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 .
Comments
The Preliminary Amendment filed on September 20, 2024, and the Supplemental Preliminary Amendment filed on October 16, 2024 have been entered and made of record.
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).
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Claims 1-24 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-21 and 29-31 of U.S. Patent No. 12,100,150 to Shen et al. Although the claims at issue are not identical, they are not patentably distinct from each other because Shen et al. claims the claimed invention as follows:
Claim 1 Shen et al.
1. A computing system comprising:
one or more processing devices; and
one or more storage devices storing instructions that, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:
receiving image data of a well plate containing a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
implementing a machine learning model to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) one or more indications of each instance of the substantially spherical droplets and (ii) one or more attributes of each instance of the substantially spherical droplets,
normalizing, based on the one or more indications and the one or more attributes, a well-to-well variation in the well plate, and
determining, based on the one or more indications outputted by the machine learning model, a fluorescence activity of one or more instances of the substantially spherical droplets, wherein the fluorescence activity comprises a live cell dye signal and a dead cell dye signal of the one or more instances of the substantially spherical droplets, and wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises performing a logical operation between the image data and the one or more indications.
1. A computer implemented method comprising:
obtaining image data of a well plate comprising a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
in response to applying a machine learning model configured to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) indications of each instance of the substantially spherical droplets and (ii) attributes of each instance of the substantially spherical droplets;
normalizing, based on the indications and the attributes, a well-to-well variation in the well plate; and
determining, based on the one or more indications outputted by the machine learning model, a fluorescence activity of one or more instances of the substantially spherical droplets, wherein the fluorescence activity comprises a live cell dye signal and a dead cell dye signal of the one or more instances of the substantially spherical droplets, and wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises performing a logical operation between the image data and the one or more indications (See for example, claim 1 at Col. 19 lines 30-55).
Claim 2 Shen et al.
2. The computing system of claim 1,
wherein the operations comprise
determining a cell viability in the normalized well plate based on a drug assay performed on the plurality of substantially spherical droplets,
wherein the drug assay measures the cell viability in response to a drug treatment to a given well in the well plate.
2. The method of claim 1, further comprising:
performing a drug assay on the plurality of substantially spherical droplets,
wherein the drug assay measures cell viability in response to a drug treatment to a given well in the well plate; and
determining the cell viability in the normalized well plate (i.e., claim 2 at Col. 19 lines 56-60).
Claim 3 Shen et al.
3. The computing system of claim 1,
wherein the substantially spherical droplets are derived from a patient-derived tissue sample.
3. The method of claim 1,
wherein the substantially spherical droplets are derived from a patient-derived tissue sample (i.e., claim 3 at Col. 19 lines 61-64).
Claim 4 Shen et al.
4. The computing system of claim 3,
wherein the patient-derived tissue sample comprises a biopsy sample from a metastatic tumor.
4. The method of claim 3,
wherein the patient-derived tissue sample comprises a biopsy sample from a metastatic tumor (i.e., claim 4 at Col. 19 lines 65-67).
Claim 5 Shen et al.
5. The computing system of claim 3,
wherein the patient-derived tissue sample comprises a clinical tumor sample comprising both cancer cells and stromal cells.
5. The method of claim 3,
wherein the patient-derived tissue sample comprises a clinical tumor sample comprising both cancer cells and stromal cells (i.e., claim 5 at Col. 20 lines 1-3).
Claim 6 Shen et al.
6. The computing system of claim 1,
wherein the image data comprise a brightfield image and a fluorescence image of the well plate.
6. The method of claim 1,
wherein the image data comprise a brightfield image and a fluorescence image of the well plate (i.e., claim 6 at Col. 20 lines 4-6).
Claim 7 Shen et al.
7. The computing system of claim 1,
wherein the one or more indications comprise a visual representation of each instance of the substantially spherical droplets in the image data, and
wherein the operations further comprise presenting the visual representation of each instance of the substantially spherical droplets on a user interface.
7. The method of claim 1,
wherein the indications comprise a visual representation of each instance of the substantially spherical droplets in the image data, and further comprising:
displaying the visual representation of each instance of the substantially spherical droplets on a user interface (i.e., claim 7 at Col. 20 lines 7-12).
Claim 8 Shen et al.
8. The computing system of claim 1,
wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises:
iteratively adjusting the one or more indications outputted from the machine learning model such that the dead cell dye signal is captured; and
outputting, based on the adjusted one or more indications, the fluorescence activity of the one or more instances of the substantially spherical droplets.
