an image with the highest reliability (Kobayashi, ¶0018; ¶0073)
As per claim 2, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning model includes at least one of a first learning model that outputs a composite ratio for each corresponding section of the plurality of filtered images (Wilbee, page 49, 3.3 Edge Detection Filter Creation, The proposed output of a system built from the proposed framework are edge filters ... The training set for the input is created by taking the training images and extracting the features from them. The training set for the output is created by taking the images and determining the best filters or methodologies in the chosen search space for that image; Kobayashi, ¶0018; Kobayashi, ¶0073), and a second learning model that outputs a set of a specified number of filters (Wilbee, page 58, 3.4.2 Fuzzy CA Neural Network, this method will select from a filter bank; Wilbee, page 73, Table 4.7, Fuzzy-Filter, list of filters used from filter bank; Kobayashi, ¶0077, The filter selection unit 203 selects a plurality of filters to be used in edge detection in a captured image 310. FIG. 7 is a diagram illustrating examples of the filters to be selected by the filter selection unit 203. Filters 401a to 401c are kernels of the Prewitt filters which detect edges in a vertical direction and filters 402a to 402c are kernels of the Prewitt filters, which detect edges in a horizontal direction).
As per claim 3, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the data regarding the plurality of filters includes data regarding at least one of types and sizes of the plurality of filters (Wilbee, pages 50-51, 3.3.1 Linear Cellular Automata, filter search to the set of Linear CA. This set is built out of a 3x3 pixel neighborhood ... The different automata are created by including or excluding different pixels from the neighborhood. This is indicated by the pixel in the neighborhood mask being a 0 for exclusion or 1 for inclusion. The automata are indexed by the binary representation of their included and excluded cells ... determining the best filter for the Non-fuzzy linear CA approach. Because the filter space was reduced to a small 512 possibilities it is simple to apply each CA filter to each image and then use the BDM to gauge how well the filter performed. The filter with the lowest BDM is then determined to be the best filter).
As per claim 4, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the data indicating a state of each predetermined section of the plurality of filtered images includes data indicating variations in values of peripheral sections of the predetermined section, or data indicating a reaction for each of the predetermined sections after threshold-processing of the plurality of filtered images (Kobayashi, ¶0018, generates conversion images by reducing a size of a captured image in different magnifications and performs edge detection on the individual conversion images. By reducing a size of an image in a plurality of magnifications, optimum edges are detected relative to the image deterioration in one of the conversion images. Since degrees of image deterioration are uneven in an image, optimum edges are detected in different conversion images. The information processing apparatus 200 computes reliability of results of the edge detection based on sharpness of a luminance gradient value ... The reliability is defined by a difference between luminance gradients of adjacent pixels. The information processing apparatus 200 selects an edge having a highest reliability in the edges detected in the plurality of conversion images as an optimum edge for each region; Wilbee, Abstract, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image ... Feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information. The set of image features are used as the input to a single hidden layer feed forward neural network trained using back propagation. The system trains against a set of linear cellular automata filters which are determined to best solve the edge image).
As per claim 5, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the data indicating a state of each predetermined section of the plurality of filtered images includes label data indicating a degree from a normal state to an abnormal state for each predetermined section (Kobayashi, ¶0042, The reliability computation unit 205 computes reliability of the edges detected by the edge detection unit 204 based on the detection edge list 320 and the luminance gradient value map; Kobayashi, ¶0045, In S1040, the reliability computation unit 205 computes reliability of all the edges included in the detection edge list 320 in accordance with Equation 2 and writes values of the reliability in the detection edge reliability 334; Kobayashi, ¶0068, an edge may not be selected when the detection edge reliability 334 is low. In step S1530, in a case where the largest detection edge reliability 334m is smaller than a predetermined threshold value, the edge selection unit 206 omits the process; Kobayashi, ¶0074, The comparison unit 103 selects an edge in which a difference between an image coordinate of the model edge and the detection edge coordinate 332a is smaller than a predetermined threshold value and in which a direction of the model edge is similar to a selection edge direction 353a).
As per claim 6, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning part converts a state of the learning model so that a feature of the workpiece extracted from a composite image composed of the plurality of filtered images based on the composite ratio of each corresponding section approaches a model feature of the workpiece extracted from a model image in which the workpiece, for which at least one of position and posture is known, is captured (Kobayashi, ¶0018, A comparison unit 103 described below computes a position and orientation of a target by comparing a selected edge with an edge of a defined 3D geometric model. By this, in this embodiment, even in a case where uneven image deterioration is generated for individual regions in a captured image due to a motion blur, for example, comparison with an edge of the target is accurately performed so that a position and orientation is computed; Kobayashi, ¶0030, The comparison unit 103 computes position and orientations of the targets 301 which have been captured in the captured image 310 based on the selection edge list 340 generated by the edge selection unit 206 and a model edge list stored in an edge model storage unit 104. The comparison unit 103 transmits the computed position and orientations of the targets 301 to the holding unit 102; Wilbee, page 43, 3.1 Learning Step, The learning step is the method by which the system learns what is and is not considered a good edge image. This system will have input image features training to an output of edge filters ... The methodology used in this research is a reference based measure. Such a measure use a training set with ground truth images and a method for rating the output edge images).
