DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The amendments to claims 1, 10, and 12 are accepted and entered.
Claim 2 is cancelled.
Claims 1 and 3-20 are pending regarding this application.
Response to Arguments
Applicant’s arguments, see Remarks, filed 03/24/2026, with respect to the 112(b) rejection applied to claim 12 have been fully considered and are persuasive. The 112(b) rejection applied to claim 12 has been withdrawn.
Applicant's arguments, filed 03/24/2026, with respect to the 103 rejection(s) previously applied to claims 1-20 have been fully considered but they are not persuasive. Applicant argues that Amthor in view of Zhalyalov fails to teach “determining a confidence value for the recognized object, wherein the confidence value expresses a probability for a correct assignment of the object to the object class, wherein the determined confidence value is output by the first artificial intelligence method” and “verifying the assignment of the object to the object class based on the second light microscopic data, wherein the step of acquiring the second light microscopic data of the sample in the second acquisition mode consists of selectively acquiring second light microscopic data of the recognized object”. However, examiner disagrees with this assertion. Regarding “determining a confidence value for the recognized object, wherein the confidence value expresses a probability for a correct assignment of the object to the object class, wherein the determined confidence value is output by the first artificial intelligence method”, support for this limitation can be found in Amthor reference in para. [0022] and para. [0057]. In these sections, Amthor teaches a verification algorithm which utilizes a machine learning algorithm to determine a verification result (confidence value). Additionally, regarding “verifying the assignment of the object to the object class based on the second light microscopic data, wherein the step of acquiring the second light microscopic data of the sample in the second acquisition mode consists of selectively acquiring second light microscopic data of the recognized object”, Amthor teaches that a new microscopic image with changed illumination parameters can be acquired of the same microscopic sample region in order to verify the assignment of the object to an object class as shown in para. [0031] and para. [0057]. In the 103 rejection below, additional citations have been added to reinforce Amthur’s teaching of the aforementioned limitations.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 03/09/2026 is considered and attached.
Claim Objections
Claims 17 and 18 are objected to because of the following informalities:
Please add an empty line between claim 17 and claim 18.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 3-7, 11-15, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al. (U.S. Publication No. 2020/0371333 A1), hereinafter Amthor in view of Zhalyalov (U.S. Publication No. 2021/0082570 A1).
Regarding claim 1, Amthor teaches a light microscopy method comprising the steps of:
− acquiring first light microscopic data of a sample in a first acquisition mode (Amthor teaches that “the light detector 85 can comprise a camera chip with which microscope images are recorded”, wherein “microscope images 10 are transmitted from the detector 85 to the image processing algorithm 20” as shown in para. [0056] and [0057], respectively),
− recognizing an object in the sample from the first light microscopic data and assigning the object to an object class (Amthor teaches “the image processing algorithm 20 detects and classifies different objects in the microscope images 10, 10A-10D” in para. [0046]; see also para. [0021]-[0022]),
− determining a confidence value for the recognized object, wherein the confidence value expresses a probability for a correct assignment of the object to the object class, wherein the determined confidence value is output by the first artificial intelligence method (Amthor teaches determining, using the verification algorithm 40, a verification result which can be positive or negative depending on the probability of the image processing result being correct as shown in para. [0051]; Amthor teaches that the verification results are determined using a machine learning algorithm in para. [0022]. See also para. [0054] and [0057]),
− comparing the confidence value with a pre-determined confidence value threshold (Amthor teaches that the verification result (confidence value) can either be positive or negative, wherein the threshold is interpreted as equivalent to criterion necessary for the image data to either be labeled as positive or negative as shown in para. [0051]),
− if the confidence value is below the confidence value threshold, acquiring second light microscopic data in a second acquisition mode (Amthor teaches that, when the verification result is negative (below the threshold), “changed image processing parameters are chosen” and a new recording of the microscopic image is captured wherein “lateral coordinates of the imaged sample region can remain the same for the new recording of a microscope image, but the illumination intensity, illumination duration, illumination wavelength or exposure or integration time of the detector 85 can be changed” as shown in para. [0057]. Here, these changed parameters are interpreted as equivalent to the second acquisition mode),
