DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on September 9, 2026 has been entered.
Priority
3. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Amendment
4. The amendment filed September 9, 2026 has been entered. Claims 1-11 remain pending in the application. Applicant’s amendments to the Claims have overcome each and every objection and 35 U.S.C. 112(a) rejections previously made in the Final rejection mailed March 9, 2026.
Response to Arguments
5. Applicant's arguments filed September 9, 2026 have been fully considered but they are not persuasive.
6. Applicant argues that Nakagawa (WIPO Patent Application Publication No. 2020/071162 A1 -- IDS) and Alasia et al. (U.S. Patent Application Publication No. 2008/0267514 A1), hereinafter referred to as Alasia, do not teach “completing processing within the manufacturing site, without transferring information externally”. The Applicant argues that Nakagawa does not teach where the learning process is performed, and that Alasia teaches on-site authentication processing rather than on-site image anomaly detection. The Applicant also argues that Alasia does not teach further implementation details of such a process.
Examiner replies that claims 1, 9, and 11 do not claim that the “processing, within a manufacturing site” is on-site image anomaly detection. Rather, the claims only claim that the processing corresponds to the selected correction. This selected correction is taught by Nakagawa in Paragraph 47.
Alasia is used to teach processing an image within a manufacturing site which Alasia does teach in Paragraph 17, stating “the captured image may be downloaded and processed on-site”. Combined with Nakagawa, this processing teaches the selected correction taught by Nakagawa. Thus, Nakagawa and Alasia combined teaches performing “processing, within a manufacturing site, corresponding to the selected correction”.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Furthermore, in response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., "completing processing within the manufacturing site, without transferring information externally") are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Thus, the scope of the claim as amended allows for information to be transferred externally as long as some processing is completed within the manufacturing site.
7. Applicant argues that Nakagawa and Alasia do not teach “learning is performed within the manufacturing site, without transferring information externally”. The Applicant argues Nakagawa does not specify where the learning process is performed. The Applicant further asserts that the essence of the application is not in “in-device learning” but rather in “learning control structure based on initiation information”.
Examiner replies that Nakagawa does teach in Paragraph 17, "in learning mode, the training data generation unit 12 generates training data based on the image of the object to be inspected 2 taken by the inspection unit 10, etc., and this training data is input to the machine learning execution unit 14 to train the trained model". This teaches the training is executed within the machine learning execution unit 14. Figure 1 and Paragraph 16 teach the inspection device, or anomaly display device, comprises the control unit 11 which also comprises both the training data generation unit 12 and the machine learning unit 14. This teaches that the machine learning unit and training data generation unit are within the anomaly display device. Thus, since the machine learning unit and training data generation unit are in the anomaly display device and conduct training, Nakagawa teaches training within the anomaly display device.
Furthermore, In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “learning is performed within the manufacturing site, without transferring information externally” and “learning control structure based on initiation information”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Thus, the scope of the claim as amended allows for information to be transferred externally as long as some training is performed within the anomaly device.
Claim Rejections - 35 USC § 112
8. 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.
9. Claim 1-11 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the learning" in lines 15, 17, 19, and 21. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the learning" in lines 15, 17, 19, and 21. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the learning" in lines 10. There is insufficient antecedent basis for this limitation in the claim.
Claims 2-8 and 10 are rejected by dependency on claims 1 and 9.
Claims 1-11 will be examined as best understood by the Examiner.
Claim Rejections - 35 USC § 103
10. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
11. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
12. Claim(s) 1-3 and 8-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Im (Japanese Patent Application Publication No. 2013-205071 A -- IDS) in view of Nakagawa (WIPO Patent Application Publication No. 2020/071162 A1 -- IDS) and Alasia et al. (U.S. Patent Application Publication No. 2008/0267514 A1), hereinafter referred to as Alasia.
