DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The office action is in response to the claims filed on May 18, 2026 for the application filed on April 15, 2024, which claims the benefit of U.S. Provisional Application No. 63/495,975 filed on April 13, 2023. Claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 are currently pending and have been examined as discussed below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Examiners should determine whether a claim satisfies the criteria for subject matter eligibility by evaluating the claim in accordance with the flowchart in MPEP 2016(III).
Eligibility Step 1:
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim as a whole falls within one of the statutory categories of invention (i.e., a process, machine, manufacture, or composition of matter). See MPEP 2106.03. In the instant application, claims 1 – 8, 21, 24, 26 – 28, and 30 – 33 are directed to a method (i.e., a process); claim 9 and 20 are directed to a method (i.e., a process); and claim 34 is directed to a method (i.e., a process).
While each one of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 appears to fall within one or more statutory categories of invention, the Office has determined that the full eligibility analysis is required because there is doubt as to whether the applicant is effectively seeking coverage for a judicial exception itself. The eligibility of each claim is not self-evident at least because each claim as a whole did not appear to clearly improve a technology or computer functionality. To the contrary, each claim as a whole appeared to merely apply one or more judicial exceptions on a computer.
Accordingly, it has been determined that each one of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 as a whole falls within one or more statutory categories under Step 1, and the Office proceeds with the full eligibility analysis (the Alice/Mayo test described in MPEP 2106(III)) as discussed below.
Eligibility Step 2A, Prong One:
Under Step 2A, Prong One of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether each claim is directed to one or more of the judicial exceptions (i.e., an abstract idea, law of nature, or natural phenomenon). See MPEP 2106.04(II)(A)(1). After evaluation, it has been determined that claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 are directed to judicial exceptions because claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 recite abstract ideas.
Independent claims 1 and 9 are determined to be directed to a judicial exception (i.e., an abstract idea in the “mental process” grouping). Representative claim 9 recites the abstract idea identified in bold as:
A method for identifying one or more tissue organoids in a plurality of tissue organoids matching a biological property of a tissue in a subject (mental process), the method comprising:
at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor:
inputting information about the test subject into a multi-task model comprising a plurality of parameters and one or more hidden layers, wherein the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising (i) a predicted effect of the pharmaceutical agent in the test subject and (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject (mental process);
obtaining a latent representation of the information about the test subject (mental process) from a respective hidden layer in the one or more hidden layers; [[and]] (This limitation is not in claim 1.)
comparing the latent representation of the information about the test subject to a plurality of latent representations, wherein each respective latent representation in the plurality of latent representations is of information about a respective tissue organoid, in a plurality of tissue organoids (mental process), obtained from the multi-task model; and (This limitation is not in claim 1.)
identifying one or more respective tissue organoids, in the plurality of tissue organoids, that satisfy a set of one or more similarity criterion based on the comparing, thereby identifying the one or more tissue organoids matching the biological property of the tissue in the subject (mental process). (This limitation is not in claim 1.)
With further respect to claim 1, the claim recites the abstract idea identified in bold as “a method for predicting an effect of a pharmaceutical agent in a test subject of a first species.”
Regarding claims 1 and 9, the limitations in bold identified as “a method for predicting an effect of a pharmaceutical agent in a test subject of a first species” in claim 1, “a method for identifying one or more tissue organoids in a plurality of tissue organoids matching a biological property of a tissue in a subject” in claim 9, “inputting information about the test subject … to generate, as output from the multi-task model, a plurality of outputs comprising (i) a predicted effect of the pharmaceutical agent in the test subject and (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject” in claims 1 and 9, “obtaining a latent representation of the information about the test subject” in claim 9, “comparing the latent representation of the information about the test subject to a plurality of latent representations, wherein each respective latent representation in the plurality of latent representations is of information about a respective tissue organoid, in a plurality of tissue organoids” in claim 9, and “identifying one or more respective tissue organoids, in the plurality of tissue organoids, that satisfy a set of one or more similarity criterion based on the comparing, thereby identifying the one or more tissue organoids matching the biological property of the tissue in the subject.” These limitations fall into the “mental process” grouping of abstract ideas because they require an assessment of abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification. See MPEP 2106.04(a)(2)(III). Thus, the limitations may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions, and claims 1 and 9 recite an abstract idea in the "mental process" grouping under Step 2A, Prong One.
