Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment and Argument
Applicant’s arguments and claim amendments, see P. 7, filed 04/30/2026, with respect to claims 1, 8, and 15 have been fully considered and are persuasive. The 35 U.S.C. 101 abstract idea rejection of 03/17/2026 has been withdrawn.
Applicant’s amendments to claims 1, 8, and 15 have been considered but are moot in view of the new ground(s) of rejection in view of Suino (US 2008/0025605 A1).
Claim Status
Claims 1-20 are pending in the present application.
Claims 1, 8, 13, 15, and 19 are rejected under 35 U.S.C. 103 as being as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1) in view of Suino (US 2008/0025605 A1).
Claims 2-3, 6, 9-11, 13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1) in view of Suino (US 2008/0025605 A1) and FU (US 2025/0182443 A1).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1) in view of Suino (US 2008/0025605 A1) and Wu et al. (US 2019/0162712 A1).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1) in view of Suino (US 2008/0025605 A1) and Ted Pella Inc. (PELCO® Silicon Aperture Frames).
No prior art rejection is currently applied to claims 4, 12, and 18.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1, hereinafter Schlaudraff) in view of Suino (US 2008/0025605 A1, hereinafter Suino).
Regarding claim 1, Schlaudraff discloses
A system, comprising: a memory that stores computer executable components; and a processor that executes the computer executable components stored in the memory (Para [0019]: “The device for examining microscope specimens comprises hardware and/or software, for example, in the form of an image processor. The device can have a memory in which, for example, one or more identification codes and/or one or more images are stored.”), wherein the computer executable components comprise:
an imaging component that captures an image of an unknown sample support comprising a material layer (Para [0095]: “In a step 165, an image 114 representing an over view picture of the object 104 is initially preferably generated by a light microscope 130”); and
a matching component that matches the unknown sample support to a known sample support based on an unknown non-uniformity profile comprising one or more non-uniformities of the material layer in the image of the unknown sample support (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0030]: “The irregularity can be generated intentionally or unintentionally.”, Para [0031]: “According to an embodiment, the at least one optical marker can comprise or represent an irregularity of a coating of the specimen carrier in the image. Such a coating can comprise, for example, a nutritional layer, a carbon layer, a membrane that can be cut by a laser, for example, by means of laser microdissection, for example, made of or containing PEN and/or PET, and/or a carbon membrane. An irregularity which is used as the at least one optical marker can in particular be a damage of the coating and/or a contamination of the object and/or a region of the object. One example of a contamination is, for example, an ice crystal or an icy point in a cryogenic sample.”, Para [0076]: “The creation of a fingerprint can be simplified if the microscope specimen 102, in particular the specimen carrier 106, is provided with a regular or irregular pattern 116. Such a pattern can be, for example, a grid or a grid structure. The grid can be generated, for example, by applying a metal layer on the specimen carrier 106 or the region 105”, Para [0077]: “The optical marker 112 in the image 114 is then preferably an irregularity 118 of the pattern 116. The pattern can thus be provided, for example, with predetermined irregularities 118, for example, irregular markings, such as letters and/or numbers. The irregularity 118 can be generated intentionally or unintentionally. An irregularity 118 can be, for example, a damage of the pattern 116 and/or a contamination in the region 105. It is additionally or alternatively also possible that the pattern 116 has a quasirandom component, for example, a quasirandom code.”, Para [0078]: “An optical marker 112, as is used for the fingerprinting 110, can also be generated by one or more marker bodies 120 which are distributed stochastically over the region 105. The marker bodies can be introduced, scattered, and/or sprayed on. Such marker bodies or fiducials can have, for example, diameters in the range of a few micrometers or of fractions of micrometers. They can be fluorescent in particular.”, Para [0079]: “The region 105 can be at least partially provided with a coating 122. The coating 122 can be, for example, a nutritional layer or a carbon layer, for example, a carbon membrane. Alternatively or additionally, the specimen carrier 106 can be coated using gold. One or more irregularities 118 in the coating 122 can be used as optical markers 112, for example, one or more damages”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”).
However, Schlaudraff does not explicitly disclose
removes one or more pixels corresponding to an image background to reduce unused information.
