DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10 February 2026 has been entered.
Response to Arguments
Claims 1, 14, 15, 17, 30, and 31 have been amended. Claims 1-33 are pending in this action.
Applicant’s arguments, see pg. 16-18, filed 10 February 2026, with respect to the interpretation of claim 17 under 35 U.S.C. 112(f) and the rejection of claims 17-31 and 33 under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) have been fully considered and are persuasive. Specifically, the examiner amended claim 17 such that it no longer invokes 35 U.S.C. 112(f). The examiner notes that this amendment has significantly changed the scope of claim 17. The rejection of claims 17-31 and 33 under 35 U.S.C. 112(a) and 35 U.S.C. 112(b) has been withdrawn.
Applicant's arguments, see pg. 18-21, filed 10 February 2026, with respect to the rejection of claims 1-13, 16, and 32 under have been fully considered but they are not persuasive. On pg. 20 of their remarks, the applicant describes their interpretation of "statistical parametric feature model" and provides third party sources supporting their interpretation. The examiner thanks the applicant for this explanation. In light of this understanding of "statistical parametric feature model" the applicant argues that Hamilton (US 20190073511 A1) does not disclose a "statistical parametric feature model" and further does not disclose storing a "statistical parametric feature model". The examiner agrees with this argument. The applicant goes on to argue that Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892 submitted 14 November 2025; hereafter, Sertel) does not disclose "wherein the number of computerized statistical parametric features models analytically model a dictionary of pre-existing diagnostically relevant visual histological patterns that defines standards for classification of a disease,". The examiner disagrees. Sertel discloses utilizing a grading standard adopted by the World Health Organization which details features for classifying a disease, which is understood as "a dictionary of pre-existing diagnostically relevant visual histological patterns that defines standards for classification of a disease," (see Sertel pg. 170 col. 1 para. 2). The grading standards, i.e. "dictionary of pre-existing . . . patterns", are used in creating model based intermediate representation (MBIR) which is understood as a statistical parametric feature model. MBIR is understood as a statistical parametric feature model because the features are represented using statistical analysis such as a probability mass function, mean, variation, etc., (see Sertel pg. 174 col. 1 para. last through col. 1 para. 1). Finally, the MBIR is understood to analytically model a dictionary of pre-existing diagnostically relevant histological patterns because in addition to being based on graded images (see Sertel pg. 173 col. 1 para. 2 applied above) the MBIR "features attempts to capture the morphological differences in different grades," (Sertel pg. 174 col. 1 para. 1). Therefore, the examiner finds that Sertel does disclose " wherein the number of computerized statistical parametric features models analytically model a dictionary of pre-existing diagnostically relevant visual histological patterns that defines standards for classification of a disease,". Further, the examiner finds that Sertel discloses:
storing a number of computerized statistical parametric feature models in a processing apparatus (pg. 170 col. 2 para. 3, the method is a computer-aided prognosis system. Pg. 179 col. 1 para. 1 "In Table 5, we report the classification results using the features constructed from MBIR." Fig. 1, the MBIR and statistical measurements, i.e. statistical parametric feature models, are determined prior to classifying the grade. A person of ordinary skill in the art would understand that a computerized process would include a memory and that if the models are determined prior to their use in classifying that they must be stored on a memory in the interim. Therefore, the statistical parametric feature models are stored in a processing apparatus),
Therefore, the examiner finds that claim 1 as a whole is disclosed by the prior art and the rejection under 35 U.S.C. 103 is maintained.
The examiner acknowledges the amendments of claims 14, 15, 30, and 31 which were rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Objections
Claims 11 and 22 are objected to because of the following informalities:
Regarding claim 11, while claim 11 was amended in the submission filed on 3 September 2025, it was not amended in the most recent submission filed on 10 February 2026. Therefore, the note preceding the claim should not read "(Currently Amended)" and should instead read "(Previously Presented)". In the future please ensure that amendments comply with 35 U.S.C. 1.121 to prevent delays in prosecution.
Regarding claim 22, on line 2 "perimeter2" should read "perimeter2".
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892 submitted 14 November 2025; hereafter, Sertel).
