DETAILED ACTIONS
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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 03/19/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
This office action is in response to the amendments/arguments submitted by the Applicant(s) on 03/19/2026.
Status of the Claims
Claims 1-2, 5-6, 9-10, 12-13, and 15-16 are pending.
Claims 1,5-6, 9, 12-13 are amended.
Claims 3-4, 7-8, 11, and 14 are cancelled
Response to Arguments
Rejections Under 35 U.S.C. §103
Applicant's arguments, see remarks pages 6-7, filed 03/19/2026.
with respect to the rejection(s) of Claims under 35 U.S.C.§103 has been considered, and are moot because the amendment has necessitated a new ground of rejections. The new rejections are set forth below.
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 1-2, 5-6, 9-10,12, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over OOTA et al. (US 2019/0325606 A1, hereinafter Oota, previously cited) and in of view of Sudo Kazuyuki (JP 2019-29273 A, hereinafter Sudo).and in further view of Sachihiro Nakagawa (US 2022/0318985 A1, hereinafter Nakagawa)
Regarding Claim 1, Oota teaches,
An inspection device (Oota, Figure 1, Inspection apparatus, 1) comprising:
an image storage unit (Oota, Figure 1, 12,13,14, [0023]) that captures a plurality of images (Oota, Figure 1, [0027], “image data input from the imaging device 70”) having different input channels for an inspection object (Oota, Figure 2, Imaging unit 102) under a predetermined imaging condition corresponding to each input channel (Oota, Figure 2, Imaging condition 1, condition 2, and Image condition N), and
stores multiple inspection images obtained by the capturing and combining the plurality of images of the inspection object (Oota, Figure 1, [0027], “The nonvolatile memory 14 stores data input from the input/output device 60, image data input from the
imaging device 70 via the interface 19, and the like”); and
a determination unit (Oota, Figure 2, Determination Unit 103), that obtains a defective quality degree for the multiple inspection images stored in the image storage unit based on a learned model (Oota, Figure 2, 0033] (“The determination unit 103 includes a learning unit 1031 and a comprehensive determination unit 1032. The learning unit 1031 uses the image data output from the imaging unit 102 as an input and performs appearance inspection, that is, determination of quality by machine
learning”). created in advance by learning using an image having a same imaging condition as the multiple inspection images (Oota, Figure 3, [0041], [0041] The learning unit 1031 herein may input all of the plurality of image data to the same learning model or may input the image data to different learning models as illustrated in FIG. 3. For example, when a plurality of learning models optimized for a specific imaging condition are constructed, it is possible to perform the inspection using the learning model suited to the imaging condition of the image data to be input”), and
determines a quality state of the inspection object by comparison between the defective quality degree and a preset threshold (Oota, Figure1-3, [0042]” The comprehensive determination unit 1032 acquires a plurality of inspection results (for the same inspection target, but based on the plurality of image data with different imaging conditions) output from the learning unit 1031 and, based on the contents of the acquired results, determines a final inspection result”. For example, the comprehensive determination unit 1032 classifies, based on the
NG degree.”. For example, the comprehensive determination unit 1032 classifies, based on the NG degree, the inspection results into three cases in which
"not good" (NG degree is 81 to 100), NOTE: See [0039], threshold, and the range of NG Degrees 0 to 100),
wherein the learned model is associated with the imaging condition for the image used for learning (Oota, Figure 2, [0040] “The learning unit 1031 inputs the plurality of image data output from the imaging unit 102 with different imaging conditions into the learning model, and obtains the inspection result corresponding to each image data”), and
wherein the learned model is learned for each type of the inspection object with respect to the image having a same imaging condition as the multiple inspection images including at least images with a defective quality (Oota, Figure 3,[0042], “Alternatively, it is also possible to estimate a comprehensive inspection result by using a learning model that can receive the plurality of inspection results output from the learning unit 1031 as an input and output the comprehensive inspection result. In this learning model, a correlation between the comprehensive inspection result determined by various methods so far and the plurality of inspection results output from the learning unit 1031 based on the comprehensive inspection result is learned in advance by a known machine learning method” NOTE: different defect quality images (good/not good) has been analyzed by the comprehensive inspection unit 1032).