Independent claim 34 is determined to be directed to a judicial exception including abstract ideas (i.e., an abstract idea in the “mental process” grouping). Claim 34 recites the abstract idea identified in bold as:
A method for training a model to predict an effect of a candidate pharmaceutical agent in a test subject of a first species, the method comprising:
at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor:
obtaining, for each respective training sample in a first plurality of training samples, wherein each respective training sample in the first plurality of training samples comprises a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation:
a corresponding plurality of abundance values comprising, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample after exposure to the candidate pharmaceutical agent (mental process),
a corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample (mental process), and
a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable (mental process);
obtaining, for each respective training sample in a second plurality of training samples, wherein each respective training sample in the second plurality of training samples comprises a biological sample from a respective test subject in a plurality of test subjects of the first species:
a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample (mental process), and
a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable (mental process);
performing a first dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the first plurality of training samples (mental process), thereby:
learning a first mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation, into a first latent feature space comprising a first plurality of dimensions that is less than the number of cellular constituents in the plurality of constituents (mental process), and
generating, for each respective training sample in the first plurality of training samples, a first corresponding representation of the corresponding plurality of abundance values in the first latent feature space according to the first mapping function (mental process);
performing a second dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the second plurality of training samples (mental process), thereby:
learning a second mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the first species, into a second latent feature space comprising the first plurality of dimensions (mental process), and
generating, for each respective training sample in the second plurality of training samples, a corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the second mapping function (mental process);
learning a third mapping function that maps a representation of a corresponding plurality of abundance values in the first latent feature space to the second latent feature space (mental process);
generating, for each respective training sample in the first plurality of training samples, a second corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the third mapping function (mental process);
inputting, for each respective training sample in the first plurality of training samples, corresponding information about the respective training sample into a multi-task model comprising a plurality of parameters, wherein the multi-task model applies the plurality of parameters to the information about the training sample through a plurality of instructions to generate, as output from the multi-task model, a corresponding plurality of outputs (mental process), wherein:
the corresponding plurality of outputs comprises (i) a predicted effect of the candidate pharmaceutical agent on the respective training sample and (ii) for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective training sample (mental process), and
the information about the respective training sample comprises the second corresponding representation of the corresponding plurality of abundance values in the second latent feature space generated according to the third mapping function (mental process);
inputting, for each respective training sample in the second plurality of training samples, corresponding information about the respective training sample into the multi-task model, wherein the information about the respective training sample comprises the corresponding representation of the corresponding plurality of abundance values in the second latent feature space generated according to the second mapping function (mental process); and
adjusting the plurality of parameters based on:
for each respective training sample in the first plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising (a) the corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample and (b) the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables (mental process), and
for each respective training sample in the second plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables (mental process).
Regarding claim 34, the limitations in bold identified as “predict an effect of a candidate pharmaceutical agent in a test subject of a first species,” “obtaining, for each respective training sample in a first plurality of training samples, wherein each respective training sample in the first plurality of training samples comprises a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation: a corresponding plurality of abundance values comprising, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample after exposure to the candidate pharmaceutical agent; a corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample; and a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable,” “obtaining, for each respective training sample in a second plurality of training samples, wherein each respective training sample in the second plurality of training samples comprises a biological sample from a respective test subject in a plurality of test subjects of the first species: a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample; and a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable,” “performing a first dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the first plurality of training samples,” “learning a first mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation, into a first latent feature space comprising a first plurality of dimensions that is less than the number of cellular constituents in the plurality of constituents (mental process),” “generating, for each respective training sample in the first plurality of training samples, a first corresponding representation of the corresponding plurality of abundance values in the first latent feature space according to the first mapping function,” “performing a second dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the second plurality of training samples,” “learning a second mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the first species, into a second latent feature space comprising the first plurality of dimensions,” “generating, for each respective training sample in the second plurality of training samples, a corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the second mapping function,” “learning a third mapping function that maps a representation of a corresponding plurality of abundance values in the first latent feature space to the second latent feature space,” “generating, for each respective training sample in the first plurality of training samples, a second corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the third mapping function,” “inputting, for each respective training sample in the first plurality of training samples, corresponding information about the respective training sample … to generate, as output from the multi-task model, a corresponding plurality of outputs, wherein: the corresponding plurality of outputs comprises (i) a predicted effect of the candidate pharmaceutical agent on the respective training sample and (ii) for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective training sample, and the information about the respective training sample comprises the second corresponding representation of the corresponding plurality of abundance values in the second latent feature space generated according to the third mapping function,” “inputting, for each respective training sample in the second plurality of training samples, corresponding information about the respective training sample … wherein the information about the respective training sample comprises the corresponding representation of the corresponding plurality of abundance values in the second latent feature space generated according to the second mapping function,” and “adjusting the plurality of parameters based on: for each respective training sample in the first plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising (a) the corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample and (b) the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables (mental process), and for each respective training sample in the second plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables.” These limitations fall into the “mental process” grouping of abstract ideas because they require a dynamic assessment of a second corresponding representation of abundance values in a second latent feature space to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification. See MPEP 2106.04(a)(2)(III). Thus, the limitations may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions, and claim 34 recites an abstract idea in the "mental process" grouping under Step 2A, Prong One.