13. Regarding claim 1, Im teaches an anomaly display device comprising a processor configure to execute a program to (Paragraph 31 teaches a control unit that is a general-purpose computer and equipped with a CPU that executes functions):
acquire starting information including information for starting an anomaly detection for detecting an anomaly in an image included in an input picture (Paragraphs 35-36 teach a device that acquires information about the distance data of the object or substrate in order to start the process of taking photos for anomaly detection. This information can be considered part of the starting information; Paragraph 40 teaches starting the inspection or anomaly detection using information, or starting information, that includes controlling the XY movement to inspect the entire object. Thus, the starting information is acquired to start anomaly detection);
acquire the input picture (Paragraph 29 teaches a camera that can capture an image of the object; Paragraph 40 teaches capturing images of the object with a camera; Paragraph 52 teaches the optical image inspection means acquires the input image from the second storage in order to start the anomaly detection. The optical image inspection means can be considered the input picture acquisition unit);
execute the anomaly detection, based on the acquired starting information, by comparing the acquired input picture with information based on a prescribed normal picture (Paragraph 52 teaches comparing pixels in the color image data to the data in a master image. The master image can be considered a prescribed normal picture and its data is the information based on a prescribed normal picture. The color image data can be considered as part of the acquired input picture);
display, in overlay on the input picture, information based on information detected by the anomaly detection (Paragraph 53 teaches displaying the anomaly information detected in overlay through a circle or arrow on the image).
However, Im does not teach: wherein the acquired starting information comprises: training execution selection information indicating whether the anomaly detection will perform training within the anomaly display device based on the prescribed normal picture or perform anomaly detection, and an information selecting what correction to make to the prescribed normal picture, or whether to make no correction, when performing the learning; wherein the processor is further configured to execute the program to: perform either the learning or the anomaly detection based on the training execution selection information included in the starting information; and when performing the learning, if its selected to make correction to the prescribed normal picture, perform processing, within a manufacturing site, corresponding to the selected correction on the prescribed normal picture before performing the learning.
Nakagawa teaches wherein the acquired starting information comprises: training execution selection information indicating whether the anomaly detection will perform training within the anomaly display device based on the prescribed normal picture or perform anomaly detection (Paragraph 17 teaches that the device has an inspection mode and a learning mode. The inspection mode is executing the anomaly detection and the learning mode is performing training. Paragraphs 45 and 53 teach that the user can select the mode on an operation interface. The user selection of the mode teaches the training execution selection information which is part of the acquired starting information. If the user selects the learning mode, then the anomaly display device will perform training based on training data which are the prescribed normal pictures, mentioned in Paragraph 5. If the user selects the inspection mode, then the anomaly display device will execute the anomaly detection; Paragraph 17 teaches "in learning mode, the training data generation unit 12 generates training data based on the image of the object to be inspected 2 taken by the inspection unit 10, etc., and this training data is input to the machine learning execution unit 14 to train the trained model". This teaches the training is executed within the machine learning execution unit 14. Figure 1 and Paragraph 16 teach the inspection device 1, or anomaly display device, comprises the control unit 11 which also comprises both the training data generation unit 12 and the machine learning unit 14. This teaches that the machine learning unit and training data generation unit are within the anomaly display device. Thus, since the machine learning unit and training data generation unit are in the anomaly display device and conduct training, Nakagawa teaches training within the anomaly display device), and an information selecting what correction to make to the prescribed normal picture, or whether to make no correction, when performing the learning (Paragraph 46-47 teaches when in learning mode, an image processing condition can be selected and performed on the input image. The input image teaches the prescribed normal picture and the selection of the image processing condition teaches the information selecting what correction to make; Paragraph 29 teaches the image processing unit can perform corrections like filtering or reducing noise);
wherein the processor is further configured to execute the program to: perform either the learning or the anomaly detection based on the training execution selection information included in the starting information (Paragraph 17 teaches that the device has an inspection mode and a learning mode. The inspection mode is executing the anomaly detection and the learning mode is performing training. If the user selects the learning mode, then the anomaly display device will perform training based on training data which are the prescribed normal pictures, mentioned in Paragraph 5. If the user selects the inspection mode, then the anomaly display device will execute the anomaly detection); and when performing the learning, if its selected to make correction to the prescribed normal picture, perform processing, (Paragraph 47 teaches performing image processing or a correction to the prescribed normal picture when an image processing condition is selected. The processed image is then output. Paragraphs 48-52 teach operating on this processed or corrected prescribed normal picture for learning. Thus, the prescribed normal picture is corrected before performing the learning).