Suino teaches
removes one or more pixels corresponding to an image background to reduce unused information (Para [0134]: “The background removal unit 2221 includes background removal means 52 for calculating a background removal threshold value for a target pixel using an averaging filter and performing a binarization process on the captured image data captured by the written image capturing unit 21 and removing a background area corresponding to an image area other than the written image area containing written characters and figures.”, Para [0136]: “The background removal unit 2221 uses the background removal means 52 to remove a background area including noise such as shadows as is shown in FIG. 6 to extract only the written image area containing characters and figures written on the writing screen 11b.”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schlaudraff with removing background pixel of Suino as both Schlaudraff and Suino are in the art of image analysis. In addition, removing background pixels is a routine process of image preprocessing in image analysis where a person with ordinary skill in the art can implement and achieve the predictable result of removing unwanted pixels in images.
Regarding claim 8, Schlaudraff discloses
A computer-implemented method, comprising: capturing, by a system operatively coupled to a processor, an image of an unknown sample support comprising a material layer (Para [0095]: “In a step 165, an image 114 representing an over view picture of the object 104 is initially preferably generated by a light microscope 130”); and
matching, by the system, the unknown sample support to a known sample support based on matching of one or more unknown non-uniformities of the material layer in the image of the unknown sample support to one or more known non-uniformities in an image of the known sample support (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0030]: “The irregularity can be generated intentionally or unintentionally.”, Para [0031]: “According to an embodiment, the at least one optical marker can comprise or represent an irregularity of a coating of the specimen carrier in the image. Such a coating can comprise, for example, a nutritional layer, a carbon layer, a membrane that can be cut by a laser, for example, by means of laser microdissection, for example, made of or containing PEN and/or PET, and/or a carbon membrane. An irregularity which is used as the at least one optical marker can in particular be a damage of the coating and/or a contamination of the object and/or a region of the object. One example of a contamination is, for example, an ice crystal or an icy point in a cryogenic sample.”, Para [0076]: “The creation of a fingerprint can be simplified if the microscope specimen 102, in particular the specimen carrier 106, is provided with a regular or irregular pattern 116. Such a pattern can be, for example, a grid or a grid structure. The grid can be generated, for example, by applying a metal layer on the specimen carrier 106 or the region 105”, Para [0077]: “The optical marker 112 in the image 114 is then preferably an irregularity 118 of the pattern 116. The pattern can thus be provided, for example, with predetermined irregularities 118, for example, irregular markings, such as letters and/or numbers. The irregularity 118 can be generated intentionally or unintentionally. An irregularity 118 can be, for example, a damage of the pattern 116 and/or a contamination in the region 105. It is additionally or alternatively also possible that the pattern 116 has a quasirandom component, for example, a quasirandom code.”, Para [0078]: “An optical marker 112, as is used for the fingerprinting 110, can also be generated by one or more marker bodies 120 which are distributed stochastically over the region 105. The marker bodies can be introduced, scattered, and/or sprayed on. Such marker bodies or fiducials can have, for example, diameters in the range of a few micrometers or of fractions of micrometers. They can be fluorescent in particular.”, Para [0079]: “The region 105 can be at least partially provided with a coating 122. The coating 122 can be, for example, a nutritional layer or a carbon layer, for example, a carbon membrane. Alternatively or additionally, the specimen carrier 106 can be coated using gold. One or more irregularities 118 in the coating 122 can be used as optical markers 112, for example, one or more damages”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”).
However, Schlaudraff does not explicitly disclose
removing, by the system, one or more pixels corresponding to an image background to reduce unused information.
Suino teaches
removing, by the system, one or more pixels corresponding to an image background to reduce unused information (Para [0134]: “The background removal unit 2221 includes background removal means 52 for calculating a background removal threshold value for a target pixel using an averaging filter and performing a binarization process on the captured image data captured by the written image capturing unit 21 and removing a background area corresponding to an image area other than the written image area containing written characters and figures.”, Para [0136]: “The background removal unit 2221 uses the background removal means 52 to remove a background area including noise such as shadows as is shown in FIG. 6 to extract only the written image area containing characters and figures written on the writing screen 11b.”).
