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 filed RCE/Amendments
Applicant’s Amendments/Remarks filed on 06/09/2026 have been received and made of record. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/09/2026 has been entered.
Claims 1-20 remained pending.
Filed Affidavit Declaration under 37 CFR 1.130(a) of 06/09/2026 has been entered and made of record.
The outstanding claim objections of dependent claims 5, 8, and 10 have been withdrawn based at least in light of the new grounds of rejection. Please refer to the action below.
Examiner Notes
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. However, the claimed subject matter, not the specification, is the measure of the invention.
Response to Remarks/Arguments
Applicants’ arguments corresponding to the file declaration under 35 CFR 1.130 regarding the prior art of Korski of pages 9-11, have been considered, however, they are moot in light of the new ground of rejection. Furthermore, the arguments of Cosatto and Zeineh of pages 11-14 regarding the claimed limitations of (“quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures”) have also been considered, however, they are also moot in light of the new grounds of rejection.
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 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.
Claim(s) 1-3, 15, 17, and 19-20 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto et al. (US 2010/0002920, previously cited), in view of Kraft et al. (NPL, A1).
Regarding claim 1, Cosatto teaches in at least the Abstract a computer-implemented method, comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Kraft discloses methods and systems of performing at least in the Abstract and Figs. 3-4 spatial pattern distribution analysis of mitotic cells, and further configured in at least in the Abstract and Figs. 3-4 determining mitotic values/metric such as further in page 315 an average nearest neighbor distance calculation to determine as cited an average nearest neighbor distance or that of an average neighbor count within a radius of the mitotic figures in the biological sample thus further calculating the density of the figures, ultimately as cited “a significant difference between observed and expected nearest-neighbor distances was investigated by use tailed normal variate test” whereby further in page 317 citing “the finding that nearest-neighbor analysis showed that the majority of mitotic and apoptotic cells to be distributes at random comes as no surprise…..our results offer quantificative data to substantiate this assumption” further indicating quantifying of said spatial distribution of the plurality of mitotic cells or figures within the biological sample, wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic cells. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Kraft are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures; Kraft’s combination of at least a random spatial distribution analysis of the mitotic cells and quantifying of the spatial distribution pattern further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified spatial distribution combination of Kraft it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 2 (according to claim 1), Cosatto further teaches wherein the mitotic metric comprises an average nearest neighbor distance (calculated distances between each pair of mitotic figures of further para. 0029 and 0034 by the nearest neighbor classifier further comprises as understood in the art an obvious average nearest neighbor distance),
the method further comprising: determining a distance between each pair of mitotic figures included in the plurality of mitotic figures (the nearest neighbor classifier of at least para. 0018 is configured to calculate as further supported in at least para. 0029 at least distances between each pair of mitotic figures included in the plurality of mitotic figures as said classifier is known to calculate smallest/shortest distance between said each pair of mitotic figures);
identifying, based at least on the distance between each pair of mitotic figures included in the plurality of mitotic figures, a shortest distance between each mitotic figure of the plurality of mitotic figures and another mitotic figure in the plurality of mitotic figures (the nearest neighbor classifier of at least para. 0018 and 0029 further configured for at least identifying, based at least on the nearest distance between each pair of mitotic figures included in the plurality of mitotic figures, an implied smallest or shortest distance between each mitotic figure of the plurality of mitotic figures and another mitotic figure in the plurality of mitotic figures);
and determining, based at least on the shortest distance between each mitotic figure of the plurality of mitotic figures and the another mitotic figure in the plurality of mitotic figures, the average nearest neighbor distance (the nearest neighbor classifier is further adapted understandably for determining, based at least on the smallest or shortest distance between each mitotic figure of the plurality of mitotic figures and the another mitotic figure in the plurality of mitotic figures, in a case the average nearest neighbor distance).
Regarding claim 3 (according to claim 1), Cosatto further teaches wherein the mitotic metric comprises an average neighbor count within a radius (calculated distances between each pair of mitotic figures of further para. 0029 and 0034 by the nearest neighbor classifier further comprises an average nearest neighbor distance within a radius of a Linhartb of mitotic figure),
the method further comprising: determining a distance between each pair of mitotic figures included in the plurality of mitotic figures (para. 0029); and determining, based at least on the distance between each pair of mitotic figures included in the plurality of mitotic figures, a count of one or more other mitotic figures that are within the radius of each mitotic figure in the plurality of mitotic figures (para. 0029).
