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 .
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.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/21/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/14/2026 has been considered by the examiner.
Meanwhile, the information disclosure statement filed 07/13/2026 fails to comply with 37 CFR 1.98(a)(1), which requires the following: (1) a list of all patents, publications, applications, or other information submitted for consideration by the Office; (2) U.S. patents and U.S. patent application publications listed in a section separately from citations of other documents; (3) the application number of the application in which the information disclosure statement is being submitted on each page of the list; (4) a column that provides a blank space next to each document to be considered, for the examiner’s initials; and (5) a heading that clearly indicates that the list is an information disclosure statement. The information disclosure statement has been placed in the application file, but the information referred to therein has not been considered.
In the IDS, box 10 (page 5) has listed US Patent Application Nos. 16693117 and 18300253. However, this IDS has not listed WO2019067795A1, but this document has been submitted with this IDS dated 07/13/2026. Thus, this IDS has failed to comply with 37 CFR 1.98(a)(1).
Status of Claims
Claims 118-142 are pending. Claims 141-142 are withdrawn as being the nonelected species. Claims 118-140 are under examination. Note that the status identifiers for claims 141-142 are incorrect as these claims are currently withdrawn.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 118-129 and 132-140 are rejected under 35 U.S.C. 103 as being unpatentable over Jabs et al. (“ Screening drug effects in patient-derived cancer cells links organoid responses to genome alterations,” Molecular Systems Biology, vol. 13 (955), pgs. 1-16 published 2017, see IDS submitted on 07/05/2022, cited no. A59) in view of KOH et al. (WO2019/035766A1, published 02/21/2019, 892 dated 02/07/2025).
With regard to claim 118, Jabs teaches cancer drug screening in patient-derived cells hold great promise for personalized oncology and drug discovery and precisely compare drug profiles in differently cultured primary cells, DeathPro, an automated microscopy-based assay to resolve drug-induced cell death and proliferation inhibition (see abstract), which would read on evaluating an effect of a therapeutic agent composition. Jabs further teaches screening cells from ovarian cancer patients in monolayer or organoid culture with clinically relevant drug (see abstract and Fig. 1), which reads on exposing a plurality of tumor organoid subsets with a therapeutic agent composition comprising one therapeutic agent. Jabs teaches an automated live cell assay and quantification workflow, which deconvolves drug-induced death and proliferation inhibition over time (pg. 2, left col., para. 1 under Results; and Fig. 1). In particular, Figs. 1A shows the imaging is identifying individual tumor organoid in a plurality of tumor organoids. For example, Fig. 1A depicts a cluster of tumor organoids and each tumor organoid is identifiable on said cluster of tumor organoids (for example, see Fig. A, under Hoechst + Propidium Iodide). Jabs teaches tumour material from serous ovarian cancer patients was collected at the Departments of Gynaecology and Obstetrics, at the University Medical Centres Mannheim and Heidelberg (see pg. 10, right col., para. 1 of Materials and Methods), which reads on each of the tumor organoid subsets comprises a plurality of tumor organoids that are derived from one cells of a tumor biopsy from a subject. Jabs teaches for drug combinations, two or three drugs were combined by using similar concentrations as for single drug testing (pg. 11, left col., para. 1 under DeathPro microscopy-based drug screens). Jabs teaches DeathPro directly compares the area of nuclei of dead and live cells and generates drug efficacy measures over time independent of cellular morphology and cytoplasmic stains. Counting dead and live cells as an alternative to area measurements would require detailed, time-consuming imaging of organoids unfeasible in high-throughput drug screens (pg. 9, left col. - right col.). Fig. 1 shows an overview of drug testing in organoid culture and cells are grown on matrigel for 4 days, stained with Hoechst (H) and propidium iodide (PI) and imaged at day 4, day 7, and day 10. Image gallery exemplifies OC12 organoid growth and cell death at start (day 4) (also see Figure 1 caption and Figure 2), which would read on exposing the plurality of tumor organoids to two fluorescent markers. Figure 1B shows proliferation area and concentration of µM. Jabs teaches drug response profiles tended to cluster based on the patients as well as the culture format (pg. 6, left col., para. 1). Fig. 4 (caption) teach hierarchical clustering of drug response profiles determined in ovarian cancer organoids. Jabs teaches cells directly derived from xenografts, 10,000 OC12 cells. Further, Jabs teaches drug screening was performed in 96-well plates and 5,000 OC cells per well (pg. 11, left col., para. 1 under DeathPro microscopy-based drug screens). Jabs teaches Aurora kinase A inhibitor MK5108 (see Fig. 5C). Jabs teaches estimated tumour cell content and ploidy values were used to compute the total and allele specific copy number for each segment (see pg. 13, right col., para. 1).
