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. Claim Status Claim s 3-6, 8, 12, 16, 22, 31, 34-42 are cancelled. Claim s 1,2,7,9-11,13-15,17,18-21,23-30, 32-33 are currently pending and under exam herein. Claims 1,2,7,9-11,13-15,17,18-21,23-30, 32-33 are rejected. Priority The instant application does not claim benefit to a provisional application. At this point in the examination, the effective filling date of the claims is 05/09/2022. Information Disclosure Statement The Information Disclosure Statements filed 09 May 2022 and 11 September 2023 are in compliance with the provisions of 37 CFR 1.97 and have therefore been considered. Drawings The Drawings filed on 09 May 2022 are accepted. Claim Rejections - 35 USC § 101 35 U.S.C. 101 reads as follows: Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title. Claims 1, 2, 9, 10-11, 13-15, 17-2 1 , 25-29, 30, 32 and 33 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more . The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgment, opinion). Subject matter eligibility evaluation in accordance with MPEP 2106 : Eligibility Step 1: Claims 1 ,7,9-1 1 ,13-15,17-21,23-29 are directed to a method ( process ) of monitoring one or more live cells. Claim 30 is directed to a n on-transitory computer readable medium ( manufacture, an article produced from materials) s toring instructions . Claim 32 is directed to a system (machine) for monitoring one or more live cells . Claim 33 is directed to a method (process) for training a computational mode. Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1. [Step 1: YES] Eligibility Step 2A : First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception. Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: (a) capturing a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein-based nuclear translocation reporters ( i.e., mental processes ); (b) capturing a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (i.e., mental processes); (c) identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells (i.e., mental processes and mathematical concepts) ; (d) identifying, based on the nuclear pixels, first pixels of the fluorescence image that correspond to the nuclei and second pixels of the fluorescence image that do not correspond to the nuclei (i.e., mental processes ) and (e) calculating, based on first intensities of the first pixels and second intensities of the second pixels, a metric representing a first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei of the one or more live cells and a second amount of the fluorescent protein-based nuclear translocation reporters not located within the nuclei of the one or more live cells (i.e., mathematical concepts) . Dependent claims 2 , 9, 10-11, 13-15, 17-2 1 , 25-29 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below. Dependent claim 2 further recites: The method of clai m 1, comprising the metric is a ratio of the first amount to the second amount (i.e., mathematical concepts) . Dependent claim 7, further recites: The method of claim 1, comprising capturing the non-fluorescence image comprises capturing a bright field image, a dark field image, or a phase contrast image ( i.e., mental processes ). Dependent claim 9 further recites: The method of claim 1, comprising calculating the metric comprises calculating a sum of the first intensities (i.e., mathematical concepts) . Dependent claim 10 further recites: The method of claim 1, comprising the sum is a first sum and calculating the metric further comprises: calculating a second sum of the second intensities; and comparing the first sum to the second sum (i.e., mathematical concepts) . Dependent claim 11 further recites: The method of claim 1, comprising comparing the first sum to the second sum comprises calculating a ratio of the first sum and the second sum (i.e., mathematical concepts) . Dependent claim 13 further recites: The method of claim 1, comprising c alculating the metric comprises calculating an average of the first intensities (i.e., mathematical concepts) . Dependent claim 1 4 further recites: The method of claim 1, comprising c alculating the average is a first average and calculating the metric further comprises: calculating a second average of the second intensities; and comparing the first average to the second average (i.e., mathematical concepts) . Dependent claim 1 5 further recites: The method of claim 1, comprising c omparing the first average to the second average comprises calculating a ratio of the first average and the second average (i.e., mathematical concepts) . Dependent claim 1 7 further recites: The method of claim 1, comprising the second pixels correspond to cytoplasm of the one or more live cells, and wherein the calculating comprises calculating, based on the first intensities of the first pixels and the second intensities of the second pixels, the metric representing the first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei and the second amount of the fluorescent protein-based nuclear translocation reporters located within the cytoplasm of the one or more live cells (i.e., mathematical concepts) . Dependent claim 1 8 further recites: The method of claim 1, comprising t he calculating comprises calculating, based on the first