DETAILED ACTION
The present application is being examined under the pre-AIA first to invent provisions.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 obviousness-type 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); and 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 a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
2. Claims 1-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12067716.
When two claims in an application are duplicates or else are so close in content that they both cover the same thing, despite a slight difference in wording, it is proper after allowing one claim to object to the other as being a substantial duplicate of the allowed claim.
The subject matter claimed in the instant application is fully disclosed in the claims 1-20 of U.S. Patent No. 12067716, as follows:
Instant application:
1. A method comprising:
obtaining a set of images of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound;
inputting the set of images into a neural network model;
predicting, using the neural network model, (i) coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box;
generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box and the associated probability score; and
determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
2. The method of claim 1, wherein the set of rules comprises (i) a rule to generate a per-subject CL count, (ii) a rule to generate a per-ovary CL count, and/or (iii) a rule to generate the CL count based on bounding boxes, (iv) a rule to generate the CL count based on a tissue area of a tissue slice, and/or (v) a rule that combines one or more rules in (i)-(iv).
3. The method of claim 1, wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
4. The method of claim 1, wherein the set of images of the tissue slices are obtained from one ovary of a same subject treated with the amount of the compound, and the method further comprises: obtaining another set of images of tissue slices from another ovary of the same subject; inputting the other set of images into the neural network model; predicting, using the neural network model, (i) coordinates for another bounding box around one or more objects within the other set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the other bounding box; and generating, using the set of rules, another CL count for the other ovary based on the other bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on an average CL count of the CL count for the one ovary and the other CL count for the other ovary.
5. The method of claim 1, further comprising: obtaining a first different set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; inputting the first different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a first different bounding box around one or more objects within the first different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the first different bounding box; and generating, using the set of rules, a first different CL count for the one or more other ovaries based on the first different bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on a trend between the CL count for the one or more ovaries and the first different CL count for the one or more other ovaries.
6. The method of claim 1, further comprising: obtaining a second different set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; inputting the second different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a second different bounding box around one or more objects within the second different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the second different bounding box; generating, using the set of rules, a second different CL count for the one or more other ovaries based on the second different bounding box and the associated probability score; and determining, using the set of rules, an ovarian toxicity of the different compound at the amount based on the second different CL count for the one or more other ovaries.
7. The method of claim 1, further comprising: providing the set of images with the bounding box around each object of the objects that is identified as the CL, the CL count for the one or more ovaries, the ovarian toxicity of the compound at the amount, or any combination thereof; and administering a treatment with the compound based on the ovarian toxicity of the compound at the amount.
8. A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including: obtaining a set of images of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound; inputting the set of images into a neural network model; predicting, using the neural network model, (i) coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box; generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box and the associated probability score; and determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
9. The system of claim 8, wherein the set of rules comprises (i) a rule to generate a per-subject CL count, (ii) a rule to generate a per-ovary CL count, and/or (iii) a rule to generate the CL count based on bounding boxes, (iv) a rule to generate the CL count based on a tissue area of a tissue slice, and/or (v) a rule that combines one or more rules in (i)-(iv).
10. The system of claim 8, wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
11. The system of claim 8, wherein the set of images of the tissue slices are obtained from one ovary of a same subject treated with the amount of the compound, and the actions further include: obtaining another set of images of tissue slices from another ovary of the same subject; inputting the other set of images into the neural network model; predicting, using the neural network model, (i) coordinates for another bounding box around one or more objects within the other set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the other bounding box; and generating, using the set of rules, another CL count for the other ovary based on the other bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on an average CL count of the CL count for the one ovary and the other CL count for the other ovary.
12. The system of claim 8, wherein the actions further include: obtaining a first different set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; inputting the first different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a first different bounding box around one or more objects within the first different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the first different bounding box; and generating, using the set of rules, a first different CL count for the one or more other ovaries based on the first different bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on a trend between the CL count for the one or more ovaries and the first different CL count for the one or more other ovaries.