Fig. 1 (A-B) depicts, for example, Day 4 with a row of carboplatin treatment, which would read on exposing a plurality of tumor organoid subsets with a therapeutic agent composition comprising one therapeutic agent, wherein each of the tumor organoid subsets (i.e., each box) comprises at least five tumor organoids and wherein the tumor organoid profile comprises a cell viability value for each of the at least five tumor organoids in each subset (see Figure 1B). Jabs teaches in the 2D co-culture screens, signals from green fluorescent fibroblast were acquired subsequently to Hoechst and PI signals with a 488 nm laser and for segmentation, a mean local threshold (radius = 35 pixel, c = -2) was used and binary images were used to filter out signals from fibroblast nuclei (see pg. 12, left col., para. 3).
Although Jabs teaches pixelated imaging and identifiable of individual tumor organoid in clusters of tumor organoids, the reference does not explicitly teach steps (d) and (e).
KOH teaches methods of providing a computational model for predicting an activity of a test agent with respect to a 3D cell structure such as spheroid, organoid and tumorsphere and specifically, machine learning algorithm is employed to generate a quantitative model of drug response in the 3D cell structure using zone-specific image featuring (see abstract). KOH teaches in Figs. 7 and 19 using images to generate data, measure cell viability, supervised learning, and drug response model. Fig. 6 of KOH shows the segmentation is conducted in the brightfield channel where the images are represented as pixels with different intensity levels and in cell profiler, identification of primary object is achieved using thresholding and filtering (also see para. [0019]). KOH teaches dataset and each dataset will have slightly different optimized parameters (see bottom of para. [0072]) and an image segmentation and analysis method that comprehensively exploits the morphology of different zones (using different image parameters such as intensity) to tumour spheroids to construct a quantitative model of the spheroid’s sensitivity to drugs and morphological changes can be measured from bright-field/digital phase contrast images of the tumour spheroids (see para. [0073]). KOH teaches image of features with spheroid viability (see Fig. 13). Fig. 13 shows a Pearson correlation graph and a linear correlation graph, depicting the optimizing of the machine learning method in LaFOS of the tumor spheroids show a significantly higher correlation with corresponding drug efficacy scores (see para. [0026]). KOH teaches morphological changes can be accurately quantified using complex computational image descriptors (see para. [0037]). KOH teaches screening can be automated (see para. [0053]). KOH teaches complex image descriptors will be required to attain more accurate quantification of the drug response at each time point (see paras. [009] and [0017]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have used the 2D images from screening drug effects on tumor organoid responses as taught by Jabs with a machine-learning computer system through images as taught by KOH because Jabs teaches its imaging system identifies individual tumor organoid in clusters of tumor organoids through pixels for screening drug effects and KOH teaches computational system is automated and accurately quantifying organoid structures in images through computational image descriptors. Because Jabs teaches image analysis of tumor organoids ultimately is to produce a quantitative analysis of tumor organoids against drug effects (Fig. 1B), it would have been obvious to the person to have individually summed up each tumor organoid by executing a computer system that recognizes individual tumor organoid structure through images. Thus, the computer system would have accurately quantified the tumor organoid structures in a sample rather than estimating the tumor organoids through area under the curve (see Jabs, Fig. 1B).
The person would have a reasonable expectation of success in using a computer system to identify each tumor organoid structure because it has been well understood by Jabs and KOH to image organoids for quantification and KOH teaches the computer system processes images for quantification, measures cell viability, and executing image descriptors for accurate quantification of the drug response for the organoid structure.
With regard to claim 119, Jabs teaches total areas covered by dead cells (PI-strained) and all cells (Hoechst or PI-stained) were determined from confocal images ( see Fig. 1 and pg. 2, left col., para 1 under Deconvolving drug-induced cell death and proliferation inhibition).