− verifying the assignment of the object to the object class based on the second light microscopic data, wherein the step of acquiring the second light microscopic data of the sample in the second acquisition mode (Amthor teaches that “new recording of a microscope image can be taken with changed microscope parameters” in para. [0031] and [0057]) consists of selectively acquiring second light microscopic data of the recognized object (Amthor teaches that “in the case of a verification result that indicates incorrect image processing, a new recording of a microscope image with subsequent image processing by way of the image processing algorithm and verification by way of the verification algorithm” in para. [0031], see also para. [0057]; Amthor additionally teaches that “in the case of a negative verification result to change illumination or detection parameters and to record another microscope image of the same sample region” … “lateral coordinates of the imaged sample region can remain the same for the new recording of a microscope image” in para. [0057], which directly implies that the same object is being captured. This acquiring step as taught by Amthur is broadly interpreted as a selective step, as a specific region is selected as the region to be captured for the second light microscopic data (i.e. the sample region captured in the first light microscopic data)).
While Amthor teaches using artificial intelligence in the verification step, and recites that “the image processing algorithm can be designed in principle in any way to calculate from one or more microscope images one or more images that are referred to here as image processing results” in para. [0021], Amthor fails to specifically teach recognizing an object in the sample from the first light microscopic data and assigning the object to an object class using a first artificial intelligence method.
However, Zhalyalov specifically teaches recognizing an object in the sample from the first light microscopic data and assigning the object to an object class using a first artificial intelligence method (Zhalyalov teaches “the diagnostic platform can … (ii) detect, classify, and count objects in the images using, for example, artificial intelligence (AI) algorithms” in para. [0023], wherein the images are images of a slide positioned in the microscope 102 as shown in para. [0033] and FIG. 1).
Amthor and Zhalyalov are both considered to be analogous to the claimed invention because they are in the same field of detecting objects in microscope images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Amthor to incorporate the teachings of Zhalyalov and include “recognizing an object in the sample from the first light microscopic data and assigning the object to an object class using a first artificial intelligence method”. The motivation for doing so would have been to provide “a cost- and resource-effective approach to analyzing blood smears by automating aspects of microscopy testing” by “identify[ing] objects representative of abnormalities through the use of artificial intelligence”, as suggested by Zhalyalov in para. [0024]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Amthor with Zhalyalov to obtain the invention specified in claim 1.
Regarding claim 3, Amthor and Zhalyalov teach the method according to claim 1,
wherein the assignment of the object to the object class is verified by means of the first artificial intelligence method or a second artificial intelligence method (Amthor teaches that the verification results are determined using a machine learning algorithm in para. [0022]. See also para. [0054] and [0057]. As noted above in the discussion of claim 1, Zhalyalov teaches the artificial intelligence aspect. The same motivation as discussed above in claim 1 apply).
Regarding claim 4, Amthor and Zhalyalov teach the method according to claim 1,
wherein the assignment of the object to an object class is repeated based on the second light microscopic data if the verification of the assignment of the object shows that the object was incorrectly assigned (Amthor teaches that, “in the case of a verification result that indicates incorrect image processing, another performance of the image processing algorithm, but with changed image processing parameters” in para. [0030], wherein the image processing algorithm is the process of assigning the object to an object class as shown in para. [0022]).
Regarding claim 5, Amthor and Zhalyalov teach the method according to claim 1,
wherein third light microscopic data of the object are acquired in the first acquisition mode, the second acquisition mode or a further acquisition mode, and the assignment of the object to an object class is repeated based on the third light microscopic data if the verification of the assignment of the object reveals that the object was incorrectly assigned (Amthor teaches that “in the case of a verification result that indicates incorrect image processing, another performance of the image processing algorithm, but with changed image processing parameters” in para. [0030], wherein the image processing algorithm is the process of assigning the object to an object class as shown in para. [0022]. See also para. [0031] which specifically recites that this process may be repeated multiple times, either with new microscope parameters (acquisition mode) or the original microscope parameters (acquisition mode)).
