DETAILED ACTION
This Office action is in response to the Application filed on December 23, 2024, which claims foreign priority to Japanese Patent Application No. 2024-000690, filed on January 5, 2024. An action on the merits follows. Claims 1-20 are pending on the application.
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 .
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 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.
Claim Objections
Claims 7-9 and 19 are objected to because of the following informalities:
Claims 7-8 recite “a GUI” in line 3 of the claims. However, the acronym GUI is undefined in the claims. To clarify that the acronym means graphical user interface, examiner suggests amending “a GUI” recited in line 3 of the claims to “a graphical user interface (GUI)”.
Claim 9 recites the limitation “a third direction corresponds to the second direction” in line 22 of the claim. However, it should instead recite “the third direction corresponds to the second direction” to avoid ambiguity issues related to the claimed “third direction” previously recited in line 4 of the claim.
Claim 19 recites the limitation “a third direction corresponds to the second direction” in line 22 of the claim. However, it should instead recite “the third direction corresponds to the second direction” to avoid ambiguity issues related to the claimed “third direction” previously recited in line 4 of the claim.
Appropriate correction is required.
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 2, 7-8, 12, and 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 2 recites the limitation “in the first operation, the processor performs the first process using one of the plurality of models, in the second operation, the processor performs the first process using another one of the plurality of models” in lines 3-6 of the claim. However, there is insufficient antecedent basis for the claimed “the first operation” and “the second operation” limitation in the claim because “a first operation” and “a second operation” were not previously recited in any of the claims. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate.
Therefore, based on above, for examination purposes the limitation “in the first operation, the processor performs the first process using one of the plurality of models, in the second operation, the processor performs the first process using another one of the plurality of models” recited in lines 3-6 of the claim as “in a first operation, the processor performs the first process using one of the plurality of models, in a second operation, the processor performs the first process using another one of the plurality of models” because both first and second operations are further defined by the claim, respectively.
Claim 7 recites the limitation “display a part of the plurality of fracture progress directions according to an orientation of the plurality of fracture progress directions” in lines 13-14 of the claim. However, the claimed “orientation” term of the claimed “orientation of the plurality of fracture progress directions” recited in lines 13-14 of claim 7 is not defined by the claims.
Par. [0079] of the specification of this application indicates that “the GUI 79 a selectively displays, for example, a part of the plurality of fracture progress directions Dr1 according to the orientation of the plurality of fracture progress directions Dr1”. Par. [0112] of the specification also indicates that “the GUI being configured to selectively display a part of the plurality of fracture progress directions according to an orientation of the plurality of fracture progress directions”.
However, the claimed “orientation of the plurality of fracture progress directions” recited in lines 13-14 of claim 7 is not defined by the claims and the specification does not define the claimed “orientation” term with respect to the claimed “orientation of the plurality of fracture progress directions” recited in lines 13-14 of claim 7.
Therefore, based on above, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite.
Claim 8 recites the limitation “the eighth display operation or the ninth display operation” in line 5 of the claim. However, there is insufficient antecedent basis for the claimed “the eighth display operation” and “the ninth display operation” limitation in the claim because “an eighth display operation” and “a ninth display operation” were not previously recited in any of the claims. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate.
Therefore, based on above, for examination purposes the limitation “the eighth display operation or the ninth display operation” recited in line 5 of the claim as “an eighth display operation or a ninth display operation” because both eighth and ninth operations are further defined by the claim.
Claim 12 recites the limitation “in the first operation, the processor performs the first process using one of the plurality of models, in the second operation, the processor performs the first process using another one of the plurality of models” in lines 3-6 of the claim. However, there is insufficient antecedent basis for the claimed “the first operation” and “the second operation” limitation in the claim because “a first display operation” and “a second display operation” were not previously recited in any of the claims. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate.
Therefore, based on above, for examination purposes the limitation “in the first operation, the processor performs the first process using one of the plurality of models, in the second operation, the processor performs the first process using another one of the plurality of models” recited in lines 3-6 of the claim as “in a first operation, the processor performs the first process using one of the plurality of models, in a second operation, the processor performs the first process using another one of the plurality of models” because both first and second operations are further defined by the claim.
Claim 17 recites the limitation “a part of the plurality of fracture progress directions being selectively displayed according to the direction of the plurality of fracture progress directions” in lines 11-12 of the claim. There is insufficient antecedent basis for the claimed “the direction of the plurality of fracture progress directions” in lines 11-12 of the claim” limitation in lines 11-12 of the claim because “a direction of the plurality of fracture progress directions” was not previously recited in any of the claims. Therefore, the lack of antecedent basis makes the scope of the claim indeterminate.
Par. [0163] of the specification of this application indicates that “a part of the plurality of fracture progress directions being selectively displayed according to the direction of the plurality of fracture progress directions”.
However, the claimed “direction of the plurality of fracture progress directions” recited in lines 11-12 of the claim was not previously recited in any of the claims and the specification does not define the claimed “direction of the plurality of fracture progress directions” recited in lines 13-14 of claim 7, as indicated above.
Therefore, based on above, the metes and bounds of the claim are not clearly set forth and the examiner cannot clearly determine which elements are encompassed by the claim language, which renders the claim indefinite.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 9-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by NAMEKAWA et al. (JP Application Publication No. 2022091453 A), hereafter referred to as NAMEKAWA, Applicant cited prior art furnished via IDS.
