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 Amendments
Applicant’s Amendments/Remarks filed on 02/02/2026 have been received and made of record.
Claims 1, and 19-20 have been amended.
Claims 1-20 remained pending.
Filed IDS of 02/02/2026 has been entered and considered.
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 of 02/02/2026, corresponding to pages 8-10 pertaining to the prior arts of record and currently amended independent claims 1 and 19-20 citing “Cosatto fails to disclose "wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures," as recited in claim 1. Cosatto discloses "counting the number of mitotic figures per square unit of tissue, which is indicative of the tumor proliferation rate." (Cosatto, [0018]; see also [0009], [0017].) Thus, Cosatto fails to disclose "wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures," as recited in amended claim 1. Accordingly, Applicant respectfully submits that claim 1 is allowable over the cited references. Claims 19 and 20 have been amended similarly to claim 1 and are allowable for at least similar reasons. Claims 2-3, 15, and 17 are allowable at least for the respective dependencies of the claims”; have been considered, however, these newly added claimed amendments and remarks are moot in light of the new ground 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 Korski et al. (NPL, cited in IDS).
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 spatial distribution of the plurality of mitotic 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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Korski teaches at least in the disclosure at least one or more mitotic detection model configured to detect and count positively ID mitotic figures in a sample, further quantifying a spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures within 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 Korski to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Korski 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; the detected and/or quantifying mitotic figures spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures of Korski further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the detected and quantified spatial distribution of Korski enables the system of Cosatto 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).
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 blob 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 (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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Korski teaches at least in the disclosure at least one or more mitotic detection model configured to detect and count positively ID mitotic figures in a sample, further quantifying a spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures within 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 Korski to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Korski 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; the detected and/or quantifying mitotic figures spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures of Korski further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the detected and quantified spatial distribution of Korski enables the system of Cosatto 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).
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 spatial distribution of the plurality of mitotic 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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Korski teaches at least in the disclosure at least one or more mitotic detection model configured to detect and count positively ID mitotic figures in a sample, further quantifying a spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures within 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 Korski to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as Cosatto in view of Korski 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; the detected and/or quantifying mitotic figures spatial distribution corresponding to a spatial pattern formed by the plurality of mitotic figures of Korski further complements the identified mitotic cells within the image of the biological sample of Cosatto in the sense that when combined with the detected and quantified spatial distribution of Korski enables the system of Cosatto 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) 1-3, 13, 15, 17, and 19-20 is/are further rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Zeineh et al. (US 2024/0233418, 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 spatial distribution of the plurality of mitotic 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); wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures; 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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Zeineh teaches at least in the disclosure specifically in para. 0053, 0065, and 0075 at least one or more mitotic detection model configured to look for and detect mitotic figures count positively ID in a sample, further in para. 0053, 0065, and 0075 detecting based on the detected mitotic image features, a highest spatial distribution and/or a density distribution corresponding to a spatial pattern and distribution formed by the plurality of mitotic figures within 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 Zeineh to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as discussed above, as Cosatto in view of 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 detected and quantified highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution of Zeineh further complements the identified mitotic cells within the image of the biological sample of Cosatto, in the sense that when combined with the highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution detection or quantification of Zeineh, enables the system of Cosatto 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).
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 blob 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 13 (according to claim 1), Cosatto is 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 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 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 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 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).
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 (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); wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures; 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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Zeineh teaches at least in the disclosure specifically in para. 0053, 0065, and 0075 at least one or more mitotic detection model configured to look for and detect mitotic figures count positively ID in a sample, further in para. 0053, 0065, and 0075 detecting based on the detected mitotic image features, a highest spatial distribution and/or a density distribution corresponding to a spatial pattern and distribution formed by the plurality of mitotic figures within 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 Zeineh to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as discussed above, as Cosatto in view of 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 detected and quantified highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution of Zeineh further complements the identified mitotic cells within the image of the biological sample of Cosatto, in the sense that when combined with the highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution detection or quantification of Zeineh, enables the system of Cosatto 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).
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 spatial distribution of the plurality of mitotic 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); wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures; 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 said spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures.
Zeineh teaches at least in the disclosure specifically in para. 0053, 0065, and 0075 at least one or more mitotic detection model configured to look for and detect mitotic figures count positively ID in a sample, further in para. 0053, 0065, and 0075 detecting based on the detected mitotic image features, a highest spatial distribution and/or a density distribution corresponding to a spatial pattern and distribution formed by the plurality of mitotic figures within 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 Zeineh to include wherein the spatial distribution corresponds to a spatial pattern formed by the plurality of mitotic figures, as discussed above, as discussed above, as Cosatto in view of 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 detected and quantified highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution of Zeineh further complements the identified mitotic cells within the image of the biological sample of Cosatto, in the sense that when combined with the highest mitotic figures spatial distribution and/or a highest mitotic figures density distribution detection or quantification of Zeineh, enables the system of Cosatto 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) 4 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in Zeineh, and further in view of Stein et al. (US 2006/0149481, previously cited).
Regarding claim 4 (according to claim 1), Cosatto in view of Zeineh 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 Zeineh, 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 Zeineh, 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 Zeineh 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 Zeineh further increase and optimize location detection of observed biological image objects which may obviously depict in Cosatto in view of Zeineh 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).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Zeineh, and further in view of LI et al. (WO 2021/236547, previously cited).
Regarding claim 7 (according to claim 1), Cosatto in view of Zeineh 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 Zeineh, 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 Zeineh, 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 Zeineh 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) 14 is/are rejected under 35 U.S.C. 103 as obvious over Cosatto in view of Zeineh, and further in view of Laurent et al. (WO 2009/150256, previously cited).
Regarding claim 14 (according to claim 1), Cosatto in view of Zeineh 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 Zeineh, 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 Zeineh, 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 Zeineh 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 Zeineh 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 Zeineh, and further in view of Zhao et al. (CN111340128, previously cited).
Regarding claim 16 (according to claim 1), Cosatto in view of Zeineh 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 Zeineh, 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 Zeineh, 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 Zeineh 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 Zeineh 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 Zeineh, 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 Zeineh, 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 Zeineh, 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 Zeineh 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 Zeineh 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).
Claims Standings
Claims 5-6, and 8-12 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
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/MARCELLUS J AUGUSTIN/Primary Examiner, Art Unit 2682 03/06/2026