Im and Nakagawa are considered analogous to the claimed invention because both are in the same field of anomaly detection. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im with the selection of training or anomaly detection taught by Nakagawa in order to quickly generate large amounts of training data while the model is in training mode and then perform highly accurate inspections with the trained model (Nakagawa Paragraph 56).
However, Im and Nakagawa do not teach: perform processing, within a manufacturing site.
Alasia teaches perform processing, within a manufacturing site (Paragraph 17 “the captured image may be downloaded and processed on-site”. This teaches the image processing can happen within a manufacturing site).
The limitation only claims that the image processing corresponds to the selected correction, not image anomaly detection. The selected correction is taught by Nakagawa in Paragraph 47.
Alasia is used to teach processing an image within a manufacturing site. Combined with Nakagawa, this processing teaches the selected correction taught by Nakagawa. Nakagawa and Alasia combined teaches performing “processing, within a manufacturing site, corresponding to the selected correction”.
Im and Nakagawa are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Nakagawa with processing on-site taught by Alasia in order to allow for a user to carry out inspection on products immediately on-site (Alasia Paragraph 63).
14. Regarding claim 2, Im in view of Nakagawa and Alasia teaches the limitations of claim 1. Im further teaches the anomaly display device wherein: the acquired starting information further includes a path indicating a location at which the input picture is stored (Paragraph 40 teaches the captured image data, which is the input picture, is stored in the second storage. The instructions for the visual inspection device to store the input picture in the second storage can be considered part of the acquired starting information);
and the processor is further configured to execute the program to acquire the input picture stored at the location indicated by the path (Paragraph 52 teaches the optical inspection means acquires the input image from the second storage in order to start the anomaly detection. Thus, the optical inspection means had instructions to acquire the input picture at the location indicated by the path which is the second storage. The optical inspection means includes the input picture acquisition unit).
15. Regarding claim 3, Im in view of Nakagawa and Alasia teaches the limitations of claim 1. Im further teaches the anomaly display device wherein: the acquired starting information includes an image capture starting signal for capturing the image (Paragraph 40 teaches the visual inspection device controls the camera to take images. Thus, this means there is a signal sent to the camera to capture the images. It also has information or instructions to store the image in the second storage);
and the processor is further configured to execute the program to acquire the input picture obtained by the image being captured (Paragraph 52 teaches the optical inspection means acquires the input image from the second storage in order to start the anomaly detection).
16. Regarding claim 8, Im in view of Nakagawa and Alasia teaches the limitations of claim 1. However, Im fails to teach the anomaly display device wherein: the starting information includes the training execution selection information indicating whether the anomaly detection is to perform training based on a prescribed normal picture or is to execute the anomaly detection; and the anomaly detection executes either the training or the anomaly detection based on the training execution selection information included in the starting information.
Nakagawa teaches the anomaly display device wherein: the starting information includes the training execution selection information indicating whether the anomaly detection is to perform training based on a prescribed normal picture or is to execute the anomaly detection; and the anomaly detection executes either the training or the anomaly detection based on the training execution selection information included in the starting information (Paragraph 17 teaches that the device has an inspection mode and a learning mode. The inspection mode is executing the anomaly detection and the learning mode is performing training. Paragraphs 45 and 53 teach that the user can select the mode on an operation interface. The user selection of the mode can be considered part of the starting information. If the user selects the learning mode, then the anomaly display device will perform training based on training data which can be considered the prescribed normal pictures, mentioned in Paragraph 5. IF the user selects the inspection mode, then the anomaly display device will execute the anomaly detection).
Im and Nakagawa are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Alasia with the selection of training or anomaly detection taught by Nakagawa in order to quickly generate large amounts of training data while the model is in learning mode and then perform highly accurate inspections with the trained model (Nakagawa Paragraph 56).
