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 .
Status of claims: claims 1-22 are examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/28/2023 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 of U.S. Patent No. 11,430,112. Although the claims at issue are not identical, they are not patentably distinct from each other because the invention defined by the claims of the instant application would have been obvious to one of ordinary skill in the art in view of the claims of the U.S. Patent No. 11,430,112.
Claims 1-22 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 11,798,163. Although the claims at issue are not identical, they are not patentably distinct from each other because the invention defined by the claims of the instant application would have been obvious to one of ordinary skill in the art in view of the claims of the U.S. Patent No. 11,798,163.
Claim Rejections - 35 USC § 103
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 1-12 and 15-22 are rejected under 35 U.S.C. 103 as being unpatentable over YU et al (US 2015/0339816) in view of Saidi et al (US 7,761,240).
Claim 1:
YU et al (US 2015/0339816) teaches the following subject matter:
A method of computer aided phenotyping of a biological tissue sample, the method comprising:
(a) receiving a digital image of the biological tissue sample, wherein the digital image indicates presence of a protein of interest in the biological tissue sample, wherein the protein of interest is a fibrillar protein (figure 1 and 0043 teach part 102 with biological image and 0044 teaches biological object of interest; 0019 teaches the use of computer system for assessing fibrosis in tissue (fibrillar protein));
(b) processing the image to quantify a plurality of parameters, each parameter associated with a feature of a plurality of features of the protein of interest in the biological tissue sample, wherein the plurality of features is expected to be descriptive of a phenotype of interest, wherein a first feature of the plurality of features is selected from a group of features consisting of (figure 1 and 0047 teaches part 106 teaches calculate quantitative values of features and 0051 teaches statistics on probabilities of tissue at certain stage of particular disease (different phenotypes of fibrosis); 0047 teaches parameter of feature such as morphological, texture, intensity and spatially related features; 0044 teaches identification and segmentation of collagen areas, cells, nuclei cells and vessels…different features):
(1) tissue level features that describe macroscopic characteristics of the protein of interest depicted in the digital image of the biological tissue sample (figure 1 and 0047 teach part 106 quantitative values of features such as tissue with intensity-base and spatially features); (2) morphometric level features that describe morphometric characteristics of the protein of interest depicted in the digital image of the biological tissue sample (figure 1 and 0047 part 106 teaches quantitative values feature such as morphological is considered; 0127 and figures 12c/13c/14c teaches changes in morphological features that are considered); and (3) texture level features that describe an organization of the protein of interest depicted in the digital image of the biological tissue sample (figure 1 and 0047 part 106 teaches quantitative values feature such as texture is considered; 0065 teach texture values considered; 0136 teaches texture values selected based on contrast agent used in the imaging procedures).
YU et al teaches all the subject matter above, but not the following which is taught by Saidi et al:
wherein at least one parameter of the plurality of parameters is a statistical parameter derived from a histogram corresponding to distributions of associated parameters across the digital image (column 3 lines 25-47 teaches morphological and texture data (feature/parameter) that are extracted for histogram data; column 6 lines 1-20 teaches image-level morphometric feature for histogram evaluation to predict tissue is cancerous or non-cancerous; column 8 lines 45-65 teaches statistical data computer from histogram data (morphological and texture data mentioned above)); and
(c) combining at least some of the plurality of parameters in (b) to obtain one or more composite scores that quantify the phenotype of interest for the biological tissue sample (figure 6 part 608 teaches histogram parameter from obtain features (of morphological and textural) and part 610 teaches generate a vector (score) from the include features above; column 13 lines 40-50 teaches parameters is summarized using the histogram, the histogram parameters are then put together (combine) to form a vector (score) of the image tissue; page 3 teaches where fibrosis grade classification is considered for diagnosis).
YU et al and Saidi et al are both in the field of image analysis, especially for fibrosis features such as morphological and textural for diagnosis such that the combine outcome is predictable.
It would have been obvious to one skill in the art at the time of the invention to modify YU et al by Saidi et al regarding the use of histogram of the features where the effect of white pixel on histogram analysis, such as removing white pixel, results in improve classification accuracy as disclosed by Saidi et al in column 14 line 65 to column 15 line 5.