Regarding claim 1, NAMEKAWA discloses a fracture section image analysis device (Par. [0001]: a detection device, a detection method, and a program… for detecting a convergence portion of a plurality of linear regions displayed in an image; Par. [0012-13]: FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece), comprising:
an acquisitor configured to acquire a first image including a fracture section of a component (Par. [0008-19]: program of the present invention is for causing a computer to function as each means of the detection device… FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100. The hardware of the detection device 100 is realized by, for example, an information processing device including a CPU, a ROM, a RAM, an HDD, and various interfaces, or a dedicated hardware… a computer associated with the scanning electron microscope may have a function of the detection device 100… a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… acquisition form of the SEM image of the fracture surface is realized by, for example, receiving data including the SEM image of the fracture surface transmitted from an external device such as a scanning electron microscope, or reading data including the SEM image of the fracture surface stored in the portable storage medium… the acquisition unit 101 acquires the SEM image of the fracture surface; acquire a first image including a fracture section of a component (e.g. facture detection device includes a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface (i.e. acquire a first image including a fracture section of a component), as indicated above), for example); and
a processor configured to perform image analysis for the first image (Par. [0008-19]: program of the present invention is for causing a computer to function as each means of the detection device… FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100. The hardware of the detection device 100 is realized by, for example, an information processing device including a CPU, a ROM, a RAM, an HDD, and various interfaces, or a dedicated hardware… a computer associated with the scanning electron microscope may have a function of the detection device 100… a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… acquisition form of the SEM image of the fracture surface is realized by, for example, receiving data including the SEM image of the fracture surface transmitted from an external device such as a scanning electron microscope, or reading data including the SEM image of the fracture surface stored in the portable storage medium… the acquisition unit 101 acquires the SEM image of the fracture surface; a processor configured to perform image analysis for the first image (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, for example, including processing (i.e. analyzing) a received SEM image (i.e. a first image) of the fracture surface (i.e. a processor configured to perform image analysis for the first image), as indicated above), for example);
the image analysis including a first process and a second process (Par. [0008-22]: program of the present invention is for causing a computer to function as each means of the detection device… FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100. The hardware of the detection device 100 is realized by, for example, an information processing device including a CPU, a ROM, a RAM, an HDD, and various interfaces, or a dedicated hardware… a computer associated with the scanning electron microscope may have a function of the detection device 100… a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… acquisition form of the SEM image of the fracture surface is realized by, for example, receiving data including the SEM image of the fracture surface transmitted from an external device such as a scanning electron microscope, or reading data including the SEM image of the fracture surface stored in the portable storage medium… the acquisition unit 101 acquires the SEM image of the fracture surface… executes preprocessing on the SEM image 300 of the fracture surface. The pre-processing is executed to divide the SEM image 300 of the fracture surface… performs binarization processing on the SEM image 300 of the fracture surface on which the preprocessing has been executed… creates a blob from the binarized image… derives the first global potential region and the second global potential region on the basis of the enlarged shape approximation blob; the image analysis including a first process and a second process (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface (i.e. the image analysis including a first process and a second process), as indicated above), for example),
the first process including deriving a plurality of fracture progress directions in the fracture section (Par. [0008-19]: program of the present invention is for causing a computer to function as each means of the detection device… a computer associated with the scanning electron microscope may have a function of the detection device 100… observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… with a scanning electron microscope. In this case, the observer searches for the reverse pattern and the tail ridge shown in the SEM image. The reverse pattern and the tear ridge are traces when the cleavage fracture surface is formed. The reverse pattern is formed so that when a single crystal grain is cleaved and broken, a step is formed on the cleavage plane without being completely the same cleavage plane. The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack… acquisition form of the SEM image of the fracture surface is realized by, for example, receiving data including the SEM image of the fracture surface transmitted from an external device such as a scanning electron microscope, or reading data including the SEM image of the fracture surface stored… potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… acquisition unit 101 acquires an SEM image of a fracture surface… The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… the acquisition unit 101 acquires the SEM image of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions; Par. [0020-23]: calculation point 302 indicated by a black circle indicates the position of the fracture start point derived by the detection device 100… the calculation point 302, and the broken line 303 are attached to the SEM image 300 of the fracture surface in order to make the vertical and horizontal directions of the SEM image 300 of the fracture surface easy to understand… the crack (reverse pattern and tail ridge) propagates radially from the fracture start point in the vicinity of the fracture start point, but macroscopically progresses downward (from the positive direction of the y-axis to the negative direction) from above (when the fracture surface is viewed as a whole)… the potential region derivation unit 102 performs binarization processing on the SEM image 300 of the fracture surface… By the binarization processing, a binarized image in which the pixel value of the pixel having a pixel value (luminance value) higher than the threshold value is 1 and the pixel value of the pixel having the pixel value equal to or less than the threshold value is 0 (zero) is created. It is known that a pixel value (luminance value) of a region indicating a reverse pattern or a tear ridge is larger (higher) than a pixel value (luminance value) of a region other than the pixel value (luminance value). Therefore, a region of a pixel having a pixel value of 1 corresponds to a region indicating a reverse pattern or a tear ridge, and a region of a pixel having a pixel value of 0 (zero) corresponds to a region other than the regio… The potential region derivation unit 102 creates a blob from the binarized image. The potential region derivation unit 102 sets, as the same blob, a plurality of pixels having a pixel value of 1 adjacent to each other in the longitudinal direction, the lateral direction, or the oblique direction in the binarized image… the potential region derivation unit 102 executes the derivation of the size and orientation of the shape approximation blob with respect to all the shape approximation blobs… the potential region derivation unit 102 derives the length of the major axis of the ellipse as the size of the shape approximation blob, and derives the direction in which the major axis of the ellipse faces as the orientation of the shape approximation blob. In addition, in the present embodiment, an example in which a direction in which the major axis of the ellipse is oriented is illustrated at an angle (°) in which the counterclockwise direction toward the paper surface is a positive direction (a negative direction in the clockwise direction toward the paper surface). In the example shown in FIG. 4B, the length D of the major axis of the ellipse and the direction θ in which the major axis of the ellipse faces are derived; Par. [0034-35]: the potential region derivation unit 102 derives, as a first global potential region, a pixel having a pixel value equal to or greater than a first predetermined value among the pixel values… the longitudinal direction of the first global potential region and the second large local potential region is included in a direction assumed as a propagation direction of a crack (reverse pattern and tear ridge) propagating in an oblique direction; the first process including deriving a plurality of fracture progress directions in the fracture section (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. the first process including deriving a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions, as indicated above), for example),
one of the plurality of fracture progress directions corresponding to one of a plurality of positions included in the fracture section (Par. [0013-17]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… the observer searches for the reverse pattern and the tail ridge shown in the SEM image. The reverse pattern and the tear ridge are traces when the cleavage fracture surface is formed… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack. Therefore, it is possible to specify the destruction starting point by tracing back the reverse pattern and the tear ridge to these spring-out points… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… output unit 104 outputs information including the position of the convergence unit derived by the convergence unit derivation unit 103… the output unit 104 outputs information including the position of the fracture start point; Par. [0019-20]: acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions… In FIG. 3, the actual measurement point 301 indicated by the white circle indicates the position of the fracture start point… calculation point 302 indicated by a black circle indicates the position of the fracture start point derived by the detection device 100… in a region below the broken line 303 (in the negative direction of the y-axis), brittle fracture occurs, and in a region above the broken line 303, the ductile fracture occurs… the calculation point 302, and the broken line 303, the measurement point 301, the calculation point 302, and the broken line 303 are attached to the SEM image 300 of the fracture surface in order to make the vertical and horizontal directions of the SEM image 300 of the fracture surface easy to understand… the crack (reverse pattern and tail ridge) propagates radially from the fracture start point in the vicinity of the fracture start point, but macroscopically progresses downward (from the positive direction of the y-axis to the negative direction) from above (when the fracture surface is viewed as a whole); Par. [0034-42]: the potential region derivation unit 102 derives, as a first global potential region, a pixel having a pixel value equal to or greater than a first predetermined value among the pixel values… the longitudinal direction of the first global potential region and the second large local potential region is included in a direction assumed as a propagation direction of a crack (reverse pattern and tear ridge) propagating in an oblique direction… As described above, the crack propagates radially in the vicinity of the fracture origin, but macroscopically progresses from the top to the bottom (in the negative direction from the positive direction of the y-axis). The macro convergence direction potential region is configured to be able to search for a path of a crack by going back along a direction y axis. It is considered that the farther from the fracture origin, the stronger the potential along the direction closer to the direction parallel to the y-axis… as the position is closer to the upper end portion (the end portion on the positive direction side of the y-axis) of the SEM image 300 of the fracture surface, the predetermined range is narrowed (in other words, the predetermined range is widened as the position is closer to the lower end portion (the end portion on the negative direction side of the y-axis) of the SEM image 300 of the fracture surface); one of the plurality of fracture progress directions corresponding to one of a plurality of positions included in the fracture section (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture, including determining crack, including a reverse pattern and tear ridge, propagating potential gradient directions, for example, including a number of points (i.e. positions, coordinates, locations, etc.) associated with the fracture (i.e. one of a plurality of positions included in the fracture section), for example, including determining points related to a direction assumed as a propagation direction of a crack propagating in a direction (i.e. one of the plurality of fracture progress directions corresponding to one of a plurality of positions included in the fracture section), as indicated above), for example),
