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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/23/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Applicant's arguments filed 12/12/2025 have been fully considered but they are not persuasive.
The applicant argues on page 9 of the remarks filed that cited prior art of Honjo et al. US PG-Pub(US 20190004298 A1) does not explicitly teach developing a second image from the first image. The examiner respectfully disagrees as Honjo does teach developing a second image from the first image as the first imaging configuration as disclosed in ¶[0018] has a first X-Y voltage pair to generate the first image and the second image is generated based off the settings of the first X-Y voltage pair. The broadest reasonable interpretation of the claim is just a second image is generated from the first image which in ¶[0018] the first image configuration is used to generate a second image which the cited section of Honjo would disclose the limitation in question.
The applicant continues to argue on page 10 of the cited prior art, Honjo does not explicitly disclose developing a score associated with the second imaging configuration that represents a difference between the first classification and the second classification. The Examiner respectfully disagrees as ¶[0058] discloses determining a confidence score associated with the images acquired from the microscope and the microscope as disclosed in ¶[0018] has different imaging configurations and the classifier is trained based on the microscopic images received and the confidence score is used to determine a difference in classification of the images received.
The applicant also argues on page 11 of the remarks filed the cited prior art of Honjo does not explicitly teach reducing image acquisition time. However, the claim limitation in question recites image acquisition time or component requirements of operating the microscopy system. Under the broadest reasonable interpretation of the claim, the examiner is only required to map to one of the conditions as the language uses “or”. The cited section of the prior art would disclose reducing computational timing. The Applicant argues on page 11, that the cited prior art does not teach the language of claim 9. However, Honjo does teach generating a second image with a second imaging configuration. The applicant argues that the second image is an actual image and not a simulation image. The examiner suggests to the applicant by clarifying wherein the second image is a simulated image and not a generated image would overcome the current interpretation of the claim language.
The applicant argues on page 12 of the arguments, the cited prior art of Bayer does not teach the idea of generating a third image from the first image. However, in ¶[0015] of the cited prior art, it appears a third image is obtained with a different configuration from the first and second images.
The Examiner suggests to the applicant, perhaps amending the independent claims to capture the idea of using a degraded image that simulates an image that would be acquired if the microscopy system were operated using the candidate production imaging configuration as disclosed in ¶[0016] of the specification of the instant application would overcome the cited prior art in question.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,2, 4-5, 9-10, 13-14, 16-17 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Honjo et al. US PG-Pub(US 20190004298 A1) in view of Kraus et al. US PG-Pub(US 20180137338 A1).
Regarding Claim 1, Honjo teaches a method of operating a microscopy system, comprising the steps of: receiving a first image of a sample acquired using a first imaging configuration (¶[0025], “receive a first image, wherein the first image provides alignment information of an objective lens in the scanning electron microscope”, ¶[0025] discloses receiving a first image based on a setting for the scanning electron microscope. )developing a second image from the first image, wherein the second image is associated with a second imaging configuration ([0046] “A second image is received at the control unit at 304. The second image provides alignment information of the objective lens and the second image is a result of settings of the first X-Y voltage pair. Thus, the settings are changed to the first X-Y voltage pair and the second image is obtained.” [0047] Using the control unit, a second X-Y voltage pair is determined 305 based on the second image. The second X-Y voltage pair provides alignment of the objective lens closer to the center of the alignment target than the first X-Y voltage pair. This second X-Y voltage pair may be determined using the same approach as is used to determine the first X-Y voltage pair or a different approach.”, ¶[0046]-¶[0047] disclose a second image is developed based on the configuration of the first image which is used to adjust the control unit so that a different configuration is used to capture the second image.)developing a score associated with the second imaging configuration that represents a difference between the first classification and the second classification([0058] “Another benefit of the classifier is that a confidence score associated with each class label output can be provided, which can be used to filter out bad sites or blurry images. The confidence scores generated by the classification network is the probability that an input image belongs to a particular voltage grid point (or class). The network outputs N confidence scores (N classes) for each input image.”, ¶[0058] discloses determining a confidence score associated with the images acquired from the microscope.); and wherein the image acquisition time or component requirements of operating the microscopy system is less when operated using the second imaging configuration than when operated using the first imaging configuration. (¶[0056], “Steps 301-303 can be referred to as a coarse process. Steps 304-306 can be referred to as a fine process. An advantage of the coarse process is that it narrows the computation time needed to perform the fine process.”, as disclosed in ¶[0056] the steps 301-303 in figure 6 are shown to reduce computation time needed to perform the fine processing of the image.)