Regarding claim 15 (according to claim 1), Cosatto further teaches wherein further comprising: segmenting the image of the biological sample into a first region corresponding to the tumor tissue and a second region corresponding to a non-tumor tissue (para. 0027-0029 further teaches filtering mitotic figures from the obtained image based at least on pixel color values into a first region corresponding to real tumor tissue or mitotic region and a second region corresponding to a non-possible tumor tissue);
wherein the non-tumor tissue includes a fat tissue and/or a normal tissue (a non-mitotic region of further para. 0027-0029 is further understood as a non-tumor tissue includes a fat tissue and/or a normal tissue); and
excluding, from the plurality of mitotic figures, one or more mitotic figures identified within the second region of the image (para. 0027-0029 further teaches to exclude those mitotic figures not meeting a threshold value).
Regarding claim 17 (according to claim 1), Cosatto further teaches wherein further comprising: determining, for each region of a plurality of regions of the tumor tissue, an intensity metric corresponding to an activity of the tumor within the region (para. 0027-0029 further teaches performing filtering and an implied segmenting proc ess to the obtained image based at least on pixel color values and further in para. 0026 based on a count intensity metric to further generate mitotic regions and non-mitotic regions corresponding to an activity of the tumor within the region); and
excluding, from the plurality of mitotic figures, one or more mitotic figures identified within a region whose intensity metric fails to satisfy one or more thresholds (the system further in para. 0027-0029 further adapted for excluding, from the plurality of mitotic figures, one or more mitotic figures identified within a region whose threshold is not met further indicative obviously to an intensity metric failing to satisfy one or more thresholds).
Regarding claim 19, Cosatto teaches a system of at least Fig. 7, comprising:
at least one data processor (Fig. 7); and
at least one memory (Fig. 7) storing instructions, which when executed by the at least one data processor, result in operations comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a spatial distribution of the plurality of mitotic figures within the biological sample); and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Kraft discloses methods and systems of performing at least in the Abstract and Figs. 3-4 spatial pattern distribution analysis of mitotic cells, and further configured in at least in the Abstract and Figs. 3-4 determining mitotic values/metric such as further in page 315 an average nearest neighbor distance calculation to determine as cited an average nearest neighbor distance or that of an average neighbor count within a radius of the mitotic figures in the biological sample thus further calculating the density of the figures, ultimately as cited “a significant difference between observed and expected nearest-neighbor distances was investigated by use tailed normal variate test” whereby further in page 317 citing “the finding that nearest-neighbor analysis showed that the majority of mitotic and apoptotic cells to be distributes at random comes as no surprise…..our results offer quantificative data to substantiate this assumption” further indicating quantifying of said spatial distribution of the plurality of mitotic cells or figures within the biological sample, wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic cells. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Kraft are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures; Kraft’s combination of at least a random spatial distribution analysis of the mitotic cells and quantifying of the spatial distribution pattern further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified spatial distribution combination of Kraft it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 20, Cosatto teaches in para. 0040 and Fig. 7 a non-transitory computer readable medium 730 storing instructions, which when executed by at least one data processor 720, result in operations comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Kraft discloses methods and systems of performing at least in the Abstract and Figs. 3-4 spatial pattern distribution analysis of mitotic cells, and further configured in at least in the Abstract and Figs. 3-4 determining mitotic values/metric such as further in page 315 an average nearest neighbor distance calculation to determine as cited an average nearest neighbor distance or that of an average neighbor count within a radius of the mitotic figures in the biological sample thus further calculating the density of the figures, ultimately as cited “a significant difference between observed and expected nearest-neighbor distances was investigated by use tailed normal variate test” whereby further in page 317 citing “the finding that nearest-neighbor analysis showed that the majority of mitotic and apoptotic cells to be distributes at random comes as no surprise…..our results offer quantificative data to substantiate this assumption” further indicating quantifying of said spatial distribution of the plurality of mitotic cells or figures within the biological sample, wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic cells. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Kraft are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures; Kraft’s combination of at least a random spatial distribution analysis of the mitotic cells and quantifying of the spatial distribution pattern further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified spatial distribution combination of Kraft it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in Kraft, and further in view of Stein et al. (US 2006/0149481, previously cited).
Regarding claim 4 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein the mitotic metric comprises a Clark-Evans (CE) index corresponding to a ratio between an average nearest neighbor distance and an expected nearest neighbor distance for the biological sample.