intensities of the first pixels and the second intensities of the second pixels, the metric representing the first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei and the second amount of the fluorescent protein-based nuclear translocation reporters located within the one or more live cells (i.e., mathematical concepts) . Dependent claim 19 further recites: The method of claim 1, comprising t he second pixels correspond to cytoplasm of the one or more live cells, and wherein the calculating comprises calculating, based on the first intensities of the first pixels and the second intensities of the second pixels, the metric representing the second amount of the fluorescent protein-based nuclear translocation reporters located within the cytoplasm and a third amount of the fluorescent protein-based nuclear translocation reporters located within the one or more live cells (i.e., mathematical concepts) . Dependent claim 20 further recites: The method of claim 1, comprising the fluorescent protein-based nuclear translocation reporters are selected from the group consisting of protein kinase translocation reporters, phosphatase translocation reporters, protease translocation reporters, and analyte responsive translocation reporters (i.e., mental processes). Dependent claim 21 further recites: The method of claim 1, comprising segmenting background from cells in the non-fluorescence image of the sample, and excluding the second pixels not belonging to cells from the calculating of the second intensities of the second pixels (i.e., mental processes and mathematical concepts) . Dependent claim 2 5 further recites: The method of claim 1, comprising t he metric provides a measure of kinase, phosphatase, or protease activity in the one or more live cells (i.e., mathematical concepts) . Dependent claim 26 further recites: The method of claim 1, comprising t he metric provides a measure of analyte concentration in the one or more live cells (i.e., mathematical concepts) . Dependent claim 27 further recites: The method of claim 1, comprising identifying the nuclear pixels comprises identifying the nuclear pixels of the non- fluorescence image that correspond to a single nucleus of a single cell of the one or more live cells, identifying the first pixels and the second pixels comprises identifying the first pixels that correspond to the single nucleus and the second pixels that are within a cytoplasm of the single cell, and calculating the metric comprises calculating the metric that represents the first amount of the fluorescent protein-based nuclear translocation reporters located within the nucleus and the second amount of the fluorescent protein-based nuclear translocation reporters located within the cytoplasm of the single cell (i.e., mental processes and mathematical concepts) . Dependent claim 2 8 further recites: The method of claim 1, comprising identifying the nuclear pixels comprises identifying the nuclear pixels of the non- fluorescence image that correspond to a single nucleus of a single cell of the one or more live cells, identifying the first pixels and the second pixels comprises identifying the first pixels that correspond to the nucleus and the second pixels that are within a cytoplasm of the single cell, and calculating the metric comprises calculating the metric that represents the first amount of the fluorescent protein-based nuclear translocation reporters located within the single nucleus and a third amount of the fluorescent protein-based nuclear translocation reporters located within the single cell (i.e., mental processes and mathematical concepts) . Dependent claim 2 9 further recites: The method of claim 1, comprising i dentifying the nuclear pixels comprises identifying the nuclear pixels of the non- fluorescence image that correspond to a single nucleus of a single cell of the one or more live cells, identifying the first pixels and the second pixels comprises identifying the first pixels that correspond to the nucleus and the second pixels that are within a cytoplasm of the single cell, and calculating the metric comprises wherein the calculating the metric representing the second amount of the fluorescent protein-based nuclear translocation reporters located within the cytoplasm of the single cell and a third amount of the fluorescent protein-based nuclear translocation reporters located within the single cell (i.e., mental processes and mathematical concepts) . Independent claim 30 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: A non-transitory computer readable medium storing instruction that, when executed by a computing device, cause the computing device to perform the method of any one of claims129 . functions comprising : ( a) capturing, via an optical microscope, a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein- based nuclear translocation reporters (i.e., mental processes) ; (b) capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (i.e., mental processes) ; (c) i dentifying , via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells (i.e., mental processes and mathematical concepts). (d) identifying, based on the nuclear pixels, first pixels of the fluorescence image that correspond to the nuclei and second pixels of the fluorescence