Regarding claim 1, Madabhushi discloses:
A computerized computational pathology method, comprising ([0037] a computer-aided prognosis system):
receiving in the processing apparatus multi-parameter cellular and/or sub-cellular imaging data for an image of a tissue sample ([0069] hematoxylin and eosin stained samples were obtained and digitized. The resulting images are considered multi-parameter by the use of plural stains);
automatically locating and segmenting a plurality of tissue components of the tissue sample in the multi-parameter cellular and sub-cellular imaging data in the processing apparatus to generate segmented multi- parameter cellular and sub-cellular imaging data ([0072] tissue component of the tissue sample are segmented by region-growing which is understood as automatic locating and segmenting);
and classifying a state of the disease in the tissue sample in the processing apparatus based on the determined quantification of each of the structural features ([0091] the SVM classifier evaluates the ability to discriminate/classify between high and low levels of the features in the images).
Madabhushi does not disclose expressly to store a number of computerized statistical parametric feature models in a processing apparatus, that the parametric feature models analytically model a dictionary of pre-existing diagnostically relevant patterns, that parametric feature models are based on information obtained through consultation of pathologist experts, that the parametric feature models define a number of quantifiable structural features, and applying the parametric feature models to tissue components in the imaging data to determine a quantification of the structural features.
Sertel discloses:
storing a number of computerized statistical parametric feature models in a processing apparatus (pg. 170 col. 2 para. 3, the method is a computer-aided prognosis system. Pg. 179 col. 1 para. 1 "In Table 5, we report the classification results using the features constructed from MBIR." Fig. 1, the MBIR and statistical measurements, i.e. statistical parametric feature models, are determined prior to classifying the grade. A person of ordinary skill in the art would understand that a computerized process would include a memory and that if the models are determined prior to their use in classifying, as shown in Fig. 1, that they must be stored on a memory in the interim. Therefore, the statistical parametric feature models are stored in a processing apparatus), wherein the number of computerized statistical parametric features models analytically model a dictionary of pre-existing diagnostically relevant visual histological patterns that defines standards for classification of a disease (pg. 170 col. 1 para. 2, the grading standard is a standard adopted by the World Health Organization and details features for classifying a disease, which is understood as a dictionary of pre-existing histological patterns. Pg. 173 col. 1 para. 2, the grades are used in creating model based intermediate representation (MBIR) which is understood as a statistical parametric feature model. MBIR is understood as a statistical parametric feature model because the features are represented using statistical analysis such as a probability mass function, mean, variation, etc., see pg. 174 col. 1 para. last through col. 1 para. 1. Finally, the MBIR is understood to analytically model a dictionary of pre-existing diagnostically relevant histological patterns because in addition to being based on graded images, see pg. 173 col. 1 para. 2 applied above, the MBIR "features attempts to capture the morphological differences in different grades," pg. 174 col. 1 para. 1), wherein the number of computerized statistical parametric feature models are based on information obtained through consultation with a number of pathologist experts (pg. 171 col. 1 para. 3, images are graded by consulting professionals. Pg. 173 col. 1 para. 2, the grades are used in creating model based intermediate representation which is understood as a statistical parametric feature model. Therefore, the statistical parametric feature model is based on consultation of pathologist experts determining the grade of histopathological images), and wherein the number of computerized statistical parametric feature models define a number of quantifiable structural features that are adapted for defining a number of disease entities for the disease that classify a state of the disease (pg. 174 col. 1 para. 1 "The [MBIR] morphological features attempts to capture the morphological differences in different grades, and consist of the length of major and minor axis and the area of ellipses." Therefore, the statistical parametric features model, i.e. MBIR, define quantifiable features, e.g. length of major and minor axis and area of ellipses, for classifying a state of the disease, e.g. "difference in different grades"),
and applying the number of computerized statistical parametric feature models to certain of the tissue components in the segmented multi-parameter cellular and sub-cellular imaging data (pg. 179 col. 1 para. 1, the MBIR, i.e. statistical parametric feature models, are applied in classifying images. The images are understood as being prepared by pathologists for training, see pg. 177 col. 2 para. 2, and as being segmented, see pg. 177 col. 1 para. 1 "We quantify this observation from the digitized FL images based on our segmentation method". Therefore, the statistical parametric feature models are applied to segmented cellular images) in the processing apparatus to determine a quantification of each of the structural features for the tissue sample (pg. 178 col. 1 para. 2, "Next, we analyzed the morphological and topological features computed from the MBIR of cytological components (i.e., nuclei and cytoplasm). These features consist of the sample mean, standard deviation, skewness, kurtosis and entropy of the estimated pmfs of 15 different measurements" therefore the statistical parametric feature model is used to determine quantified measurements of structural features);
Sertel is combinable with Madabhushi because it is from the same field of endeavor of computer aided prognosis of pathology images (Madabhushi, [0002]; Sertel, pg. 170 col. 2 para. 3).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the characteristic of statistical parametric feature models of Sertel with the invention of Madabhushi.