Honjo does not explicitly teach applying a sequence of image processing steps to the first image to develop a first classification of first objects represented in the first image and applying the sequence of image processing steps to the second image to develop a second classification of second objects represented in the second image;
Kraus teaches applying a sequence of image processing steps to the first image to develop a first classification of first objects represented in the first image and applying the sequence of image processing steps to the second image to develop a second classification of second objects represented in the second image (¶[0021], “The convolutional MIL network described herein uses MIL to simultaneously classify and segment microscopy images with populations of cells. In an embodiment, the CNN outputs class-specific feature maps representing the probabilities of the classes for different locations in the input image and the MIL pooling layer is applied to these feature maps. The system can be trained using only whole microscopy images with image level labels, without requiring any segmentation steps. Processed images can be of arbitrary size and contain varying number of cells. Individual cells can be classified by passing segmented cells through the trained CNN or by mapping the probabilities in class specific feature maps back to the input space.”, ¶[0021] discloses using a neural network to classify and segment the cells in the images and ¶[0052] discloses classification includes cropping certain objects to identify a object location in the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo with Kraus in order to classify objects in the microscopy images acquired. One skilled in the art would have been motivated to modify Kraus in this manner in order to produce predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells. (Kraus, Abstract)
Regarding Claim 2, the combination of Honjo and Kraus teach the method of claim 1, where Kraus further teaches wherein the first classification of the first objects is associated with identification of one or more objects represented in the first image, identification of one or more objects having a particular characteristic, or metrics associated with one or more objects represented in the first image. (¶[0052], “Following training, an image of any size can be passed through the convolutional MIL network. This can be useful for classifying individual cropped objects or image patches. One could use a separate segmentation algorithm (such as Otsu thresholding, mixture of Gaussians, region growing, graphical models, etc.) to identify object locations, crop bounding boxes around them, and pass them through the convolutional MIL network in order to produce single cell predictions. Alternatively, the cellular regions can be identified by back propagating errors through the network to the input space, as earlier described.”, ¶[0052] discloses the prior art classifies cropped objects or patches by using bounding boxes to determine the object in the image.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo with Kraus in order to classify objects in the microscopy images acquired. One skilled in the art would have been motivated to modify Kraus in this manner in order to produce predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells. (Kraus, Abstract)
Regarding Claim 4, the combination of Honjo and Kraus teach the method of claim 1, where Kraus further teaches including the further steps of: selecting a set of training parameters in accordance with an image processing step of the sequence of image processing steps (¶[0030], “The memory 106 may comprise a database for storing activations and learned weights for each feature detector, as well as for storing datasets of microscopy information and extra information and optionally for storing outputs from the CNN 101 or MIL pooling layer 109. The microscopy information may provide a training set comprising training data. The training data may, for example, be used for training the CNN 101 to generate feature maps, in which visually assigning annotations from a known screen may be provided; specifically, optionally labelling proteins that are annotated to localize to more than one sub-cellular compartment.”, ¶[0030] discloses determining the training data used to train the neural network to classify objects to determine in the images.)configuring an untrained machine learning system with the selected set of training parameters to develop a trained machine learning system and operating the trained machine learning system to develop the first classification. (¶[0052], “Following training, an image of any size can be passed through the convolutional MIL network. This can be useful for classifying individual cropped objects or image patches. One could use a separate segmentation algorithm (such as Otsu thresholding, mixture of Gaussians, region growing, graphical models, etc.) to identify object locations, crop bounding boxes around them, and pass them through the convolutional MIL network in order to produce single cell predictions. Alternatively, the cellular regions can be identified by back propagating errors through the network to the input space, as earlier described.”, as disclosed in this section of the prior art, the machine learning system is trained to detect objects in the image and to classify the object in the image as well.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo with Kraus in order to classify objects in the microscopy images acquired. One skilled in the art would have been motivated to modify Kraus in this manner in order to produce predictions for one or more reference cellular phenotype variables based on microscopy images with populations of cells. (Kraus, Abstract)
Regarding Claim 5, the combination of Honjo and Kraus teach the method of claim 1, where Honjo further teaches including the further step of developing the second imaging configuration from the first imaging configuration. (¶[0046]-¶[0047] disclose a second image is developed based on the configuration of the first image which is used to adjust the control unit so that a different configuration is used to capture the second image.)
Regarding Claim 9, the combination of Honjo and Kraus teach the method of claim 1, where Honjo further teaches wherein the second image simulates an image of the sample that would be acquired if the microscopy system were operated with the second imaging configuration. (¶[0046]-¶[0047] disclose a second image is developed based on the configuration of the first image which is used to adjust the control unit so that a different configuration is used to capture the second image.)