image that do not correspond to the nuclei (i.e., mental processes) ; (e) calculating, based on first intensities of the first pixels and second intensities of the second pixels, a metric representing a first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei of the one or more live cells and a second amount of the fluorescent protein-based nuclear translocation reporters not located within the nuclei of the one or more live cells (i.e., mathematical concepts). Independent claim 3 2 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: A system for monitoring one or more live cells, the system comprising: an optical microscope; a fluorescence microscope; one or more processors; and a non-transitory computer readable medium storing instructions that, when executed by the one or more processors, cause the system to perform functions comprising: (a) capturing, via an optical microscope , a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein- based nuclear translocation reporters (i.e., mental processes) ; (b) capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (i.e., mental processes); (c) identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells (i.e., mental processes and mathematical concepts). (d) identifying, based on the nuclear pixels, first pixels of the fluorescence image that correspond to the nuclei and second pixels of the fluorescence image that do not correspond to the nuclei (i.e., mental processes) ; (e) calculating, based on first intensities of the first pixels and second intensities of the second pixels, a metric representing a first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei of the one or more live cells and a second amount of the fluorescent protein-based nuclear translocation reporters not located within the nuclei of the one or more live cells (i.e., mathematical concepts). Independent claim 3 3 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas: A method for training a computational model to identify pixels of non- fluorescence images that represent nuclei, the method comprising: generating first labels for first pixels of fluorescence images of samples, wherein the first labels indicate whether the first pixels represent a nucleus within the samples (i.e., mathematical concepts). generating, based on the first labels, second labels for second pixels of first non- fluorescence images of the samples, wherein the second labels indicate whether the second pixels represent a nucleus within the samples (i.e., mental processes and mathematical concepts). and training a computational model to identify pixels of second non-fluorescence images that represent nuclei using the second labels and the first non-fluorescence images (i.e., mathematical concepts). Therefore, claims 1 , 2, 9, 10-11, 13-15, 17-2 1 , 25-29 , 30, 32 and 33 recite an abstract idea. [Step 2A Prong One: YES] Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)). The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below. Dependent claims 2,7,9,10,11,13-15,17,18-21,25-29 do not recite any elements in addition to the judicial exception , and thus are part of the judicial exception. The additional element in independent claim 1 include: (c) identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells The additional elements in independent claim 30 include: A non-transitory computer readable medium storing instruction that, when executed by a computing device, cause the computing device to perform functions comprising: (a) capturing, via an optical microscope, a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein- based nuclear translocation reporters (i.e., mental processes); (b) capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (i.e., mental processes); (c) identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells (i.e., mental processes and mathematical concepts). The additional elements in independent claim 3 2 include : A system for monitoring one or more live cells, the system comprising : an optical microscope; a fluorescence microscope; one or more processors; and a non-transitory computer readable medium storing instructions that, when executed by the one or more processors, cause the system to perform functions comprising: (a) capturing, via an optical microscope, a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein- based nuclear translocation reporters (i.e., mental processes); (b) capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (i.e., mental processes); (c) identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells (i.e., mental processes and mathematical concepts). The additional elements of a non-transitory computer readable medium storing instruction that, when executed by a computing device, cause the computing device to perform functions (claim 30 and claim 32); capturing, via an optical microscope, a non-fluorescence image of a sample that includes one or more live cells (claim 30 and claim 32); capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample (claim 30 and claim 32); identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells ( claim 1, claim 30 and claim 32) ; are insignificant extra-solution activities that are part of the data gathering process used in the recited judicial exceptions (see