13. The system of claim 8, wherein the actions further include: obtaining a second different set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; inputting the second different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a second different bounding box around one or more objects within the second different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the second different bounding box; generating, using the set of rules, a second different CL count for the one or more other ovaries based on the second different bounding box and the associated probability score; and determining, using the set of rules, an ovarian toxicity of the different compound at the amount based on the second different CL count for the one or more other ovaries.
14. The system of claim 8, wherein the actions further include: providing the set of images with the bounding box around each object of the objects that is identified as the CL, the CL count for the one or more ovaries, the ovarian toxicity of the compound at the amount, or any combination thereof; and administering a treatment with the compound based on the ovarian toxicity of the compound at the amount.
15. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: obtaining a set of images of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound; inputting the set of images into a neural network model; predicting, using the neural network model, (i) coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box; generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box and the associated probability score; and determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
16. The computer-program product of claim 15, wherein the set of rules comprises (i) a rule to generate a per-subject CL count, (ii) a rule to generate a per-ovary CL count, and/or (iii) a rule to generate the CL count based on bounding boxes, (iv) a rule to generate the CL count based on a tissue area of a tissue slice, and/or (v) a rule that combines one or more rules in (i)-(iv).
17. The computer-program product of claim 15, wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
18. The computer-program product of claim 15, wherein the set of images of the tissue slices are obtained from one ovary of a same subject treated with the amount of the compound, and the actions further include: obtaining another set of images of tissue slices from another ovary of the same subject; inputting the other set of images into the neural network model; predicting, using the neural network model, (i) coordinates for another bounding box around one or more objects within the other set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the other bounding box; and generating, using the set of rules, another CL count for the other ovary based on the other bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on an average CL count of the CL count for the one ovary and the other CL count for the other ovary.
19. The computer-program product of claim 15, wherein the actions further include: obtaining a first different set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; inputting the first different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a first different bounding box around one or more objects within the first different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the first different bounding box; and generating, using the set of rules, a first different CL count for the one or more other ovaries based on the first different bounding box and the associated probability score, wherein the ovarian toxicity of the compound at the amount is determined based on a trend between the CL count for the one or more ovaries and the first different CL count for the one or more other ovaries.
20. The computer-program product of claim 15, wherein the actions further include: obtaining a second different set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; inputting the second different set of images into the neural network model; predicting, using the neural network model, (i) coordinates for a second different bounding box around one or more objects within the second different set of images that are identified as CL, and (ii) an associated probability score of the CL being present in the second different bounding box; generating, using the set of rules, a second different CL count for the one or more other ovaries based on the second different bounding box and the associated probability score; and determining, using the set of rules, an ovarian toxicity of the different compound at the amount based on the second different CL count for the one or more other ovaries.
Patent No. 12067716:
1. A method comprising:
obtaining a set of images of tissue slices from one or more ovaries treated with an amount of a compound;
inputting the set of images into a neural network model constructed as a one-stage detector using focal loss as at least a portion of the loss function;
predicting, using the neural network model, coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL);
outputting, using the neural network model, the set of images with the bounding box around the one or more objects that are identified as the CL based on the coordinates predicted for the bounding box;
counting the bounding boxes within the set of images output from the neural network model to obtain a CL count for the ovary; and
determining an ovarian toxicity of the compound at the amount based on the CL count for the ovary,
wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
2. The method of claim 1, further comprising identifying, using the neural network model, the one or more objects within the set of images as being the CL, wherein the identifying comprises generating a probability score for each object of the one or more objects that is identified as the CL, and the counting comprises only counting the bounding boxes within the set of images around the one or more objects identified as the CL and having a probability score greater than a predetermined probability score threshold.
3. The method of claim 1, wherein the examining the CL count for the ovary comprises comparing the CL count for the ovary to a predetermined toxicity threshold, when the CL count is above the predetermined toxicity threshold, determining the compound at the amount is not toxic to the ovary, and when the CL count is below or equal to the predetermined toxicity threshold, determining the compound at the amount is toxic to the ovary.