Regarding claim 6, Amthor and Zhalyalov teach the method according to claim 1,
wherein the assignment of the object to the object class and/or the determination of the confidence value is repeated based on the second light microscopic data (Amthor teaches that “in the case of a verification result that indicates incorrect image processing, another performance of the image processing algorithm, but with changed image processing parameters” in para. [0030], wherein the image processing algorithm is the process of assigning the object to an object class as shown in para. [0022]. Here, the new recording of a microscope image that is used in the process outlined in para. [0030]-[0031] is interpreted as equivalent to the second light microscopic data) or based on a combination of the first light microscopic data and the second light microscopic data.
Regarding claim 7, Amthor and Zhalyalov teach the method according to claim 1,
wherein the first light microscopic data are acquired in the first acquisition mode in a first color channel and that the second light microscopic data are acquired in the second acquisition mode in a second color channel which is different from the first color channel (Amthor teaches that “the control device 60 can drive the light source 70 or the detector 85 in the case of a negative verification result to change illumination or detection parameters and to record another microscope image of the same sample region. In particular, lateral coordinates of the imaged sample region can remain the same for the new recording of a microscope image, but the illumination intensity, illumination duration, illumination wavelength or exposure or integration time of the detector 85 can be changed” in para. [0057], wherein the change in illumination wavelength directly implies that the first light microscopic data may be in a first color channel and the second light microscopic data may be in a second color channel, which differs from the first color channel).
Regarding claim 11, Amthor and Zhalyalov teach the method according to claim 1,
wherein the method is carried out automatically for a plurality of objects (Amthur teaches “the image processing algorithm 20 detects and classifies different objects in the microscope images 10, 10A-10D” in para. [0046]).
Regarding claim 12, Amthor and Zhalyalov teach the method according to claim 1,
wherein the first artificial intelligence method and/or the second artificial intelligence method is a deep learning type method (Amthur teaches that “a deep learning algorithm or another learning algorithm that is known in principle can be used for the machine learning algorithm” in para. [0035]. See para. [0051] which clarifies that the machine learning algorithm is trained and para. [0035] which specifies that a convolutional neural network may be used to carry out the data processing), wherein the first artificial intelligence method is carried out by means of a first trained data processing network (Zhalyalov teaches a object detection module 516 (first AI method) which utilizes AI as shown in para. [0066], wherein “the diagnostic platform may identify a detection model comprised of object detection algorithm(s) trained to identify abnormal cells whose presence is indicative of malaria” as shown in para. [0079]),
wherein the second artificial intelligence method is carried out by means of a second trained data processing network (Amthur teaches that “a deep learning algorithm or another learning algorithm that is known in principle can be used for the machine learning algorithm” in para. [0035]. See para. [0051] which clarifies that the machine learning algorithm is trained and para. [0035] which specifies that a convolutional neural network may be used to carry out the data processing) (Zhalyalov teaches a classification module 518 (second AI method) wherein “the classification module 518 may employ AI-driven algorithms to classify the objects detected by the detection module 516” and “the classification model may be a neural network that is trained using a supervised machine learning algorithm” as shown in para. [0068]).
Similar motivations as applied to claim 1 can be applied here to claim 12.
Regarding claim 13, Amthor and Zhalyalov teach the method according to claim 1,
wherein the object is a biological entity (Amthur teaches that one “use [of the invention] is counting (biological) cells in a microscope image. In this case, for example, the cell walls/membranes are ascertained as bounding boxes, wherein the number of such bounding boxes, which are in each case closed, is the variable of interest” in para. [0022]).