MPEP 2106.05(g) . When all limitations in claims 1 , 2, 9, 10-11, 13-15, 17-2 1 , 25-29 , 30, 32 and 33 have been considered as a whole , the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1 , 2, 9, 10-11, 13-15, 17-2 1 , 25-29 , 30, 32 and 33 are directed to an abstract idea (MPEP 2106.04(d)). [Step 2A Prong Two: NO] Eligibility Step 2B : Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i -vi). The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because the reasons noted below. Dependent claims 2,7,9,10,11,13-15,17,18-21,25-29 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception (s). The additional elements recited in independent claim 1 , independent claim 30 and independent claim 32 , are identified above, and carried over from Step 2A : Prong Two along with their conclusions for analysis at Step 2B . Any additional element or combination of elements that was considered to be insignificant extra-solution activity at step Step 2A : Prong Two was re-evaluated at step 2B , because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d). The additional elements of “a non-transitory computer readable medium storing instruction that, when executed by a computing device ” is conventional. Evidence for conventionally is shown by Scholz, Karl W. (“A Floppy Disk Operating System for the 8080 and Z80 .” Behavior Research Methods , vol. 10, no. 4, 1 July 1978, pp. 527–530). Scholz shows a floppy disk storing program instructions readable by the 8080 or Z80 microprocessor, demonstrating that such physical media for storing executable instructions that direct a computing device’s operation has existed for decades, and it is well-understood and conventional in the field. The additional elements of “ capturing, via an optical microscope, a non-fluorescence image of a sample that includes one or more live cells ” is conventional. Evidence for conventionally is shown by Hooke (“ Hooke, R. Micrographia or some physiological descriptions of minutes bodies made by magnifying glasses. (J. Martyn and J. Allestry , 1665 ” ). Hooke published a collection of his hand-drawn observations of biological samples in 1665 in a book entitled Micrographia . Among the studies of different plants or animals by Hooke, one of the most famous remains the first observation of cork cells from Quercus suber . Optical microscopy allowed this first representation of the basic biological unit by Hooke, which was then defined as a “cell” , demonstrating that an optical microscope is a well-understood and conventional way of capturing a non-fluorescence image of a sample since 1665. The additional elements of “ capturing, via a fluorescence microscope, a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample ” is conventional. Evidence for conventionally is shown by Yuste ( Yuste , Rafael. “Fluorescence Microscopy Today.” Nature Methods , vol. 2, no. 12, Dec. 2005, pp. 902–904 ). Yuste shows that capturing a fluorescence image of fluorescence-protein reporters in live cells has been standard practice for decades (since 2005) . The review article further explains that green fluorescent protein ( GFP ) enabled routine fluorescence microscopy of protein localization in living tissues, establishing this type of imaging as a widely used and well-understood technique in the field. The additional elements of “ i dentifying, via a computational model , nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells ” is conventional. Evidence for conventionally is shown by Stringer et al. ( Stringer et al. “ Cellpose : A Generalist Algorithm for Cellular Segmentation.” Nature Methods, vol. 18, no. 1, 14 Dec. 2020, pp. 100–106 ). Stringer et al. describe the Cellpose model, which assigns pixels to cellular regions based on predicted spatial gradients and produces segmentation masks from brightfield and phase-contrast microscopy images without relying on fluorescence. Accordingly, the technique of pixel-level identification of nuclear regions from non-fluorescent optical images has been used since 202 0 , and is a well-understood and routine technique in the field. Therefore, when taken alone, all additional elements in independent claim 30 and independent claim 32 do not amount to significantly more than the above-identified judicial exceptions(s). Even when evaluated as combination, the additional elements fail to transform the exceptions (s) into patent-eligible application of that exception. Thus, claims 1-20 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)). [Step 2B : NO] 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 non - obviousness. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention. Claims 1 , 2, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 23, 26, 27, 28, 29, 30 and 32 are rejected under 35 U.S.C. 103(a) as being unpatentable over Kelley and Paschal. ( “Fluorescence-Based Quantification of Nucleocytoplasmic Transport.” Methods , vol. 157, 1 Mar. 2019, pp. 106–114 ), in view of Stringer et al. (“ Cellpose : A Generalist Algorithm for Cellular Segmentation.” Nature Methods, vol. 18, no. 1, 1 Jan. 2021, pp. 100–106) , and in further view of Koyuncu et al. ( “Object-Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images.” Cytometry Part A , vol. 93, no. 10, 13 Sept. 2018, pp. 1019–1028 ) . Claims 1, 2, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 23, 26, 27, 28, 29, 30 and 32 are drawn to a computer-based method for monitoring one or more living cells by first capturing non-fluorescent image s of a sample that includes live cells containing fluorescent protein-based nuclear translocation reporters, then capturing a fluorescent image of those same reporters within the live cells. A computational model is used to identify which pixels in the non-fluorescent image correspond to the nuclei, and those nuclear pixels are then used to determine which pixels in the fluorescent image belong to the nuclei (first pixels) and which do not (second pixels). Finally, using the intensity of the first and second pixels, the method calculates a metric that reflects the amount of fluorescent protein-based reporters located inside the nuclei and the amount located outside the nuclei (claim 1, claim 30, and claim 32) . In some embodiments: the metric is a ratio of the first amount to the second amount (claim 2) ; the non-fluorescence image can be taken using bright-field imaging, dark-field imaging, or phase-contrast imaging (claim 7); calculating the metric includes adding up the intensities of all the first pixels (claim 9); calculating the metric also includes adding up the intensities of all the second pixels to get a second sum, and comparing the first sum with the second sum (claim 10); comparing the first sum to the second sum includes calculating the ratio of the first sum to the second sum (claim 11); calculating the metric comprises calculating an average of the first intensities (claim 13); calculating a second average from the intensities of the second pixels, and comparing the first average to the second average (claim 14); comparing the first average to the second average includes calculating the ratio of the first average to the second average (claim 15); the second pixels correspond to the cytoplasm of the one or more living cells. Calculating the metric includes determining, from the intensities of the first pixels and the intensities of the second pixels, the amount of fluorescent protein-based nuclear translocation reporters inside the nuclei and the amount of those reporters located in the cytoplasm of the living cells (claim 17); calculating the metric includes determining, based on the intensities of the first pixels and the intensities of the second pixels, a first amount of fluorescent protein-based nuclear translocation reporters that are inside the nuclei, and a second amount of fluorescent protein-based nuclear translocation reporters that are located within the live cells (claim 18); ] the second pixels come from the cytoplasm of the live cells. The calculation includes determining, using the intensities of the first pixels and the second pixels, a metric representing a second amount of fluorescent protein based nuclear translocation reporters that are in the cytoplasm, and a third amount of those reporters that are somewhere else in the live cells (claim 19), using fluorescent protein-based nuclear translocation reporters that are chosen protein kinase translocation reporters, phosphatase translocation reporters, protease translocation reporters, and analyte responsive translocation reporters (claim 20); separating the background from the cells in a non-fluorescence image of the sample, and after separating them, excluding any second pixels that do not belong to cells from the calculation of the second intensities of the second pixels (claim 21); performing the method for the purpose of monitoring signaling pathways inside one or more live cells (claim 23); the calculated metric provides a measure of analyte concentration in the one or more live cells (claim 26); identifying the nuclear pixels by finding, in the non-fluorescence image, the pixels that correspond to one nucleus of one cell in the sample, then identifying the first pixels as those corresponding to that nucleus, and the second pixels as those in the cytoplasm of the same cell. The metric is calculated to represent the amount of fluorescent reporter in the nucleus and the amount in the cytoplasm of that cell (claim 27); identifying the nuclear pixels by finding the pixels in the non-fluorescence image that correspond to one nucleus of one cell, the first pixels correspond to that nucleus and the second pixels as those in the cytoplasm of the same cell. The metric is then calculated to represent the amount of fluorescent reporter in the nucleus and a third amount of the fluorescent reporter in the cytoplasm of that cell (claim 28); identifying nuclear pixels by finding in the non-fluorescence image, the pixels that correspond to one nucleus of one cell. It identifies the first pixels as those corresponding to the nucleus and the second pixels as those in the cell’s cytoplasm. The metric is then calculated to represent the second amount of fluorescent reporter in the cytoplasm and a third amount of the fluorescent reporter in the same cell (claim 29) . With respect to the limitation of capturing a non-fluorescence image of a sample that includes one or more live cells, wherein the one or more live cells contain fluorescent protein-based nuclear translocation reporters , Kelley and