the second process including deriving a fracture origin position in the fracture section based on at least a part of the plurality of fracture progress directions (Par. [0013-17]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… the observer searches for the reverse pattern and the tail ridge shown in the SEM image. The reverse pattern and the tear ridge are traces when the cleavage fracture surface is formed… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack. Therefore, it is possible to specify the destruction starting point by tracing back the reverse pattern and the tear ridge to these spring-out points… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… output unit 104 outputs information including the position of the convergence unit derived by the convergence unit derivation unit 103… the output unit 104 outputs information including the position of the fracture start point; Par. [0019-20]: acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions… In FIG. 3, the actual measurement point 301 indicated by the white circle indicates the position of the fracture start point… calculation point 302 indicated by a black circle indicates the position of the fracture start point derived by the detection device 100… in a region below the broken line 303 (in the negative direction of the y-axis), brittle fracture occurs, and in a region above the broken line 303, the ductile fracture occurs… the calculation point 302, and the broken line 303, the measurement point 301, the calculation point 302, and the broken line 303 are attached to the SEM image 300 of the fracture surface in order to make the vertical and horizontal directions of the SEM image 300 of the fracture surface easy to understand… the crack (reverse pattern and tail ridge) propagates radially from the fracture start point in the vicinity of the fracture start point, but macroscopically progresses downward (from the positive direction of the y-axis to the negative direction) from above (when the fracture surface is viewed as a whole); Par. [0034-42]: the potential region derivation unit 102 derives, as a first global potential region, a pixel having a pixel value equal to or greater than a first predetermined value among the pixel values… the longitudinal direction of the first global potential region and the second large local potential region is included in a direction assumed as a propagation direction of a crack (reverse pattern and tear ridge) propagating in an oblique direction… As described above, the crack propagates radially in the vicinity of the fracture origin, but macroscopically progresses from the top to the bottom (in the negative direction from the positive direction of the y-axis). The macro convergence direction potential region is configured to be able to search for a path of a crack by going back along a direction y axis. It is considered that the farther from the fracture origin, the stronger the potential along the direction closer to the direction parallel to the y-axis… as the position is closer to the upper end portion (the end portion on the positive direction side of the y-axis) of the SEM image 300 of the fracture surface, the predetermined range is narrowed (in other words, the predetermined range is widened as the position is closer to the lower end portion (the end portion on the negative direction side of the y-axis) of the SEM image 300 of the fracture surface); the second process including deriving a fracture origin position in the fracture section based on at least a part of the plurality of fracture progress directions (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions (i.e. based on at least a part of the plurality of fracture progress directions), for example, including a number of points (i.e. positions, coordinates, locations, etc.) associated with the fracture (i.e. one of a plurality of positions included in the fracture section), for example, including a position of a fracture start (i.e. origin) point of the fracture derived by the detection device (i.e. the second process including deriving a fracture origin position in the fracture section based on at least a part of the plurality of fracture progress directions), as indicated above), for example).
Regarding claim 2, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the processor includes a plurality of models (Par. [0008-14]: program of the present invention is for causing a computer to function as each means of the detection device… the detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece... FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100. The hardware of the detection device 100 is realized by, for example, an information processing device including a CPU, a ROM, a RAM, an HDD, and various interfaces, or a dedicated hardware. Further, a computer associated with the scanning electron microscope may have a function of the detection device 100… In FIG. 1, the detection device 100 includes an acquisition unit 101, a potential region derivation unit 102, a convergence unit derivation unit 103, and an output unit 104; the processor includes a plurality of models (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions of the detection device, for example, including a plurality of units (i.e. models, structures, etc.) configured to perform a number (i.e. first, second, third.. Nth) functions, processes, or operations of the detection device (i.e. the processor includes a plurality of models), as indicated above), for example),
in the [a] first operation, the processor performs the first process using one of the plurality of models (Par. [0014-15]: detection device 100 includes an acquisition unit 101, a potential region derivation unit 102, a convergence unit derivation unit 103, and an output unit 104… The potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction); in a first operation, the processor performs the first process using one of the plurality of models (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions of the detection device, for example, including a plurality of units (i.e. models, structures, etc.) configured to perform a number (i.e. first, second, third.. Nth) functions, processes, or operations of the detection device, for example, including a potential region derivation unit configured (i.e. one of the plurality of models) to perform processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress) directions of the fracture (i.e. in a first operation, the processor performs the first process using one of the plurality of models), for example, including determining crack, including a reverse pattern and tear ridge, propagating potential gradient directions, as indicated above), for example),
in the [a] second operation, the processor performs the first process using another one of the plurality of models (Par. [0013-17]: the detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece... In FIG. 1, the detection device 100 includes an acquisition unit 101, a potential region derivation unit 102, a convergence unit derivation unit 103, and an output unit 104… The convergence unit derivation unit 103 derives the convergence unit on the basis of a result of searching for a route from the first end to the second end of the detection range of the convergence unit… the convergence unit is a fracture start point… the convergence unit derivation unit 103… causes the path to proceed in a direction along the potential region in the pixel included in the potential region, and causes the path to proceed in a predetermined direction so as to approach the second end in the pixel not included in the potential region. The output unit 104 outputs information including the position of the convergence unit derived by the convergence unit derivation unit 103; in a second operation, the processor performs the first process using another one of the plurality of models (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions of the detection device, for example, including a plurality of units (i.e. models, structures, etc.) configured to perform a number (i.e. first, second, third.. Nth) functions, processes, or operations of the detection device, for example, including a convergence unit derivation unit (i.e. another one of the plurality of models) configured to determine a number of points (i.e. positions, coordinates, locations, etc.) associated with the fracture (i.e. in a second operation, the processor performs the first process using another one of the plurality of models), for example, including determining points related to a direction assumed as a propagation direction of a crack propagating in a direction, as indicated above), for example),
at least one of a fracture mode of the fracture section, a material of the component, a shape of the component, or a molding conditions of the component are different between the one of the plurality of models and the other one of the plurality models (Par. [0124-135]: FIG. 13 is a diagram illustrating an example of a functional configuration of the detection device 1300. The hardware of the detection device 1300 is realized in the same manner as the detection device 100… Similarly to the potential region derivation unit 102 of the first embodiment, the convergence unit derivation unit 1304 derives the fracture origin on the basis of a result of searching for a route from the first end to the second end of the detection range of the fracture origin… FIG. 14 is a flowchart illustrating an example of a detection method executed using the detection device 1300. With reference to FIG. 14, an example of a detection method executed using the detection device 1300 will be described together with a function of the detection device 1300… the detection range derivation unit 1302 derives a detection range of the fracture start point with respect to the SEM image of the fracture surface on the basis of the SEM image of the fracture surface acquired… the detection range derivation unit 1302 classifies the region of the SEM image of the fracture surface into the region of the brittle fracture surface and the region of the ductile fracture surface by the machine learning model… the detection range derivation unit 1302 creates teacher data by using the notch as the machined surface and the striation pattern formed on the surface as the ductile fracture surface. Similarly, for example, the detection range derivation unit 1302 extracts a partial image that can be clearly identified as a brittle fracture surface from an image obtained by imaging a fracture surface… . The teacher data for such a ductile fracture surface and the teacher data for the brittle fracture surface are prepared for a number suitable for machine learning, respectively… A machine learning model is created based on an algorithm of the machine learning model using the teacher data; at least one of a fracture mode of the fracture section, a material of the component, a shape of the component, or a molding conditions of the component are different between the one of the plurality of models and the other one of the plurality models (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions of the detection device, for example, including a plurality of units (i.e. models, structures, etc.) configured to perform a number (i.e. first, second, third.. Nth) functions, processes, or operations of the detection device, for example, including a detection range derivation unit configured to classify regions of the SEM image of the fracture surface into a region of a brittle fracture surface and a region of a ductile fracture surface (i.e. a fracture mode of the fracture section), by using trained machine learning models, for example, including teacher data for a ductile fracture surface and teacher data for a brittle fracture surface prepared for a number suitable for machine learning, respectively, including machine learning models created based on an algorithm of each machine learning model using the teacher data (i.e. at least one of a fracture mode of the fracture section different between the one of the plurality of models and the other one of the plurality models), as indicated above), for example).