Regarding claim 14, Amthor and Zhalyalov teach the method according to claim 13,
wherein the object class describes a cell type, an organelle type, a phenotype, a cell division stage, a localization of components of the object or a pattern of components of the object (Amthur teaches that one “use [of the invention] is counting (biological) cells in a microscope image. In this case, for example, the cell walls/membranes are ascertained as bounding boxes, wherein the number of such bounding boxes, which are in each case closed, is the variable of interest” in para. [0022] wherein “the shape and location of a bounding box may be indicated as the image processing result, for example the size, orientation and location of a square shape in the case of a square cover slip” as shown in para. [0014]. Here a localization of components of the object/ a pattern of components of the object is taught).
Regarding claim 15, Amthor and Zhalyalov teach the method according to claim 1,
wherein the object class describes a rare and/or transient state of the object (Zhalyalov teaches “the diagnostic platform 512 may be configured to obtain a series of images generated based on light reflected through the eyepiece of a microscope and then analyze the series of images in real time to detect, classify, and count regions of pixels that are, for example, representative of abnormal cells” in para. [0064]. See also para. [0058], [0070], [0079], and FIG. 6A. Here, the abnormal cells are interpreted as equivalent to a rare state of the object (cell). Additionally, abnormal cells can also broadly be interpreted as transient since they’ve transitioned from a normal state to an abnormal state).
Similar motivations as applied to claim 1 can be applied here to claim 15.
Regarding claim 19, Amthor and Zhalyalov teach a device for carrying out the method according to claim 1, comprising:
− a light microscope which is configured to acquire first light microscopic data of a sample in a first acquisition mode and to acquire second light microscopic data of the sample in a second acquisition mode (Amthur teaches that images are recorded using a microscope in para. [0046], wherein the microscope is configured to capture images in multiple acquisition modes as shown in para. [0057]),
− a processor which is configured to recognize an object in the sample from the first light microscopic data using a first artificial intelligence method, to assign the object to an object class, to determine a confidence value for the recognized object, the confidence value expressing a probability for a correct assignment of the object to the object class, and to compare the confidence value with a predetermined confidence value threshold (Amthur teaches that “the computer program described can be executed in particular on a computer that is operatively connected to a microscope or to the microscope described or is part of said microscope” in para. [0033], wherein the image processing algorithm and the verification algorithm can be carried out by the computer as shown in para. [0033]-[0034]. Both of these algorithms carry out the processes outlined in the above claim limitation as shown in claim 1),
− wherein the processor is further configured to verify the assignment of the object to the object class based on the second light microscopic data (See above citation wherein the verification algorithm is also carried out by the computer, and the process outlined in the above claim limitation is shown in claim 1).
Regarding claim 20, Amthor and Zhalyalov teach a non-transitory computer-readable medium for storing computer instructions for carrying out a light microscopy method (Zhalyalov teaches “the processing system 900 may include a processor 902, main memory 906, non-volatile memory 910” in para. [0083]. See also para. [0085] and para. [0086] wherein the processor/non-volatile memory is configured to carry out the method(s) of the invention) that, when executed by one or more processors associated with a device comprising a light microscope is configured to perform the method according to claim 1 (Amthor additionally teaches using a computer associated with a microscope to carry out the method as recited in the citations applied to claim 1 in para. [0033]-[0034]).
Claims 8, 16, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al. (U.S. Publication No. 2020/0371333 A1), hereinafter Amthor in view of Zhalyalov (U.S. Publication No. 2021/0082570 A1) and Andrew et al. (U.S. Publication No. 2025/0111473 A1), hereinafter Andrew.
Regarding claim 8, Amthor and Zhalyalov teach the method according to claim 1.
Amthor and Zhalyalov fail to teach wherein the first light microscopic data are acquired in the first acquisition mode at a first resolution and that the second light microscopic data are acquired in the second acquisition mode at a second resolution which is higher than the first resolution.