As per claim 7, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning data acquisition part calculates the difference between the filtered images and a model feature extraction image extracted from a model image in which the workpiece, for which at least one of position and posture is known, is captured, and acquires label data indicating the degree from a normal state to an abnormal state for each of the predetermined sections of the plurality of filtered images (Kobayashi, ¶0018, A comparison unit 103 described below computes a position and orientation of a target by comparing a selected edge with an edge of a defined 3D geometric model. By this, in this embodiment, even in a case where uneven image deterioration is generated for individual regions in a captured image due to a motion blur, for example, comparison with an edge of the target is accurately performed so that a position and orientation is computed; Kobayashi, ¶0030, The comparison unit 103 computes position and orientations of the targets 301 which have been captured in the captured image 310 based on the selection edge list 340 generated by the edge selection unit 206 and a model edge list stored in an edge model storage unit 104. The comparison unit 103 transmits the computed position and orientations of the targets 301 to the holding unit 102; Wilbee, page 43, 3.1 Learning Step, The learning step is the method by which the system learns what is and is not considered a good edge image. This system will have input image features training to an output of edge filters ... The methodology used in this research is a reference based measure. Such a measure use a training set with ground truth images and a method for rating the output edge images).
As per claim 8, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning data acquisition part acquires label data indicating a degree from a normal state to an abnormal state for each predetermined section of the plurality of filtered images using one or more model feature extraction images extracted from the model image when one or more changes are made to the model image in which the workpiece, for which at least one of position and posture is known, is captured (Kobayashi, ¶0018, A comparison unit 103 described below computes a position and orientation of a target by comparing a selected edge with an edge of a defined 3D geometric model. By this, in this embodiment, even in a case where uneven image deterioration is generated for individual regions in a captured image due to a motion blur, for example, comparison with an edge of the target is accurately performed so that a position and orientation is computed; Kobayashi, ¶0030, The comparison unit 103 computes position and orientations of the targets 301 which have been captured in the captured image 310 based on the selection edge list 340 generated by the edge selection unit 206 and a model edge list stored in an edge model storage unit 104. The comparison unit 103 transmits the computed position and orientations of the targets 301 to the holding unit 102; Kobayashi, ¶0050, The comparison unit 103 computes translation and rotation components of the targets 301 of a smallest coordinate difference in the captured image 310 between the model edge and the selection edge information 35; Wilbee, page 43, 3.1 Learning Step, The learning step is the method by which the system learns what is and is not considered a good edge image. This system will have input image features training to an output of edge filters ... The methodology used in this research is a reference based measure. Such a measure use a training set with ground truth images and a method for rating the output edge images).
As per claim 9, Wilbee and Kobayashi disclose the machine learning device according to claim 8, wherein the one or more changes made to the model image include one or more changes that are used when comparing features of the workpiece extracted from an image of the workpiece and model features of the workpiece extracted from the model image (Kobayashi, ¶0050, he comparison unit 103 obtains the selection edge list 340 generated by the edge selection unit 206 and the model edge list stored in the edge model storage unit 104 and computes position and orientations of the targets 301 which have been captured as the captured image 310 by means of a repetitive process. Specifically, the comparison unit 103 compares a coordinate and a direction obtained by projecting a 3D coordinate and a direction of a model edge to the captured image 310 in accordance with position and orientations of the current targets 301 with the selection edge list 340 and performs association. The comparison unit 103 computes translation and rotation components of the targets 301 of a smallest coordinate difference in the captured image 310 between the model edge and the selection edge information 350).
As per claim 10, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning part generates the learning model using a result of detecting at least one of the position and posture of the workpiece by comparing the feature of the workpiece extracted from the image in which the workpiece is captured with a model feature extracted from a model image in which the workpiece, for which at least one of position and posture is known, is captured (Wilbee, Abstract, a framework for a system which will intelligently assign an edge detection filter to an image based on features taken from the image ... Feature extraction is performed using a GIST methodology which extracts color, intensity, and orientation information; Wilbee, pages 45-47, 3.2 Image Feature Extraction, a GIST feature set .. The feature set is constructed from orientation, color and intensity information ... Four orientation channels were created using Garbor filters ... Each feature image which was created, 34 in total, was then subdivided into 16 equal regions; Kobayashi, ¶0018, A comparison unit 103 described below computes a position and orientation of a target by comparing a selected edge with an edge of a defined 3D geometric model. By this, in this embodiment, even in a case where uneven image deterioration is generated for individual regions in a captured image due to a motion blur, for example, comparison with an edge of the target is accurately performed so that a position and orientation is computed).