8. The method of claim 1,
wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises:
iteratively adjusting the indications outputted from the machine learning model such that the dead cell dye signal is captured; and
outputting, based on the adjusted indications, the fluorescence activity of the one or more instances of the substantially spherical droplets (i.e., claim 8 at Col. 20 lines 13-22).
Claim 9 Shen et al.
9. The computing system of claim 1,
wherein the one or more attributes of each instance of the substantially spherical droplets comprise a total surface area of each instance, the live cell dye signal, and the dead cell dye signal.
9. The method of claim 1,
the attributes of each instance of the substantially spherical droplets comprise a total surface area of each instance, the live cell dye signal, and the dead cell dye signal (i.e., claim 9 at Col. 20 lines 23-26).
Claim 10 Shen et al.
10. The computing system of claim 1,
wherein normalizing the well-to-well variation in the well plate comprises:
obtaining a total surface area of each instance of the substantially spherical droplets in the well plate,
wherein the total surface area is correlated with a level of adenosine triphosphate (ATP) of each instance of the substantially spherical droplets;
obtaining, for each well of the well plate, a cell viability in response to a drug treatment; and
adjusting, based on the total surface area, respective cell viability across a plurality of wells in the well plate.
10. The method of claim 1,
wherein normalizing the well-to-well variation in the well plate comprises:
obtaining a total surface area of each instance of the substantially spherical droplets in the well plate,
wherein the total surface area is correlated with a level of adenosine triphosphate (ATP) of each instance of the substantially spherical droplets;
obtaining, for each well of the well plate, a cell viability in response to a drug treatment; and
adjusting, based on the total surface area, respective cell viability across a plurality of wells in the well plate (i.e., claim 10 at Col. 20 lines 27-37).
Claim 11 Shen et al.
11. The computing system of claim 1,
wherein the operations comprise
determining an integrated cell viability in a given well in the well plate, the given well being treated with a live cell dye corresponding to the live cell dye signal and a dead cell dye corresponding to the dead cell dye signal prior to performance of a drug assay on a plurality of substantially spherical droplets in the given well.
11. The method of claim 1, further comprising:
treating a given well, in the well plate, with a live cell dye corresponding to the live cell dye signal and a dead cell dye corresponding to the dead cell dye signal;
performing a drug assay on a plurality of substantially spherical droplets in the given well; and
determining an integrated cell viability in the given well (i.e., claim 11 at Col. 20 lines 38-44).
Claim 12 Shen et al.
12. The computing system of claim 11,
wherein the drug assay comprises a CellTiter-Glo (CTG) luminescent cell viability assay.
12. The method of claim 11,
wherein the drug assay comprises a CellTiter-Glo (CTG) luminescent cell viability assay (i.e., claim 12 at Col. 20 lines 45-47).
Claim 13 Shen et al.
13. The computing system of claim 11,
wherein the live cell dye comprises a calcein-AM and a mitotracker viewer.
13. The method of claim 11,
wherein the live cell dye comprises a calcein-AM and a mitotracker viewer (i.e., claim 13 at Col. 20 lines 48-49).
Claim 14 Shen et al.
14. The computing system of claim 11,
wherein the dead cell dye comprises an ethidium homodimer-2 and a fluorescent conjugated annexin V.
14. The method of claim 11,
wherein the dead cell dye comprises an ethidium homodimer-2 and a fluorescent conjugated annexin V (i.e., claim 14 at Col. 20 lines 50-52).
Claim 15 Shen et al.
15. The computing system of claim 11,
wherein the operations comprise
determining, based on the integrated cell viability, cytotoxic or cytostatic drug responses.
15. The method of claim 11, further comprising:
determining, based on the integrated cell viability, cytotoxic or cytostatic drug responses (i.e., claim 15 at Col. 20 lines 53-55).
Claim 16 Shen et al.
16. The computing system of claim 1,
wherein the operations comprise filtering out stromal cells and non-tumorspheres in the one or more indications in response to applying a size filter to the one or more indications,
wherein the size filter removes cells under a pre-defined size from the one or more indications.
16. The method of claim 1, further comprising:
in response to applying a size filter to the indications, filtering out stromal cells and non-tumorspheres in the indications,
wherein the size filter removes cells under a pre-defined size from the indications (i.e., claim 16 at Col. 20 lines 56-60).
Claim 17 Shen et al.
17. The computing system of claim 1,
wherein one or more wells in the well plate are treated with a stain that non-specifically binds to organic tissue and the base material of the plurality of substantially spherical droplets.
17. The method of claim 1, further comprising
treating one or more wells in the well plate with a stain that non-specifically binds to organic tissue and a base material of the plurality of substantially spherical droplets (i.e., claim 17 at Col. 20 lines 61-64).