Regarding claim 3, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the first process includes deriving one of the plurality of fracture progress directions with respect to one of a plurality of patch regions obtained by dividing the first image acquired by the acquisitor (Par. [0013-21]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack… potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… the acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the potential region derivation unit 102 executes preprocessing on the SEM image 300 of the fracture surface. The pre-processing is executed to divide the SEM image 300 of the fracture surface into a region indicating a reverse pattern or a tear ridge and a region other than the reverse pattern with high accuracy by binarization processing; wherein the first process includes deriving one of the plurality of fracture progress directions with respect to one of a plurality of patch regions obtained by dividing the first image acquired by the acquisitor (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface, for example, including pre-processing executed to divide (i.e. segment, separate, etc.) the SEM image of the fracture surface into a region indicating a reverse pattern or a tear ridge and a region other than the reverse pattern (i.e. one of a plurality of patch regions obtained by dividing the first image acquired), for example, including processing (i.e. analyzing) the received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions (i.e. wherein the first process includes deriving one of the plurality of fracture progress directions with respect to one of a plurality of patch regions obtained by dividing the first image acquired by the acquisitor), as indicated above), for example).
Regarding claim 4, claim 3 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the first process includes changing a range included in at least one of the plurality of patch regions according to a magnification of imaging of the first image (Par. [0013-28]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack… potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… the acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the potential region derivation unit 102 executes preprocessing on the SEM image 300 of the fracture surface. The pre-processing is executed to divide the SEM image 300 of the fracture surface into a region indicating a reverse pattern or a tear ridge and a region other than the reverse pattern with high accuracy by binarization processing… The potential region derivation unit 102 creates a blob from the binarized image… There is a possibility that the reverse pattern and the tear ridge are wider than the binarized image. In addition, it is assumed that the influence of the reverse pattern and the tail ridge extends to a region in the vicinity thereof. Therefore, in the present embodiment, the potential region derivation unit 102 executes the enlargement of the size of the shape approximation blob with respect to all the shape approximation blobs… For example, the potential region derivation unit 102 multiplies the length of the major axis of the shape approximation blob by a constant… The constant may be changed according to the imaging magnification of the SEM image 300 of the fracture surface. As the imaging magnification of the SEM image 300 of the fracture surface is lower, in the SEM image 300 of the fracture surface, a region affected by the reverse pattern and the tear ridge becomes wider. Therefore, for example, when the imaging magnification of the SEM image 300 of the fracture surface is 200 times or less, the constant is set to 6, and when the imaging magnification of the SEM image 300 of the fracture surface is more than 200 times and 500 times or less, the constant is set to 2. In addition, in a case where it can be confirmed that the shape approximation blob approximates the reverse pattern and the tear ridge with high accuracy, the constant may be a value smaller than these values (= 2, 6); wherein the first process includes changing a range included in at least one of the plurality of patch regions according to a magnification of imaging of the first image (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface, for example, including pre-processing executed to divide (i.e. segment, separate, etc.) the SEM image of the fracture surface into a region (i.e. at least one of the plurality of patch regions) indicating a reverse pattern or a tear ridge and a region other than the reverse pattern (i.e. one of a plurality of patch regions obtained by dividing the first image acquired), for example, including performing imaging magnification of the SEM image of the fracture surface, including corresponding divided regions, by changing values (i.e. changing a range) of a constant magnification factor according to the imaging magnification of the SEM image of the fracture surface (i.e. wherein the first process includes changing a range included in at least one of the plurality of patch regions according to a magnification of imaging of the first image), as indicated above), for example).
Regarding claim 5, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the first process further includes deriving a certainty factor regarding each of the plurality of fracture progress directions being derived (Par. [0013-22]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack… potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… the acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the potential region derivation unit 102 executes preprocessing on the SEM image 300 of the fracture surface. The pre-processing is executed to divide the SEM image 300 of the fracture surface into a region indicating a reverse pattern or a tear ridge and a region other than the reverse pattern with high accuracy by binarization processing… the potential region derivation unit 102 performs binarization processing on the SEM image 300 of the fracture surface on which the preprocessing has been executed. By the binarization processing, a binarized image in which the pixel value of the pixel having a pixel value (luminance value) higher than the threshold value is 1 and the pixel value of the pixel having the pixel value equal to or less than the threshold value is (zero) is created. It is known that a pixel value (luminance value) of a region indicating a reverse pattern or a tear ridge is larger (higher) than a pixel value (luminance value) of a region other than the pixel value (luminance value). Therefore, a region of a pixel having a pixel value of 1 corresponds to a region indicating a reverse pattern or a tear ridge, and a region of a pixel having a pixel value of 0 (zero) corresponds to a region other than the region; wherein the first process further includes deriving a certainty factor regarding each of the plurality of fracture progress directions being derived (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions, for example, including binarization processing to generate a binarized image having pixel values (luminance value) higher than a threshold value (i.e. a certainty factor, a determined reference value, etc.) and pixel values equal to or less than the threshold value is created, for example, in which a region of a pixel having a pixel value of 1 corresponds to a region indicating a reverse pattern or a tear ridge corresponding to crack propagating potential gradient directions, and a region of a pixel having a pixel value of 0 (zero) corresponds to a region other than the region (i.e. wherein the first process further includes deriving a certainty factor regarding each of the plurality of fracture progress directions being derived), as indicated above), for example),
the second process includes deriving the fracture origin position without using at least one of the plurality of fracture progress directions (Par. [0013-20]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… In FIG. 3, the actual measurement point 301 indicated by the white circle indicates the position of the fracture start point specified by the skilled person; the second process includes deriving the fracture origin position without using at least one of the plurality of fracture progress directions (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface, including a position of the fracture start point (i.e. the fracture origin position) specified by an observer (i.e. the second process includes deriving the fracture origin position without using at least one of the plurality of fracture progress directions), as indicated above), for example), and
the certainty factor corresponding to the at least one of the plurality of fracture progress directions is less than a determined reference value (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions, for example, including binarization processing to generate a binarized image having pixel values (luminance value) higher than a threshold value (i.e. a certainty factor, a determined reference value, etc.) and pixel values equal to or less than the threshold value is created, for example, in which a region of a pixel having a pixel value of 1 corresponds to a region indicating a reverse pattern or a tear ridge corresponding to crack propagating potential gradient directions, and a region of a pixel having a pixel value of 0 (zero) corresponds to a region other than the region (i.e. and the certainty factor corresponding to the at least one of the plurality of fracture progress directions is less than a determined reference value), as indicated above), for example).