17. Regarding claim 9, Im teaches a non-transitory computer-readable storage medium storing an anomaly display program causing a computer to implement the steps of (Paragraph 31 teaches a control unit that is a general-purpose computer and equipped with a CPU and memories like a hard disk and RAM to execute functions):
acquiring starting information including information for starting an anomaly detection for detecting an anomaly in an image included in an input picture (Paragraphs 35-36 teach a device that runs an anomaly display program. It acquires information about the distance data of the object or substrate in order to start the process of taking photos for anomaly detection. This information can be considered part of the starting information; Paragraph 40 teaches starting the inspection or anomaly detection using information, or starting information, that includes controlling the XY movement to inspect the entire object. Thus, the starting information is acquired to start anomaly detection);
acquiring the input picture (Paragraph 29 teaches a camera that can capture an image of the object; Paragraph 40 teaches capturing images of the object with a camera; Paragraph 52 teaches the optical image inspection means acquires the input image from the second storage in order to start the anomaly detection. The optical image inspection means can be considered the input picture acquisition unit);
executing the anomaly detection, based on the acquired starting information, by comparing the acquired input picture with a prescribed normal picture (Paragraph 52 teaches comparing pixels in the color image data to the data in a master image. The master image can be considered a prescribed normal picture. The color image data can be considered as part of the acquired input picture);
displaying, in overlay on the input picture, information based on information detected by the anomaly detection (Paragraph 53 teaches displaying the anomaly information detected in overlay through a circle or arrow on the image).
However, Im does not teach: wherein the acquired starting information comprises: training execution selection information indicating whether the anomaly detection will perform training within the anomaly display device based on the prescribed normal picture or perform anomaly detection, and an information selecting what correction to make to the prescribed normal picture, or whether to make no correction, when performing the learning; wherein the processor is further configured to execute the program to: perform either the learning or the anomaly detection based on the training execution selection information included in the starting information; and when performing the learning, if its selected to make correction to the prescribed normal picture, perform processing, within a manufacturing site, corresponding to the selected correction on the prescribed normal picture before performing the learning.
Nakagawa teaches wherein the acquired starting information comprises: training execution selection information indicating whether the anomaly detection will perform training within the anomaly display device based on the prescribed normal picture or perform anomaly detection (Paragraph 17 teaches that the device has an inspection mode and a learning mode. The inspection mode is executing the anomaly detection and the learning mode is performing training. Paragraphs 45 and 53 teach that the user can select the mode on an operation interface. The user selection of the mode teaches the training execution selection information which is part of the acquired starting information. If the user selects the learning mode, then the anomaly display device will perform training based on training data which are the prescribed normal pictures, mentioned in Paragraph 5. If the user selects the inspection mode, then the anomaly display device will execute the anomaly detection; Paragraph 17 teaches "in learning mode, the training data generation unit 12 generates training data based on the image of the object to be inspected 2 taken by the inspection unit 10, etc., and this training data is input to the machine learning execution unit 14 to train the trained model". This teaches the training is executed within the machine learning execution unit 14. Figure 1 and Paragraph 16 teach the inspection device 1, or anomaly display device, comprises the control unit 11 which also comprises both the training data generation unit 12 and the machine learning unit 14. This teaches that the machine learning unit and training data generation unit are within the anomaly display device. Thus, since the machine learning unit and training data generation unit are in the anomaly display device and conduct training, Nakagawa teaches training within the anomaly display device), and an information selecting what correction to make to the prescribed normal picture, or whether to make no correction, when performing the learning (Paragraph 46-47 teaches when in learning mode, an image processing condition can be selected and performed on the input image. The input image teaches the prescribed normal picture and the selection of the image processing condition teaches the information selecting what correction to make; Paragraph 29 teaches the image processing unit can perform corrections like filtering or reducing noise);
wherein the processor is further configured to execute the program to: perform either the learning or the anomaly detection based on the training execution selection information included in the starting information (Paragraph 17 teaches that the device has an inspection mode and a learning mode. The inspection mode is executing the anomaly detection and the learning mode is performing training. If the user selects the learning mode, then the anomaly display device will perform training based on training data which are the prescribed normal pictures, mentioned in Paragraph 5. If the user selects the inspection mode, then the anomaly display device will execute the anomaly detection); and when performing the learning, if its selected to make correction to the prescribed normal picture, perform processing, (Paragraph 47 teaches performing image processing or a correction to the prescribed normal picture when an image processing condition is selected. The processed image is then output. Paragraphs 48-52 teach operating on this processed or corrected prescribed normal picture for learning. Thus, the prescribed normal picture is corrected before performing the learning).