Claim 18 Shen et al.
18. The computing system of claim 1,
wherein the image data of the well plate was captured at a single focal plane or using a two-dimensional projection of three-dimensional confocal microscopy Z-stacks.
18. The method of claim 1,
wherein obtaining the image data of the well plate comprises
obtaining the image data for one or more wells in the well plate at a single focal plane or using a two-dimensional projection of three-dimensional confocal microscopy Z-stacks (i.e., claim 18 at Col. 20 line 65 through Col. 21 line 2).
Claim 19 Shen et al.
19. The computing system of claim 1,
wherein obtaining the one or more indications of each instance of the substantially spherical droplets in the image data comprises
generating a corresponding mask represented using a Fourier series representation,
wherein the Fourier series representation is generated based on coefficients output by the machine learning model.
19. The method of claim 1,
wherein obtaining the indications of each instance of the substantially spherical droplets in the image data comprises
generating a corresponding mask represented using a Fourier series representation,
wherein the Fourier series representation is generated based on coefficients output by the machine learning model (i.e., claim 19 at Col. 21 lines 2-8).
Claim 20 Shen et al.
20. The computing system of claim 19,
wherein the machine learning model is trained using images that include labeled droplet instances, the labeled droplet instances being labeled using a pre-trained neural network configured to generate image instance segmentation masks.
20. The method of claim 19,
wherein the machine learning model is trained using images that include labeled droplet instances, the labeled droplet instances being labeled using a pre-trained neural network configured to generate image instance segmentation masks (i.e., claim 20 at Col. 21 lines 9-14).
Claim 21 Shen et al.
21. The computing system of claim 20,
wherein the images that include the labeled droplet instances are generated from unlabeled images, and
wherein the unlabeled images are pre-processed using (i) a mathematical transformation that enhances fluorescence corresponding to stained droplets present in the unlabeled images, (ii) a mathematical morphology operation that enhances disk-like objects that are within a defined range of sizes, and (iii) an image-resizing operation.
21. The method of claim 20,
wherein the images that include the labeled droplet instances are generated from unlabeled images, and
wherein the unlabeled images are pre-processed using (i) a mathematical transformation that enhances fluorescence corresponding to stained droplets present in the unlabeled images, (ii) a mathematical morphology operation that enhances disk-like objects that are within a defined range of sizes, and (iii) an image-resizing operation (i.e., claim 21 at Col. 21 lines 15-23).
Claim 22 Shen et al.
22. A computing system comprising:
one or more processing devices; and
one or more storage devices storing instructions that, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:
receiving image data of a well plate containing a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
implementing a machine learning model to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) one or more indications of each instance of the substantially spherical droplets and (ii) one or more attributes of each instance of the substantially spherical droplets, and
normalizing, based on the one or more indications and the one or more attributes, a well-to-well variation in the well plate, and
determining, based on the one or more indications outputted by the machine learning model, a fluorescence activity of one or more instances of the substantially spherical droplets,
wherein the fluorescence activity comprises a live cell dye signal and a dead cell dye signal of the one or more instances of the substantially spherical droplets, and
wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises
(i) iteratively adjusting the indications outputted from the machine learning model such that the dead cell dye signal is captured and
(ii) outputting, based on the adjusted indications, the fluorescence activity of the one or more instances of the substantially spherical droplets.
29. A computer implemented method comprising:
obtaining image data of a well plate comprising a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
in response to applying a machine learning model configured to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) indications of each instance of the substantially spherical droplets and (ii) attributes of each instance of the substantially spherical droplets;
normalizing, based on the indications and the attributes, a well-to-well variation in the well plate; and
determining, based on the one or more indications outputted by the machine learning model, a fluorescence activity of one or more instances of the substantially spherical droplets,
wherein the fluorescence activity comprises a live cell dye signal and a dead cell dye signal of the one or more instances of the substantially spherical droplets, and
wherein determining the fluorescence activity of the one or more instances of the substantially spherical droplets comprises
(i) iteratively adjusting the indications outputted from the machine learning model such that the dead cell dye signal is captured and
(ii) outputting, based on the adjusted indications, the fluorescence activity of the one or more instances of the substantially spherical droplets (See for example, claim 29 at Col. 22 lines 9-38).
Claim 23 Shen et al.