Regarding claim 6, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the first image is obtained from an imaging device configured to capture the fractured section (Par. [0008-19]: program of the present invention is for causing a computer to function as each means of the detection device… FIG. 1 is a diagram illustrating an example of a functional configuration of a detection device 100. The hardware of the detection device 100 is realized by, for example, an information processing device including a CPU, a ROM, a RAM, an HDD, and various interfaces, or a dedicated hardware… a computer associated with the scanning electron microscope may have a function of the detection device 100… a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… acquisition form of the SEM image of the fracture surface is realized by, for example, receiving data including the SEM image of the fracture surface transmitted from an external device such as a scanning electron microscope, or reading data including the SEM image of the fracture surface stored in the portable storage medium… the acquisition unit 101 acquires the SEM image of the fracture surface; acquire a first image including a fracture section of a component (e.g. facture detection device includes a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, for example, including receiving (i.e. acquiring, obtaining, etc.) data including a captured scanning electron microscope (SEM) image (i.e. a first image) of a fracture surface (i.e. wherein the first image is obtained from an imaging device configured to capture the fractured section), as indicated above), for example),
the second process includes deriving the fracture origin position using a coordinate being common for the plurality of fracture progress directions (Par. [0013-17]: observer specifies a position of a fracture start point from a captured image (hereinafter, referred to as an SEM image) obtained by capturing an image of a fracture surface of a test piece… the observer searches for the reverse pattern and the tail ridge shown in the SEM image. The reverse pattern and the tear ridge are traces when the cleavage fracture surface is formed… The tear ridge is formed by ductile fracture when the cleavage fracture surface between the crystal grains is connected. The reverse pattern also leaves a trace that reflects the propagation direction of the crack. Therefore, it is possible to specify the destruction starting point by tracing back the reverse pattern and the tear ridge to these spring-out points… detection device 100 detects the position of the fracture origin from the SEM image of the fracture surface of the test piece… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction)… output unit 104 outputs information including the position of the convergence unit derived by the convergence unit derivation unit 103… the output unit 104 outputs information including the position of the fracture start point; Par. [0019-20]: acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions … In FIG. 3, the actual measurement point 301 indicated by the white circle indicates the position of the fracture start point… calculation point 302 indicated by a black circle indicates the position of the fracture start point derived by the detection device 100… in a region below the broken line 303 (in the negative direction of the y-axis), brittle fracture occurs, and in a region above the broken line 303, the ductile fracture occurs… the calculation point 302, and the broken line 303, the measurement point 301, the calculation point 302, and the broken line 303 are attached to the SEM image 300 of the fracture surface in order to make the vertical and horizontal directions of the SEM image 300 of the fracture surface easy to understand… the crack (reverse pattern and tail ridge) propagates radially from the fracture start point in the vicinity of the fracture start point, but macroscopically progresses downward (from the positive direction of the y-axis to the negative direction) from above (when the fracture surface is viewed as a whole); Par. [0030-35]: the potential region derivation unit 102 sets the pixel value of each pixel in the detection range of the fracture origin according to a function indicating the relationship between the direction in which the major axis of the ellipse of the enlarged shape approximation blob faces and the pixel value… the potential region derivation unit 102 derives, as a first global potential region, a pixel having a pixel value equal to or greater than a first predetermined value among the pixel values… the longitudinal direction of the first global potential region and the second large local potential region is included in a direction assumed as a propagation direction of a crack (reverse pattern and tear ridge) propagating in an oblique direction; the second process includes deriving the fracture origin position using a coordinate being common for the plurality of fracture progress directions (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions (i.e. the plurality of fracture progress directions), for example, including a number of points (i.e. positions, coordinates, locations, etc.) associated with the fracture (i.e. one of a plurality of positions included in the fracture section), for example, including a position of a fracture start (i.e. origin) point of the fracture derived by the detection device and x-y coordinates given to indicate the relationship between the directions in the SEM image (i.e. the second process includes deriving the fracture origin position using a coordinate being common for the plurality of fracture progress directions), as indicated above), for example), and
the coordinate is a reference coordinate at a time of imaging of the imaging device (Par. [0014-15]: detection device 100 includes an acquisition unit 101… Theacquisition unit 101 acquires an SEM image of a fracture surface… The potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… the linear region is a reverse pattern and a tear ridge. The potential region is a region including a reverse pattern and a tear ridge, and is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (a reverse pattern and a tear ridge are formed in a potential gradient direction… the acquisition unit 101 acquires the SEM image of the fracture surface; Par. [0019-20]: acquisition unit 101 acquires the SEM image of the fracture surface. FIG. 3 is a diagram illustrating an example of the SEM image 300 of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions… In FIG. 3, the actual measurement point 301 indicated by the white circle indicates the position of the fracture start point… calculation point 302 indicated by a black circle indicates the position of the fracture start point derived by the detection device 100… in a region below the broken line 303 (in the negative direction of the y-axis), brittle fracture occurs, and in a region above the broken line 303, the ductile fracture occurs… the calculation point 302, and the broken line 303, the measurement point 301, the calculation point 302, and the broken line 303 are attached to the SEM image 300 of the fracture surface in order to make the vertical and horizontal directions of the SEM image 300 of the fracture surface easy to understand… the crack (reverse pattern and tail ridge) propagates radially from the fracture start point in the vicinity of the fracture start point, but macroscopically progresses downward (from the positive direction of the y-axis to the negative direction) from above (when the fracture surface is viewed as a whole); and the coordinate is a reference coordinate at a time of imaging of the imaging device (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. a plurality of fracture progress directions in the fracture section), for example, including determining crack (reverse pattern and tear ridge) propagating potential gradient directions (i.e. the plurality of fracture progress directions), for example, including a number of points (i.e. positions, coordinates, locations, etc.) associated with the fracture (i.e. one of a plurality of positions included in the fracture section), for example, including a position of a fracture start (i.e. origin) point of the fracture derived by the detection device and x-y coordinates given to indicate the relationship between the directions in the SEM image (i.e. and the coordinate is a reference coordinate), for example, including a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture (i.e. and the coordinate is a reference coordinate at a time of imaging of the imaging device), as indicated above), for example).
Regarding claim 9, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the plurality of fracture progress directions include a first direction, a second direction, and a third direction (Par. [0019-23]: the acquisition unit 101 acquires the SEM image of the fracture surface… the x-y coordinates are given to indicate the relationship between the directions… the potential region derivation unit 102 performs binarization processing on the SEM image 300 of the fracture surface … potential region derivation unit 102 creates a blob from the binarized image. The potential region derivation unit 102 sets, as the same blob, a plurality of pixels having a pixel value of 1 adjacent to each other in the longitudinal direction, the lateral direction, or the oblique direction in the binarized image; wherein the plurality of fracture progress directions include a first direction, a second direction, and a third direction (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine a number (i.e. first, second, third.. Nth) of propagation (i.e. progress, growth, etc.) directions of the fracture (i.e. wherein the plurality of fracture progress directions include a first direction, a second direction, and a third direction), as indicated above), for example),
the first direction is a direction from a first origin to a first end point,
the second direction is a direction from a second origin to a second end point,