Paschal . show that the “ The example images in Figs. 4–7 are Saccharomyces cerevisiae expressing Gsp1-GFP ” (Figs. 4-7 page 107) - Gsp1-GFP is a fluorescent protein-based construct used to analyze and monitor protein localization, including observing its translocation properties. The authors further display these cells in D IC (differential interference contrast) which is a non-fluorescence imaging method ( Fig.4 , page 110). With respect to the limitation of capturing a fluorescence image of the fluorescent protein-based nuclear translocation reporters in the one or more live cells in the sample , Kelley and Paschal . show that the “ The example images in Figs. 4–7 are Saccharomyces cerevisiae expressing Gsp1-GFP ” (Figs. 4-7 page 107) - Gsp1-GFP is a fluorescent protein-based construct used to analyze and monitor protein localization, including observing its translocation properties. The authors further display these cells in DAPI , a fluorescence dye used to visualize the nucleus of a cell under fluorescence microscopy ( Fig.4 , page 110). With respect to the limitation of calculating, based on first intensities of the first pixels and second intensities of the second pixels, a metric representing a first amount of the fluorescent protein-based nuclear translocation reporters located within the nuclei of the one or more live cells , Kelley and Paschal. teach segmenting nuclear “ Use an image of a nuclear marker, DAPI for example, to generate a mask that segments nuclei ” (Figs. 4-7, col. 1, “ 5.1 generate nuclear mask”, lines 1-2, page 111) and cytoplasmatic compartments ( col.1 , “5.1 generate a mask that contains the cytoplasm”, lines 1-2, page 111), meaning nuclear first pixels and cytoplasmic second pixels. Kelley and Paschal. then calculate the “ mean fluorescence intensity per pixel ”, which equals “ the total fluorescence intensity divided by the number of pixels measured ” (Fig 1., col.2 , 3.1 calculations, eq.1 ), meaning its calculated from the intensity values of the pixels inside the nucleus, corresponding to first intensities of first pixels. Kelley and Paschal. also teach computing cytoplasmatic values relying on intensities of pixels outside the nuclei and corresponding to the second intensities of the second pixels “to calculate the cytoplasmic mean from this data, calculate the cytoplasmic intensity and the cytoplasmic area ” ( col.1 , “5.7 calculate N/C’, lines 4-8”, eq 4-6, page 112). The metric is then calculated “the ratio of the nuclear mean fluorescence to the cytoplasmic mean fluorescence” ( col.2 , “3.3. Calculation of N/C”, eq.3 , page 110), meaning a first amount inside the nuclei and a second amount outside the nuclei. With respect to claim 2 , Kelley and Paschal. teach the metric being a ratio of the first amount to the second amount, “the ratio of the nuclear mean fluorescence to the cytoplasmic mean fluorescence” ( col.2 , “3.3. Calculation of N/C”, eq.3 , page 110), meaning a first amount inside the nuclei and a second amount outside the nuclei. With respect to claim 9 , Kelley and Paschal. teach calculating a sum of the first intensities as the digital images are “matrix of light intensity values” and “total fluorescence is the sum of the intensity of every pixel measured" ( Fig.1 , col.1 , “3. Calculations”, Line s 1 -11 , page 108) , the total is calculated over the nuclear region, meaning it is the sum of the intensity values of the pixels. With respect to Claim 10 , Kelley and Paschal. teach calculating the metric also includes adding up the intensities of all the second pixels to get a second sum, and comparing the first sum with the second sum, “ Digital images are a matrix of light intensity values ” and “ Total fluorescence intensity is the sum of the intensity of every pixel measured ” ( Fig.1 , col.1 , lines 1-10, page 108), meaning the total nuclear intensity and total cytoplasmatic intensity correspond to sums of pixel intensities. The authors then compare the first sum and the second sum within one calculation “metric” ( Eq.2 , col.2 , page 108). With respect to claim 11 , Kelley and Paschal. teach comparing the first sum to the second sum includes calculating the ratio of the first sum to the second sum , “ Digital images are a matrix of light intensity values ” and “ Total fluorescence intensity is the sum of the intensity of every pixel measured ” ( Fig.1 , col.1 , lines 1-10, page 108), meaning the total nuclear intensity and total cytoplasmatic intensity correspond to sums of pixel intensities. The authors then calculate a ratio “ the N/C calculation required is simply the mean nuclear intensity divided by the mean cytoplasmic intensity ” of the first sum and the second sum ( Eq.3 , col.2 , lines 1-8, page 110). With respect to claim 13 , Kelley and Paschal. teach calculating an average of the first intensities “ Digital images are a matrix of light intensity values ” ( Fig.1 , col.1 , lines 1-10, page 108), “ mean fluorescence intensity per pixel ”, which equals “ the total fluorescence intensity divided by the number of pixels measured ” (Fig 1., col.2 , 3.1 calculations, eq.1 ), meaning the calculation is based on dividing the sum of nuclear pixels intensities by the number of nuclear pixels, so an average of the first intensities