Dependent claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 are directed to one or more judicial exceptions (i.e., abstract idea exceptions) under Step 2A, Prong One of the full eligibility analysis as follows:
Regarding claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33, the claims recite the abstract idea identified in bold as “the predicted effect comprises a prediction for cell death of a cancer cell in the subject in response to administration of the pharmaceutical agent to the subject” in claim 2, “the pharmaceutical agent is a chemotherapeutic agent” in claim 3, “the pharmaceutical agent is selected from the group consisting of lenalidomid, pembrolizumab, trastuzumab, bevacizumab, rituximab, ibrutinib, human papillomavirus quadrivalent (types 6, 11, 16, and 18) vaccine, pertuzumab, pemetrexed, nilotinib, denosumab, abiraterone acetate, promacta, imatinib, everolimus, palbociclib, erlotinib, bortezomib, bortezomib, nivolumab, atezolizumab,daratumumab, enzalutamide, obinutuzumab, ruxolitinib, venetoclax, osimertinib, and pomalidomide” in claim 4, “a respective output in the plurality of outputs” in claim 5, “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, recommending a first therapy that comprises administration of the pharmaceutical agent to the subject; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, recommending a second therapy that is different from the first therapy” in claim 6, “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, administering a first therapy to the subject, wherein the first therapy that comprises administration of the pharmaceutical agent; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, administering a second therapy to the subject, wherein the second therapy is different from the first therapy” in claim 7, “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, recommending the test subject for a clinical trial of the pharmaceutical agent to the subject; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, not recommending the test subject for the clinical trial” in claim 8, “the plurality of tissue organoids comprises a plurality of tumor organoids” in claim 20, “a linear mapping function that transforms the plurality of abundance values into a first latent feature space comprising fewer dimensions than the number of respective cellular constituents in the plurality of cellular constituents” in claim 21, “the set of one or more cell type variables comprises a variable selected from the group consisting of cell histology, disease type, disease stage, disease grade, tissue type, and tissue site” in claim 24, “each respective cellular constituent in the plurality of cellular constituents is a different mRNA species” in claim 26, “obtaining, in electronic form, a plurality of nucleic acid sequences for mRNA from the biological sample of the test subject; and determining, for each respective cellular constituent in the plurality of cellular constituents, the corresponding abundance value from the plurality of nucleic acid sequences” in claim 27, “the obtaining (i) comprises sequencing the mRNA from the biological sample of the test subject, thereby obtaining the plurality of nucleic acid sequences” in claim 28, “the biological sample of the subject comprises a diseased tissue of the subject” in claim 30, “the diseased tissue of the subject is a cancerous tissue” in claim 31, “the biological sample of the subject comprises a biological fluid from the subject” in claim 32, and “the subject has a cancer selected from the group consisting of a carcinoma, lymphoma, blastoma, glioblastoma, sarcoma, leukemia, breast cancer, squamous cell cancer, lung cancer, small-cell lung cancer, non-small cell lung cancer (NSCLC), adenocarcinoma of the lung, squamous carcinoma of the lung, head and neck cancer, cancer of the peritoneum, hepatocellular cancer, gastric or stomach cancer, pancreatic cancer, ovarian cancer, cervical cancer, liver cancer, bladder cancer, hepatoma, colon cancer, colorectal cancer, endometrial or uterine carcinoma, salivary gland carcinoma, kidney or renal cancer, liver cancer, prostate cancer, vulval cancer, thyroid cancer, hepatic carcinoma, B-cell lymphoma, low grade/follicular non-Hodgkin's lymphoma (NHL), small lymphocytic (SL) NHL, intermediate grade/follicular NHL, intermediate grade diffuse NHL, high grade immunoblastic NHL, high grade lymphoblastic NHL, high grade small non-cleaved cell NHL, bulky disease NHL, mantle cell lymphoma, AIDS-related lymphoma, Waldenstrom's Macroglobulinemia, chronic lymphocytic leukemia (CLL), acute lymphoblastic leukemia (ALL), hairy cell leukemia, and chronic myeloblastic leukemia” in claim 33. These limitations fall into the “mental process” grouping of abstract ideas because they require a dynamic assessment of abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification. See MPEP 2106.04(a)(2)(III). Thus, the limitations may be practically performed in the human mind using observation, evaluation, judgment, and/or opinions, and claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 recite an abstract idea in the "mental process" grouping under Step 2A, Prong One.