Oota is silent on wherein the multiple inspection images comprise a low-energy X-ray image and a high- energy X-ray image that show different transmission characteristics and that are acquired by irradiating the inspection object with X-rays having a wavelength and intensity from an X-ray tube,
However, Sudo teaches wherein the multiple inspection images comprise a low-energy X-ray image and a high- energy X-ray image that show different transmission characteristics and that are acquired by irradiating the inspection object with X-rays having a wavelength and intensity from an X-ray tube, (Sudo, Page 7, lower middle and Bottom paragraph, a substance is first irradiated with X-rays 12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired.” Note: different photon energy distribution reads on high and low energy and plurality of high energy and low energy images are transmitted.)
wherein the imaging condition includes a tube current and a tube voltage of the X-ray tube (Sudo, Figure 1, Page 7, middle paragraph, the maximum X-ray photon energy value and the X-ray photon energy value that is the maximum number of photons are the voltage value of the high-voltage power source applied between the cathode and the anode, that is, the tube voltage. It is determined by the current value, that is, the tube current”),
that are set by test imaging using the inspection object to generate the X-rays, (Sudo, Page 7, bottom paragraph, and page 8 top paragraph, The intensity of the transmitted X-ray, which is the X-ray 12 that has passed through the sample, is measured with respect to the intensity of the incident X-ray, which is the X-ray 12 that enters the sample and has a predetermined X-ray photon energy. And the intensity | strength of a transmission X ray is similarly measured with respect to the incident X ray which changed the X ray photon energy of the X ray 12. FIG. the material of the substance can be identified by calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image”).
.Sudo, teaches identification of a sample matter based on analysis of the transmitted X-ray I mages, Page 7, lower middle paragraph, identifies a substance based on calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image the material of the substance can be identified by calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract).
Sudo is silent on a defective quality due to containing foreign matter
However, Nakagawa teaches identifies a defective quality due to containing foreign matter (Nakagawa, Figure 7a, [0062] After the position(s) where the seal section(s) is/are present has/have been specified, blob F indicating the section
where foreign matter is present may further be detected as shown in FIG. 7A by binarizing the image of the inspection target 2 by using as a threshold, the darkness of the image corresponding to the section where the foreign matter is present in the seal section(s) 2s, and a predetermined area including this blob F may be specified as inspection area A as shown in FIG. 7B. FIGS. 7A and 7B illustrate the case with a single blob F, but it is possible to specify a plurality of inspection areas A in a similar manner in the case where there is a plurality of blobs F.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s and Sudo’s X-ray image processing method to incorporate an analysis of the identifying foreign object as taught by Nakagawa image analysis method with a X-ray inspection apparatus and identify from image analysis the presence of the presence of foreign matters. (Nakagawa [0002]-[0004]).
Regarding Claim 2, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 1,
Oota further teaches wherein the predetermined imaging condition includes at
least position information indicating an imaging position of the inspection object for
each input channel. (Oota, [0031] " [0031] The imaging condition decision unit 101 determines the imaging condition of the appearance of the inspection target. The imaging conditions include, for example, an angle of the light source or the camera with respect to the inspection target a positional relationship between the camera and the light source, a type (color, temperature, brightness, and the like) of the light source" NOTE: operator sets the imaging condition parameters and adjust as needed. (Oota, [0045], the imaging condition decision unit 101 can change a predetermined imaging condition according to a predetermined rule. For example, the imaging condition decision unit 101 determines to change relative positions of an inspection target”).
Regarding Claim 5, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 1,
Oota is silent on wherein the multiple inspection images include images obtained by spectroscopy of light transmitting through the inspection object.
However, Sudo teaches wherein the multiple inspection images include images obtained by spectroscopy of light transmitting through the inspection object. (Sudo, Page 7, Lower bottom paragraph, “a substance is first irradiated with X-rays
12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract).
Regarding Claim 6, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 2,
Oota is silent wherein the multiple inspection images include images obtained by spectroscopy of light transmitting through the inspection object.
However, Sudo teaches wherein the multiple inspection images include images obtained by spectroscopy of light transmitting through the inspection object. (Sudo, Page 7, Lower bottom paragraph, “a substance is first irradiated with X-rays
12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract).