Claim 2:
YU et al teaches:
The method of claim 1, wherein the protein of interest is collagen, laminin, elastin, resilin, fibrinogen, or myosin, and wherein the phenotype of interest comprises a phenotype associated with a fibrillar structure of the protein of interest (figure 1 and 0043 teach part 102 with biological image and 0044 teaches biological object of interest; 0019 teaches the use of computer system for assessing fibrosis in tissue (fibrillar protein)).
Claim 3:
YU et al teaches:
The method of claim 1, wherein a second feature of the plurality of features is selected from the group of features different from that of the first feature (0047 teaches parameter of feature such as morphological, texture, intensity and spatially related features; 0044 teaches identification and segmentation of collagen areas, cells, nuclei cells and vessels…different features, which all can be selected as second feature depending on user preference).
Claim 4:
YU et al teach:
The method of claim 3, wherein the plurality of features comprises one tissue level feature (figure 1 and 0047 teach part 106 quantitative values of features such as tissue with intensity-base and spatially features), one morphometric level feature (figure 1 and 0047 part 106 teaches quantitative values feature such as morphological is considered; 0127 and figures 12c/13c/14c teaches changes in morphological features that are considered), and one texture level feature (figure 1 and 0047 part 106 teaches quantitative values feature such as texture is considered; 0065 teach texture values considered; 0136 teaches texture values selected based on contrast agent used in the imaging procedures).
Claim 5:
YU et al teach:
The method of claim 1, wherein the digital image is obtained from a modality of imaging that distinguishes between a presence and absence of the protein of interest in the biological tissue sample (0007 teaches measurement of collagen percentage area in the biopsy tissue sample to assess fibrosis; 0012 teaches identifying different type of collagen; 0081 teaches collagen percentage in region of interest (ROI) due to collagen pixel located, where 0082 teaches decrease or maximum of collagen percentage; 0085 teaches collagen pixel not identified is view as absence of collagen).
Claim 6:
YU et al teach:
The method of claim 5, wherein the modality of imaging comprises stained histopathology slides (teaches stained biopsy tissue), two photon microscopy (0019), fluorescence imaging (0041), structured imaging, polarized imaging (0100 teaches structure of groups of pixels), Coherent anti-Stokes Raman Scattering (CARS) (0041), Optical Coherence Tomography (OCT) images, fresh tissue imaging, and endoscopy.
Claim 7:
YU et al teach:
The method of claim 1, wherein indicating the presence of the protein of interest in the images results from an optical marker that is specific to any form of the protein of interest (0062 teaches optical marker by means of staining (optical marker) in regard of histological status of each stained section assessed with scoring system; 0007 teaches stained tissue sample in regard to collagen and 0013 teaches identifying specific collagen such as a portal collagen area, a septal collagen area and a fibrillar collagen of the tissue).
Claim 8:
YU et al teach:
The method of claim 7, wherein the optical marker is a stain specific to the protein of interest used in a histopathology method (0062 teaches optical marker by means of staining in regard of histological status of each stained section assessed with scoring system; 0007 teaches stained tissue sample in regard).
Claim 9:
YU et al teach:
The method of claim 7, wherein the optical marker is an intrinsic bio-optical marker specific to one or more forms of the protein of interest that is intrinsic to an optical imaging method (0062 teaches optical marker by means of staining (optical marker) in regard of histological status of each stained section assessed with scoring system).
Claim 10:
YU et al teach:
The method of claim 1, wherein pixels of the digital image indicate presence and quantity of the protein of interest in corresponding volumes of the biological tissue sample (0007 teaches quantitative method for measurement of collagen percentage (presence) area (CPA – percentage of collagen in the biopsy tissue sample); 0081 teaches collagen percentage in region of interest (ROI)).
Claim 11:
Saidi et al teach:
The method of claim 1, wherein the statistical parameter derived from the histogram is associated with a morphometric level feature or a texture level feature (column 3 lines 30-50 teaches morphological and texture data (feature/parameter) that are extracted for histogram data; column 6 lines 1-20 teaches image-level morphometric feature for histogram evaluation to predict tissue is cancerous or non-cancerous; column 8 lines 45-65 teaches statistical data computed from histogram data (morphological and texture data mentioned above).