Stein teaches in at least para. 0092 a cluster analysis and a Clark-Evans quantified metric of a biological sample corresponding to a ratio between an average nearest neighbor distance and an expected nearest neighbor distance for the biological sample. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in Kraft, and further in view of Stein to include Clark-Evans (CE) index corresponding to a ratio between an average nearest neighbor distance and an expected nearest neighbor distance for the biological sample, as discussed above, as Cosatto in Kraft, and further in view of Stein are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample, Stein’s cluster analysis and a Clark-Evans quantified metric further complements the cluster analysis of the identified mitotic figures within the image of the biological sample of Cosatto in view of Kraft with a supplemented Clark-Evans quantified metric associated with a nearest neighbor distance of the plurality of biological image depictions which when added to the nearest distance mitotic figures metric of Cosatto in view of Kraft further increase and optimize location detection of observed biological image objects which may obviously depict in Cosatto in view of Kraft a mitosis process of said objects which would also optimize generated treatment plans according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 5 (according to claim 4), Cosatto is silent regarding wherein further comprising: determining, for each mitotic figure of the plurality of mitotic figures, a distance to another mitotic figure of the plurality of mitotic figures that is a nearest neighbor; determining, for the plurality of mitotic figures, the average nearest neighbor distance for the biological sample; and determining, based at least on a count of the plurality of mitotic figures and a size of the tumor in the biological sample, the expected average neighbor distance for the biological sample.
Kraft further teaches at least in Figs. 3-4 and pages 315-318 of the disclosure performed quantitative calculation of the spatial distribution pattern of the mitotic figures employing at least the nearest-neighbor distances calculations for each figure pair essentially determining, for each mitotic figure of the plurality of mitotic figures, a distance to another mitotic figure of the plurality of mitotic figures that is a nearest neighbor and determining, for the plurality of mitotic figures, the average nearest neighbor distance for the biological sample; and Kraft further may obviously further determining, based at least on a count of the plurality of mitotic figures and a size of the tumor in the biological sample, the expected average neighbor distance for the biological sample. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft to include wherein said determining, as discussed above, as Cosatto in view of Kraft are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures; Kraft’s combination of at least a random spatial distribution pattern analysis of the mitotic cells and quantifying of the spatial distribution pattern further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified spatial distribution combination of Kraft it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view of LI et al. (WO 2021/236547, previously cited).
Regarding claim 7 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein the mitotic metric comprises a measure of local spatial autocorrelation.
LI teaches a distance and spatial distribution metric of para. 0029, 0040 and 0152 for at least in a case detected mitotic and tumor cells wherein said metric of at least para. 0152 comprises a measure of local spatial autocorrelation. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of LI to include wherein mitotic metric comprises a measure of local spatial autocorrelation, as discussed above, as Cosatto in view of Kraft, and further in view of LI are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample, LI’s local spatial autocorrelation further complements the identified mitotic cells within the image of the biological sample with a supplemented quantified local spatial autocorrelation metric associated with a local Geary metric which when added to the nearest distance mitotic figures metric of Cosatto in view of Kraft further increase and optimize location detection of the mitosis process which would also optimize generated treatment plans according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view Rogan et al. (US, 20210057109, A1).
Regarding claim 8 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein the mitotic metric comprises a local Moran's I statistic for each region of a plurality of regions in the image of the biological sample, and wherein a first local Moran's I statistic for a first region of the plurality of regions is determined based on a first count of mitotic figures present in the first region and a weighted sum of mitotic figures present in each of a plurality of other regions.
Rogan teaches in at least para. 0038-0039 determining features which cluster spatially with other neighboring features using methods such as Local Moran's I test and cited spatial autocorrelation to quantify and count features in the sample and further using neighbouring data for determining at least a weighted sum of the features present in each of a plurality of other regions. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of Rogan to include wherein said comprise local Moran's I statistic for each region of a plurality of regions in the image of the biological sample, and wherein said first local Moran's I statistic and said weighted sum of mitotic figures present in each of a plurality of other regions, as discussed above, as Cosatto in view of Kraft, and further in view of Rogan are in the same of endeavor of identifying, within an image of a biological sample, a plurality of features associated with the present sample based on at least a performed spatial distribution analysis of the sample, Rogan’s combination of the performed spatial autocorrelation analysis, in addition to the determined local Moran's I statistic and the weighted sum of the features further complements the identified and quantified mitotic cells within the image of the biological sample of Cosatto in view of Kraft with a supplemented quantified spatial autocorrelation metric where the clustered counts further correlate to specific identified regions of figures further optimizing the mitotic figures detection undergoing the mitosis process which would also ultimately optimize the tumor grading classification and detection of Cosatto in view of Kraft according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view BI et al. (CN, 110378461, A1).