Regarding claim 15, Schlaudraff discloses
A computer program product facilitating a process for sample support recognition, the computer program product comprising a computer readable storage medium having program instructions embodied therewith (Para [0019]: “The device for examining microscope specimens comprises hardware and/or software, for example, in the form of an image processor. The device can have a memory in which, for example, one or more identification codes and/or one or more images are stored.”), and the program instructions executable by a processor to cause the processor to:
compare, by the processor, an unknown non-uniformity profile comprising one or more non-uniformities of an unknown sample support, at an image of the unknown sample support, to a known non-uniformity profile of an image of a known sample support; and based on a result of the comparing, identify, by the processor, the unknown sample support as being the known sample support (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0030]: “The irregularity can be generated intentionally or unintentionally.”, Para [0031]: “According to an embodiment, the at least one optical marker can comprise or represent an irregularity of a coating of the specimen carrier in the image. Such a coating can comprise, for example, a nutritional layer, a carbon layer, a membrane that can be cut by a laser, for example, by means of laser microdissection, for example, made of or containing PEN and/or PET, and/or a carbon membrane. An irregularity which is used as the at least one optical marker can in particular be a damage of the coating and/or a contamination of the object and/or a region of the object. One example of a contamination is, for example, an ice crystal or an icy point in a cryogenic sample.”, Para [0076]: “The creation of a fingerprint can be simplified if the microscope specimen 102, in particular the specimen carrier 106, is provided with a regular or irregular pattern 116. Such a pattern can be, for example, a grid or a grid structure. The grid can be generated, for example, by applying a metal layer on the specimen carrier 106 or the region 105”, Para [0077]: “The optical marker 112 in the image 114 is then preferably an irregularity 118 of the pattern 116. The pattern can thus be provided, for example, with predetermined irregularities 118, for example, irregular markings, such as letters and/or numbers. The irregularity 118 can be generated intentionally or unintentionally. An irregularity 118 can be, for example, a damage of the pattern 116 and/or a contamination in the region 105. It is additionally or alternatively also possible that the pattern 116 has a quasirandom component, for example, a quasirandom code.”, Para [0078]: “An optical marker 112, as is used for the fingerprinting 110, can also be generated by one or more marker bodies 120 which are distributed stochastically over the region 105. The marker bodies can be introduced, scattered, and/or sprayed on. Such marker bodies or fiducials can have, for example, diameters in the range of a few micrometers or of fractions of micrometers. They can be fluorescent in particular.”, Para [0079]: “The region 105 can be at least partially provided with a coating 122. The coating 122 can be, for example, a nutritional layer or a carbon layer, for example, a carbon membrane. Alternatively or additionally, the specimen carrier 106 can be coated using gold. One or more irregularities 118 in the coating 122 can be used as optical markers 112, for example, one or more damages”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”).
However, Schlaudraff does not explicitly disclose
remove, by the processor, one or more pixels corresponding to an image background to reduce unused information.
Suino teaches
remove, by the processor, one or more pixels corresponding to an image background to reduce unused information (Para [0134]: “The background removal unit 2221 includes background removal means 52 for calculating a background removal threshold value for a target pixel using an averaging filter and performing a binarization process on the captured image data captured by the written image capturing unit 21 and removing a background area corresponding to an image area other than the written image area containing written characters and figures.”, Para [0136]: “The background removal unit 2221 uses the background removal means 52 to remove a background area including noise such as shadows as is shown in FIG. 6 to extract only the written image area containing characters and figures written on the writing screen 11b.”).
Regarding claim 19, dependent upon claim 15,
Schlaudraff further discloses
the image of the unknown sample support is an optical microscope image (Para [0082]: “The device 100 can have a light microscope 130 and/or an electron microscope 132 for generating the digital image 114.”), and
the one or more non-uniformities of the unknown sample support are provided at an internal material layer of the unknown sample support (Para [0031]: “According to an embodiment, the at least one optical marker can comprise or represent an irregularity of a coating of the specimen carrier in the image. Such a coating can comprise, for example, a nutritional layer, a carbon layer, a membrane that can be cut by a laser, for example, by means of laser microdissection, for example, made of or containing PEN and/or PET, and/or a carbon membrane. An irregularity which is used as the at least one optical marker can in particular be a damage of the coating and/or a contamination of the object and/or a region of the object. One example of a contamination is, for example, an ice crystal or an icy point in a cryogenic sample.”).
Claims 2-3, 6, 9-11, 13, 16-17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1, hereinafter Schlaudraff) in view of Suino (US 2008/0025605 A1, hereinafter Suino) and FU (US 2025/0182443 A1, hereinafter Fu).