Claim 12:
Saidi et al teach:
The method of claim 11, comprising cut-off values that split the histogram into subsets of sample values, and wherein the statistical parameter is derived from one subset of sample values (figure 13 lines 45-55 teaches five fractal code vector, each vector is view a single subset of sample values such as one from morphological and textural; above teaches histogram which is statistical).
Claim 15:
Saidi et al teach:
The method of claim 1, wherein the plurality of parameters that are combined in (c) are selected from a list of candidate parameters using a calibration technique involving a calibration data set of calibration digital images taken from biological samples having known variants of the phenotype of interest (above teaches the plurality of parameter in combination in (c); figure 3 part 306 and 0045 teach use of reference image (calibration data set of biological sample having known phenotypes of fibrosis) with matching histogram, where matching is view as calibration technique).
Claim 16:
YU et al teach
The method of claim 1, wherein the method quantifies the phenotype of interest on a continuous scale (claim 26 teaches diagnosing level; 0005 teaches stage, where level and stage are on a continuous scale for measurement to quantify).
Claim 17:
YU et al teach
The method of claim 1, wherein the parameters that describe texture level features include at least one statistical parameter describing the distribution of one or more properties of the image pixel intensity grey level co-occurrence matrix (GLCM) defined on a spatial dimension across the image, the GLCM properties including at least one of the group consisting of: energy, homogeneity, contrast, correlation, inertia, entropy, skewness, and kurtosis (figure 8 and 0094 teaches processing image to different area with different shades of grey toe effectively extract the different collagen areas in a liver tissue sample regardless of the fibrosis stage of the tissue sample; 0116 detail gray table for relevant fibrillar features; 0118).
Claim 18:
YU et al teach:
The method of claim 1, wherein the plurality of parameters that are combined in (c) are selected from a set of candidate parameters to reduce the dimension of the set of candidate parameters (0056 teaches appropriate for the statistic learning or training of the model, and including the steps can help reduce the dimension of the quantitative values which can in turn help reduce the computational effort).).
Claim 19:
YU et al teach
The method of claim 1, wherein the plurality of parameters that are combined in (c) are selected using artificial intelligence and machine learning (figure 2 and 0054 teaches use of artificial neural network).
Claim 20:
YU et al (US 2015/0339816) teaches the following subject matter:
A method of computer aided phenotyping of a biological tissue sample, the method comprising:
(a) receiving a digital image of the biological tissue sample, wherein the digital image indicates presence of a fibrillar structure formed by filamentous cells in the biological tissue sample (figure 1 and 0043 teach part 102 with biological image and 0044 teaches biological object of interest; 0019 teaches the use of computer system for assessing fibrosis in tissue (fibrillar protein));
(b) processing the image to quantify a plurality of parameters, each parameter associated with a feature of a plurality of features of the filamentous cells in the biological tissue sample that is expected to be different for a phenotype of interest, wherein a first feature of the plurality of features is selected from a group of features consisting of (figure 1 and 0047 teaches part 106 teaches calculate quantitative values of features and 0051 teaches statistics on probabilities of tissue at certain stage of particular disease (different phenotypes of fibrosis); 0047 teaches parameter of feature such as morphological, texture, intensity and spatially related features; 0044 teaches identification and segmentation of collagen areas, cells, nuclei cells and vessels…different features): (1) tissue level features that describe macroscopic characteristics of the filamentous cells depicted in the digital image of the biological tissue sample (figure 1 and 0047 teach part 106 quantitative values of features such as tissue with intensity-base and spatially features); (2) morphometric level features that describe morphometric characteristics of the filamentous cells depicted in the digital image of the biological tissue sample (figure 1 and 0047 part 106 teaches quantitative values feature such as morphological is considered; 0127 and figures 12c/13c/14c teaches changes in morphological features that are considered); and (3) texture level features that describe an organization of the filamentous cells depicted in the digital image of the biological tissue sample (figure 1 and 0047 part 106 teaches quantitative values feature such as texture is considered; 0065 teach texture values considered; 0136 teaches texture values selected based on contrast agent used in the imaging procedures).