The motivation for doing so would have been "We introduced a model-based intermediate representation (MBIR) [i.e. statistical parametric feature models] that allows describing the cytological components of the tissue in a quantitative way. This representation is very rich and an extensive set of features can be extracted from this intermediate representation. Using MBIR, we constructed features that characterizes the morphology and topology of nuclei and cytoplasm components in the tissue and allows discrimination of histological grades for FL" (Sertel, pg. 180 col. 1 para. last through pg. 180 col. 1 para. 1). The model of Sertel improves quantitative assessment of pathology images.
Therefore, it would have been obvious to combine Sertel with Madabhushi to obtain the invention as specified in claim 1.
Regarding claim 2, Madabhushi in view of Sertel discloses the subject matter of claim 1.
Madabhushi further discloses:
wherein the structural features include a number of cell morphology features (Fig. 4(a), [0026] and [0062], identifying the morphology features of nuclei shape and size which are distinct between healthy and diseased cells) and a number of spatial cell organization features ([0086] the nucleus centroids are linked and length between them is calculated, thus a spatial cell organization feature).
Regarding claim 3, Madabhushi in view Sertel discloses the subject matter of claim 1.
Madabhushi further discloses:
wherein the locating and segmenting the plurality of tissue components comprises locating and segmenting a plurality of nuclei in the tissue sample, the certain of the tissue components comprising the plurality of nuclei ([0072] the segmentation process locates and segments a plurality of nuclei in the image. See also [0062] that a key part of the process is segmenting nuclei), and wherein the number of cell morphology features includes a number of size features each based on a nuclear size of each of the nuclei ([0063] "incorporating size and luminance information from each detected object to temporarily label it as either a BC [breast cancer] or lymphocyte nucleus." [0026] and Fig. 4a, "In general, lymphocyte nuclei are distinguished from cancer cell nuclei by their smaller size"), a number of shape features each based on a nuclear shape of each of nuclei ([0082] shape parameters are included in modeling the distribution of cell features. [0026] and Fig. 4a, "In general, lymphocyte nuclei are distinguished from cancer cell nuclei by . . . more circular shape,"), and a number of spatial spread features each based on a degree of nuclear spacing of each of the nuclei ([0064] various graphs are used to describe the spatial arrangement of nuclei).
Regarding claim 17, claim 17 recites a system with elements corresponding to the steps recited in claim 1. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 1. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel, presented in rejection of claim 1, apply to this claim. Finally, Madabhushi discloses:
a processing apparatus comprising at least one processor and a memory storing executable instructions ([0037] a computer-aided prognosis system. A person of ordinary skill in the art would understand a computer as comprising a processor and a memory),
Regarding claim 18, claim 18 recites a system with elements corresponding to the steps recited in claim 2. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 2. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel, presented in rejection of claim 2, apply to this claim.
Regarding claim 19, claim 19 recites a system with elements corresponding to the steps recited in claim 3. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 3. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel, presented in rejection of claim 2, apply to this claim.
Claims 4-7, 9-10, 20-23, and 25-26 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Wirth (“Shape Analysis and Measurement”; as included in the IDS received 01/05/2023).
Regarding claim 4, Madabhushi in view of Sertel discloses the subject matter of claim 3.
Madabhushi further discloses:
wherein the number of size features includes a nuclear smallness feature and a nuclear largeness feature (Fig. 4a and [0026], lymphocyte nuclei are distinguished from cancer cell nuclei (largeness) by their smaller size. Therefore, cancer cell nuclei are large, a largeness feature, and lymphocyte nuclei are small, a smallness feature), wherein the number of shape features includes a nuclear roundness feature ([0026] lymphocyte cells are distinguished from cancer cells by their more circular shape, a roundness feature), and wherein the number of spatial spread features includes a nuclear crowdedness feature and a nuclear spacedness feature ([0088] the global density of nuclei is calculated. Low values are understood to describe a spacedness and high values are understood to describe a crowdedness).
Madabhushi in view of Sertel does not disclose expressly a nuclear ellipticity feature.