Regarding claims 2 and 9, dependent upon claims 1 and 8 respectively, Schlaudraff in view of Suino teaches everything regarding claims 1 and 8.
Schlaudraff further discloses
a comparing component that compares the unknown non-uniformity profile of the image of the unknown sample support to a known non-uniformity profile of an image of the known sample support (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0030]: “The irregularity can be generated intentionally or unintentionally.”, Para [0031]: “According to an embodiment, the at least one optical marker can comprise or represent an irregularity of a coating of the specimen carrier in the image. Such a coating can comprise, for example, a nutritional layer, a carbon layer, a membrane that can be cut by a laser, for example, by means of laser microdissection, for example, made of or containing PEN and/or PET, and/or a carbon membrane. An irregularity which is used as the at least one optical marker can in particular be a damage of the coating and/or a contamination of the object and/or a region of the object. One example of a contamination is, for example, an ice crystal or an icy point in a cryogenic sample.”, Para [0076]: “The creation of a fingerprint can be simplified if the microscope specimen 102, in particular the specimen carrier 106, is provided with a regular or irregular pattern 116. Such a pattern can be, for example, a grid or a grid structure. The grid can be generated, for example, by applying a metal layer on the specimen carrier 106 or the region 105”, Para [0077]: “The optical marker 112 in the image 114 is then preferably an irregularity 118 of the pattern 116. The pattern can thus be provided, for example, with predetermined irregularities 118, for example, irregular markings, such as letters and/or numbers. The irregularity 118 can be generated intentionally or unintentionally. An irregularity 118 can be, for example, a damage of the pattern 116 and/or a contamination in the region 105. It is additionally or alternatively also possible that the pattern 116 has a quasirandom component, for example, a quasirandom code.”, Para [0078]: “An optical marker 112, as is used for the fingerprinting 110, can also be generated by one or more marker bodies 120 which are distributed stochastically over the region 105. The marker bodies can be introduced, scattered, and/or sprayed on. Such marker bodies or fiducials can have, for example, diameters in the range of a few micrometers or of fractions of micrometers. They can be fluorescent in particular.”, Para [0079]: “The region 105 can be at least partially provided with a coating 122. The coating 122 can be, for example, a nutritional layer or a carbon layer, for example, a carbon membrane. Alternatively or additionally, the specimen carrier 106 can be coated using gold. One or more irregularities 118 in the coating 122 can be used as optical markers 112, for example, one or more damages”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”).
However, Schlaudraff in view of Suino does not explicitly teach
generates a comparison score based on the comparing, wherein the comparison score comprises a value that defines a similarity level between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.
Fu teaches
generates a comparison score based on the comparing (Para [0169]: “comparing, by determining a similarity between, the template contour and the extracted contour points based on a plurality of distances between locations on the template contour and the extracted contour points, wherein the plurality of distances is adaptively weighted based on the locations on the template contour and whether the locations on the template contour overlap with blocking structures in the image”),
wherein the comparison score comprises a value that defines a similarity level between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support (Para [0172]: “determining a coarse similarity score based on a weighted sum of the plurality of distances multiplied by the total weights”).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schlaudraff in view of Suino with generating comparison score that defines a similarity between contours of Fu to effectively increase the accuracy and throughput in defect detection and inspection.
Regarding claims 3 and 10, dependent upon claims 2 and 9 respectively, Schlaudraff in view of Suino and Fu teaches everything regarding claims 2 and 9.
Fu further teaches
wherein the matching component further matches the unknown sample support to the known sample support based on the comparison score comprising a highest value as compared to one or more other comparison scores generated by the comparing component relative to another image of another unknown sample support or of another known sample support (Para [0173]: “repeating the multiplying and determining the coarse similarity score operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized coarse position of the template contour relative to the extracted contour points”, Para [0178]: “repeating the adjusting, the multiplying, and the determining the first and second fine similarity operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized fine position of the template contour relative to the extracted contour points”, Para [0179]: “based on the comparing, determining a matching geometry or a matching position of the template contour with the extracted contour points from the image”).
Regarding claim 6, dependent upon claims 2, Schlaudraff in view of Suino and Fu teaches everything regarding claims 2.
Schlaudraff further discloses
an obtaining component that obtains a set of images of known sample supports, including the image of the known sample support (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”)
Regarding claims 11, dependent upon claims 9, Schlaudraff in view of Suino and Fu teaches everything regarding claims 9.