With regard to claim 120, Jabs teaches in Fig. 1B therapeutic agent composition drug response curve of LD50. However, Jabs does not teach a therapeutic agent composition dosage curve from the tumor organoid profile. KOH teaches machine-learning methods to generate multivariate models of image features with improved predictability of the drug response (see para. [0076]). Thus, it would have been obvious to have used the quantitative analysis to produce a drug response curve because both Jabs and KOH recognize generating a drug response from the data.
With regard to claim 121, Jabs teaches in Fig. 3 treatment dosage (see caption A). Jabs further teaches to identify patient-specific treatment options, the drug with the highest TI can be selected from each individual. As stated above, Jabs does not teach a therapeutic agent composition dosage curve from the tumor organoid profile. However, it would have been obvious to have used the generated dosage curve LD50 to assess the treatment dosage.
With regard to claim 122, Jabs teaches in Fig. 3 the three clusters arose based on differential cytotoxicity (i) drugs effectively inducing cell death and growth inhabitation (red cluster); medium cytotoxic drugs (yellow cluster) and ineffective drugs (blue cluster).
With regard to claim 123, Jabs teaches determining greater than 1% cell viability in days 4, 7 and 10 (see Fig. 1).
With regard to claims 124 and 125, Jabs teaches in Fig. 1A and B (AUC) the probability or likelihood that the subject's tumor is resistant to the therapeutic agent composition at a particular concentration based on the proportion of numeric cell viability values of the plurality of tumor organoids that have greater than 1% or 50% cell viability after contact with the therapeutic agent composition at the particular concentration.
With regard to claims 126-128, Jabs teaches in Fig. 3 the tumor of the subject is determined to be likely to be resistant to the therapeutic agent composition at a particular concentration when 1% or more of the numeric cell viability values of the plurality of tumor organoids contacted with the therapeutic agent composition at the particular concentration are 100% viable. Jabs teaches in Fig. 3 the three clusters arose based on differential cytotoxicity (i) drugs effectively inducing cell death and growth inhabitation (red cluster); medium cytotoxic drugs (yellow cluster) and ineffective drugs (blue cluster).
With regard to claim 129, Jabs teaches in Fig. 5B analyzing the plurality of tumor organoids for one or more genetic variants associated with resistance to the therapeutic agent composition if the tumor of the subject is designated as likely to be resistant to the therapeutic agent composition.
With regard to claim 132, Jabs teaches in Fig. 1 the tumor organoid profile comprises a numeric cell viability value for each of at least 10 tumor organoids in the plurality of organoids.
With regard to claim 133, Jabs teaches drug combinations of two drugs were combined by using similar concentrations as for single drug testing (see pg. 11, para. 1 of DeathPro microscopy-based drug screens and Fig. 3).
With regard to claim 134, Jabs teaches lung cancer organoids (pg. 2, para. 2 of Results).
With regard to claims 135 and 137, Jabs teaches screens with belinostat, BM120 and carboplatin are the most potent drugs (see pg. 3., right col., para. 2 and Fig. 1). However, Jabs does not explicitly teach administered to the subject or administering a therapy that is not the therapeutic agent. KOH teaches drug preparation and in vivo treatment and Gefitinib was administered (see para. [00103]). Thus, it would have been obvious to have administered the appropriate dosage to the patient after testing of the patient’s samples against the drug responses and changing the therapy if the drug is not responsive.
With regard to claim 136, Jabs teaches screens with belinostat, BM120 and carboplatin are the most potent drugs (see pg. 3., right col., para. 2 and Fig. 1). Additionally, Jabs teaches Aurora kinase A inhibitor MK5108 (see Fig. 5C).
With regard to claim 138, Jabs teaches the claimed fluorescent image and Fig. 1 depicts each pixel in the pixelated digital fluorescent image has a value between 0 and 216.
With regard to claim 139, Jabs teaches in Fig. 1A 3D confocal image is based on taking a set of two or more 2D pixelated digited fluorescent images of the tumor organoid for maximum intensity projection at a different Z-plane in a plurality of Z-planes.
With regard to claim 140, Fig. 1A shows the imaging C) produces a plurality of two-dimensional pixelated digital fluorescent images that are used by the obtaining D) to form the tumor organoid profile, wherein the one or more fluorescent markers is a plurality of fluorescent markers, and wherein the plurality of two-dimensional pixelated digital fluorescent images constitutes a multichannel image.