Regarding Claim 10, the combination of Honjo and Kraus teach the method of claim 1, where Honjo further teaches wherein the second imaging configuration is one of a plurality of candidate production imaging configurations, wherein a score is developed for each of the plurality of candidate production imaging configurations, further including the step of selecting a recommended production imaging configuration from those candidate production imaging configurations having scores that exceed a predetermined amount. (¶[0058], “Another benefit of the classifier is that a confidence score associated with each class label output can be provided, which can be used to filter out bad sites or blurry images. The confidence scores generated by the classification network is the probability that an input image belongs to a particular voltage grid point (or class). The network outputs N confidence scores (N classes) for each input image. The class of the highest score is assigned to the input image, which assigns the corresponding voltage to the image as well. A low confidence score can mean the network is not sure which voltage grid point it should assign the input image to. This can happen if the image is acquired from an area on the resolution standard where tin spheres are missing or damaged, in which case the low confidence score can tell system to skip the area and move to another area to grab a new image.”, as disclosed in ¶[0058], a score is calculated for each image and based on the score if it is too low the prior art can adjust the microscope to grab a new image.)
Regarding Claim 13, it is substantially similar to claim 1 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 14, it is substantially similar to claim 2 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 16, it is substantially similar to claim 4 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 17, it is substantially similar to claim 5 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 21, it is substantially similar to claim 9 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 22, it is substantially similar to claim 10 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 3, 8, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Honjo et al. US PG-Pub(US 20190004298 A1) in view of Kraus et al. US PG-Pub(US 20180137338 A1) in view of Bayer et al. US PG-Pub(US 20210333537 A1).
Regarding Claim 3, while the combination of Honjo and Kraus teach the method of claim 1, they do not explicitly teach wherein the score comprises a first score and further including the steps of: developing a third image from the first image, wherein the third image is associated with a third imaging configuration; applying the sequence of image processing steps to the third image to develop a third classification; developing a second score that represents a difference between the first classification and the third classification; and automatically selecting a recommended production imaging configuration, wherein the recommended production imaging configuration is the second imaging configuration if the first score is better than the second score and the recommended production imaging configuration is the third imaging configuration if the second score is better than the first score.
Bayer teaches wherein the score comprises a first score and further including the steps of: developing a third image from the first image, wherein the third image is associated with a third imaging configuration (¶[0015], “An image classifier is programmed to color the object in the first image with a first color to form a first colored image, color the object in the second image with a second color to form a second colored image, color the object in the third image with a third color to form a third colored image”, ¶[0015] discloses obtaining a third image with a different configuration from the first and second images.)applying the sequence of image processing steps to the third image to develop a third classification(¶[0051]-¶[0052], “The processor module may be trained to detect biofilms, pathogens and other objects. The processor model 62 may be trained with moving and non-moving objects. If only biofilms are being detected, moving objects may not be trained for recognition in the model. The image processor 62 results or classifications include the location of the biofilms and the area and or volume and confidence score of the biofilm structure in each field of view (FOV) 90.”, as disclosed in this section a model is used to determine the classification of the areas in each of the images acquired.); developing a second score that represents a difference between the first classification and the third classification (¶[0086], “Another indicator is a biofilm density ratio 616 calculated as a total volume of biofilms divided by the total volume of the field of views. Another indicator is an average confidence score 618 is calculated as sum of the individual biofilm confidence scores divided by the biofilm occurrences. Yet another indicator is biofilm volume weighted confidence score 620 calculated as a (sum of the biofilm confidence scores*total volume of biofilm)/biofilm occurrences. Another indicator is the field of view (FOV) volume weighted confidence score 622 calculated as (sum of the biofilm confidence scores*total volume of the field of views)/biofilm occurrences. For each of the 616-622 a variance may be provided as well. A first variance is generated based upon the biofilm density ratio. A second variance is generated relative to the average confidence score. A third variance is generated based on the biofilm volume weighted confidence score. A fourth variance is determined based upon the field of view volume weighted confidence score.”, ¶[0086], discloses determining a confidence score for each image classification and comparing the differences between the scores.); and automatically selecting a recommended production imaging configuration, wherein the recommended production imaging configuration is the second imaging configuration if the first score is better than the second score and the recommended production imaging configuration is the third imaging configuration if the second score is better than the first score. ([0046], “The controller 26 may also control or include a magnification actuator 34. The magnification actuator 34 may control the power of magnification of the microscope 12. Because the system 10 may be used for detecting biofilms of various stages of development and various objects such as pathogens within the bodily fluid, the magnification may be changed in order to make such detections of different size biofilm instances and other objects such as pathogens.” [0047] “A focus actuator 36 is also in communication with the controller 26. The focus actuator 36 may automatically adjust the focus of the microscope. By adjusting the focus of the microscope, various three dimensional images or videos may be obtained. Focusing the lens 28 accommodate the various depths to allow clear mages to be taken”, ¶[0046]-¶[0047], disclose determining a configuration for the microscope based on if the image captured is blurred or not.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo and Kraus with Bayer in order to generate a third image and adjust the microscope based on the imaging conditions. One skilled in the art would have been motivated to modify Honjo and Kraus in this manner in order to identify and characterize biofilm structures in a blood sample. (Bayer, ¶[0002])
Regarding Claim 8, while the combination of Honjo and Kraus teach the method of claim 1, they do not explicitly teach wherein the step of receiving the first image comprises the step of acquiring the first image using a first microscopy system, including the further step of acquiring a third image using a second microcopy system in accordance with the second imaging configuration.