Regarding claim 10 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein the mitotic metric comprises a local Geary's C statistic for each region of a plurality of regions in the image of the biological sample, wherein a first local Geary's C statistic of a first region of the plurality of regions corresponds to a weighted sum of differences in a first count of mitotic figures present in the first region and a count of mitotic figures present in each of a plurality of other regions.
BI teaches at least in the disclosure the Local Geary's statistic depicted of the detected spatial distribution of the identified features such as “in said step 4, the Local Geary's C space from the calculation method of the correlation coefficient as follows: local spatial statistical analysis is by evaluating the space between current data and all space interval data of self-correlation is performed, compared with the global analysis method can more accurately extracts space related error. in all local spatial statistics analysis method, Local Geary's C and is widely used due to its sensitive self-correlation result to the space”, said features are clustered for each region of a plurality of regions in the image, wherein a first local Geary's C statistic of a first region of the plurality of regions corresponds to an understood weighted sum of differences in the said regions. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of BI to include wherein mitotic metric comprises a local Geary's C statistic for each region of a plurality of regions in the image of the biological sample, as discussed above, as Cosatto in view of Kraft, and further in view of BI are in the same of endeavor of identifying, within an image of a biological sample, a plurality of features associated with the present sample based on at least a performed spatial distribution analysis of the sample, BI’s combination of the performed spatial autocorrelation analysis, in addition to the determined local Geary's statistic further complements the identified and quantified mitotic cells within the image of the biological sample of Cosatto in view of Kraft with a supplemented quantified spatial autocorrelation metric where the clustered counts further based on at least the self-correlation known to one ordinary skill in the art to at least optimizing the mitotic figures detection of Cosatto in view of Kraft for obviously ultimately optimize the tumor grading classification and detection according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view Zeineh et al. (US, 2024/0233418, previously cited).
Regarding claim 13 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein the mitotic metric comprises an average mitotic density corresponding to a ratio between a count of the plurality of mitotic figures and an area of the tumor tissue.
Zeineh further teaches in at least para. 0053 calculating at least tumor cell density and the ratio thereof further comprising in the art at least an average mitotic density corresponding to a ratio between a count of the plurality of mitotic figures and an area of the tumor tissue. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view Zeineh to include wherein mitotic metric comprises an average mitotic density corresponding to a ratio between a count of the plurality of mitotic figures and an area of the tumor tissue, as discussed above, as Cosatto in view of Kraft, and further in view Zeineh are in the same of endeavor of employing at least mitotic cells detection models for identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; the combined detection of the highest mitotic figures spatial and density distribution in addition to quantified density ratio of the mitotic figures of Zeineh further complements the mitotic cells figures count detection within the image of the biological sample of Cosatto in view of Kraft, in the sense that when combined with the mitotic metric comprises an average mitotic density corresponding to a ratio between a count of the plurality of mitotic figures and an area of the tumor tissue of Zeineh, enables the system of Cosatto in view of Kraft to more positively identified the counts of tumorous and/or cancerous cells grading and proliferation according to at least their mitosis patterns and spatial distribution of the said biological sample, according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view of Laurent et al. (WO 2009/150256, previously cited).
Regarding claim 14 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein determining, based at least on the tumor grade, a survival prognosis for the patient associated with the biological sample.
Laurent teaches identifying tumor features in an image sample of at least para. 0018 and further determining, in at least para. 0036 based at least on the tumor grade/stage, a survival prognosis for the patient associated with the biological sample. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of Laurent to include determining, based at least on the tumor grade, a survival prognosis for the patient associated with the biological sample, as discussed above, as Cosatto in view of Kraft, and further in view of Laurent are in the same of endeavor of identifying, within an image of a biological sample, a plurality of tumor cells associated with a tumor tissue present in the biological sample, Laurent’s survival prognosis determination further complements the identified mitotic figures within the image of the biological sample of Cosatto in view of Kraft with a supplemented survival prognosis based at least on the tumor which when added to the nearest distance mitotic figures metric of Cosatto in view of Kraft further increase and optimize generated treatment plans according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view of Zhao et al. (CN111340128, previously cited).