4. The method of claim 1, wherein the set of images of the tissue slices are obtained from an ovary of a subject treated with the amount of the compound, and the method further comprises: obtaining another set of images of tissue slices from another ovary of the subject treated with the amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the another ovary, wherein the examining the CL count for the ovary comprises: averaging the CL count for the ovary and the another CL count for the another ovary to obtain an averaged CL count, wherein the ovarian toxicity of the compound at the amount is determined based on the average CL.
5. The method of claim 1, further comprising: obtaining another set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary, wherein the examining the CL count for the ovary comprises determining a trend between the CL count for the one or more ovaries and the another CL count for the one or more other ovaries, and the ovarian toxicity of the compound is determined at the amount or at the different amount based on the trend.
6. The method of claim 1, further comprising: obtaining another set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary of the another subject; and determining an ovarian toxicity of the different compound at the amount based on the CL count for the one or more other ovaries.
7. The method of claim 1, further comprising: providing the set of images with the bounding box around each object of the objects that is identified as the CL, the CL count for the one or more ovaries, the ovarian toxicity of the compound at the amount, or any combination thereof; and administering a treatment with the compound based on the ovarian toxicity of the compound at the amount.
8. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including: obtaining a set of images of tissue slices from one or more ovaries treated with an amount of a compound; inputting the set of images into a neural network model constructed as a one-stage detector using focal loss as at least a portion of the loss function; predicting, using the neural network model, coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL); outputting, using the neural network model, the set of images with the bounding box around the one or more objects that are identified as the CL based on the coordinates predicted for the bounding box; counting the bounding boxes within the set of images output from the earning neural network model to obtain a CL count for the ovary; and determining an ovarian toxicity of the compound at the amount based on the CL count for the ovary, wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
9. The computer-program product of claim 8, wherein the actions further include identifying, by the neural network model, the one or more objects within the set of images as being the CL, and wherein the identifying comprises generating a probability score for each object of the one or more objects that is identified as the CL, and the counting comprises only counting the bounding boxes within the set of images around the one or more objects identified as the CL and having a probability score greater than a predetermined probability score threshold.
10. The computer-program product of claim 8, wherein the examining the CL count for the ovary comprises comparing the CL count for the ovary to a predetermined toxicity threshold, when the CL count is above the predetermined toxicity threshold, determining the compound at the amount is not toxic to the ovary, and when the CL count is below or equal to the predetermined toxicity threshold, determining the compound at the amount is toxic to the ovary.
11. The computer-program product of claim 8, wherein the set of images of the tissue slices are obtained from an ovary of a subject treated with the amount of the compound and the actions further include: obtaining another set of images of tissue slices from another ovary of the subject treated with the amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the another ovary, wherein the examining the CL count for the ovary comprises: averaging the CL count for the ovary and the another CL count for the another ovary to obtain an averaged CL count, wherein the ovarian toxicity of the compound at the amount is determined based on the average CL.
12. The computer-program product of claim 8, wherein the actions further include: obtaining another set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary, wherein the examining the CL count for the ovary comprises determining a trend between the CL count for the one or more ovaries and the another CL count for the one or more other ovaries, and the ovarian toxicity of the compound is determined at the amount or at the different amount based on the trend.
13. The computer-program product of claim 8, the actions further include: obtaining another set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary of the another subject; and determining an ovarian toxicity of the different compound at the amount based on the CL count for the one or more other ovaries.
14. The computer-program product of claim 8, wherein the actions further include providing the set of images with the bounding box around each object of the objects that is identified as the CL, the CL count for the one or more ovaries, the ovarian toxicity of the compound at the amount, or any combination thereof.