Wirth discloses:
a nuclear ellipticity feature (pg. 26, how to measure ellipticity is described, therefore, an ellipticity feature),
Wirth is combinable with Madabhushi in view of Sertel because they are from the same field of endeavor of measuring features of images of tissue (Madabhushi, [0084]; Wirth, pg. 2).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the ellipticity feature of Wirth with the system of Madabhushi in view of Sertel.
The motivation for doing so would have been "Shape measurements are physical dimensional measures that characterize the appearance of an object. The goal is to use the fewest necessary measures to characterize an object adequately so that it may be unambiguously classified" (Wirth, pg. 3).
Therefore, in order to unambiguously classify cell structures, it would have been obvious to combine Wirth with Madabhushi in view of Hamilton in further view of Sertel to obtain the invention as specified in claim 4.
Regarding claim 5, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 4.
Madabhushi further discloses:
wherein the nuclear smallness feature is based on a first histogram of nuclear areas obtained from prototypical regions containing nuclei of a first size classification comprising a small classification, wherein the first histogram is modelled with a Gamma distribution (Fig. 4a and [0026], lymphocyte nuclei are distinguished from cancer cell nuclei because they are smaller than cancer cell nuclei, i.e. a smallness feature. [0082] cell features are modeled with Gamma distributions, including a Gamma distribution capturing scale, i.e. size or smallness), and wherein the nuclear largeness feature is based on a second histogram of nuclear areas obtained from prototypical regions containing nuclei of a second size classification comprising a large classification, wherein the second histogram is modelled with a Gamma distribution (Fig. 4a and [0026], caner cell nuclei are distinguished from lymphocyte nuclei because they are larger than lymphocyte nuclei, i.e. a largeness feature. [0082] cell features are modeled with Gamma distributions, including a Gamma distribution capturing scale, i.e. size or largeness).
Regarding claim 6, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 4.
Madabhushi in view of Sertel does not disclose expressly a formula for calculating roundness and ellipticity.
Wirth discloses:
wherein the nuclear roundness feature is based on a number of first measurements each given by (4*π*area)/perimeter2 (Pg. 30, roundness is calculated as roundness = 4*π*area/perimeter2) and wherein the nuclear ellipticity feature is based on a number of second measurements each given a ratio of a length of a minor-axis to a length of a major-axis (Pg. 26-27, ellipticity is measured as a ratio of minor axis to major axis).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the roundness and ellipticity measurements of Wirth with the method of Madabhushi in view of Sertel.
The motivation for doing so would have been "Shape measurements are physical dimensional measures that characterize the appearance of an object. The goal is to use the fewest necessary measures to characterize an object adequately so that it may be unambiguously classified" (Wirth, pg. 3).
Therefore, in order to unambiguously classify cell structures, it would have been obvious to combine Wirth with Madabhushi in view of Sertel to obtain the invention as specified in claim 6.
Regarding claim 7, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 6.
Madabhushi in view of Sertel does not disclose that roundness is between 0 to 1 and that low values of ellipticity denote highly elliptical nuclei.
Wirth discloses:
wherein the nuclear roundness feature ranges from 0 (indicative of an irregular star-like appearance) to 1 (indicative of a perfect circle) (Pg. 30, roundness is 1 for a circular object and less than 1 for a non-circular object. As a nucleus becomes less circular, the area will decrease while the perimeter increases. Taking the limit of the area going to zero and the perimeter going to infinity, a person of ordinary skill in the art would determine that a completely non-circular object (such as a line) would have a roundness of 0 by the roundness formula), and wherein the nuclear ellipticity feature characterizes a flatness of a nucleus wherein lower values denote highly elliptical nuclei (Pg. 26-27, an example is shown of something that is highly elliptical and something that is less elliptical. Something that is highly elliptical will have a small minor axis and a large major axis. By referencing the formula for ellipticity, pg. 26, a person having ordinary skill in the art would determine that a low value would be highly elliptical).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the roundness and ellipticity measurements of Wirth with the system of Madabhushi in view of Sertel.
The motivation for doing so would have been "Shape measurements are physical dimensional measures that characterize the appearance of an object. The goal is to use the fewest necessary measures to characterize an object adequately so that it may be unambiguously classified" (Wirth, pg. 3).
Therefore, in order to unambiguously classify cell structures, it would have been obvious to combine Wirth with Madabhushi in view of Sertel to obtain the invention as specified in claim 7.
Regarding claim 9, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 4.