Regarding Claim 9, Oota teaches
Oota teaches a learned model creation method ((Oota, Figure 2, [0040] “The learning unit 1031 inputs the plurality of image data output from the imaging unit 102 with different imaging conditions into the learning model, and obtains the inspection result corresponding to each image data”),) comprising:
a step of acquiring a non-defective image of an inspection object and an image with only defective quality (Oota, Figure 3,[0042], “Alternatively, it is also possible to estimate a comprehensive inspection result by using a learning model that can receive the plurality of inspection results output from the learning unit 1031 as an input and output the comprehensive inspection result. In this learning model, a correlation between the comprehensive inspection result determined by various methods so far and the plurality of inspection results output from the learning unit 1031 based on the comprehensive inspection result is learned in advance by a known machine learning method” NOTE: different defect quality images (good/not good) has been analyzed by the comprehensive inspection unit 1032).
a step of creating a learned model by performing machine learning of the learning defective quality synthesis image and associating the learned model with the imaging condition (Oota, Figure 3, [0041], [0041] The learning unit 1031 herein may input all of the plurality of image data to the same learning model or may input the image data to different learning models as illustrated in FIG. 3. For example, when a plurality of learning models optimized for a specific imaging condition are constructed, it is possible to perform the inspection using the learning model suited to the imaging condition of the image data to be input”),
Oota is silent on learning images that comprise a low-energy X-ray image and a high-energy X-ray image that show different transmission characteristics acquired and that are acquired by irradiating the inspection object with X-rays from an X-ray tube;
imaging condition that includes a tube current and a tube voltage of the X-ray tube that are set by test imaging using the inspection object.
However, Sudo teaches wherein the multiple inspection images comprise a low-energy X-ray image and a high- energy X-ray image that show different transmission characteristics and that are acquired by irradiating the inspection object with X-rays having a wavelength and intensity from an X-ray tube, (Sudo, Page 7, lower middle and Bottom paragraph, a substance is first irradiated with X-rays 12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired.” Note: different photon energy distribution reads on high and low energy and plurality of high energy and low energy images are transmitted.)
wherein the imaging condition includes a tube current and a tube voltage of the X-ray tube (Sudo, Figure 1, Page 7, middle paragraph, the maximum X-ray photon energy value and the X-ray photon energy value that is the maximum number of photons are the voltage value of the high-voltage power source applied between the cathode and the anode, that is, the tube voltage. It is determined by the current value, that is, the tube current”),
that are set by test imaging using the inspection object to generate the X-rays, (Sudo, Page 7, bottom paragraph, and page 8 top paragraph, The intensity of the transmitted X-ray, which is the X-ray 12 that has passed through the sample, is measured with respect to the intensity of the incident X-ray, which is the X-ray 12 that enters the sample and has a predetermined X-ray photon energy. And the intensity | strength of a transmission X ray is similarly measured with respect to the incident X ray which changed the X ray photon energy of the X ray 12. FIG. the material of the substance can be identified by calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image”).
.Sudo, teaches identification of a sample matter based on analysis of the transmitted X-ray I mages, Page 7, lower middle paragraph, identifies a substance based on calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image the material of the substance can be identified by calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract
Oota and Subo is silent on a defective quality due to containing foreign matter in the inspection object as learning images;
step of creating a learning defective quality synthesis image in which the image with only defective quality due to containing foreign matter is synthesized with the non-defective image of the inspection object using the learning image and
a learning defective quality label showing position of foreign matter in the learning defective quality synthesis image having a same imaging condition as the non-defective image of an inspection object;
However, Nakagawa teaches identifies a defective quality due to containing foreign matter step of creating a learning defective quality synthesis image in which the image with only defective quality due to containing foreign matter is synthesized with the non-defective image of the inspection object using the learning image (Nakagawa, Figures 3, 7A-7B, and 10-11, [0062] After the position(s) where the seal section(s) is/are present has/have been specified, blob F indicating the section where foreign matter is present may further be detected as shown in FIG. 7A by binarizing the image of the inspection target 2 by using, as a threshold, the darkness of the image corresponding to the section where the foreign
matter is present in the seal section(s) 2s, and a predetermined area including this blob F may be specified as inspection area A as shown in FIG. 7B. FIGS. 7A and 7B illustrate the case with a single blob F, but it is possible to specify a plurality of inspection areas A in a similar manner in the case where there is a plurality of blobs F.” [0064] The cut-out means 121 cuts out the image of the
inspection area(s) specified as described above from the
image of the inspection target 2 and outputs the same as a
learning-target image to the sorting means 124.).
a learning defective quality label showing position of foreign matter in the learning defective quality synthesis image having a same imaging condition as the non-defective image of an inspection object; Nakagawa, Figure 11, step 200, set inspection condition, [0066] The extraction means 122 outputs the thus extracted sub-images to the sorting means 124 as learning target images. [0067] In this way, by using the sub-images obtained by subdividing the inspection area A as the learning-target ”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s and Sudo’s X-ray image processing method to incorporate an analysis of the identifying foreign object as taught by Nakagawa image analysis method with a X-ray inspection apparatus and identify from image analysis the presence of the presence of foreign matters. (Nakagawa [0002]-[0004]).