Bayer teaches wherein the step of receiving the first image comprises the step of acquiring the first image using a first microscopy system, including the further step of acquiring a third image using a second microcopy system in accordance with the second imaging configuration. (¶[0015], “An image classifier is programmed to color the object in the first image with a first color to form a first colored image, color the object in the second image with a second color to form a second colored image, color the object in the third image with a third color to form a third colored image”, ¶[0015] discloses obtaining aa third image with a different configuration from the first and second images.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo and Kraus with Bayer in order to generate a third image. One skilled in the art would have been motivated to modify Honjo and Kraus in this manner in order to identify and characterize biofilm structures in a blood sample. (Bayer, ¶[0002])
Regarding Claim 15, it is substantially similar to claim 3 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Regarding Claim 20, it is substantially similar to claim 8 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Honjo et al. US PG-Pub(US 20190004298 A1) in view of Kraus et al. US PG-Pub(US 20180137338 A1) in view of Amthor et al. US Patent(US 11985415 B2).
Regarding Claim 6, while the combination of Honjo and Kraus teach the method of claim 5, they do not explicitly teach wherein the first imaging configuration specifies a first value associated with an imaging parameter and the second imaging configuration specifies a second value associated with the imaging parameter, and the first and second values are different and further including the steps of selecting training parameters of an untrained machine learning system in accordance with the difference between the first imaging configuration and the second imaging configuration; training the untrained machine learning system with the selected training parameters to develop a trained machine learning system; and operating the trained machine learning system with the first image as an input to generate the second image.
Amthor teaches wherein the first imaging configuration specifies a first value associated with an imaging parameter and the second imaging configuration specifies a second value associated with the imaging parameter (Col 9, Lines 5-11, “The computing device 20 is intended to output a high-quality overview image based on a plurality of captured raw overview images. To this end, the computing device 20 is configured to control the microscope 1 in order to capture a plurality of raw overview images with different capture parameters. The raw overview images differ from one another by the different capture parameters, for example different exposure times, illumination light intensities, illumination wavelengths or filter settings.”, as disclosed in this section of the prior art, the computing device controls the operation of the microscope and there are different configurations when capturing microscopic images.)and the first and second values are different and further including the steps of selecting training parameters of an untrained machine learning system in accordance with the difference between the first imaging configuration and the second imaging configuration (Col 10, Lines 9-21, “The machine learning model M can optionally take into account contextual data i, which relates either to an individual raw overview image of the raw overview images 11-15 or to all raw overview images 11-15 alike. The contextual data i can relate, for example, to the capture parameters, a sample type or a sample carrier type. Calculation steps of the machine learning model M are based on model parameter values, for example entries of convolutional matrices of a convolutional neural network of the machine learning model M. The model parameter values are defined using training data,”, as disclosed in this section of the prior art, training data is used as parameter values to train the machine learning model.)and training the untrained machine learning system with the selected training parameters to develop a trained machine learning system (Col 6, Lines 23-31, “A training of the machine learning model can occur as a supervised learning process. Each of the training raw overview images is thus labelled with an annotation indicating an assessment of the corresponding training raw overview image. The assessment can be specified manually and comprise, e.g., scores within a predetermined range, for example from 1 to 10. Through the training, a (given) learning algorithm can adjust model parameter values of the machine learning model using the training raw overview images”, this section of the prior art discloses training the machine learning model with the selected parameters.); and operating the trained machine learning system with the first image as an input to generate the second image (Col 11, Lines 12-27, “FIG. 6 shows schematically processes of a further example method according to the invention for generating an overview image 30 based on a plurality of raw overview images 11-15. In this case, the raw overview images 11-15 are input collectively into the machine learning model M which, instead of outputting an assessment, outputs an overview image 30 directly. The overview image 30 can be, depending on the training of the machine learning model M, either a selection of one of the raw overview images 11-15, a linear combination of a plurality of the raw overview images 11-15 or a more complex combination calculated based on the raw overview images 11-15. An architecture of the machine learning model M can comprise, for example, one or more CNNs which generate a mapping of a plurality of input images onto one output image.”, as disclosed in this section of the prior art, the trained model is used to generate a new image based on the input image into the network.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo and Kraus with Amthor in order to train the machine learning model with a specific configuration. One skilled in the art would have been motivated to modify Honjo and Kraus in this manner in order to control the microscope to capture a plurality of raw overview images showing the sample environment with different capture parameters. (Amthor, Abstract)
Regarding Claim 18, it is substantially similar to claim 6 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Claims 11 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Honjo et al. US PG-Pub(US 20190004298 A1) in view of Kraus et al. US PG-Pub(US 20180137338 A1) in view of Plihal et al. US PG-Pub(US 20190302031 A1).