Madabhushi further discloses:
wherein the nuclear crowdedness feature is quantified by computing, for each nucleus, an average distance to a plurality of nearest neighbor nuclei ([0086] nuclei are connected to each other by lines forming a grid of triangles. Each nuclei is connected to the nuclei closest to it and summary statistics are quantified describing the distance, including the mean or average).
Regarding claim 10, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 4.
Madabhushi further discloses:
wherein the nuclear spacedness feature is quantified by, for each nucleus, placing a grid cell centered at a reference nucleus and measuring a density of a plurality of neighboring nuclei by counting a population of nuclei in the grid cell ([0088] the global density is calculated by counting the number of nuclei in a scene. [0070] A scene is a subset of pixels in the image, and can be understood as a grid. [0088] the nuclear neighborhood is considered for any nuclear centroid).
Regarding claim 20, claim 20 recites a system with elements corresponding to the steps recited in claim 4. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 4. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 4, apply to this claim.
Regarding claim 21, claim 21 recites a system with elements corresponding to the steps recited in claim 5. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 5. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 5, apply to this claim.
Regarding claim 22, claim 22 recites a system with elements corresponding to the steps recited in claim 6. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 6. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 6, apply to this claim.
Regarding claim 23, claim 23 recites a system with elements corresponding to the steps recited in claim 7. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 7. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 7, apply to this claim.
Regarding claim 25, claim 25 recites a system with elements corresponding to the steps recited in claim 9. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 9. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 9, apply to this claim.
Regarding claim 26, claim 26 recites a system with elements corresponding to the steps recited in claim 10. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 10. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth, presented in rejection of claim 10, apply to this claim.
Claims 8, 16, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Wirth (“Shape Analysis and Measurement”; as included in the IDS received 01/05/2023) and Keller (US 20140099659 A1).
Regarding claim 8, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 6.
Madabhushi further discloses:
models the distributions of roundness with a Gamma distribution ([0081] shape parameters, i.e. roundness, are modeled with a Gamma distribution),
Madabhushi in view of Sertel does not disclose expressly that the nuclear roundness feature considers a spatial neighborhood around the nucleus and that the ellipticity feature considers a spatial neighborhood around the nucleus.
Wirth discloses:
wherein the nuclear roundness feature considers a spatial neighborhood around each nucleus (Pg. 75-76, the roundness is considered with radial distance measures. The center of the shape is understood as the nucleus and the radial distance in a neighborhood around it is considered), and wherein the nuclear ellipticity feature considers a spatial neighborhood around each nucleus (Pg. 75-76, the roundness is considered with radial distance measures. The center of the shape is understood as the nucleus and the radial distance in a neighborhood around it is considered. These values would also indicate the ellipticity by the values varying from high near the major axis and low near the minor axis).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the roundness and ellipticity measurements of Wirth with the system of Madabhushi in view of Sertel.
The motivation for doing so would have been "Shape measurements are physical dimensional measures that characterize the appearance of an object. The goal is to use the fewest necessary measures to characterize an object adequately so that it may be unambiguously classified" (Wirth, pg. 3).
Therefore, in order to unambiguously classify cell structures, it would have been obvious to combine Wirth with Madabhushi in view of Sertel.
Madabhushi in view of Sertel in further view of Wirth does not disclose expressly to model the distribution of ellipticity with a mixture of Gaussians model.
Keller discloses:
models the distributions of ellipticity with a 2- component mixture of Gaussians (MoG) model ([0197] the nuclei positions, sizes, shape information, and tracking are performed by modeling each image as a mixture of Gaussians. Each image may be understood as a neighborhood around each nucleus. Fig. 15C, the nuclei are shown to include elliptical shapes, therefore, elliptical nuclei are included in the modeling of a mixture of Gaussians).
Keller is combinable with Madabhushi in view of Sertel in further view of Wirth because it is in the same field of endeavor of imaging tissue samples (Keller, abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel in further view of Wirth with the modeling as taught by Keller.
The motivation for doing so would have been that "Approximating nuclear shape intensity by a Gaussian provides a good trade-off between model complexity and shape information” (Keller, [0197]).
Therefore, it would have been obvious to combine Keller with Madabhushi in view of Sertel in further view of Wirth to obtain the invention as specified in claim 8.
Regarding claim 16, Madabhushi in view of Sertel discloses the subject matter of claim 1.
Madabhushi does not disclose expressly a non-transitory computer readable medium storing instructions to be executed by a computer.