Regarding Claim 10, combination of Oota, Sudo and Nakagawa teaches the inspection method according to claim 9,
An inspection method (Oota, Figure 4) comprising:
a step of determining a quality state of an inspection object by capturing a plurality of images (Oota, Figure 1, [0027], “image data input from the imaging device 70”) having different input channels for the inspection object under an imaging condition corresponding to each input channel, (Oota, Figure1-3, [0042]” The comprehensive determination unit 1032 acquires a plurality of inspection results (for the same inspection target, but based on the plurality of image data with different imaging conditions) output from the learning unit 1031 and, based on the contents of the acquired results, determines a final inspection result”. For example, the comprehensive determination unit 1032 classifies, based on the
NG degree.”. For example, the comprehensive determination unit 1032 classifies, based on the NG degree, the inspection results into three cases in which
"not good" (NG degree is 81 to 100), NOTE: See [0039],
Oota is silent on obtaining a degree of containing foreign matter for multiple inspection images obtained by the capturing and combining the plurality of images of the inspection object, based on a learned model created using an image having a same imaging condition as the multiple inspection images by the learned model creation method of claim 9, and comparing degree of containing the foreign matter and a preset threshold.
However, Nakagawa teaches obtaining a degree of containing foreign matter for; multiple inspection images obtained by the (Nakagawa Figures 3, 7A-7B, and 10-11, [0062] After the position(s) where the seal section(s) is/are present has/have been specified, blob F indicating the section where foreign matter is present may further be detected as shown in FIG. 7A by binarizing the image of the inspection target 2 by using, as a threshold, the darkness of the image corresponding to the section where the foreign matter is present in the seal section(s) 2s, and a predetermined area including this blob F may be specified as inspection area A as shown in FIG. 7B. [0064] The cut-out means 121 cuts out the image of the inspection area(s) specified as described above from the image of the inspection target 2 and outputs the same as a learning-target image to the sorting means 124.”). capturing and combining the plurality of images of the inspection object based on a learned model created using an image having a same imaging condition as the multiple inspection images by the learned model creation method of claim 9 [0070] In the case where the cut-out means 121 cuts out a learning-target image when performing additional learning on the learned model, which is generated on the basis of the image of the inspection area, the cut-out conditions for the cut-out means 121 are set so that the image of the inspection area of the size and shape used to generate such learned model is cut out”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s in view of Nakatani’s image capturing device with a X-ray inspection apparatus and identify from image analysis the presence of the presence of foreign matters. (Nakatani, [0066]-[0073]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s and Sudo’s X-ray image processing method to incorporate an analysis of the identifying foreign object as taught by Nakagawa image analysis method with a X-ray inspection apparatus and identify from image analysis the presence of the presence of foreign matters. (Nakagawa [0002]-[0004]).
Regarding Claim 12, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 1,
Oota is silent on wherein the low-energy X-ray image and a-the high-energy X-ray image are acquired by a photon counting-type X-ray detect, and wherein the imaging condition includes an energy threshold for the photon counting- type X-ray detector and the energy threshold associated with the learned model.
However, Subo teaches wherein the low-energy X-ray image and the high-energy X-ray image are acquired by a photon counting-type X-ray detect, (Sudo, Page 7, lower middle and Bottom paragraph, a substance is first irradiated with X-rays 12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired.” Note: different photon energy distribution reads on high and low energy and plurality of high energy and low energy images are transmitted.)
and wherein the imaging condition includes an energy threshold for the photon counting- type X-ray detector and the energy threshold associated with the learned model. (Subo, Page 7, middle paragraph, the maximum X-ray photon energy value and the X-ray photon energy value that is the maximum number of photons are the voltage value of the high-voltage power source applied between the cathode and the anode, that is, the tube voltage. It is determined by the current value, that is, the tube current. “)
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract)
Regarding Claim 13, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 1,
Oota is silent on wherein the multiple inspection images comprise X-ray image, a high-energy, X-ray image and a difference image created from the low-energy image and the high-energy image.