Fu further teaches
wherein the matching further comprises: aggregating, by the system, the comparison score with a parameter score that is based on a comparison of a secondary parameter of the unknown sample support, related to other than the one or more unknown non-uniformities, and a corresponding secondary parameter of the known sample support, related to other than the one or more known non-uniformities (Para [0176]: “determining a first fine similarity score based on a weighted sum of the plurality of distances multiplied by the total weights”, Para [0177]: “determining a second fine similarity score based on a weighted sum of the plurality of distances multiplied by the total weights only for unblocked locations on the contour that do not overlap with the blocking structures”, Para [0179]: “based on the comparing, determining a matching geometry or a matching position of the template contour with the extracted contour points from the image.”), and
matching, by the system, the unknown sample support to the known sample support based on an aggregated score, resulting from the aggregating, comprising a highest value as compared to one or more other aggregated scores, generated by the comparing and aggregating, relative to another image of another unknown sample support or of another known sample support (Para [0173]: “repeating the multiplying and determining the coarse similarity score operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized coarse position of the template contour relative to the extracted contour points”, Para [0178]: “repeating the adjusting, the multiplying, and the determining the first and second fine similarity operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized fine position of the template contour relative to the extracted contour points”, Para [0179]: “based on the comparing, determining a matching geometry or a matching position of the template contour with the extracted contour points from the image”).
Regarding claims 13, dependent upon claims 9, Schlaudraff in view of Suino and Fu teaches everything regarding claims 9.
Schlaudraff further discloses
updating, by the system, a data record associated with the known sample to reference the unknown sample (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”; It can be inferred from this step that once the current image's identification code is generated, it now becomes part of the comparison for the next image, which includes updating the memory to associate the current unknown identification code to known identification code).
Regarding claim 16, dependent upon claim 15, Schlaudraff in view of Suino teaches everything regarding claim 15
However, Schlaudraff in view of Suino does not explicitly teach
generate, by the processor, a comparison score based on the comparing, wherein the comparison score comprises a value that defines a similarity level between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support.
Fu teaches
generate, by the processor, a comparison score based on the comparing (Para [0169]: “comparing, by determining a similarity between, the template contour and the extracted contour points based on a plurality of distances between locations on the template contour and the extracted contour points, wherein the plurality of distances is adaptively weighted based on the locations on the template contour and whether the locations on the template contour overlap with blocking structures in the image”),
wherein the comparison score comprises a value that defines a similarity level between the unknown non-uniformity profile of the unknown sample support and the known non-uniformity profile of the known sample support (Para [0172]: “determining a coarse similarity score based on a weighted sum of the plurality of distances multiplied by the total weights”).
Regarding claim 17, dependent upon claims 16, Schlaudraff in view of Suino and Fu teaches everything regarding claim 16.
Fu further teaches
match, by the processor, the unknown sample support to the known sample support based on the comparison score comprising a highest value as compared to one or more other comparison scores, generated by the comparing, relative to another image of another unknown sample support or of another known sample support (Para [0173]: “repeating the multiplying and determining the coarse similarity score operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized coarse position of the template contour relative to the extracted contour points”, Para [0178]: “repeating the adjusting, the multiplying, and the determining the first and second fine similarity operations for multiple geometries or positions of the template contour relative to the extracted contour points to determine an optimized fine position of the template contour relative to the extracted contour points”, Para [0179]: “based on the comparing, determining a matching geometry or a matching position of the template contour with the extracted contour points from the image”).
Regarding claim 20, dependent upon claims 16, Schlaudraff in view of Suino and Fu teaches everything regarding claim 16.