Claims 130-131 are rejected under 35 U.S.C. 103 as being unpatentable over Jabs et al. in view of KOH et al. as applied to claim 124 above, and further in view of Skardal et al. (WO2019/152767A1, published 08/08/2019).
Jabs and KOH have been discussed above. Jabs further teaches genetic alterations and predict or functionally link drug sensitivities to genetic alterations, several studies have integrated drug sensitivity data from viability assays of patient cells or cell lines with genome sequencing data (pg. 6, left col. last para. – right col., para. 1). Figure 5B shows genomic alterations at least 50%. Jabs teaches cell clusters washed out from mice ascites where subjected to erythrocyte lysis (pg. 11, left col., para. 3).
However, the references do not teach the isolating a set of tumor organoids within the plurality of tumor organoids having 0% cell viability and genetically sequencing (claim 130) wherein isolating is performed using Ficoll-Paque isolation (claim 131).
Skardal teaches preparing and using organoids (see abstract). Skardal teaches two or more populations of cells are labeled and the tissue sample and/or tumor biopsy may be genetically sequenced in part or in full in order to identify mutations (see pg. 15, lines 25-32). Skardal teaches ficoll-Paque PLUS and carefully layer the blood on top of the ficoll layer, avoid mixing the blood with the ficoll (see pg. 45, lines 21-30).
It would have been obvious to have isolated the tumor organoids having no cell viability as taught by Jabs with the sequencing and isolating method of Skardal because it has been recognized by Skardal to study organoids with immunotherapy by sequencing and isolating target materials for identification. The person would have sequenced tumor organoids having zero % viability to study the genetic makeup of tumor organoids that are effective with the drug. Additionally, the person would have a reasonable expectation of success because Skardal teaches using organoids for immunotherapy.
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 118-140 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-27 of U.S. Patent No. 11415571B2.
Although the claims at issue are not identical, they are not patentably distinct from each other because claim 1 in Patent 11415571 read on the claimed method by reciting a method of evaluating an effect of a therapeutic agent composition for a subject, the method comprising: A) exposing a plurality of tumor organoid subsets with a therapeutic agent composition comprising one or more therapeutic agents, wherein each of the tumor organoid subsets comprises at least five tumor organoids, wherein each tumor organoid subset in the plurality of tumor organoid subsets is in a different well in one or more multi-well plates; wherein each tumor organoid subset in the plurality of tumor organoid subsets comprises a plurality of tumor organoids that are derived from one or more cells of a tumor biopsy from the subject, and wherein each of the tumor organoid subsets in the plurality of tumor organoid subsets is exposed to a particular concentration of the therapeutic agent composition; B) exposing each respective tumor organoid subset in the plurality of tumor organoid subsets to one or more fluorescent markers; C) imaging the plurality of tumor organoid subsets thereby forming a plurality of two-dimensional pixelated digital fluorescent images; D) obtaining, using a computer system, for each respective tumor organoid subset in the plurality of tumor organoid subsets, a corresponding tumor organoid profile using numeric values for individual pixels within one or more corresponding two-dimensional pixelated digital fluorescent images in the plurality of two-dimensional pixelated digital fluorescent images, thereby obtaining a plurality of tumor organoid profiles, wherein each respective tumor organoid profile in the plurality of tumor organoid profiles comprises a separate numeric cell viability value for each of the at least five tumor organoids in the respective tumor organoid subset that corresponds to the respective tumor organoid profile; and E) assessing the effect of the therapeutic agent composition on each of the tumor organoids of the at least five tumor organoids in each of the tumor organoid subsets in the plurality of tumor organoid subsets based on the separate numeric cell viability value for each tumor organoid in each tumor organoid subset in the plurality of tumor organoid subsets. Thus, the Patent claim reads on the broader instant claim.
With respect to instant claim 119, Patent claim 2 recites wherein the one or more fluorescent markers comprises one or more cell death detection agents and a total cell detection agent.
With respect to instant claim 120, Patent claim 4 recites wherein the assessing step E) comprises determining a probability or a likelihood that the subject's tumor is resistant to the therapeutic agent composition at a particular concentration based on the proportion of numeric cell viability values of a given tumor organoid subset in the plurality of tumor organoid subsets that have greater than 1% cell viability after contact with the therapeutic agent composition at the particular concentration.