15. A system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including: obtaining a set of images of tissue slices from one or more ovaries treated with an amount of a compound; inputting the set of images into a neural network model constructed as a one-stage detector using focal loss as at least a portion of the loss function; predicting, using the neural network model, coordinates for a bounding box around one or more objects within the set of images that are identified as corpora lutea (CL); outputting, using the neural network model, the set of images with the bounding box around the one or more objects that are identified as the CL based on the coordinates predicted for the bounding box; counting the bounding boxes within the set of images output from the neural network model to obtain a CL count for the ovary; and determining an ovarian toxicity of the compound at the amount based on the CL count for the ovary, wherein the determining the ovarian toxicity comprises examining the CL count for the ovary in relation to a given condition, indicative of whether the compound at the amount is toxic to the ovary.
16. The system of claim 15, wherein the actions further include identifying, by the neural network model, the one or more objects within the set of images as being the CL, and wherein the identifying comprises generating a probability score for each object of the one or more objects that is identified as the CL, and the counting comprises only counting the bounding boxes within the set of images around the one or more objects identified as the CL and having a probability score greater than a predetermined probability score threshold.
17. The system of claim 15, wherein the examining the CL count for the ovary comprises comparing the CL count for the ovary to a predetermined toxicity threshold, when the CL count is above the predetermined toxicity threshold, determining the compound at the amount is not toxic to the ovary, and when the CL count is below or equal to the predetermined toxicity threshold, determining the compound at the amount is toxic to the ovary.
18. The system of claim 15, wherein the set of images of the tissue slices are obtained from an ovary of a subject treated with the amount of the compound, and the actions further include: obtaining another set of images of tissue slices from another ovary of the subject treated with the amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the another ovary, wherein the examining the CL count for the ovary comprises: averaging the CL count for the ovary and the another CL count for the another ovary to obtain an averaged CL count, wherein the ovarian toxicity of the compound at the amount is determined based on the average CL.
19. The system of claim 15, wherein the actions further include: obtaining another set of images of tissue slices from one or more other ovaries that are either untreated or treated with a different amount of the compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; and counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary, wherein the examining the CL count for the ovary comprises determining a trend between the CL count for the one or more ovaries and the another CL count for the ovary, and the ovarian toxicity of the compound is determined at the amount or at the different amount based on the trend.
20. The system of claim 15, the actions further include: obtaining another set of images of tissue slices from one or more other ovaries that are treated with an amount of a different compound; generating, using the neural network model, the another set of images with a bounding box around one or more objects of objects that are identified as the CL within the another set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the another set of images output from the neural network model to obtain another CL count for the ovary of the another subject; and determining an ovarian toxicity of the different compound at the amount based on the CL count for the one or more other ovaries.
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, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yip et al. (US 20200258223 A1) in view of Walk et al. (US 20140342379 A1).
Considering claim 1, Yip teaches a method comprising:
obtaining a set of images (histopathology images) of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound (Fig.1-3, [0015] ovarian, [0108], [0137]-[0138] organoid modeling lab 116 may collect various types of data, such as, for example, the sensitivity of an organoid to a drug (for example, determined by measuring cell death or cell viability after exposure to the drug));
inputting the set of images (histopathology images) into a neural network model (deep learning framework 150, Fig.1-3, [0137]-[0138]);
predicting, using the neural network model ([0010], deep learning framework), (i) coordinates for a bounding box (1502, Fig.15, [0358]) around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box (Fig.12-17, [0161] identify a percentage TILs within a tile image, the cell segmenter 316 determines the cell boundary, and the biomarker classification model 322 classifies the tile image based on the percentage of TILs within a cell interior resulting in a classification: (i) Tumor—IHC/Lymphocyte positive or (ii) Non-tumor—IHC/Lymphocyte positive, [0290]);
generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box (1502, Fig.15) and the associated probability score (Fig.12-17, [0203] generate an absolute count of how many biomarkers are on the image, a percentage of the number of cells in tumor regions that are associated with each of the biomarkers, and/or a designation of any biomarker classifications or other information); and
Yip do not clearly teach determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries ([0035]-[0037]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine or modify of Walk to Yip to order to determining whether a compound is capable of inducing such gonadal toxicity in a subject and to a method of identifying a drug for treating gonadal toxicity. Furthermore, the present invention relates to a device and a kit for diagnosing gonadal toxicity.