the third direction is a direction from a third origin to a third end point (Par. [0015-16]: potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… The potential region… is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture… convergence unit derivation unit 103 derives the convergence unit on the basis of a result of searching for a route from the first end to the second end of the detection range of the convergence unit… the convergence unit is a fracture start point. The detection range of the fracture origin is included in the SEM image of the fracture surface… The first end portion and the second end portion are opposite sides of the detection range (quadrangle) of the fracture start point. When searching for a path from the first end to the second end of the detection range of the fracture origin, the convergence unit derivation unit 103 causes the path to proceed in a direction along the potential region in the pixel included in the potential region; Par. [0042-49]: the crack propagates radially in the vicinity of the fracture origin… The macro convergence direction potential region is configured to be able to search for a path of a crack by going back along a direction close to a direction parallel to the y axis. It is considered that the farther from the fracture origin, the stronger the potential along the direction closer to the direction parallel to the y-axis. Therefore… as the position is closer to the upper end portion (the end portion on the positive direction side of the y-axis) of the SEM image 300 of the fracture surface, the predetermined range is narrowed (in other words, the predetermined range is widened as the position is closer to the lower end portion (the end portion on the negative direction side of the y-axis) of the SEM image 300 of the fracture surface)… the convergence unit derivation unit 103 executes a route search process for searching for a route from the first end to the second end of the detection range of the destruction starting point on the basis of the first large-station potential region and the second large-station potential region… and the macro-convergence-direction potential region… a route from the first end to the second end of the detection range of the destruction starting point in the case where each of the four ends of the detection range of the destruction starting point is set as the first end is searched… The convergence unit derivation unit 103 executes the derivation of the setting value in each of the plurality of pixels located at the second end as the first end portion of each of the four end portions (the upper end portion, the lower end portion, the left end portion, and the right end portion) of the detection range of the fracture start point. Therefore, in a case where each of the four end portions (the upper end portion, the lower end portion, the left end portion, and the right end portion) of the detection range of the fracture start point is defined as the first end portion, the setting values in the plurality of pixels located at the second end portion (the lower end portion, the upper end portion, the right end portion, and the left end portion) are derived… detection range 700 of the fracture start point coincides with the SEM image 300 of the fracture surface illustrated in FIG. 3. Here, it is assumed that the first end portion is selected in the order of the lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the fracture start point. When the lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the fracture start point are the first end portions, each of the second end portions becomes the upper end portion 702, the lower end portion 701, the right end portion 704, and the left end portion 703 of the detection range 700 of the fracture start point; the first direction is a direction from a first origin to a first end point, the second direction is a direction from a second origin to a second end point, the third direction is a direction from a third origin to a third end point (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine a number (i.e. first, second, third.. Nth) of propagation (i.e. progress, growth, etc.) directions of the fracture including searching for propagation directions routes of the fracture from a number (i.e. first, second, third.. Nth) of end points of a detection range (i.e. first, second, third.. Nth) of destruction starting (i.e. origin) points (i.e. the first direction is a direction from a first origin to a first end point, the second direction is a direction from a second origin to a second end point, the third direction is a direction from a third origin to a third end point), as indicated above), for example),
a position of the second end point in a straight line along the first direction is between a position of the second origin in the straight line and a position of the first end point in the straight line,
a position of the first origin in the straight line is between the position of the second end point in the straight line and the position of the first end point in the straight line (Par. [0015-16]: potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… The potential region… is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture… convergence unit derivation unit 103 derives the convergence unit on the basis of a result of searching for a route from the first end to the second end of the detection range of the convergence unit… the convergence unit is a fracture start point. The detection range of the fracture origin is included in the SEM image of the fracture surface… The first end portion and the second end portion are opposite sides of the detection range (quadrangle) of the fracture start point. When searching for a path from the first end to the second end of the detection range of the fracture origin, the convergence unit derivation unit 103 causes the path to proceed in a direction along the potential region in the pixel included in the potential region; Par. [0038-49]: the macro convergence direction, which is a direction set in advance as the macro direction in which the linear region converges to the convergence portion, corresponds to the positive direction of the y-axis of the SEM image 300 of the fracture surface. The direction inclined in the first inclination direction with respect to the macro-convergence direction corresponds to a direction inclined in a negative direction (clockwise direction toward the paper surface) with respect to a straight line (upward straight line of the y-axis) extending in the positive direction of the y-axis, and a direction inclined in the second inclination direction with respect to the macro-convergence direction corresponds to a direction inclined in a positive direction (counterclockwise direction toward the paper surface) with respect to a straight line extending in the positive direction of the y-axis… the crack propagates radially in the vicinity of the fracture origin… The macro convergence direction potential region is configured to be able to search for a path of a crack by going back along a direction close to a direction parallel to the y axis. It is considered that the farther from the fracture origin, the stronger the potential along the direction closer to the direction parallel to the y-axis. Therefore… as the position is closer to the upper end portion (the end portion on the positive direction side of the y-axis) of the SEM image 300 of the fracture surface, the predetermined range is narrowed (in other words, the predetermined range is widened as the position is closer to the lower end portion (the end portion on the negative direction side of the y-axis) of the SEM image 300 of the fracture surface)… the potential region derivation unit 102 derives a macro convergence direction potential region based on the enlarged shape approximation blob as follows. First, the potential region derivation unit 102 derives a position in a vertical direction (a direction parallel to the y-axis) of a pixel (for example, a pixel corresponding to the center of gravity) corresponding to a predetermined position of the enlarged shape approximation blob. Next, the potential region derivation unit 102 determines whether or not the direction in which the major axis of the ellipse of the extracted enlarged shape approximation blob faces is within the macro convergence direction deviation range corresponding to the derived position… the convergence unit derivation unit 103 executes a route search process for searching for a route from the first end to the second end of the detection range of the destruction starting point on the basis of the first large-station potential region and the second large-station potential region… and the macro-convergence-direction potential region… a route from the first end to the second end of the detection range of the destruction starting point in the case where each of the four ends of the detection range of the destruction starting point is set as the first end is searched… The convergence unit derivation unit 103 executes the derivation of the setting value in each of the plurality of pixels located at the second end as the first end portion of each of the four end portions (the upper end portion, the lower end portion, the left end portion, and the right end portion) of the detection range of the fracture start point. Therefore, in a case where each of the four end portions (the upper end portion, the lower end portion, the left end portion, and the right end portion) of the detection range of the fracture start point is defined as the first end portion, the setting values in the plurality of pixels located at the second end portion (the lower end portion, the upper end portion, the right end portion, and the left end portion) are derived… detection range 700 of the fracture start point coincides with the SEM image 300 of the fracture surface illustrated in FIG. 3. Here, it is assumed that the first end portion is selected in the order of the lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the fracture start point. When the lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the fracture start point are the first end portions, each of the second end portions becomes the upper end portion 702, the lower end portion 701, the right end portion 704, and the left end portion 703 of the detection range 700 of the fracture start point; a position of the second end point in a straight line along the first direction is between a position of the second origin in the straight line and a position of the first end point in the straight line, a position of the first origin in the straight line is between the position of the second end point in the straight line and the position of the first end point in the straight line (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine a number (i.e. first, second, third.. Nth) of propagation (i.e. progress, growth, etc.) directions of the fracture, for example, including searching for propagation directions routes of the fracture from a number (i.e. first, second, third.. Nth) of end points of a detection range (i.e. first, second, third.. Nth) of destruction starting (i.e. origin) points by determining a number (i.e. first, second, third.. Nth) of positions between staring points and corresponding end portions (i.e. end points), for example, with respect to a straight line extending in the positive direction of the y-axis, as indicated above), for example),
the second process includes repeating an origin point derivation process (Par. [0160]: detection device 1300 executes the derivation of the potential region and the derivation of the fracture start point in order from the SEM image of the fracture surface… the position of the fracture start point can be detected at the level of the crystal grain by narrowing down the position of the fracture start point by the repeating main method),
one of the origin derivation processes includes determining the second direction based on the first direction,