With respect to instant claims 121-122, Patent claim 17 recites wherein the method further comprises: F) exposing one or more tumor organoid subsets in the plurality of tumor organoid subsets that are designated as likely to be resistant to the therapeutic agent composition to a second therapeutic agent composition; G) obtaining a second tumor organoid profile for each of the one or more tumor organoid subsets in step F), wherein each of the second tumor organoid profiles comprises a separate numeric cell viability value for at least five tumor organoids in the subset; and H) assessing an effect of the second therapeutic agent composition based on the second tumor organoid profiles.
With respect to instant claim 123, Patent claim 3 recites the obtaining step D) comprises determining the proportion of numeric cell viability values of a given tumor organoid subset that have greater than 1% cell viability after contact with the therapeutic agent composition.
With respect to instant claim 124, Patent claim 4 recites the assessing step E) comprises determining a probability or a likelihood that the subject's tumor is resistant to the therapeutic agent composition at a particular concentration based on the proportion of numeric cell viability values of a given tumor organoid subset in the plurality of tumor organoid subsets that have greater than 1% cell viability after contact with the therapeutic agent composition at the particular concentration.
With respect to instant claim 125, Patent claim 5 recites the subject's tumor is determined to be likely resistant to the therapeutic agent composition at a particular concentration when 50% or more of the numeric cell viability values for a tumor organoid subset in the plurality of tumor organoid subsets contacted with the therapeutic agent composition at the particular concentration are 50% or more viable.
With respect to instant claim 126, Patent claim 6 recites the subject's tumor is determined to be likely to be resistant to the therapeutic agent composition at a particular concentration when 1% or more of the numeric cell viability values for a tumor organoid subset in the plurality of tumor organoid subsets is contacted with the therapeutic agent composition at the particular concentration are 100% or more viable.
With respect to instant claim 127, Patent claim 7 recites the subject's tumor is designated as likely to be resistant to the therapeutic agent composition and the method further comprises recommending to the subject a monitoring frequency that is more frequent than a standard monitoring frequency.
With respect to instant claim 128, Patent claim 8 recites the subject's tumor is not designated as likely to be resistant to the therapeutic agent composition and the method further comprises recommending to the subject a standard monitoring frequency.
With respect to instant claim 129, Patent claim 9 recites the method further comprising isolating a plurality of tumor organoids from a tumor organoid subset that is designated as likely to be resistant to the therapeutic agent composition and analyzing the plurality of tumor organoids for one or more genetic variants associated with resistance to the therapeutic agent composition.
With respect to instant claim 130, Patent claim 10 recites the method further comprising the step of isolating a set of tumor organoids within the plurality of tumor organoid subsets having 0% cell viability and genetically sequencing the isolated set of tumor organoids to detect one or more genetic variants associated with at least 50% of the isolated tumor organoids, wherein one or more genetic variants associated with at least 50% of the isolated set of tumor organoids is determined to be associated with susceptibility to the therapeutic agent composition.
With respect to instant claim 131, Patent claim 11 recites the isolating is performed using Ficoll-Paque isolation.
With respect to instant claim 132, Patent claim 12 recites the tumor organoid profile comprises a separate numeric cell viability value for each of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, or 1×10.sup.3, 1×10.sup.4, 1×10.sup.5, or 1×10.sup.6 tumor organoids in each of the tumor organoid subsets.
With respect to instant claim 133, the therapeutic agent composition comprises two or more therapeutic agents.
With respect to instant claim 134, Patent claim 16 recites he subject has a basal cell skin cancer, a squamous cancer, a breast cancer, a bladder cancer, a cervical cancer, a colon cancer, an endometrial cancer, a head and neck cancer, a hepatobiliary cancer, a kidney cancer, a gastric cancer, a lung cancer, a mesothelial cancer of the pleural cavity, a mesothelial cancer of the peritoneal cavity, an ovarian cancer, prostate cancer, or a rectal cancer.
With respect to instant claim 135, Patent claim 19 recites further comprising administering a therapy to the subject based on step E).