as the intensities of fluorescence image pixels are located inside the nuclei. With respect to claim 14 and claim 15 , Kelley and Paschal. teach calculating a second average of the second intensities and comparing the first average to the second average and comparing the first average to the second average includes calculating the ratio of the first average to the second average, “ In this case, a mean nuclear fluorescence value would have to be accompanied by a mean cytoplasmic value for comparison ” and “ For this reason, a commonly used metric for the nucleocytoplasmic distribution of a molecule is the ratio of the nuclear mean fluorescence to the cytoplasmic mean fluorescence ” ( col.2 , 3.1 calculations, lines 10-14, page 108), meaning the a first average is calculated (mean nuclear intensity), a second average is calculated (mean cytoplasmic intensity) and a comparison is made between the two averages (ratio). With respect to claim 17 , Kelley and Paschal. teach that second pixels correspond to cytoplasm ( col.1 , 5.7 calculate N/C, lines 4-8, eq 4-6, page 112), the values are calculated from the cytoplasmatic regions, so the underlying pixels are from outside of the nucleus (second pixels). The authors further teach calculating a metric using the intensity of first and second pixels ( Fig.1 , col.1 , lines 1-10, page 108), and explain that the calculation uses pixels intensities from both nuclear regions corresponding to first pixels and cytoplasmic regions corresponding to second pixels (Fig 1., col.2 , 3.1 calculations, eq.1 ) . Kelley and Paschal. also identify the fluorescence inside the nuclei by the “ mean fluorescence intensity per pixel ” for the nuclear mask ( Fig 1., col.2 , 3.1 calculations, eq.1 ), corresponding to the first amount, and also identifies the fluorescence inside the cytoplasm by the “ mean cytoplasmic intensity ” ( Eq.3 , col.2 , lines 1-8, page 110) . With respect to claim 18 , Kelley and Paschal. teach calculating a metric based on the first intensities of nuclear pixels and the second intensities of cytoplasmic pixel , defining t he images as matrix of light intensity values ( Fig.1 , col.1 , “3. Calculations”, Lines 1-11, page 108) and then calculating the mean fluorescence per pixel ( Fig 1., col.2 , 3.1 calculations, eq.1 ), as well as cytoplasmatic fluorescence” ( Eq.3 , col.2 , lines 1-8, page 110). The nuclear and cytoplasmic intensities correspond to the claimed first amount and (fluorescence in the nucleus) and second amount (fluorescence within the cytoplasm), and the calculation uses the intensities of the first pixels and second pixels. With respect to claim 19 , claim 28 , and claim 29 , Kelley and Paschal . teach calculating a metric and including a third amount of fluorescence protein in the calculation by creating a “whole cell mask approach” (Supplementary Fig S1 , page 112) and they use this whole cell mask to represent an amount of fluorescence protein, corresponding to a third amount ( the first amount comes from the nucleus and the second amount from the cytoplasm, as established in independent claims 1, 30 and 32). With respect to claim 20 , Kelley and Paschal. teach using fluorescent protein-based nuclear translocation reporters that are analyte responsive translocation reporters , they show that the “ The example images in Figs. 4–7 are Saccharomyces cerevisiae expressing Gsp1-GFP ” (Figs. 4-7 page 107) - Gsp1-GFP is a fluorescent protein-based construct and its localization changes in response to cellular signaling conditions, and is used to analyze and monitor protein localization observing its translocation. With respect to claim 21, Kelley and Paschal. teach segmentation background from cells using a non-fluorescence image, “Use an image of a nuclear marker, DAPI for example, to generate a mask that segments nuclei” (Figs 4-7, col.1 , lines 1-2, page 111). This segmentation step identifies cell regions and separates them from the background. Kelley and Paschal. further teach calculating the cytoplasmatic intensity coming only from the pixels within the cytoplasmic mask ( col.1 , 5.7 calculate N/C, lines 4-8, eq 4-6, page 112), meaning the cytoplasmic mean and the cytoplasmic intensity come from only the pixels inside the cytoplasmic mask, excluding all background. With respect to claim 23 , Kelley and Paschal. teach performing the method to monitor signaling pathways within live cells. The authors show that “ Nucleocytoplasmic transport plays a key role in numerous cellular pathways ” ( col.1 , introduction, para 1, lines 15-17) and provide guidance for quantifying protein levels using fluorescence microscopy and ImageJ software (abstract). With respect to claim 26 , Kelley and Paschal. teach that the fluorescence-based calculation provides a measure of reactant concentration, “ concentration of reactants is what drives biochemical processes forward ” and “ same intensity from a large area would represent a lower concentration than that same intensity in a small area ” (col 1., “3. calculations”, para. 1, lines 4-12) meaning that the measured fluorescence metric provides a measure related to the concentration in live cells. With respect to claim 27 , Kelley and Paschal. t each applying the overall calculations applied