However, Andrew teaches wherein the first light microscopic data are acquired in the first acquisition mode at a first resolution and that the second light microscopic data are acquired in the second acquisition mode at a second resolution which is higher than the first resolution (Andrew teaches “the first imaging data can be used as a low-image-quality (e.g., low-resolution) input and the pre-processed and registered/aligned second imaging data can be used as a high-image-quality (e.g., high-resolution) desired output” in para. [0044]. See also para. [0094] and FIG. 7).
Amthor, Zhalyalov, and Andrew are all considered to be analogous to the claimed invention because they are in the same field of detecting objects in microscope images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Amthor (as modified by Zhalyalov) to incorporate the teachings of Andrew and include “wherein the first light microscopic data are acquired in the first acquisition mode at a first resolution and that the second light microscopic data are acquired in the second acquisition mode at a second resolution which is higher than the first resolution”. The motivation for doing so would have been to “facilitate training of the neural network used to generate the output imaging data, as well as potentially also facilitating certain pre-processing techniques (e.g., registration and/or alignment)” and “generate output imaging data of high image quality for the entire large region of the sample (e.g., the entire sample) for which first imaging data was obtained”, as suggested by Andrew in para. [0042] and [0044], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Amthor and Zhalyalov with Andrew to obtain the invention specified in claim 8.
Regarding claim 16, Amthor and Zhalyalov teach the method according to claim 1.
While Amthor teaches carrying out a super-resolution method (see para. [0021]), Amthor and Zhalyalov fail to specifically teach wherein a super-resolution light microscopy method is carried out in the second acquisition mode.
However, Andrew teaches wherein a super-resolution light microscopy method is carried out in the second acquisition mode (Andrew teaches a process of applying super-resolution to second input imaging data in order “to avoid or minimize certain artefacts or other problems known to occur in the first imaging data” as shown in para. [0035]. See also para. [0095] and FIG. 8).
Amthor, Zhalyalov, and Andrew are all considered to be analogous to the claimed invention because they are in the same field of detecting objects in microscope images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Amthor (as modified by Zhalyalov) to incorporate the teachings of Andrew and include “wherein a super-resolution light microscopy method is carried out in the second acquisition mode”. The motivation for doing so would have been “to avoid or minimize certain artefacts or other problems known to occur in the first imaging data” and that “the improved-image-quality image 800 can further demonstrate reduced artefacts, such as reduced noise, as compared to the low-resolution image 600 of FIG. 6 and/or the high-resolution image 700 of FIG. 7”, as suggested by Andrew in para. [0035] and [0095], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Amthor and Zhalyalov with Andrew to obtain the invention specified in claim 16.
Regarding claim 17, Amthor, Zhalyalov, and Andrew teach method according to claim 16, wherein the super-resolution light microscopy method (See Andrew’s teaching of the super-resolution light microscopy method in claim 16) is
a STED microscopy method,
a RESOLFT microscopy method,
a MINFLUX method,
a STED-MINFLUX method,
a PALM/STORM method,
a SIM method (Amthur teaches “the image processing algorithm can also be designed for microscopy-specific calculations, as are used for example in SIM (Structured Illumination Microscopy) or PALM (Photoactivated Localization Microscopy). In the case of SIM, the image processing algorithm calculates a single image from a plurality of microscope images as the image processing result, wherein the microscope images differ in the illumination used with respect to the orientation and phase of a structured illumination” as shown in para. [0021]) or
a SIMFLUX method.
Similar motivation as applied to claim 16 can be applied here to claim 17.
Claims 9, 10, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Amthor et al. (U.S. Publication No. 2020/0371333 A1), hereinafter Amthor in view of Zhalyalov (U.S. Publication No. 2021/0082570 A1) and Lemmer et al. (U.S. Publication No. 2009/0237501 A1), hereinafter Lemmer.
Regarding claim 9, Amthor and Zhalyalov teach the method according to claim 1.
Amthor and Zhalyalov fail to teach wherein the second light microscopic data are three-dimensional light microscopic data.