Considering claim 8, Yip teaches a system comprising: one or more data processors; and a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform actions including:
obtaining a set of images (histopathology images) of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound (Fig.1-3, [0015] ovarian, [0108], [0137]-[0138] organoid modeling lab 116 may collect various types of data, such as, for example, the sensitivity of an organoid to a drug (for example, determined by measuring cell death or cell viability after exposure to the drug));
inputting the set of images (histopathology images) into a neural network model (deep learning framework 150, Fig.1-3, [0137]-[0138]);
predicting, using the neural network model ([0010], deep learning framework), (i) coordinates for a bounding box (1502, Fig.15, [0358]) around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box (Fig.12-17, [0161] identify a percentage TILs within a tile image, the cell segmenter 316 determines the cell boundary, and the biomarker classification model 322 classifies the tile image based on the percentage of TILs within a cell interior resulting in a classification: (i) Tumor—IHC/Lymphocyte positive or (ii) Non-tumor—IHC/Lymphocyte positive, [0290]);
generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box (1502, Fig.15) and the associated probability score (Fig.12-17, [0203] generate an absolute count of how many biomarkers are on the image, a percentage of the number of cells in tumor regions that are associated with each of the biomarkers, and/or a designation of any biomarker classifications or other information); and
Yip do not clearly teach determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries ([0035]-[0037]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine or modify of Walk to Yip to order to determining whether a compound is capable of inducing such gonadal toxicity in a subject and to a method of identifying a drug for treating gonadal toxicity. Furthermore, the present invention relates to a device and a kit for diagnosing gonadal toxicity.
Considering claim 15, Yip teaches a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform actions including:
obtaining a set of images (histopathology images) of tissue slices from one or more ovaries of one or more subjects treated with an amount of a compound (Fig.1-3, [0015] ovarian, [0108], [0137]-[0138] organoid modeling lab 116 may collect various types of data, such as, for example, the sensitivity of an organoid to a drug (for example, determined by measuring cell death or cell viability after exposure to the drug));
inputting the set of images (histopathology images) into a neural network model (deep learning framework 150, Fig.1-3, [0137]-[0138]);
predicting, using the neural network model ([0010], deep learning framework), (i) coordinates for a bounding box (1502, Fig.15, [0358]) around one or more objects within the set of images that are identified as corpora lutea (CL), and (ii) an associated probability score of the CL being present in the bounding box (Fig.12-17, [0161] identify a percentage TILs within a tile image, the cell segmenter 316 determines the cell boundary, and the biomarker classification model 322 classifies the tile image based on the percentage of TILs within a cell interior resulting in a classification: (i) Tumor—IHC/Lymphocyte positive or (ii) Non-tumor—IHC/Lymphocyte positive, [0290]);
generating, using a set of rules, a CL count for the one or more ovaries based on the bounding box (1502, Fig.15) and the associated probability score (Fig.12-17, [0203] generate an absolute count of how many biomarkers are on the image, a percentage of the number of cells in tumor regions that are associated with each of the biomarkers, and/or a designation of any biomarker classifications or other information); and
Yip do not clearly teach determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries.
determining, using the set of rules, an ovarian toxicity of the compound at the amount based on the CL count for the one or more ovaries ([0035]-[0037]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine or modify of Walk to Yip to order to determining whether a compound is capable of inducing such gonadal toxicity in a subject and to a method of identifying a drug for treating gonadal toxicity. Furthermore, the present invention relates to a device and a kit for diagnosing gonadal toxicity.
Conclusion
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/KHAI M NGUYEN/Primary Examiner, Art Unit 2641
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