Regarding claim 16 (according to claim 1), Cosatto in view of Kraft are silent regarding wherein further comprising: identifying, within the image of the biological sample, one or more background portions of the image; and omitting the one or more background portions of the image during the identifying of the plurality of mitotic figures.
Zhao teaches at least in the disclosure and the Abstract metastatic lymph pathological image identification system wherein identifying, within the image of the biological sample, one or more biological objects of the sample undergoing a mitosis process image; Zhao further teaches the separating and excluding one or more background portions of the image during the implied identifying of the plurality of the mitosis process. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of Zhao to include said identifying, within the image of the biological sample, one or more background portions of the image; and omitting the one or more background portions of the image during the identifying of the plurality of mitotic figures, as discussed above, as Cosatto in view of Kraft, and further in view of Zhao are in the same of endeavor of identifying divided cells within an image of a biological sample, Zhao’s background image portions identification and omission further complements the determined count number of mitotic figures undergoing mitosis within an image of a biological sample of Cosatto in view of Kraft with supplemented background image portions identification and omittance of the said one or more background portions of the image during the identifying of the plurality of mitosis processing, which when added to the nearest distance mitotic figures count detection of Cosatto in view of Kraft undergoing mitosis further increase and optimize mitotic figures detection accuracy for at least further generating an optimized treatment plan according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Kraft, and further in view of Vasilev et al. (RU 2677872, previously cited).
Regarding claim 18 (according to claim 1), Cosatto is silent regarding wherein the tumor grade is further determined based on an another mitotic metric corresponding a count of mitotic figures identified in one or more fields-of-view of the image of the biological sample, and wherein the one or more fields of view are associated with a magnification level satisfying one or more thresholds.
Vasilev teaches at least in the disclosure and the Abstract the identifying and counts of mitotic figures in a biological sample where a tumor grade or malignancy is further determined based on mitotic metric corresponding said count of mitotic figures identified in one or more fields-of-view of the image of the biological sample, and wherein the one or more fields of view are associated with a lens magnification level satisfying obviously one or more thresholds. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Kraft, and further in view of Vasilev to include said tumor grade is further determined based on an another mitotic metric corresponding a count of mitotic figures identified in one or more fields-of-view of the image of the biological sample, and wherein the one or more fields of view are associated with a magnification level satisfying one or more thresholds, as discussed above, as Cosatto in view of Kraft, and further in view of Vasilev are in the same of endeavor of identifying and determining a count number of mitotic figures within an image of a biological sample, Vasilev’s
Identified mitotic metric count determination of mitotic figures in the one or more fields-of-view of the image of the biological sample further complements the determined count number of mitotic figures undergoing mitosis within an image of a biological sample of Cosatto in view of Kraft with a supplemented mitotic metric corresponding to a count of mitotic figures identified in one or more fields-of-view of the image of the biological sample further associated with a magnification level satisfying one or more thresholds, which when added to the nearest distance mitotic figures count detection of Cosatto in view of Kraft further increase and optimize based at least on the fields of view and the magnification level the level or grade of mitosis of the tumor cells for further generating an optimized treatment plan according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claim(s) 1-3, 15, 17, and 19-20 is/are further rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Linhart et al. (WO 2021171281, A1).