Eligibility Step 2A, Prong Two:
Independent claims 1 and 9 recite additional limitations beyond the judicial exceptions. Representative claim 9 recites the additional limitations identified in bold as:
A method for identifying one or more tissue organoids in a plurality of tissue organoids matching a biological property of a tissue in a subject, the method comprising:
at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor:
inputting information about the test subject into a multi-task model comprising a plurality of parameters and one or more hidden layers, wherein the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising (i) a predicted effect of the pharmaceutical agent in the test subject and (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject;
obtaining a latent representation of the information about the test subject from a respective hidden layer in the one or more hidden layers; [[and]] (This limitation is not in claim 1.)
comparing the latent representation of the information about the test subject to a plurality of latent representations, wherein each respective latent representation in the plurality of latent representations is of information about a respective tissue organoid, in a plurality of tissue organoids, obtained from the multi-task model; and (This limitation is not in claim 1.)
identifying one or more respective tissue organoids, in the plurality of tissue organoids, that satisfy a set of one or more similarity criterion based on the comparing, thereby identifying the one or more tissue organoids matching the biological property of the tissue in the subject. (This limitation is not in claim 1.)
Claims 1 and 9 recite the additional limitations identified in bold as “a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor,” “a multi-task model comprising a plurality of parameters and one or more hidden layers, wherein the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions,” “hidden layer in the one or more hidden layers,” and “the multi-task model.”
Regarding the consideration under MPEP 2106.04(d)(2), each one of the claims as a whole does not amount to a particular treatment or prophylaxis, but rather to an assessment of abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification.
Regarding the consideration under MPEP 2106.05(a), each one of claims 1 and 9 as a whole does not purport to improve the functioning of a computer itself or improve any other technology or technical field. Claims 1 and 9 recite a combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “inputting information about the test subject into a multi-task model comprising a plurality of parameters and one or more hidden layers, wherein the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising (i) a predicted effect of the pharmaceutical agent in the test subject and (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.” The claims do not improve the functionality of the computer, the processor, the memory, the computer program, or the multi-task model (i.e., comprising a plurality of parameters and one or more hidden layers), but rather invoke these generic computer components as tools to automate the abstract idea (i.e., the mental process of assessing abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification). Furthermore, the computer, the processor, the memory, the computer program, and the multi-task model are claimed at a high level of generality using inputs to generate outputs without explaining how the model processes those inputs and generates the outputs. The claims fail to cover a technical solution to a technological problem (i.e., with a computer functionality). At best, the combination of all claim elements amounts to an improvement to the abstract idea itself rather than improving any technology. Thus, even when considering any combination of the elements, each one of claims 1 and 9 as a whole still does not integrate the recited exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in bold above is determined to be mere instructions to apply an abstract idea. For instance, claims 1 and 9 recite a combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “inputting information about the test subject into a multi-task model comprising a plurality of parameters and one or more hidden layers, wherein the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising (i) a predicted effect of the pharmaceutical agent in the test subject and (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.” Each claim as a whole (1) recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) invokes computers or other machinery merely as a tool to perform an existing process; and (3) covers the application of the judicial exception at a high level of generality. See MPEP 2106.05(f). Each claim recites no details about a particular multi-task model. The multi-task model is used to generally apply the abstract idea (i.e., assessing abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification) without placing any limitation on how the multi-task model processes abundance values to generate both pharmaceutical-effect and cell-type-classification outputs. In addition, the limitation would cover every mode of implementing the recited abstract idea using a multi-task model. The claim omits any details as to how the multi-task model solves a technical problem and instead recites only the idea of a solution or outcome. Therefore, the limitation represents no more than mere instructions to implement the abstract idea (i.e., assessing abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification), which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the judicial exception to the technological environment of a multi-task model by generally linking the use of the judicial exception to the recited multi-task model. Therefore, this general multi-task model recitation does not integrate the judicial exception into a practical application under MPEP 2106.05(h), and it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment (i.e., multi-task models). Accordingly, even when considering any combination of the elements, each one of claims 1 and 9 as a whole fails to integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), even if the steps of inputting, obtaining, and comparing were additional elements (which the Office does not concede), these steps would not add more than insignificant extra-solution activity to the judicial exception (i.e., the abstract idea of predicting anti-cancer drug response in the test subject). These steps represent the well-known pre-solution activity of necessary input data gathering, which is incidental to the primary process of using that input data to generate the outputs, including the predicted drug response. Thus, the steps of inputting, obtaining, and comparing would merely be nominal or tangential additions to the claim; however, the Office maintains that the steps of inputting, obtaining, and comparing are not actually additional elements, but rather the abstract idea as described above. Accordingly, even when considering any combination of the elements, each one of claims 1 and 9 as a whole does not integrate the abstract idea into a practical application under Step 2A, Prong Two.