Im and Nakagawa are considered analogous to the claimed invention because both are in the same field of anomaly detection. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the non-transitory computer-readable storage medium storing an anomaly display program taught by Im with the selection of training or anomaly detection taught by Nakagawa in order to quickly generate large amounts of training data while the model is in learning mode and then perform highly accurate inspections with the trained model (Nakagawa Paragraph 56).
However, Im and Nakagawa do not teach: perform processing, within a manufacturing site.
Alasia teaches perform processing, within a manufacturing site (Paragraph 17 “the captured image may be downloaded and processed on-site”. This teaches the image processing can happen within a manufacturing site).
The limitation only claims that the image processing corresponds to the selected correction, not image anomaly detection. The selected correction is taught by Nakagawa in Paragraph 47.
Alasia is used to teach processing an image within a manufacturing site. Combined with Nakagawa, this processing teaches the selected correction taught by Nakagawa. Nakagawa and Alasia combined teaches performing “processing, within a manufacturing site, corresponding to the selected correction”.
Im and Nakagawa are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the non-transitory computer-readable storage medium storing an anomaly display program taught by Im in view of Nakagawa with processing on-site taught by Alasia in order to allow for a user to carry out inspection on products immediately on-site (Alasia Paragraph 63).
18. Regarding claim 10, Im teaches an anomaly display system a processor configured to execute a program to (Paragraph 31 teaches a control unit that is a general-purpose computer and equipped with a CPU that executes functions):
capture an image (Paragraph 29 teaches a camera that can capture an image of the object; Paragraph 40 teaches capturing images of the object with a camera. The camera is the image capture unit);
and wherein the anomaly display system comprise the anomaly display device according to claim 3 (See rejection for claim 3 above),
which executes the anomaly detection on the input picture (Paragraph 52 teaches comparing pixels in the color image data to the data in a master image to detect abnormal pixels or anomalies), and which displays information obtained as a result of the execution (Paragraph 53 teaches displaying the anomaly information detected in overlay through a circle or arrow on the image).
19. Regarding claim 11, claim 11 is the method claim of anomaly display program claim 9 and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 9.
20. Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Im (Japanese Patent Application Publication No. 2013-205071 A -- IDS) in view of Nakagawa (WIPO Patent Application Publication No. 2020/071162 A1 -- IDS) and Alasia et al. (U.S. Patent Application Publication No. 2008/0267514 A1), hereinafter referred to as Alasia, as applied to claim 1 above, and further in view of Omura (Japanese Patent Application Publication No. 2018-084443 A -- IDS).
21. Regarding claim 4, Im in view of Nakagawa and Alasia teaches the limitations of claim 1. Im further teaches the anomaly display device, wherein the processor is further configured to execute the program to: (Paragraph 52 teaches comparing pixels in the color image data to the data in a master image. The master image can be considered a prescribed normal picture. The color image data can be considered part of the input picture).
However, Im fails to teach correcting the input picture.
Omura teaches correcting the input picture (Paragraph 35 teaches correcting the input image based on a comparison with a reference picture).
Im, Nakagawa, and Omura are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Nakagawa and Alasia with the correction unit taught by Omura in order to improve the accuracy in analyzing an object (Omura Paragraph 6).
22. Regarding claim 5, Im in view of Nakagawa, Alasia, and Omura teaches the limitations of claim 4. However, Im fails to teach the anomaly display device, wherein the processor is further configured to execute the program to acquire correction selection information acquisition for selecting a type of correction process to be executed.