Keller discloses:
A non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform the method of claim 1 ([0124] the system includes a storage device including at least hard disk drives which are non-transitory computer readable medium).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the non-transitory computer readable medium of Keller with the invention of Madabhushi in view of Sertel.
The motivation for doing so would have been combining known elements, the method of Madabhushi in view of Sertel and the non-transitory computer readable medium of Keller, in a known way, Keller shows it is known to combine a non-transitory computer readable medium for performing a process, to obtain a predictable result, stored instructions for performing a method. Further, while Madabhushi in view of Sertel does not disclose expressly a non-transitory computer readable medium, a person of ordinary skill in the art would understand that a digital image processing method such as that of Madabhushi in view of Sertel would use a non-transitory computer readable medium for storing program instructions.
Therefore, it would have been obvious to combine Keller with Madabhushi in view of Sertel to obtain the invention as specified in claim 16.
Regarding claim 24, claim 24 recites a system with elements corresponding to the steps recited in claim 8. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 8. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth and Keller, presented in rejection of claim 8, apply to this claim.
Claim 11 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Wirth (“Shape Analysis and Measurement”; as included in the IDS received 01/05/2023) and Rey et al. ("pointpats: Point Pattern Analysis in PySAL", full reference in PTO-892; hereafter, Rey).
Regarding claim 11, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 10.
Madabhushi in view of Sertel in further view of Wirth does not disclose expressly that the population is compared against an expected number of nuclei under a complete spatial randomness hypothesis.
Rey discloses:
wherein the population is compared against an expected number of nuclei under a complete spatial randomness hypothesis (Pg. 1 "Point Pattern Analysis", the pattern of points is analyzed against the hypothesis of complete spatial randomness).
Rey is combinable with Madabhushi in view of Sertel in further view of Wirth because it solves a related problem to nuclei analysis by analyzing point distributions (Rey, pg. 1).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel in further view of Wirth with the comparison against a spatial randomness hypothesis as taught by Rey.
The motivation for doing so would have been that doing so provides a means of analyzing on the assumption that point events arise independently of one another and with constant probability across the space (Rey, Pg. 1, "Point Pattern Analysis"). Further, Rey is admitted by the applicant as being relied upon for performing this step (applicant’s specification, pg. 12 line 20-23).
Therefore, it would have been obvious to combine Rey with Madabhushi in view of Sertel in further view of Wirth to obtain the invention as specified in claim 11.
Regarding claim 27, claim 27 recites a system with elements corresponding to the steps recited in claim 11. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 11. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth and Rey, presented in rejection of claim 11, apply to this claim.
Claim 12 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Wirth (“Shape Analysis and Measurement”; as included in the IDS received 01/05/2023) and Azar et al. ("Automated classification of glandular tissue by statistical proximity sampling", full reference in PTO-892; hereafter, Azar) and Tsubaki et al. ("Characterization of histopathology and gene-expression profiles of synovitis in early rheumatoid arthritis using targeted biopsy specimens", full reference in PTO-892; hereafter, Tsubaki).
Regarding claim 12, Madabhushi in view of Sertel in further view of Wirth discloses the subject matter of claim 4.
Madabhushi in view of Sertel in further view of Wirth does not disclose expressly wherein the number of spatial cell organization feature includes a feature indicating the degree of cribriform in the tissue.
Azar discloses:
wherein the number of spatial cell organization features includes a cribriform feature indicative of a degree to which the certain of the tissue components exhibit a cribriform pattern (Pg. 7, lumens are analyzed and scored between 0 to 1, wherein a score close to 0 indicates a cribriform lumen)
Azar is combinable with Madabhushi in view of Sertel in further view of Wirth because it is in the same field of endeavor of analyzing images of tissue samples (Azar, abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel in further view of Wirth with the cribriform feature of Azar.
The motivation for doing so would have been Azar et al. "demonstrate the efficacy of the method in extracting discriminative features for obtaining high classification rates for tubular formation in both healthy and cancerous tissue, which is an important component in Gleason and tubule-based Elston grading" (Azar, abstract).
Therefore, it would have been obvious to combine Azar with Madabhushi in view of Sertel in further view of Wirth.
Madabhushi in view of Sertel in further view of Wirth and Azar does not disclose expressly a feature indicating the degree of picket-fence patterns in the tissue.