Regarding claim 1, Cosatto teaches in at least the Abstract a computer-implemented method, comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a figures within the biological sample (determining, at least in para. 0017-0018, count of mitotic figures per unit area in the biological sample, as said figures count per unit area further indicates in the art a distributed count of said figures regarding how cancerous, if any, the tissue is, further generating at least in para. 0017-0018 a proliferation rate including further an at least a mitotic metric corresponding to a mitosis distribution of mitotic image features between the mitotic figures counts further quantifying a distribution activity level further obviously indicative of the spatial distribution of the plurality of mitotic figures within the biological sample); and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Linhart discloses at least in para. 0004, and 0040 automated and realtime tracking and analysis of pathology images and “quantification of (IHC) slides and mitosis count, a percentage per grade, an IHC quantification, an indication of a distance between cancerous cells and a border of a tissue, and an indication of whether cancerous cells have metastasized beyond the predefined border” further insinuating tracking in realtime spatial distribution of the plurality of mitotic figures within the biological sample and quantifying by calculating said at least cited distances between the figures, and the mitosis count as the determined metric further quantifying as noted also in para. 0141 and 0159 said mitoses or the spatial distribution of the plurality of mitotic figures within the biological sample as said mitotic spatial distribution as understood in the art corresponds to a spatial pattern formed by the plurality of mitotic figures. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Linhart to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Linhart are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures and determined based on quantification a tumor grading or the like; Linhart’s combination of at least the mitoses quantification and the tumor grading determination further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified mitoses calculation combination of Linhart it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 2 (according to claim 1), Cosatto further teaches wherein the mitotic metric comprises an average nearest neighbor distance (calculated distances between each pair of mitotic figures of further para. 0029 and 0034 by the nearest neighbor classifier further comprises as understood in the art an obvious average nearest neighbor distance),
the method further comprising: determining a distance between each pair of mitotic figures included in the plurality of mitotic figures (the nearest neighbor classifier of at least para. 0018 is configured to calculate as further supported in at least para. 0029 at least distances between each pair of mitotic figures included in the plurality of mitotic figures as said classifier is known to calculate smallest/shortest distance between said each pair of mitotic figures);
identifying, based at least on the distance between each pair of mitotic figures included in the plurality of mitotic figures, a shortest distance between each mitotic figure of the plurality of mitotic figures and another mitotic figure in the plurality of mitotic figures (the nearest neighbor classifier of at least para. 0018 and 0029 further configured for at least identifying, based at least on the nearest distance between each pair of mitotic figures included in the plurality of mitotic figures, an implied smallest or shortest distance between each mitotic figure of the plurality of mitotic figures and another mitotic figure in the plurality of mitotic figures);
and determining, based at least on the shortest distance between each mitotic figure of the plurality of mitotic figures and the another mitotic figure in the plurality of mitotic figures, the average nearest neighbor distance (the nearest neighbor classifier is further adapted understandably for determining, based at least on the smallest or shortest distance between each mitotic figure of the plurality of mitotic figures and the another mitotic figure in the plurality of mitotic figures, in a case the average nearest neighbor distance).
Regarding claim 3 (according to claim 1), Cosatto further teaches wherein the mitotic metric comprises an average neighbor count within a radius (calculated distances between each pair of mitotic figures of further para. 0029 and 0034 by the nearest neighbor classifier further comprises an average nearest neighbor distance within a radius of mitotic figures),
the method further comprising: determining a distance between each pair of mitotic figures included in the plurality of mitotic figures (para. 0029); and determining, based at least on the distance between each pair of mitotic figures included in the plurality of mitotic figures, a count of one or more other mitotic figures that are within the radius of each mitotic figure in the plurality of mitotic figures (para. 0029).
Regarding claim 15 (according to claim 1), Cosatto further teaches wherein further comprising: segmenting the image of the biological sample into a first region corresponding to the tumor tissue and a second region corresponding to a non-tumor tissue (para. 0027-0029 further teaches filtering mitotic figures from the obtained image based at least on pixel color values into a first region corresponding to real tumor tissue or mitotic region and a second region corresponding to a non-possible tumor tissue);
wherein the non-tumor tissue includes a fat tissue and/or a normal tissue (a non-mitotic region of further para. 0027-0029 is further understood as a non-tumor tissue includes a fat tissue and/or a normal tissue); and
excluding, from the plurality of mitotic figures, one or more mitotic figures identified within the second region of the image (para. 0027-0029 further teaches to exclude those mitotic figures not meeting a threshold value).
Regarding claim 17 (according to claim 1), Cosatto further teaches wherein further comprising: determining, for each region of a plurality of regions of the tumor tissue, an intensity metric corresponding to an activity of the tumor within the region (para. 0027-0029 further teaches performing filtering and an implied segmenting proc ess to the obtained image based at least on pixel color values and further in para. 0026 based on a count intensity metric to further generate mitotic regions and non-mitotic regions corresponding to an activity of the tumor within the region); and
excluding, from the plurality of mitotic figures, one or more mitotic figures identified within a region whose intensity metric fails to satisfy one or more thresholds (the system further in para. 0027-0029 further adapted for excluding, from the plurality of mitotic figures, one or more mitotic figures identified within a region whose threshold is not met further indicative obviously to an intensity metric failing to satisfy one or more thresholds).