Accordingly, in view of these considerations, the Office has determined that, even when considering any combination of the elements, each one of claims 1 and 9 as a whole does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Independent claim 34 recite additional limitations beyond the judicial exceptions. Claim 34 recites the additional limitations identified in bold as:
A method for training a model to predict an effect of a candidate pharmaceutical agent in a test subject of a first species, the method comprising:
at a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor:
obtaining, for each respective training sample in a first plurality of training samples, wherein each respective training sample in the first plurality of training samples comprises a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation:
a corresponding plurality of abundance values comprising, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample after exposure to the candidate pharmaceutical agent,
a corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample, and
a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable;
obtaining, for each respective training sample in a second plurality of training samples, wherein each respective training sample in the second plurality of training samples comprises a biological sample from a respective test subject in a plurality of test subjects of the first species:
a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in the respective training sample, and
a corresponding set of one or more cell type classifications comprising, for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective cell type variable;
performing a first dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the first plurality of training samples, thereby:
learning a first mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a tissue organoid or tissue organoid culture, formed from cells of the first species, that has been exposed to a perturbation, into a first latent feature space comprising a first plurality of dimensions that is less than the number of cellular constituents in the plurality of constituents, and
generating, for each respective training sample in the first plurality of training samples, a first corresponding representation of the corresponding plurality of abundance values in the first latent feature space according to the first mapping function;
performing a second dimensionality reduction analysis across the corresponding plurality of abundance values for each respective training sample in the second plurality of training samples, thereby:
learning a second mapping function that maps a corresponding plurality of abundance values comprising, for each respective cellular constituent in the plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the first species, into a second latent feature space comprising the first plurality of dimensions, and
generating, for each respective training sample in the second plurality of training samples, a corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the second mapping function;
learning a third mapping function that maps a representation of a corresponding plurality of abundance values in the first latent feature space to the second latent feature space;
generating, for each respective training sample in the first plurality of training samples, a second corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the third mapping function;
inputting, for each respective training sample in the first plurality of training samples, corresponding information about the respective training sample into a multi-task model comprising a plurality of parameters, wherein the multi-task model applies the plurality of parameters to the information about the training sample through a plurality of instructions to generate, as output from the multi-task model, a corresponding plurality of outputs, wherein:
the corresponding plurality of outputs comprises (i) a predicted effect of the candidate pharmaceutical agent on the respective training sample and (ii) for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective training sample, and
the information about the respective training sample comprises the second corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the third mapping function;
inputting, for each respective training sample in the second plurality of training samples, corresponding information about the respective training sample into the multi-task model, wherein the information about the respective training sample comprises the corresponding representation of the corresponding plurality of abundance values in the second latent feature space according to the second mapping function; and
adjusting the plurality of parameters based on:
for each respective training sample in the first plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising (a) the corresponding experimentally measured effect of the candidate pharmaceutical agent on the respective training sample and (b) the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables, and
for each respective training sample in the second plurality of training samples, one or more differences between (i) the corresponding plurality of outputs and (ii) a set of labels comprising the corresponding cell type classification for each respective cell type variable in the set of one or more cell type variables.
Claim 34 recites the additional limitations identified in bold as “a method for training a model,” “a computer system comprising at least one processor and a memory storing at least one program for execution by the at least one processor,” and “a multi-task model comprising a plurality of parameters, wherein the multi-task model applies the plurality of parameters to the information about the training sample through a plurality of instructions.”
Regarding the consideration under MPEP 2106.04(d)(2), claim 34 as a whole does not amount to a particular treatment or prophylaxis, but an assessment of abundance values to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification.
Regarding the consideration under MPEP 2106.05(a), claim 34 as a whole does not purport to improve the functioning of a computer itself or improve any other technology or technical field. Claim 34 recites a combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “inputting, for each respective training sample in the first plurality of training samples, corresponding information about the respective training sample into a multi-task model comprising a plurality of parameters, wherein the multi-task model applies the plurality of parameters to the information about the training sample through a plurality of instructions to generate, as output from the multi-task model, a corresponding plurality of outputs.” The claim does not improve the functionality of the computer, the processor, the memory, the computer program, or the multi-task model (i.e., comprising a plurality of parameters and one or more hidden layers), but rather invokes these generic computer components as tools to automate the abstract idea (i.e., the mental process of assessing a second corresponding representation of abundance values in a second latent feature space to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification). Furthermore, the computer, the processor, the memory, the computer program, and the multi-task model are claimed at a high level of generality using inputs to generate outputs without explaining how the model processes the representation of abundance values in the second latent