However, wherein the multiple inspection images comprise X-ray image, the a-high-energy, X-ray image and a difference image created from the low-energy image and the high-energy image (Sudo, Page 7, bottom paragraph, and page 8 top paragraph, The intensity of the transmitted X-ray, which is the X-ray 12 that has passed through the sample, is measured with respect to the intensity of the incident X-ray, which is the X-ray 12 that enters the sample and has a predetermined X-ray photon energy. And the intensity | strength of a transmission X ray is similarly measured with respect to the incident X ray which changed the X ray photon energy of the X ray 12. FIG. the material of the substance can be identified by calculating the X-ray absorption coefficient from the luminance of each transmitted X-ray image”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date to modify Oota’s inspection device with Sudo’s X-ray inspection device to inspect object with plurality of as taught by Sudo’s inspection device and methos with transmitted X-ray of different photon energy inspection apparatus and identify from the X-ray images the presence of a subject matter. (Sudo, abstract).
Regarding Claim 15, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 12,
Oota is silent on wherein the multiple inspection images further comprise the difference image created from the low-energy X-ray image and the high-energy X-ray image. Sudo, Page 7, lower middle and Bottom paragraph, a substance is first irradiated with X-rays 12 under a condition that results in a plurality of different X-ray photon energy distributions, and a plurality of transmitted X-ray images are acquired.” Note: different photon energy distribution reads on high and low energy and plurality of high energy and low energy images are transmitted.)
Regarding Claim 16, combination of Oota, Sudo and Nakagawa teaches the inspection device according to Claim 1,
Oota further teaches wherein the learned model is learned for each type of the inspection object with respect to the image having a same imaging condition as the multiple inspection images. (Oota, Figure 3,[0042], “Alternatively, it is also possible to estimate a comprehensive inspection result by using a learning model that can receive the plurality of inspection results output from the learning unit 1031 as an input and output the comprehensive inspection result. In this learning model, a correlation between the comprehensive inspection result determined by various methods so far and the plurality of inspection results output from the learning unit 1031 based on the comprehensive inspection result is learned in advance by a known machine learning method” NOTE: different defect quality images (good/not good) has been analyzed by the comprehensive inspection unit 1032). including further images with a defective quality due to a shape defect (Oota, Figure 1, [0009] “An inspection apparatus according to an embodiment of the present invention is an inspection apparatus for performing an appearance inspection using a plurality of images obtained by imaging an inspection targe”).
Conclusion
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Yurugi et al. (WO 2019/235022 A1) recites “An x-ray inspection device (1) is equipped with an X-ray irradiation unit (6), an X-ray detection unit (7) for detecting X-rays, and a control unit (10) for determining whether an article contains foreign matter or not by using a plurality of algorithms, on the basis of an image generated from the X-ray detection results. The control unit (10) determines whether an article contains foreign matter or not by subjecting an image to first processing using a first algorithm among a plurality of algorithms, determines whether the article contains foreign matter or not by subjecting the image to second processing using a second algorithm among the plurality of algorithms, and determines that the article does contain foreign matter when foreign matter which is common to the determination results of both the first and second processing is contained in the article”(Abstract).
b. HU et al (US 2022/0114729 A1) discloses “This X-ray imaging apparatus includes an X-ray irradiation unit, an X-ray detection unit, an X-ray image generation unit, and a control unit. The control unit includes: an enhanced image generation unit for generating an enhanced image in which a foreign object included in the X-ray image has been enhanced; an identification image generation unit for generating an identification image for identifying the foreign object by coloring a portion corresponding to the enhanced foreign object based on the enhanced image; and an image output unit for outputting an identification image (Abstract).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DILARA SULTANA whose telephone number is (571)272-3861. The examiner can normally be reached Mon-Fri, 9 AM-5:30 PM.
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supervisor, EMAN ALKAFAWI can be reached on (571) 272-4448. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DILARA SULTANA/Examiner, Art Unit 2858
/EMAN A ALKAFAWI/Supervisory Patent Examiner, Art Unit 2858
4/8/2026