a [the] third direction corresponds to the second direction specified by the repeating the origin derivation process (Par. [0140-160]: the potential region derivation unit 1303 derives the horizontal potential region based on the enlarged shape approximation blob… the longitudinal direction of the horizontal potential region is a direction within the horizontal deviation range with respect to the macro convergence direction. The horizontal direction deviation range is determined so as not to overlap the range of the inclination angle in the first inclination direction with respect to the first large station potential region, the range of the inclination angle in the second inclination direction with respect to the second large station potential region, and the macro convergence direction deviation range… the potential region in the direction close to the horizontal direction (the direction parallel to the x-axis) in the case where the macro-convergence direction is the up-down direction (the direction parallel to the y-axis) is derived as the horizontal-direction potential region, and based on the number of the horizontal-direction potential regions, the effective range, which is the range in which the macro-convergence direction potential region is used in searching for the path, and the invalid range, which is the range in which the macro-convergence-direction potential region is not used in searching for the path, are set for the detection range 1520 of the destruction starting point. In the following description, the horizontal direction when the macro convergence direction is the up-down direction is simply referred to as a horizontal direction as necessary… the potential region derivation unit 1303 executes the derivation of the position in the vertical direction (the direction parallel to the y-axis) of the pixel (for example, the pixel corresponding to the center of gravity) corresponding to the predetermined position in the horizontal potential region as the position in the vertical direction of the horizontal potential region, for all the horizontal potential regions. The potential region derivation unit 1303 derives the number of horizontal potential regions located in each pixel in the vertical direction (direction parallel to the y-axis) of the detection range 1520 of the fracture start point on the basis of the vertical position of the horizontal potential region derived in this way. This is because, based on the vertical position of the horizontal potential region, the vertical direction (direction parallel to the y-axis) of the detection range 1520 of the fracture origin is a direction parallel to the macro-convergence direction… the potential region derivation unit 1303 derives the macro convergence direction potential region… detection device 1300 executes the derivation of the potential region and the derivation of the fracture start point in order from the SEM image of the fracture surface… the position of the fracture start point can be detected at the level of the crystal grain by narrowing down the position of the fracture start point by the repeating main method; one of the origin derivation processes includes determining the second direction based on the first direction, the third direction corresponds to the second direction specified by the repeating the origin derivation process (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine a number (i.e. first, second, third.. Nth) of propagation (i.e. progress, growth, etc.) directions of the fracture including searching for propagation directions routes of the fracture from a number (i.e. first, second, third.. Nth) of end points of a detection range (i.e. first, second, third.. Nth) of destruction starting (i.e. origin) points by determining a number (i.e. first, second, third.. Nth) of positions between staring points and corresponding end portions (i.e. end points), for example, including determining a number (i.e. first, second, third.. Nth) of directions with respect to a macro-convergence direction corresponding to a direction inclined in a negative direction (clockwise direction toward the paper surface) with respect to a straight line (upward straight line of the y-axis) extending in the positive direction of the y-axis, and a direction inclined in the second inclination direction with respect to the macro-convergence direction corresponds to a direction inclined in a positive direction (counterclockwise direction toward the paper surface) with respect to a straight line extending in the positive direction of the y-axis, as indicated above), for example),
the second process causing a position on an extension line of an orientation from the third end point to the third origin obtained by repeating the origin point derivation process to be a candidate for the fracture origin position (Par. [0015-30]: potential region derivation unit 102 derives the potential region on the basis of the linear region extracted from the SEM image of the fracture surface… The potential region… is a region assumed as a region where a potential having a fracture start point as an apex occurs at the time of fracture… convergence unit derivation unit 103 derives the convergence unit on the basis of a result of searching for a route from the first end to the second end of the detection range of the convergence unit… the convergence unit is a fracture start point. The detection range of the fracture origin is included in the SEM image of the fracture surface… The first end portion and the second end portion are opposite sides of the detection range (quadrangle) of the fracture start point. When searching for a path from the first end to the second end of the detection range of the fracture origin, the convergence unit derivation unit 103 causes the path to proceed in a direction along the potential region in the pixel included in the potential region… The output unit 104 outputs information including the position of the convergence unit derived by the convergence unit derivation unit 103… the output unit 104 outputs information including the position of the fracture start point… the length D of the major axis of the ellipse and the direction θ in which the major axis of the ellipse faces are derived. In FIG. 4B, a straight line extending in the positive direction of the x-axis is referred to as a start line (0°)… it is assumed that the influence of the reverse pattern and the tail ridge extends to a region in the vicinity thereof… it is assumed that the angle indicated by the following first to sixth conditions is an angle at which a straight line extending in the positive direction of the x-axis is a starting line (0°); Par. [0116]: the potential region includes a macro convergence direction potential region. The longitudinal direction of the macro convergence direction potential region is within a predetermined range (macro convergence direction deviation range) with respect to the convergence direction (positive direction of the y axis)… Therefore, it is possible to easily and reliably search for a path extending in a direction close to the macro-convergence direction; Par. [0154-161]: the detection accuracy of the fracture start point is improved by preventing the search of the path using the macro convergence direction potential region from being executed in the region where the reverse pattern and the tear ridge extending in the direction close to the horizontal direction are present… detection device 1300 executes the derivation of the potential region and the derivation of the fracture start point in order from the SEM image of the fracture surface… the position of the fracture start point can be detected at the level of the crystal grain by narrowing down the position of the fracture start point by the repeating main method… the detection device 1300 searches for the path assuming that the macro convergence direction potential region exists only in the effective range 1550 of the detection range 1520 of the destruction origin. In addition, the detection device 1300 searches for the path assuming that the horizontal potential region is not present even when the pixels of the detection range 1520 of the destruction starting point are included in the horizontal direction potential region. Therefore, it is possible to prevent the route from being searched in accordance with the macro convergence direction potential region in a reverse pattern extending in a direction close to the macro convergence direction and in a region in which a reverse pattern and a tear ridge extending in a direction closer to the horizontal direction are larger than the reverse pattern and the tear ridge extending in a direction close to the macro convergence direction. Therefore, the detection accuracy of the destruction starting point can be further improved. Since the reverse pattern and the tear ridge extending in the direction close to the horizontal direction are likely to occur in the vicinity of the fracture start point; the second process causing a position on an extension line of an orientation from the third end point to the third origin obtained by repeating the origin point derivation process to be a candidate for the fracture origin position (e.g. facture detection device includes an information processing device including a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including processing (i.e. analyzing) a received SEM image of the fracture surface to determine a number (i.e. first, second, third.. Nth) of propagation (i.e. progress, growth, etc.) directions of the fracture, for example, including searching for propagation directions routes of the fracture from a number (i.e. first, second, third.. Nth) of end points of a detection range (i.e. first, second, third.. Nth) of destruction starting (i.e. origin) points by determining a number (i.e. first, second, third.. Nth) of positions between staring points and corresponding end portions (i.e. end points), for example, with respect to a straight line extending in the positive direction of the y-axis (i.e. the second process causing a position on an extension line of an orientation from an end point to an origin obtained), for example, by the repeating the process to provide output information including the position of the fracture start point, as indicated above), for example).
Regarding claim 10, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), wherein
the first process includes a process by a regression processor (Par. [0133]: The machine learning model is, for example, a logistic regression model or a neural network…; Par. [0147]: potential region derivation unit 1303 derives a curve indicating the relationship between the position in the vertical direction (direction parallel to the y-axis) of the detection range 1520 of the fracture origin and the number of the horizontal potential regions using a method such as regression analysis;) configured to perform processing based on machine learning related to a plurality of teacher images including the fracture section and the plurality of fracture progress directions (Par. [0134]: the detection range derivation unit 1302 creates teacher data. For example, the detection range derivation unit 1302 extracts a partial image that can be clearly identified as a ductile fracture surface from an image obtained by imaging a fracture surface… and sets the partial image and data (correct answer data) indicating that the partial image is a ductile fracture surface as one training data… the detection range derivation unit 1302 extracts a partial image that can be clearly identified as a brittle fracture surface from an image obtained by imaging a fracture surface… and sets the partial image and data (correct answer data) indicating that the partial image is a brittle fracture surface as one training data. The teacher data for such a ductile fracture surface and the teacher data for the brittle fracture surface are prepared for a number suitable for machine learning, respectively; Par. [0135]: A machine learning model is created based on an algorithm of the machine learning model using the teacher data. For example, when the SEM image of the fracture surface is input, the machine learning model derives the region of the brittle fracture surface from the SEM image of the fracture surface, and outputs the region of the brittle fracture surface as the detection range of the fracture start point; ).