feature space to determine the predicted effect of the pharmaceutical agent on the test subject or training sample and determine the corresponding cell type classification. The claim fails to cover a particular technical solution to a technological problem. At best, the combination of all claim elements amounts to an improvement to the abstract idea itself rather than improving any technology. Thus, even when considering any combination of the elements, claim 34 as a whole still does not integrate the recited exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in bold above is determined to be mere instructions to apply an abstract idea. For instance, claim 34 recites a combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “inputting, for each respective training sample in the first plurality of training samples, corresponding information about the respective training sample into a multi-task model comprising a plurality of parameters, wherein the multi-task model applies the plurality of parameters to the information about the training [subject] through a plurality of instructions to generate, as output from the multi-task model, a corresponding plurality of outputs.” The claim as a whole (1) recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) invokes computers or other machinery merely as a tool to perform an existing process; and (3) covers the application of the judicial exception at a high level of generality. See MPEP 2106.05(f). The claim recites no details about a particular multi-task model. The multi-task model is used to generally apply the abstract idea (i.e., assessing a second corresponding representation of abundance values in a second latent feature space to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification) without placing any limitation on how the multi-task model processes a second corresponding representation of abundance values in a second latent feature space to generate both pharmaceutical-effect and cell-type-classification outputs. In addition, the limitation would cover every mode of implementing the recited abstract idea using a multi-task model. The claim omits any details as to how the multi-task model solves a technical problem and instead recites only the idea of a solution or outcome. Therefore, the limitation represents no more than mere instructions to implement the abstract idea (i.e., assessing a second corresponding representation of abundance values in a second latent feature space to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification), which is equivalent to adding the words “apply it” to the recited judicial exception. In addition, the claim confines the use of the judicial exception to the technological environment of a multi-task model by generally linking the use of the judicial exception to the recited multi-task model. Therefore, this general multi-task model recitation does not integrate the judicial exception into a practical application under MPEP 2106.05(h), and it can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to a particular field of use or a technological environment (i.e., multi-task models). Accordingly, even when considering any combination of the elements, claim 34 as a whole fails to integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), claim 34 recites the step of obtaining, for each respective training sample in an associated on of the first and second pluralities of training samples, the abundance values and the cell type classifications. Claim 34 further recites the step of obtaining, for each respective training sample in the first plurality of training samples, the experimentally measured effect. Claim 34 further recites the steps of performing the first dimensionality reduction analysis, learning the first mapping function, generating the first corresponding representation, performing the second dimensionality reduction analysis, learning the second mapping function, generating the corresponding representation, learning the third mapping function, generating the second corresponding representation, and inputting the second corresponding representation. These steps represent the abstract idea as explained under Step 2A, Prong One above. Even if one or more of these steps were an additional element (which the Office does not concede), those steps would not add more than insignificant extra-solution activity to the judicial exception (i.e., the abstract idea of dynamically predicting anti-cancer drug response in the test subject). These steps represent the well-known pre-solution activity of necessary input data gathering, which is incidental to the primary process of using that input data to generate the outputs, including the predicted drug response. Thus, the step of preparing and gathering necessary input data would merely be nominal or tangential additions to the claim; however, the Office maintains that these steps are not actually additional elements, but rather the abstract idea as described above. Accordingly, even when considering any combination of the elements, claim 34 as a whole does not integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. In the instant application, the additional limitations of the computer, the processor, the memory, the computer program, or the multi-task model do no more than link the abstract idea (i.e., the mental process of assessing to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification) to a particular technological environment, i.e., the multi-task model. Thus, the additional limitations fail to add an inventive concept to the claims.
Accordingly, in view of these considerations, the Office has determined that, even when considering any combination of the elements, claim 34 as a whole does not integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Dependent claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 present additional information in tandem with further details regarding elements from independent claim 1 and are therefore directed to one or more abstract ideas for similar reasons as given Under Step 2A, Prong One above. With further regard to claims 5, 7, and 21, these claims further recite additional limitations, and these additional limitations fail to integrate the abstract idea into a practical application under Step 2A, Prong Two of the full eligibility analysis as follows:
Regarding claims 5, 7, and 21, the claims recite the additional limitations identified in bold as “the multi- task model comprises a partially connected neural network defining a plurality of tasks, each respective task in the plurality of tasks corresponding to a respective output in the plurality of outputs, wherein the partially-connected neural network comprises (a) a first set of layers shared between the plurality of tasks and (b) for each respective task in the plurality of tasks a corresponding second set of layers unique to the respective task” in claim 5, “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, administering a first therapy to the subject, wherein the first therapy that comprises administration of the pharmaceutical agent; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, administering a second therapy to the subject, wherein the second therapy is different from the first therapy” in claim 7, and “the multi- task model comprises a linear mapping function that transforms the plurality of abundance values into a first latent feature space comprising fewer dimensions than the number of respective cellular constituents in the plurality of cellular constituents” in claim 21.