Tsubaki discloses:
a picket-fence feature indicative of a degree to which the certain of the tissue components exhibit a picket-fence pattern (Pg. 4, Results, palisading, i.e. tissue with picket fencing characteristics, is scored).
Tsubaki is combinable with Madabhushi in view of Sertel in further view of Wirth and Azar because it is from the same field of endeavor of analyzing digital images of tissue samples (Tsubaki, abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel in further view of Wirth and Azar with the picket-fence feature taught by Tsubaki.
The motivation for doing so would have been that "The results of the study [Tsubaki’s study] suggest that a combination of histopathology and gene-expression profiling is a useful tool for diagnostic and prognostic studies of early RA [rheumatoid arthritis]" (Tsubaki, Pg. 9 last paragraph).
Therefore, in order to better diagnose diseases such as rheumatoid arthritis, it would have been obvious to combine Tsubaki with Madabhushi in view of Sertel in further view of Wirth and Azar to obtain the invention as specified in claim 12.
Regarding claim 28, claim 28 recites a system with elements corresponding to the steps recited in claim 12. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 12. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Wirth and Azar and Tsubaki, presented in rejection of claim 12, apply to this claim.
Claim 13 and 29 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Joerg et al. (WO 2016075096, as included in the IDS; hereafter, Joerg).
Regarding claim 13, Madabhushi in view of Sertel discloses the subject matter of claim 1.
Madabhushi further discloses:
wherein the number of structural features comprises a number of unary features ([0026], [0086], [0088], [0101], a number of unary features have been considered, for example, size, circularity, nuclei spacing, nuclei density, and average diameter),
Madabhushi in view of Sertel does not disclose a number of binary features and a number of ternary features.
Joerg discloses:
a number of binary features comprising a combination of two of the unary features ([0049] a variety of features are considered in combination, the textual features and nuclear feature metrics, the texton histogram feature and nuclear feature metrics, and more), and number of ternary features comprising a combination of three or more features selected from the unary features or other structural features ([0049] the variety of methods are all combined with "nuclear feature metrics". Nuclear feature metrics includes, in itself, several unary features, such as morphology features including shape or dimensions [0071], appearance features including pixel intensity [0072] and background features including stain around the cell [0073]. [0074] nuclear features may also capture other metrics such as occurrence and density. Therefore, combination of nuclear features with other methods as shown in [0049] may be understood as ternary features).
Joerg is combinable with Madabhushi in view of Sertel because it is in the same field of endeavor of classifying cells by nuclear features (Joerg, abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel with the binary and ternary features of Joerg.
The motivation for doing so would have been "it is believed that a cell nucleus can be more confidently labeled (for example as being a nucleus in a tumor cell) by taking into account the cells and other biological structures in its neighborhood" (Joerg, [0050]).
Therefore, it would have been obvious to combine Joerg with Madabhushi in view of Sertel to obtain the invention as specified in claim 13.
Regarding claim 29, claim 29 recites a system with elements corresponding to the steps recited in claim 13. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 13. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Joerg, presented in rejection of claim 13, apply to this claim.
Claims 32 and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Madabhushi et al. (US 20120106821 A1; as included in the IDS received 01/05/2023; hereafter, Madabhushi) in view of Sertel et al. ("Histopathological Image Analysis Using Model-Based Intermediate Representations and Color Texture: Follicular Lymphoma Grading", full reference on PTO-892; hereafter, Sertel) in further view of Gurcan et al. ("Histopathological Image Analysis: A Review," full reference in PTO-892; hereafter Gurcan).
Regarding claim 32, Madabhushi in view of Sertel discloses the subject matter of claim 1.
Madabhushi in view of Sertel does not disclose expressly that the number of disease entities comprise organ specific disease entities including both tumor and non-tumor pathology.
Gurcan discloses:
wherein the number of disease entities comprise a number of organ-specific disease entities, including both tumor and non-tumor pathology (The systems described consider various organs and diseases. Fig. 8 - Determining difference between brain tissue that is cancerous, healthy, inflamed. Fig. 13 - Discriminating between high grade and low grade cancer. Fig. 15 - Detecting prostate cancer).
Gurcan is combinable with Madabhushi in view of Sertel because it is in the same field of endeavor of performing image analysis on images of tissue samples (Gurcan, abstract).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to modify Madabhushi in view of Sertel with the disease entities taught by Gurcan.
The motivation for doing so would have been "The use of computer-aided diagnosis for digitized histopathology could begin to be employed for disease prognostics, allowing physicians to predict which patients may be susceptible to a particular disease and also predicting disease outcome and survival" (Gurcan, Pg. 20 last paragraph).