Regarding claim 19, Cosatto teaches a system of at least Fig. 7, comprising:
at least one data processor (Fig. 7); and
at least one memory (Fig. 7) storing instructions, which when executed by the at least one data processor, result in operations comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a count of mitotic figures per unit area in the biological sample, as said figures count per unit area further indicates in the art a distributed count of said figures regarding how cancerous, if any, the tissue is, further generating at least in para. 0017-0018 a proliferation rate including further an at least a mitotic metric corresponding to a mitosis distribution of mitotic image features between the mitotic figures counts further quantifying a distribution activity level further obviously indicative of the spatial distribution of the plurality of mitotic figures within the biological sample); and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Linhart discloses at least in para. 0004, and 0040 automated and realtime tracking and analysis of pathology images and “quantification of (IHC) slides and mitosis count, a percentage per grade, an IHC quantification, an indication of a distance between cancerous cells and a border of a tissue, and an indication of whether cancerous cells have metastasized beyond the predefined border” further insinuating tracking in realtime spatial distribution of the plurality of mitotic figures within the biological sample and quantifying by calculating said at least cited distances between the figures, and the mitosis count as the determined metric further quantifying as noted also in para. 0141 and 0159 said mitoses or the spatial distribution of the plurality of mitotic figures within the biological sample as said mitotic spatial distribution as understood in the art corresponds to a spatial pattern formed by the plurality of mitotic figures. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Linhart to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Linhart are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures and determined based on quantification a tumor grading or the like; Linhart’s combination of at least the mitoses quantification and the tumor grading determination further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified mitoses calculation combination of Linhart it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Regarding claim 20, Cosatto teaches in para. 0040 and Fig. 7 a non-transitory computer readable medium 730 storing instructions, which when executed by at least one data processor 720, result in operations comprising:
identifying, within an image of a biological sample, a plurality of mitotic figures associated with a tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis (identifying, at least in para. 0017-0018 further supported by para. 0029, within an image of a biological sample, a plurality of mitotic figures associated with a cited proliferating tumor tissue present in the biological sample, each mitotic figure of the plurality of mitotic figures corresponding to a tumor cell that is undergoing mitosis);
determining, based at least on the plurality of mitotic figures in the biological sample, a mitotic metric quantifying a and
determining, based at least on the mitotic metric, a tumor grade for the tumor tissue present in the biological sample (the system of para. 0017-0018 and 0029, based on the number of mitotic figures counts and their neighboring distances further corresponding to the mitotic metric, further ascertains a proliferating tumor rate as said tumor grade for the tumor tissue present in the biological sample).
However, Cosatto is silent regarding the above lined-out items such as specifically citing quantifying specifically spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Linhart discloses at least in para. 0004, and 0040 automated and realtime tracking and analysis of pathology images and “quantification of (IHC) slides and mitosis count, a percentage per grade, an IHC quantification, an indication of a distance between cancerous cells and a border of a tissue, and an indication of whether cancerous cells have metastasized beyond the predefined border” further insinuating tracking in realtime spatial distribution of the plurality of mitotic figures within the biological sample and quantifying by calculating said at least cited distances between the figures, and the mitosis count as the determined metric further quantifying as noted also in para. 0141 and 0159 said mitoses or the spatial distribution of the plurality of mitotic figures within the biological sample as said mitotic spatial distribution as understood in the art corresponds to a spatial pattern formed by the plurality of mitotic figures. It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Cosatto in view of Linhart to include wherein said quantifying spatial distribution of the plurality of mitotic figures within the biological sample and wherein said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Linhart are in the same of endeavor of identifying, within an image of a biological sample, a plurality of mitotic cells associated with a tumor tissue present in the biological sample; and quantify a distribution amount of distributed mitotic figures and determined based on quantification a tumor grading or the like; Linhart’s combination of at least the mitoses quantification and the tumor grading determination further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the quantified mitoses calculation combination of Linhart it enables the system of Cosatto to more positively identify and classify mitotic counts/figures into at least one of non-tumorous and/or cancerous cells and more effectively determine based on at least their mitosis patterns and spatial distribution a tumor grading and proliferation according to further known methods to yield predictable results since known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art as said combination is thus the adaptation of an old idea or invention using newer technology that is either commonly available and understood in the art thereby a variation on already known art (See MPEP 2143, KSR Exemplary Rationale F).
Claims Standings
Claims 6, 9, and 11-12 remained objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, and/or if properly incorporated in the indepddent claims including all of the limitations of the base claim and any intervening claims.
Conclusion
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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 06/16/2026
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