Regarding the consideration under MPEP 2106.04(d)(2), each one of claims 5, 7, and 21 as a whole does not amount to a particular treatment or prophylaxis. At best, claim 7 recites “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, administering a first therapy to the subject, wherein the first therapy that comprises administration of the pharmaceutical agent; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, administering a second therapy to the subject, wherein the second therapy is different from the first therapy.” However, these steps are not particular at least because the steps do not cover any particular criterion, any particular therapy, way of satisfying the criterion, or any particular pharmaceutical agent. The steps are instead merely instructions to "apply" the judicial exception in a generic way. Furthermore, the administration steps are extra-solution activities (i.e., post-solution activity for the main process of identifying a tissue organoid matching the test subject). Thus, the administration step does not integrate the mental analysis step into a practical application.
Regarding the consideration under MPEP 2106.05(a), each one of claims 5, 7, and 21 as a whole does not purport to improve the functioning of a computer itself or improve any other technology or technical field. At best, claim 5 recites a combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) ““the multi- task model comprises a partially connected neural network defining a plurality of tasks, each respective task in the plurality of tasks corresponding to a respective output in the plurality of outputs, wherein the partially-connected neural network comprises (a) a first set of layers shared between the plurality of tasks and (b) for each respective task in the plurality of tasks a corresponding second set of layers unique to the respective task.” Claim 5 does not improve the functionality of the multi-task model (i.e., being a generic multi-task model comprising the partially connected neural network defining a plurality of tasks, wherein the partially-connected neural network comprises (a) a first set of layers shared between the plurality of tasks and (b) for each respective task in the plurality of tasks a corresponding second set of layers unique to the respective task). To the contrary, claim 5 merely defines the generic multi-task model as a tool to automate the abstract idea (i.e., the mental process of assessing to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification). Furthermore, the partially connected neural network, the plurality of tasks, the first set of layers shared between the plurality of tasks, and, for each respective task in the plurality of tasks, a corresponding second set of layers unique to the respective task are claimed at a high level of generality using inputs to generate outputs without explaining how any of this computer technology processes those inputs and generates the outputs. Claim 5 fails to cover a technical solution to a technological problem (i.e., with a computer functionality). At best, the combination of all claim elements amounts to an improvement to the abstract idea itself rather than improving any technology. Thus, even when considering any combination of the elements, each one of claims 5, 7, and 21 as a whole still does not integrate the recited exception into a practical application.
Regarding the consideration under MPEP 2106.05(f), each one of the additional limitations in claims 5, 7, and 21 is determined to be mere instructions to apply an abstract idea. . At best, claim 5 recites the combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) ““the multi- task model comprises a partially connected neural network defining a plurality of tasks, each respective task in the plurality of tasks corresponding to a respective output in the plurality of outputs, wherein the partially-connected neural network comprises (a) a first set of layers shared between the plurality of tasks and (b) for each respective task in the plurality of tasks a corresponding second set of layers unique to the respective task.” The partially connected neural network, the plurality of tasks, the first set of layers, and the corresponding second set of layers are claimed at a high level of generality, and thus, the claims merely amount to instructions to implement the abstract idea on generic computer technology. Accordingly, even when considering any combination of the elements, claim 5 as a whole fails to integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(g), claim 7 recites the combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “when the predicted effect of the pharmaceutical agent in the test subject satisfies a first set of one or more criterion, administering a first therapy to the subject, wherein the first therapy that comprises administration of the pharmaceutical agent; and when the predicted effect of the pharmaceutical agent in the test subject does not satisfy the first set of one or more criterion, administering a second therapy to the subject, wherein the second therapy is different from the first therapy.” This combination represents the insignificant post-solution activity of administering a first and second therapies based on the identified tissue organoid matching with the test subject and the predicted effect of the pharmaceutical agent on the test subject. This administration step is well-known, and also nominal and incidental to the main process of using that input data to generate the outputs, including the predicted drug response. Claim 21 recites the combination of limitations (including the abstract idea in italicized font and the additional limitation in bold font) “the multi- task model comprises a linear mapping function that transforms the plurality of abundance values into a first latent feature space comprising fewer dimensions than the number of respective cellular constituents in the plurality of cellular constituents.” This combination amounts to the insignificant pre-solution activity of necessary data gathering, i.e., obtaining the first latent feature space, which is one of multiple steps used to obtain the inputs for the multi-task model. This transforming step is well-known, and also nominal and incidental to the main process of using that input data to generate the outputs, including the predicted drug response. Accordingly, even when considering any combination of the elements, each one of claims 7 and 21 as a whole does not integrate the abstract idea into a practical application.
Regarding the consideration under MPEP 2106.05(h), the additional limitations, individually or in combination, also amount to merely indicating a field of use or technological environment in which to apply the judicial exception. In the instant application, the additional limitations of the computer, the processor, the memory, the computer program, the multi-task model, the partially connected neural network, the plurality of tasks, the first set of layers, and the corresponding second set of layers do no more than link the abstract idea (i.e., the mental process of assessing to determine a predicted effect of a pharmaceutical agent on the test subject or training sample and determine a corresponding cell type classification) to a particular technological environment, i.e., the multi-task model. Thus, the additional limitations fail to add an inventive concept to the claims.