Therefore, it would have been obvious to combine Gurcan with Madabhushi in view of Sertel to obtain the invention as specified in claim 32.
Regarding claim 33, claim 33 recites a system with elements corresponding to the steps recited in claim 32. Therefore, the recited elements of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding method claim, claim 32. Additionally, the rationale and motivation to combine Madabhushi in view of Sertel in further view of Gurcan, presented in rejection of claim 32, apply to this claim.
Allowable Subject Matter
Claims 14-15 and 30-31 are allowed.
The following is an examiner' s statement of reasons for allowance:
Regarding claim 14, the prior art fails to disclose or reasonably suggest that each binary feature comprises a joint distribution of z-scores from the unary features thereof with a mixture of Gaussians distribution. The closest prior art, Keller et al. (U.S. Publ. No. 20140099659), discloses using a mixture of Gaussians to model nuclei features, but does not disclose expressly using the z-scores in that modeling.
The claim as a whole is found non-obvious over the prior art including:
wherein each binary feature comprises a joint distribution of z-scores from the unary feature thereof with a two-component, two-dimensional mixture of Gaussian distribution.
Regarding claim 15, the prior art fails to disclose or reasonably suggest the specific binary features and ternary features disclosed in claim 15. Madabhushi et al. (U.S. Publ. No. 20120106821) discloses unary features. Wirth ("Shape Analysis and Measurement" as included in the IDS received 01/05/2023) discloses unary features. Joerg et al. (WO 2016075096 as included in the IDS received 01/05/2023) discloses various binary and ternary features but fails to disclose the specific features disclosed in claim 15. The indicated art individually or in combination fails to disclose or reasonably suggest the specific binary and ternary features of claim 15.
The claim as a whole is found non-obvious over the prior art including:
wherein the number of binary features includes a nuclear largeness-roundness feature, a nuclear smallness-ellipticity feature, a nuclear spacedness-largeness feature, a nuclear crowdedness-smallness feature, a nuclear spacedness-smallness feature, a nuclear crowdedness-ellipticity feature, and a nuclear spacedness-roundness feature, and wherein the number of ternary features includes a nuclear largeness-roundness-spacedness feature, the cribriform feature and the picket-fence feature.
Regarding claim 30, the prior art fails to disclose or reasonably suggest that each binary feature comprises a joint distribution of z-scores from the unary features thereof with a mixture of Gaussians distribution. The closest prior art, Keller et al. (U.S. Publ. No. 20140099659), discloses using a mixture of Gaussians to model nuclei features, but does not disclose expressly using the z-scores in that modeling.
The claim as a whole is found non-obvious over the prior art including:
wherein each binary feature comprises a joint distribution of z-scores from the unary feature thereof with a two-component, two-dimensional mixture of Gaussian distribution.
Regarding claim 31, the prior art fails to disclose or reasonably suggest the specific binary features and ternary features disclosed in claim 30. Madabhushi et al. (U.S. Publ. No. 20120106821) discloses unary features. Wirth ("Shape Analysis and Measurement" as included in the IDS received 01/05/2023) discloses unary features. Joerg et al. (WO 2016075096 as included in the IDS received 01/05/2023) discloses various binary and ternary features but fails to disclose the specific features disclosed in claim 31. The indicated art individually or in combination fails to disclose or reasonably suggest the specific binary and ternary features of claim 30.
The claim as a whole is found non-obvious over the prior art including:
wherein the number of binary features includes a nuclear largeness-roundness feature, a nuclear smallness-ellipticity feature, a nuclear spacedness-largeness feature, a nuclear crowdedness-smallness feature, a nuclear spacedness-smallness feature, a nuclear crowdedness-ellipticity feature, and a nuclear spacedness-roundness feature, and wherein the number of ternary features includes a nuclear largeness-roundness-spacedness feature, the cribriform feature and the picket-fence feature.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chukka et al., US 20200342597 A1, discloses a system which considers pixels identified by a pathologist and uses probability density functions to describe the identified pixels.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA B CROCKETT whose telephone number is (571)270-7989. The examiner can normally be reached Monday-Thursday 8am-5pm.
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/JOSHUA B. CROCKETT/ Examiner, Art Unit 2661 /JOHN VILLECCO/ Supervisory Patent Examiner, Art Unit 2661
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