Regarding claim 11, is a corresponding method claim rejected as applied to the apparatus claim 1 above.
Regarding claim 12, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 2 above.
Regarding claim 13, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 3 above.
Regarding claim 14, claim 13 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 4 above.
Regarding claim 15, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 5 above.
Regarding claim 16, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 6 above.
Regarding claim 19, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 9 above.
Regarding claim 20, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 10 above.
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 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over NAMEKAWA, as applied to claim 1 above, in view of ISOSAKI et al. (US PG Publication No. 2022/0270235 A1), hereafter referred to as ISOSAKI, Applicant cited prior art furnished via IDS.
Regarding claim 8, claim 1 is incorporated and NAMEKAWA discloses the device (Par. [0001]), further comprising:
a GUI [a graphical user interface (GUI)] (Par. [0156]: processing to the user interface of the detection device 1300),
the GUI being configured to perform at least one of the [an] eighth display operation or the [a] ninth display operation (Par. [0008-17]: program of the present invention is for causing a computer to function as each means of the detection device… the detection device 100 includes an acquisition unit 101, a potential region derivation unit 102, a convergence unit derivation unit 103, and an output unit 104… output form of information can employ, for example, at least one of display on a computer display; the GUI being configured to perform at least one of the [an] eighth display operation or the [a] ninth display operation (e.g. facture detection device includes a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including providing output information to a computer display, as indicated above), for example),
in the eighth display operation, the GUI being configured to display the plurality of fracture progress directions based on each of the plurality of fracture progress directions, and
in the ninth display operation, the GUI being configured to display the plurality of fracture progress directions used in deriving the fracture origin position (Par. [0008-17]: program of the present invention is for causing a computer to function as each means of the detection device… the detection device 100 includes an acquisition unit 101, a potential region derivation unit 102, a convergence unit derivation unit 103, and an output unit 104… output form of information can employ, for example, at least one of display on a computer display; Par. [0098-110]: processing of steps S223 to S233 is repeatedly executed until the processing of steps S224 to S232 is executed for all the rows or all the columns of the detection range 700 of the destruction starting point… FIG. 11A, FIG. 11B, FIG. 11C, and FIG. 11D are diagrams illustrating an example of a path derived by processing the flowchart of FIGS. 2A and 2B with the lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the destruction starting point as the first end portion. In FIGS. 11A -11 D, a portion indicated by white in the detection range 700 of the fracture start point indicates a path. The lower end portion 701, the upper end portion 702, the left end portion 703, and the right end portion 704 of the detection range 700 of the destruction starting point are defined as the first end portions, and the setting values of the respective pixels derived by the processing of the flowcharts of FIGS. 2A and 2B are proportional to the number of merging of the paths leading to the pixels … the convergence unit derivation unit 103 May derive, as a pixel serving as a destruction starting point, a pixel in which the final setting value indicates a maximum value in at least one of a longitudinal direction (a direction parallel to the y-axis) and a lateral direction (a direction parallel to the x-axis) of the SEM image 300 of the fracture surface among the pixels in the detection range of the fracture origin; in the eighth display operation, the GUI being configured to display the plurality of fracture progress directions based on each of the plurality of fracture progress directions, and in the ninth display operation, the GUI being configured to display the plurality of fracture progress directions used in deriving the fracture origin position (e.g. facture detection device includes a computer (i.e. a processor, CPU, etc.) programmed (i.e. configured) to perform functions, processes, or operations of the detection device, including a number (i.e. first, second, third.. Nth) of processing steps, for example, including providing output information to a computer display, for example, including the plurality of fracture progress directions determined and used for determining the fracture origin position (i.e. display the plurality of fracture progress directions based on each of the plurality of fracture progress directions and display the plurality of fracture progress directions used in deriving the fracture origin position), as shown on Figs. 11A-11D), for example), but fails to teach the following as further cited in claim 8.
However, ISOSAKI teaches selectively display a part of the plurality of fracture based on a certainty factor regarding each of the plurality of fracture and selectively display the part of the plurality of fracture (Par. [0032-45]: micro region observation unit 101 images the surface of the object to be analyzed 3 installed on the stage 4 by the imaging means 2 for a micro region, and stores the image in the microscopic observation image storage means 5. The observation image stored in the microscopic observation image storage means 5 is processed by the calculation means 6 for the degree of certainty of the fracture surface mode of the observation image and the calculation means 7 for the observation condition for improving the degree of certainty, and is displayed as information of the micro region on the observation recommendation condition visualization means 1 such as a monitor… FIG. 3 is a diagram illustrating an example of processing by the calculation means 6 for a degree of certainty of a fracture surface mode of an observation image… As in the example displayed in the small region R1 of FIG. 2, the observation image 201 is an entire image including an observation image fatigue fracture surface region 202, a ductile fracture surface region 203, and a crack initiation point 204, and a partial region 502 thereof is displayed in the small region R2 of FIG. 2. The micro region observation unit 101 performs observation for the image 207 of the partial region 502… When the micro region fracture surface image 207 is input to the calculation means 6 for a degree of certainty of a fracture surface mode of an observation image using a learned neural network, the degree of certainty with respect to various fracture mechanisms such as fatigue fracture and ductile fracture of the micro region fracture surface image 207 is output as an output to the display region… The calculation means 7 for an observation condition for improving a degree of certainty displays the processing result in the display region R3 of FIG. 2. In the display region R3 of FIG. 2, an operation direction, magnification, and the like are displayed as operation conditions for increasing the degree of certainty of observation… as a result, in processing steps S6 and S7, it is calculated that the degree of certainty in the case of the observation condition A is 0.95 and the degree of certainty in the case of the observation condition B is 0, 70 as the degree of certainty that the image is a fatigue fracture surface, and the calculated results are displayed in the small region; selectively display a part of the plurality of fracture based on a certainty factor regarding each of the plurality of fracture and selectively display the part of the plurality of fracture (e.g. calculate a degree of certainty of a fracture surface mode of an observation image (i.e. a certainty factor regarding each of the plurality of fracture), for example, including a partial region fracture surface region displayed as an output to the display region based on the degree of certainty with respect to various fracture mechanisms (i.e. selectively display a part of the plurality of fracture based on a certainty factor regarding each of the plurality of fracture and selectively display the part of the plurality of fracture), as indicated above), for example).
NAMEKAWA and ISOSAKI are considered to be analogous art because they pertain to image processing applications related to analysis of facture surfaces. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the facture detection device (as disclosed by NAMEKAWA) with selectively display a part of the plurality of fracture based on a certainty factor regarding each of the plurality of fracture and selectively display the part of the plurality of fracture (as taught by ISOSAKI, Abstract, Par. [0037-45) to provides a fracture surface analysis apparatus and a fracture surface analysis method and to improve a degree of certainty of a fracture mode to a visualization means (ISOSAKI, Abstract, Par. [0005-10, 32-42]).
Regarding claim 18, claim 11 is incorporated and is a corresponding method claim rejected as applied to the apparatus claim 8 above.
Conclusion
The prior art made of record cited in PTO-892 and not relied upon is considered pertinent to claims 7 and 17, respectively. However, due to the inability to determine a reasonable interpretation of claims 7 and 17, as indicated above, no prior art rejection or determination of allowability over the prior art was possible during examination.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GUILLERMO M RIVERA-MARTINEZ whose telephone number is (571) 272-4979. The examiner can normally be reached on 9 am to 5 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GUILLERMO M RIVERA-MARTINEZ/ Primary Examiner, Art Unit 2677
Prosecution Insights