23. A computing system comprising:
one or more processing devices; and
one or more storage devices storing instructions that, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:
receiving image data of a well plate containing a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
implementing a machine learning model to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) one or more indications of each instance of the substantially spherical droplets and (ii) one or more attributes of each instance of the substantially spherical droplets, and
normalizing, based on the one or more indications and the one or more attributes, a well-to-well variation in the well plate,
wherein normalizing the well-to-well variation in the well plate comprises:
obtaining a total surface area of each instance of the substantially spherical droplets in the well plate,
wherein the total surface area is correlated with a level of adenosine triphosphate (ATP) of each instance of the substantially spherical droplets;
obtaining, for each well of the well plate, a cell viability in response to a drug treatment; and
adjusting, based on the total surface area, respective cell viability across a plurality of wells in the well plate.
30. A computer implemented method comprising:
obtaining image data of a well plate comprising a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
in response to applying a machine learning model configured to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) indications of each instance of the substantially spherical droplets and (ii) attributes of each instance of the substantially spherical droplets; and
normalizing, based on the indications and the attributes, a well-to-well variation in the well plate, wherein normalizing the well-to-well variation in the well plate comprises:
obtaining a total surface area of each instance of the substantially spherical droplets in the well plate,
wherein the total surface area is correlated with a level of adenosine triphosphate (ATP) of each instance of the substantially spherical droplets;
obtaining, for each well of the well plate, a cell viability in response to a drug treatment; and
adjusting, based on the total surface area, respective cell viability across a plurality of wells in the well plate (See for example, claim 30 at Col. 22 lines 39-64).
Claim 24 Shen et al.
24. A computing system comprising:
one or more processing devices; and
one or more storage devices storing instructions that, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:
receiving image data of a well plate containing a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
implementing a machine learning model to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) one or more indications of each instance of the substantially spherical droplets and (ii) one or more attributes of each instance of the substantially spherical droplets, and
normalizing, based on the one or more indications and the one or more attributes, a well-to-well variation in the well plate,
wherein obtaining the indications of each instance of the substantially spherical droplets in the image data comprises generating a corresponding mask represented using a Fourier series representation,
wherein the Fourier series representation is generated based on coefficients output by the machine learning model, and
wherein the machine learning model is trained using images that include labeled droplet instances, the labeled droplet instances being labeled using a pre-trained neural network configured to generate image instance segmentation masks.
31. A computer implemented method comprising:
obtaining image data of a well plate comprising a plurality of substantially spherical droplets, each of the droplets comprising a base material and one or more three-dimensional cell aggregates;
in response to applying a machine learning model configured to identify instances of at least some of the plurality of substantially spherical droplets in the image data, obtaining (i) indications of each instance of the substantially spherical droplets and (ii) attributes of each instance of the substantially spherical droplets; and
normalizing, based on the indications and the attributes, a well-to-well variation in the well plate,
wherein obtaining the indications of each instance of the substantially spherical droplets in the image data comprises generating a corresponding mask represented using a Fourier series representation,
wherein the Fourier series representation is generated based on coefficients output by the machine learning model, and
wherein the machine learning model is trained using images that include labeled droplet instances, the labeled droplet instances being labeled using a pre-trained neural network configured to generate image instance segmentation masks (See for example, claim 31 at Col. 22 line 65 through Col. 23 line 23).
Allowable Subject Matter
Claims 1-24 would be allowable upon the filing of a Terminal Disclaimer obviating the nonstatutory double patenting rejection(s) as set forth above.
The following is an examiner’s statement of reasons for allowance: the closest prior art made of record (i.e., Ding et al., “Patient-derived micro-organospheres enable clinical precision oncology”, Cell Stem Cell 29, 2 June 2022, pp. 905-917; Salahudeen et al., U.S. Pub. No. 2022/0392640; and Gritti et al., “MOrgAna: accessible quantitative analysis of organoids with machine learning”, The Company of Biologists, Development (2021) 148, pp. 1-8) fails to disclose, teach, and/or suggest, inter alia, the performing of imaging-based of substantially spherical droplets drug assay that overcome tissue heterogeneity and well-to-well variations by at least obtaining image data of a well plate comprising a plurality of the droplets, the droplets comprising a base material and one or more three-dimensional cell aggregates, implementing a trained machine learning model to identify instances of at least some of the droplets in the image data, obtaining indications and attributes of each instance of the droplets, and normalizing, based on the attributes and indications, a well-to-well variation in the well plate. The machine learning model being trained using brightfield and fluorescence image data of the droplets, and ground-truth label representing each droplet. Further comprising the determination of a fluorescence activity of one or more instances of the droplets, normalizing the well-to-well variation using a total surface area of each instance of the droplets, and a cell viability in response to a drug treatment, or obtaining the indications of each instance of the droplets using a Fourier series representation, as claimed.
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
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/JOSE M TORRES/Examiner, Art Unit 2664 09/03/2026
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