Schlaudraff further discloses
determine, by the processor, a match of a second unknown sample support to a second known sample support based on the result of the comparing (Para [0020]: “identify the specimen carrier or the object by comparing the calculated digital identification code to a stored and/or (previously) calculated identification code or to calculate a digital identification code for a stored image by fingerprinting and to compare the two identification codes. The stored identification code can represent an image or can have been calculated on the basis of at least one image which has a different resolution, location, orientation, and/or enlargement than the at least one image for ascertaining the (previously) calculated identification code.”, Para [0096]: “On the basis of at least one image 114, preferably on the basis of a plurality of images 114, the digital identification code 108 of the object 104 or of the specimen carrier 106 carrying the object 104 is then calculated by fingerprinting 110. The calculation takes place in a data processing system 168, for example, a PC”, Para [0098]: “In this case, as already mentioned, a plurality of different images 114 of the microscope specimens can be created to generate the identification code 108 from this plurality. The identification codes 108a of the different images 114 can be contained in the identification code 108, for example, in the form of a blockchain 171. The individual images 114 can have a different magnification and/or resolution here. The images 114 can have been recorded in different color channels and different color spaces. Furthermore, the images 114 can represent different regions of the region 105 and/or the object 104. The identification code 108 can be calculated by applying a hash function to an image, a subset of the plurality of the images 114, or all images 114.”).
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1, hereinafter Schlaudraff) in view of Suino (US 2008/0025605 A1, hereinafter Suino) and Wu et al. (US 2019/0162712 A1, hereinafter Wu).
Regarding claims 7 and 14, dependent upon claims 1 and 8 respectively, Schlaudraff in view of Suino teaches everything regarding claims 1 and 8.
However, Schlaudraff in view of Suino does not explicitly teach
a notifying component that generates a notification defining whether the match was generated corresponding to the unknown sample support.
Wu teaches
a notifying component that generates a notification defining whether the match was generated corresponding to the unknown sample support (Para [0085]: “The extracted second feature quantity and the extracted feature quantity of the culture vessel 10b are checked against the first feature quantity and the feature quantity of the culture vessel 10a which are stored in the fertilized egg information DB 35 (Step 116). That is, the comparison unit 32 compares the first feature quantity and the second feature quantity with each other. On a basis of that comparison result, the determination unit 33 determines whether or not the transfer of the fertilized egg 5 is normal.”, Para [0094]: “The notification unit 34 makes notification of the determination result of the determination unit 33 (Step 120).”)
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schlaudraff in view of Suino with producing notification of the determination result of Wu since both Schlaudraff and Wu are related to comparing microscopic images. In addition in Para [0115], Schlaudraff’s system comprises a display and one or more loud speaker; and Wu’s notification, as stated in Para [0094], is made by using an image and a sound. As such, it is reasonable for person of ordinary skill in the art to produce the notification of Wu on and through Schlaudraff’s display and speaker.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over SCHLAUDRAFF (US 2023/0161146 A1, hereinafter Schlaudraff) in view of Suino (US 2008/0025605 A1, hereinafter Suino) and Ted Pella Inc. (PELCO® Silicon Aperture Frames, hereinafter Ted Pella).
Regarding claim 5, dependent upon claim 1, Schlaudraff in view of Suino teaches everything regarding claim 1.
Schlaudraff further discloses
wherein the image of the unknown sample support is an optical microscope image (Para [0082]: “The device 100 can have a light microscope 130 and/or an electron microscope 132 for generating the digital image 114”).
However, Schlaudraff in view of Suino does not explicitly teach
the material layer comprises a silicon frame
Ted Pella teaches
the material layer comprises a silicon frame (P. 2 Ultra-Flat SiNx Coated Substrates: 5 x 5mm Diced Ultra-Flat SiNx on Silicon Substrates
PNG
media_image1.png
254
233
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Greyscale
).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Schlaudraff in view of Suino with SiNx frame on top of a SiNx coated Wafer of Ted Pella as Schlaudraff suggested in Para [0016]: “The specimen carrier is preferably made of glass or another material transmissive to light and electron beams, so that the images can be generated using greatly varying electromagnetic radiation.” This can be observed in the image of the Gel-Pak® box as it had a transparent lid with 5 x 5 mm SiNx frame.
Relevant Prior Art Directed to State of Art
Liu et al. (US 9,874,526 B2, hereinafter Liu) is prior art not applied in the rejection(s) above. Liu discloses a system that comprises an illumination optics subsystem for generating and directing an incident beam towards a defect on a surface of a wafer.
Gallarda et al. (US 6,539,106 B1, hereinafter Gallarda) is prior art not applied in the rejection(s) above. Gallarda discloses methods and apparatus for inspecting a patterned substrate, comprising: preparing a reference image and a test image, extracting features from the reference image and extracting features from the test image, matching features of the reference image and features of the test image; and comparing features of the reference image and of the test image to identify defects.
Allowable Subject Matter
Claims 4, 12, and 18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J. C./Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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