to independent claims 1, 30 and 32 above, to single cells and single nucleus, as can be seen in figure 8 “ Different methods yield the same relative measurements. Graphs of the Ran N/C results of each cell, using the different selection methods discussed above. While the absolute values differ (most notably between Nuclear Dilation and the other methods), the relative values of the N/C ratios are well maintained through all three methods ” ( Fig.8 , legend, page 113). Kelley and Paschal. do not t each the limitation of identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells . Kelley and Paschal. also do not t each the limitation of identifying, based on the nuclear pixels, first pixels of the fluorescence image that correspond to the nuclei and second pixels of the fluorescence image that do not correspond to the nuclei Stringer et al . does teach identifying nuclei directly from non-fluorescent images by using a computational method to separate nuclear pixels from background pixels. Koyuncu et al. does teach identifying nuclear pixels directly from fluorescence image itself by separating nuclear pixels from background pixels. With respect to the limitation of identifying, via a computational model, nuclear pixels of the non-fluorescence image that correspond to nuclei of the one or more live cells , Stringer et al . teach “ We used image set MoNuSeg , which consists of 30 H&E-stained histological images of various human organs. Because the images contain many small nuclei (roughly 700 per image), we divided each of these images into nine separate images. We inverted the polarity of these images so that foreground nuclear pixels had higher intensity values than the background ”. With respect to claim 7, Stringer et al . teach the non-fluorescence image can be taken using bright-field imaging, dark-field imaging, or phase-contrast imaging ( col.2 , “training dataset”, lines 7-10). With respect to the limitation of identifying, based on the nuclear pixels, first pixels of the fluorescence image that correspond to the nuclei and second pixels of the fluorescence image that do not correspond to the nuclei , Koyuncu et al. teach “ segmentation in fluorescence microscopy images typically starts with differentiating nuclear pixels from background to obtain a binary mask (…), this is quite straightforward when nuclei appear isolated in an image; each connected component on the binary mask corresponds to a nucleus ” meaning first pixels, the method identifies which fluorescence-image pixels belong to nuclei (“fluorescence”, para.2 , lines 2-8, page 1019). Thus, the second pixels of the fluorescence image that do not correspond to the nuclei corresponds to the background, which are distinct from nuclear pixels. It would have been obvious to one of ordinary skill in the art at the time of the invention was made to modify the method of Kelley and Paschal. by using the nuclear identification techniques taught by Stringer et al. and Koyuncu et al. Kelley and Paschal. show that their approach “has multiple advantages over biochemical fractionation” and rely on predefined nuclear masks (conclusion) . Stringer et al. teach that nuclei can be identified directly from non-fluorescent images using a deep learning-based segmentation method is very precise and does not require model retraining (introduction), while Koyuncu et al. shows that its method solves current issues with c ell nucleus segmentation and leads to better segmentation (introduction and conclusion). A person of ordinary skill in the art would therefore have been motivated to combine these teachings because Stringer et al. and Koyuncu et al. show improved, precise and straightforward ways to identify nuclear pixels, supporting Kelley and Paschal ’s fluorescence-based quantification of nucleocytoplasmic transport method. One would have had a reasonable expectation of success for making the combination because all references are not only related to the same field , but provide improvements , and advantages, leading to a more precise and faster method. Claim 24 is rejected under 35 U.S.C. 103(a) as being unpatentable over Kelley and Paschal. “Fluorescence-Based Quantification of Nucleocytoplasmic Transport.” Methods , vol. 157, 1 Mar. 2019, pp. 106–114 ), in view of Stringer et al. (“ Cellpose : A Generalist Algorithm for Cellular Segmentation.” Nature Methods, vol. 18, no. 1, 1 Jan. 2021, pp. 100–106) , and in further view of Koyuncu et al. ( “Object-Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images.” Cytometry Part A , vol. 93, no. 10, 13 Sept. 2018, pp. 1019–1028 ), as applied to claims 1, 2, 7, 9 - 11, 13 - 15, 17 - 21, 23, 26 - 30 and 32 above, and in further view of Moczko et al. ( “Fluorescence-Based Assay as a New Screening Tool for Toxic Chemicals.” Scientific Reports , vol. 6, no. 1, 22 Sept. 2016, p. 33922 ). Claim 24 is drawn to the sample being exposed to a test compound, and the earlier image-analysis steps are repeated several times to see how the compound affects the system. Through these repeated measurements, the method determines how the test compound changes in amount of fluorescent nuclear-translocation reporter found inside the nuclei and the amount found outside the nuclei of the live cells. Kelley an