YU et al teaches all the subject matter above, but not the following which is taught by Saidi et al:
wherein at least one parameter of the plurality of parameters is a statistical parameter derived from a histogram corresponding to distributions of associated parameters across the digital image (column 3 lines 25-47 teaches morphological and texture data (feature/parameter) that are extracted for histogram data; column 6 lines 1-20 teaches image-level morphometric feature for histogram evaluation to predict tissue is cancerous or non-cancerous; column 8 lines 45-65 teaches statistical data computer from histogram data (morphological and texture data mentioned above)); and
(c) combining at least some of the plurality of parameters in (b) to obtain one or more composite scores that quantify the phenotype of interest for the biological tissue sample on a continuous scale sample (figure 6 part 608 teaches histogram parameter from obtain features (of morphological and textural) and part 610 teaches generate a vector (score) from the include features above; column 13 lines 40-50 teaches parameters is summarized using the histogram, the histogram parameters are then put together (combine) to form a vector (score) of the image tissue; page 3 teaches where fibrosis grade classification is considered for diagnosis).
YU et al and Saidi et al are both in the field of image analysis, especially for fibrosis features such as morphological and textural for diagnosis such that the combine outcome is predictable.
It would have been obvious to one skill in the art at the time of the invention to modify YU et al by Saidi et al regarding the use of histogram of the features where the effect of white pixel on histogram analysis, such as removing white pixel, results in improve classification accuracy as disclosed by Saidi et al in column 14 line 65 to column 15 line 5.
Claim 21:
YU et al teach:
The method of claim 20, wherein the filamentous cell comprises a stellate cell, a neuron, a fibroblast, or a dendritic cell (0005 teaches assessing liver fibrosis by architectural features; 0006 teaches hepatic fibrosis, pericellular/perisinusoidal fibrosis).
Claim 22:
YU et al teach:
The method of claim 20, wherein the filamentous cells comprise Hepatic Stellate Cells (HSC), and wherein the phenotype of interest comprises a HCS network (006 teaches hepatic fibrosis, pericellular/perisinusoidal fibrosis).
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over YU et al (US 2015/039816) in view of Saidi et al (US 2006/0064248) as applied to claim 1 above, and further in view of Wehrli et al (US 2002/0191823).
Claim 13:
YU et al and Saidi et al teaches the subject matter “quantifying at least some of the plurality of parameters associated with” above but not the following which is taught by Wehrli et al:
wherein quantifying the statistical parameter derived from the histogram comprises processing the histogram to identify multiple modes by deconvoluting the histogram (0038, 0106 and 0116 teaches histogram decolvution with such modes such as deshading and noise reduction; 0011 teach this application for consideration of bone that undergoes slight uniform thickening(fibrosis)).
YU et al, Saidi et al and Wehrli et al are all in the field of image analysis especially with fibrosis/thickening of the body such that the combine outcome is predictable.
It would have been obvious to one skill in the art at the time of the invention to modify YU et al and Saidi et al by Wehrli et al using histogram deconvoluation would resulting images with resolution that are enhanced as disclosed by Wehrli et al in paragraph 0116.
Claim 14:
Wehrli et al further teach:
The method of claim 13, wherein at least one mode of the multiple modes of the histogram corresponds to a phenotypic signature of the phenotype of interest, and wherein deconvoluting the histogram comprises: filtering the histogram to determine whether the histogram exhibits the phenotypic signature; and quantify the exhibited phenotypic signature (0038, 0106 and 0116 teaches histogram deconvoluting with modes (deshading and noise reduction) where exhibit phenotypic signature such as bone network undergoes slight uniform thickening with change in mechanical properties in paragraph 0011).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Milici et al (US 2018/0011000) teaches METHODS FOR QUANTITATIVE ASSESSMENT OF MUSCLE FIBERS IN MUSCULAR DYSTROPHY – 0076 teaches category of features relate to the muscular dystrophy-linked protein staining and morphometric features assessed on a muscle fiber-by-fiber basis. For this category of features, each muscle fiber identified by the algorithm process is characterized by staining (i.e. mean dystrophin staining intensity, maximum dystrophin staining intensity, etc.) and morphometric (i.e. completeness of dystrophin staining, average width of fiber membrane, length of fiber membrane, uniformity in width of the fiber membrane, etc.) features of the muscular dystrophy-linked proteins within said fiber's membrane. Each fiber-by-fiber feature can be summarized for the tissue section (i.e. average membrane staining intensity, average completeness of dystrophin staining, etc.), or a sub-region of the tissue section, to capture the histogram statistics of said features (i.e. mean, median, mode, standard deviation, etc.).
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656