With respect to instant claim 136, Patent claim 20 recites the therapeutic agent composition consists of one of the following: a DNA damage response modulator, a cell cycle inhibitor, a metabolic inhibitor, a DNA synthesis inhibitor, an RNA synthesis inhibitor, a chemotherapy, an antimetabolite antineoplastic agent, an antimicrotubular antineoplastic agent, an antimetabolite antineoplastic agent, an antimitotic antineoplastic agent, an alkylating antineoplastic agent, a topoisomerase inhibitor, an apoptosis inducer inducers, a kinase inhibitor, a proteasome inhibitor, a PARP inhibitor, a MEK inhibitor, an Akt inhibitor, an ATM/ATR inhibitor, a TGF-beta/Smad inhibitor, a HDAC inhibitor, or a retinoic acid receptor antagonist or agonist.
With respect to instant claim 137, Patent claim 21 recites the assessing step E) comprises determining a sensitivity of the plurality of tumor organoids subsets to the therapeutic agent composition, and wherein the method further comprises: F) administering the therapeutic agent composition to the subject when the therapeutic agent composition satisfies a sensitivity threshold, and administering a therapy that is not the therapeutic agent composition when the therapeutic agent composition does not satisfy the sensitivity threshold, wherein the therapeutic agent composition comprises a PARP inhibitor.
With respect to instant claim 138, Patent claim 22, each pixel in each pixelated digital fluorescent image in the plurality of two-dimensional pixelated digital fluorescent images has a value between 0 and 2.sup.16.
With respect to instant claim 139, Patent claim 23, the imaging C) comprises (i) taking a set of two or more two-dimensional pixelated digital fluorescent images of a first tumor organoid subset in the plurality of tumor organoid subsets in the different well, wherein each respective image in the set of two-dimensional pixelated digital fluorescent images of the first tumor organoid subset is at a different Z-plane in a plurality of Z-planes, and (ii) projecting the set of two-dimensional pixelated digital fluorescent images into a single two-dimensional pixelated digital fluorescent image for the obtaining D) step.
With respect to instant claim 140, Patent claim 25 recites the one or more fluorescent markers is a plurality of fluorescent markers and wherein each two-dimensional pixelated digital fluorescent image in the plurality of two-dimensional pixelated digital fluorescent images is a multichannel image.
Response to Arguments
Applicant's arguments filed 01/21/2026 have been fully considered but they are not persuasive.
Applicant argues on page 8 that the individual pixel intensities of zeros and ones represented by the presence of fluorescent markers do not necessarily equate with separate numeric cell viability value for each tumor organoid. Applicant further argues page 9 that machine-learning system could simply aggregate all the individual pixel intensities and provide a total cell viability as opposed to a separate numerate numerical cell viability value for each tumor organoid as forth in the present claims. Applicant argues that KOH does not cure the deficiencies because a machine-learning system that generates quantitative models through images that are represented by pixels with different intensity level. Again, Applicant argues on page 10 that individual pixel intensities of zeros and ones represented by the presence of fluorescent markers do not necessarily equate with separate numeric cell viability value for each tumor organoid.
The arguments are not found persuasive for the following reasons. Jabs teaches the claimed tumor organoids, fluorescent markers, and forming a two-dimensional pixelated digital fluorescent image. Thus, Jabs teaches the images are capturing separate cell viability for each tumor organoid in a plurality of tumor organoids, as Jabs captures the claimed elements of A) to C). Although Jabs teaches quantifying by measuring area under the curve (i.e., estimation), it would have been obvious to the person to have used a computer system that recognizes structures of an organoid for quantitative analysis, as taught by KOH. By executing a computer system that automatically recognizes each tumor organoid structure, the quantitative analysis would be more accurate than estimating area under the curve. Thus, KOH does cure the deficiencies by using a computer system that uses images to quantify organoid structures. In other word, the computer system would have a higher accuracy and efficiency than a quantitative analysis that estimates area under the curve. Meanwhile, KOH does teach the structure of the claimed computer system. For the reasons above, the combination of Jabs and KOH would read on the claimed invention.
With respect to nonstatutory double patenting rejection over Patent No. 11415571B2, Applicant argues that the rejection be held in abeyance until there is an indication of allowable subject matter.
The argument is not found persuasive. The rejection is maintained because Applicant has not filed a terminal disclaimer nor amended the claims to overcome the current rejection.
Conclusion
No claim is allowed.
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/N.P.N/Examiner, Art Unit 1678
/SHAFIQUL HAQ/Primary Examiner, Art Unit 1678
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