Regarding Claim 11, while the combination of Honjo and Kraus teach The method of claim 1, they do not explicitly teach wherein the second imaging configuration is one of a plurality of candidate production imaging configurations and a classification and a score are developed for each of the plurality of candidate production imaging configurations, further including the steps of: selecting a recommended production imaging configuration from the plurality of candidate production imaging configurations; and instructing a computer to display information regarding each candidate production imaging configuration of the plurality of candidate production imaging configurations and an indicator identifying the candidate production imaging configuration selected as the recommended production imaging configuration; and receiving from the computer a selection of one of the candidate production imaging configurations with which to configure the imaging system; wherein the information displayed for each candidate production imaging configuration includes one or more of the score, an estimate of a time savings, an image, and the classification of objects associated with the candidate production imaging configuration.
Plihal teaches wherein the second imaging configuration is one of a plurality of candidate production imaging configurations and a classification and a score are developed for each of the plurality of candidate production imaging configurations(¶[0049], “The one or more computer subsystems are configured for determining a separability score for each of one or more of the multiple modes thereby generating one or more separability scores”, as disclosed in ¶[0049], the prior art generates a score for each different configuration of the microscope.), further including the steps of: selecting a recommended production imaging configuration from the plurality of candidate production imaging configurations(¶[0062], “In one such embodiment, altering the one or more parameters of the defect detection method includes identifying a portion of the multiple modes expected (or known) to be capable of detecting the defects in greater numbers than another portion of the multiple modes and selecting only the output generated during the scanning performed with the identified portion of the multiple modes as the input to the defect detection method”, ¶[0062] discloses determining which mode is selected when operating the microscope.); and instructing a computer to display information regarding each candidate production imaging configuration of the plurality of candidate production imaging configurations and an indicator identifying the candidate production imaging configuration selected as the recommended production imaging configuration (¶[0063] “the computer subsystem(s)) are configured for displaying the defects and the nuisances to a user and receiving a selection of one or more of the defects and one or more of the nuisances from the user. For example, during the discovery analyzer (OMSSA) phase, the computer subsystem(s) may be configured for diagnostics and visualization of discovery results. In the diagnostics and visualization of discovery results phase, a user may identify important DOI types.”, ¶[0063] discloses using a computer to display a selection from the user in which on a display shows the diagnostics and visualization information pertaining to the selection.)and receiving from the computer a selection of one of the candidate production imaging configurations with which to configure the imaging system (¶[0065] discloses a user is able to select the imaging configuration of the microscope.)wherein the information displayed for each candidate production imaging configuration includes one or more of the score, an estimate of a time savings, an image, and the classification of objects associated with the candidate production imaging configuration.(¶[0065] discloses displaying diagnostic information from the mode selected from the user alongside the score of the defect.)
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the claimed invention as taught by Honjo and Kraus with Plihal in order to determine the imaging mode for the microscope. One skilled in the art would have been motivated to modify Honjo and Kraus in this manner in order to select a mode for inspection of a specimen. (Plihal, ¶[0001])
Regarding Claim 23, it is substantially similar to claim 11 respectively, and is rejected in the same manner, the same art, and reasoning applying.
Conclusion
THIS ACTION IS MADE FINAL. 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 HAN D HOANG whose telephone number is (571)272-4344. The examiner can normally be reached Monday-Friday 8-5.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JOHN M VILLECCO can be reached at 571-272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/HAN HOANG/Examiner, Art Unit 2661