As per claim 11, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the data indicating a state of each predetermined section of the plurality of filtered images includes data indicating a reaction for each predetermined section after threshold-processing of the plurality of filtered images processed by the plurality of filters exceeding a specified number (Wilbee, page 52, 3.3.2 Fuzzy Cellular Automata, The fuzzy transition rule ... is a near neighborhood CA [Equations 3.8, 3.9] where t is the edge pixel threshold; Kobayashi, ¶0068, an edge may not be selected when the detection edge reliability 334 is low. In step S1530, in a case where the largest detection edge reliability 334m is smaller than a predetermined threshold value, the edge selection unit 206 omits the process; Kobayashi, ¶0077, The filter selection unit 203 selects a plurality of filters to be used in edge detection in a captured image 310. FIG. 7 is a diagram illustrating examples of the filters to be selected by the filter selection unit 203. Filters 401a to 401c are kernels of the Prewitt filters which detect edges in a vertical direction and filters 402a to 402c are kernels of the Prewitt filters, which detect edges in a horizontal direction).
As per claim 12, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning part generates the learning model that outputs a set of a specified number of filters using a model image in which the workpiece, for which at least one of position and posture is known, is captured (Kobayashi, ¶0017, an apparatus which accurately performs the edge comparison of a target so that a position and orientation is computed even in a case where a motion blur occurs in a captured image due to a shift of an imaging unit or the target); Kobayashi, ¶0018, A comparison unit 103 described below computes a position and orientation of a target by comparing a selected edge with an edge of a defined 3D geometric model. By this, in this embodiment, even in a case where uneven image deterioration is generated for individual regions in a captured image due to a motion blur, for example, comparison with an edge of the target is accurately performed so that a position and orientation is computed; Kobayashi, ¶0030, The comparison unit 103 computes position and orientations of the targets 301 which have been captured in the captured image 310 based on the selection edge list 340 generated by the edge selection unit 206 and a model edge list stored in an edge model storage unit 104. The comparison unit 103 transmits the computed position and orientations of the targets 301 to the holding unit 102; Kobayashi, ¶0077, The filter selection unit 203 selects a plurality of filters to be used in edge detection in a captured image 310. FIG. 7 is a diagram illustrating examples of the filters to be selected by the filter selection unit 203. Filters 401a to 401c are kernels of the Prewitt filters which detect edges in a vertical direction and filters 402a to 402c are kernels of the Prewitt filters, which detect edges in a horizontal direction).
As per claim 13, Wilbee and Kobayashi disclose the machine learning device according to claim 1, wherein the learning part generates the learning model for outputting a set of a specified number of filters so that after threshold-processing of the plurality of filtered images processed by the plurality of filters exceeding the specified number, the reaction for each predetermined section becomes a maximum for each predetermined section (Wilbee, page 58, 3.4.2 Fuzzy CA Neural Network, this method will select from a filter bank; Wilbee, page 59, Results, The number and type of filter which is selected; Kobayashi, ¶0077, The filter selection unit 203 selects a plurality of filters to be used in edge detection in a captured image 310. FIG. 7 is a diagram illustrating examples of the filters to be selected by the filter selection unit 203. Filters 401a to 401c are kernels of the Prewitt filters which detect edges in a vertical direction and filters 402a to 402c are kernels of the Prewitt filters, which detect edges in a horizontal direction).
Allowable Subject Matter
Claims 14 and 15 are objected to but would be allowable if the 35 U.S.C. §112(b) rejections were overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claims 14 and 15 are objected to but would be allowable if the 35 U.S.C. §112(b) rejections were overcome because while the prior art discloses multi-filer processing for processing a plurality images, the prior art does not disclose the limitations, “a section number specification part for specifying a number of sections in which the plurality of filtered images are composited according to separate composite ratios; a filter set specification part for specifying a set of a specified number of filters; a composite ratio specification part for specifying the composite ratio for each predetermined section; and a threshold specification part for specifying a threshold value for extracting the features of the workpiece from the composite image composed of the plurality of filtered images, or a threshold value for extracting a feature of the workpiece from a plurality of filtered images” as recited in independent claims 14 and 15.
Conclusion
Applicants’ amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRACY MANGIALASCHI whose telephone number is (571)270-5189. The examiner can normally be reached M-F, 9:30AM TO 6:00PM.
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/TRACY MANGIALASCHI/Primary Examiner, Art Unit 2668