However, Lemmer teaches wherein the second light microscopic data are three-dimensional light microscopic data (Lemmer teaches a “novel technique [which] allows fast three dimensional (3D) imaging of nanostructures, in particular biological nanostructures with an effective 3D optical resolution (x, y, z) of single molecules of approximately 20 nm in the lateral and 50 nm in the axial direction corresponding to about 1/25.sup.th to 1/10.sup.th of the exciting wavelength” in para. [0183]. See para. [0138]. See also Amthor’s teaching of the second light microscopic data in claim 1).
Amthor, Zhalyalov, and Lemmer are all considered to be analogous to the claimed invention because they are in the same field of detecting objects in microscope images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Amthor (as modified by Zhalyalov) to incorporate the teachings of Lemmer and include “wherein the second light microscopic data are three-dimensional light microscopic data”. The motivation for doing so would have been “a method and an apparatus for improvement of the localization precision and the achievable object information in the localization microscopy by combining it with far-field techniques with structured illumination” and to “allow[] fast three dimensional (3D) imaging of nanostructures, in particular biological nanostructures with an effective 3D optical resolution (x, y, z) of single molecules”, as suggested by Andrew in para. [0296] and [0183], respectively. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Amthor and Zhalyalov with Lemmer to obtain the invention specified in claim 9.
Regarding claim 10, Amthor, Zhalyalov, and Lemmer teach the method according to claim 9,
wherein the first light microscopic data are two-dimensional light microscopic data (Lemmer teaches an obtained time series of two-dimensional images (microscopic data) in para. [0057]),
wherein the second light microscopic data are generated by acquiring an axial stack of images (Lemmer teaches “the three dimensional image data are obtained by combining "n" copies of a single two-dimensional image from the obtained series of wide field images to a three-dimensional data stack” in para. [0138]. See para. [0239] wherein the 3D stack displays the axial position in para. [0239]. See also Amthor’s teaching of the second light microscopic data in claim 1).
Similar motivations as applied to claim 9 can be applied here to claim 10.
Regarding claim 18, Amthor and Zhalyalov teach the method according to claim 1.
Amthor and Zhalyalov fail to teach wherein a confocal scanning microscopy method or a wide-field luminescence microscopy method is carried out in the first acquisition mode.
However, Lemmer teaches wherein a confocal scanning microscopy method (Lemmer teaches “one light microscope with confocal set-up of the optical illumination path is employed, wherein at least one additional optical element is built in, respectively positioned within the illumination optical path of the light microscope, so as to enable an adjustment of the determined optimal intensity of the optical radiation to the and/or within range of 1 kW/cm.sup.2 to 1 MW/cm.sup.2" in para. [0289]) or a wide-field luminescence microscopy method (Lemmer teaches “the fluorescence localization microscope may have a confocal set-up of the optical illumination path, a wide field set-up of the optical illumination path” in para. [0092]) is carried out in the first acquisition mode (See Amthor’s teaching of the first acquisition mode in claim 1).
**Although both paths are taught in the prior art, please note only one path need be found here due to the “or” language in the claim.
Amthor, Zhalyalov, and Lemmer are all considered to be analogous to the claimed invention because they are in the same field of detecting objects in microscope images. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Amthor (as modified by Zhalyalov) to incorporate the teachings of Lemmer and include “wherein a confocal scanning microscopy method or a wide-field luminescence microscopy method is carried out in the first acquisition mode”. The motivation for doing so would have been that “it becomes possible to efficiently combine localization microscopic measurements with other far or wide field measurements, so as to obtain additional spatial information, in particular additional spatial information in the direction perpendicular to the observed by localization microscopy object plane. This may lead to an increase of the axial resolution in comparison with conventional methods by a factor of about 30”, as suggested by Lemmer in para. [0044]. See also para. [0096]-[0097]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Amthor and Zhalyalov with Lemmer to obtain the invention specified in claim 18.
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 extension fee 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 date of this final action.
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
Prosecution Insights