Omura teaches the anomaly display device wherein the processor is further configured to execute the program to acquire correction selection information acquisition for selecting a type of correction process to be executed (Paragraph 35 teaches a correction selection information that selects a method based on the comparison result of the input image pixel and reference image. If it is less than a threshold, it will convert the pixel to the reference pixel. Thus, the correction selection information that chooses a type of correction process is acquired through the comparison result).
Im, Nakagawa, and Omura are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Nakagawa and Alasia with the correction unit taught by Omura in order to improve the accuracy in analyzing an object (Omura Paragraph 6).
23. Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Im (Japanese Patent Application Publication No. 2013-205071 A -- IDS) in view of Nakagawa (WIPO Patent Application Publication No. 2020/071162 A1 -- IDS) and Alasia et al. (U.S. Patent Application Publication No. 2008/0267514 A1), hereinafter referred to as Alasia, as applied to claim 1 above, and further in view of Wang (Chinese Patent Application Publication No. 112418353 A -- IDS).
Regarding claim 6, Im in view of Nakagawa and Alasia teaches the limitations of claim 1. However, Im fails to teach the anomaly display device wherein the anomaly detection is pretrained by a prescribed normal picture.
Wang teaches the anomaly display device wherein the anomaly detection is pretrained by a prescribed normal picture (Paragraph 29 teaches a training image set consists of images that pass the inspection, thus having no anomaly).
Im, Nakagawa, and Wang are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Nakagawa and Alasia with the training taught by Wang in order to train the detection unit to know what are positive samples and what are abnormal features (Wang Paragraph 10).
24. Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Im (Japanese Patent Application Publication No. 2013-205071 A -- IDS) in view of Nakagawa (WIPO Patent Application Publication No. 2020/071162 A1 -- IDS) and Alasia et al. (U.S. Patent Application Publication No. 2008/0267514 A1), hereinafter referred to as Alasia, as applied to claim 1 above, and further in view of Saito et al. (Japanese Patent Application Publication No. 2007-132757 A -- IDS), hereinafter referred to as Saito.
Regarding claim 7, Im, Nakagawa and Alasia teaches the limitations of claim 1. However, Im fails to teach the anomaly display device wherein: the information based on the prescribed normal picture includes information on a mean value vector and variance of the prescribed normal picture; and the processor is further configured to execute the program to perform anomaly detection corresponding to multiple split regions, which are regions into which the input picture has been split; and display, in overlay on the input picture, the information detected, so as to be associated with the multiple split regions.
Saito teaches the anomaly display device wherein: the information based on the prescribed normal picture includes information on a mean value vector and variance of the prescribed normal picture (Paragraph 78 teaches calculating an average vector, or mean value vector, and a covariance matrix, or variance, from the images of non-defective products. Thes values are set as feature reference values. The images of non-defective products are the prescribed normal pictures. Thus, the feature reference values which have information on the mean value vector and variance are based on a prescribed normal picture);
and the processor is further configured to execute the program to perform anomaly detection corresponding to multiple split regions, which are regions into which the input picture has been split (Paragraph 11 teaches an area division step which divides the picture into multiple split regions);
and display, in overlay on the input picture, the information detected, so as to be associated with the multiple split regions (Paragraph 109-111 teach splitting the image into multiple regions and then if a defect is found during that process, it will display the location on the monitor. Displaying the location of the defect associates it with at least one of the split regions since each split region has its own location).
Im, Nakagawa, and Saito are considered analogous to the claimed invention because both are in the same field of anomaly detection. Alasia is considered analogous to the claimed invention because it is in the same field of processing an object and determining whether it meets certain criteria. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the anomaly display device taught by Im in view of Nakagawa and Alasia with the prescribed normal picture information and splitting the input image into regions as taught by Saito in order to identify defects that are not just local but also global (Saito Paragraphs 9-10).
Conclusion
25. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
- Liu et al. (U.S. Patent Application Publication No. 2019/0266437 A1) teaches anomalies within images through comparison.
- Nefian et al. (U.S. Patent Application Publication No. 2024/0005491 A1) teaches identifying anomalies in images by splitting an image and calculating the mean and variance of each split region.
26. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 9-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571)272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/JAMES A THOMPSON/Primary Examiner, Art Unit 2615