Accordingly, in view of these considerations, the Office has determined that each one of claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 as a whole do not have one or more additional limitations, individually or in combination, that integrate the abstract idea exception into a practical application under Step 2A, Prong Two.
Eligibility Step 2B:
Regarding independent claims 1, 9, and 34, the Office carries over its identification of the additional elements from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B.
Under Step 2B of the 2019 Revised Patent Subject Matter Eligibility Guidance, it must be determined whether provide an inventive concept by determining if the claims include additional elements or a combination of elements that are sufficient to amount to significantly more than the judicial exception. After evaluation, there is no indication that an additional element or combination of elements are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations amount to mere instructions to apply an abstract idea under MPEP 2106.05(f). Evidence that using multi-task models (i.e., having latent components and mapping functions to predict drug responses) is well-understood, routine and conventional is provided by NPL Ammad-ud-din. Furthermore, the claimed inputting steps amount to the insignificant pre-solution activity of mere necessary data gathering under MPEP 2106.05(g). Evidence that retrieving information and performing repetitive calculations are well-understood, routine and conventional is provided by MPEP 2106.05(d), subsection II.
Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements amounts to an inventive concept.
Therefore, whether taken individually or as an ordered combination, claims 1, 9, and 34 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Regarding dependent claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33, the Office carries over its identification of the additional elements from Step 2A, Prong Two so as to apply the same additional elements in Step 2B. See MPEP 2106.05(II). The Office further carries over its conclusions from the considerations discussed in MPEP 2106.05(a) through (c), (e) through (h) in Step 2A, Prong Two so as to apply the same considerations in Step 2B.
The dependent claims present additional abstract information in tandem with further details regarding the elements from the independent claim 1 and are, therefore, directed to an abstract idea under Step 2A, Prong Two for similar reasons as given above. With further regard to claims 5, 7, and 21, these claims further recite additional limitations, and these additional limitations fail to integrate the abstract idea into a practical application under Step 2A, Prong Two as described above. Evidence that using multi-task models (i.e., having latent components and mapping functions to predict drug responses) is well-understood, routine and conventional is provided by NPL Ammad-ud-din. Furthermore, the claimed inputting steps amount to the insignificant pre-solution activity of mere necessary data gathering under MPEP 2106.05(g). Evidence that retrieving information and performing repetitive calculations are well-understood, routine and conventional is provided by MPEP 2106.05(d), subsection II. Claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 are all encompassed by the abstract idea grouping of mental processes.
Therefore, whether taken individually or as an ordered combination, claims 2 – 8, 20 – 21, 24, 26 – 28, and 30 – 33 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's amendment and arguments (Fifth Paragraph to Seventh Paragraph on Page 11 of the Amendment filed May 18, 2026) regarding the objections to claims 20 and 34 have been fully considered and are persuasive. Thus, the objections to claims 20 and 34 have been withdrawn.
Applicant's amendment and arguments (First Paragraph to Second Paragraph on Page 12 of the Amendment filed May 18, 2026) regarding the rejection of claim 20 under 35 U.S.C. § 112 have been fully considered and are persuasive. Thus, the rejection of claim 20 has been withdrawn.
Applicant's arguments (Third Paragraph on Page 12 to Second Paragraph on Page 29 of the Amendment filed May 18, 2026) regarding the rejection of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 under 35 U.S.C. § 103 have been fully considered and are persuasive. Thus, the rejections of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 under 35 U.S.C. § 103 have been withdrawn.
In the Amendment (Last Paragraph on Page 16 to First Paragraph on Page 18), Applicant argued that Yang does not disclose the limitation in independent claim 1 of “the multi-task model applies the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising … (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises a plurality of abundance values, the plurality of abundance values comprising, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.” The Office agrees. None of the cited art of record discloses this limitation.
In the Amendment (First Paragraph to Third Paragraph on Page 22), Applicant argued that Yang does not disclose the limitation in independent claim 9 of “the multi-task model is trained to apply the plurality of parameters to the information about the test subject through a plurality of instructions to generate, as output from the multi-task model, a plurality of outputs comprising … (ii) for each respective cell type variable in a set of one or more cell type variables, a corresponding cell type classification, wherein the information about the test subject comprises, for each respective cellular constituent in a plurality of cellular constituents, a corresponding abundance value for the abundance of the respective cellular constituent in a biological sample of the test subject.” The Office agrees. None of the cited art of record discloses this limitation.
In the Amendment (Last Paragraph on Page 27), Applicant argued that Yang does not disclose the limitation in independent claim 34 of “inputting … into a multi-task model … to generate… (ii) for each respective cell type variable in the set of one or more cell type variables, a corresponding cell type classification for the respective training sample.” The Office agrees. None of the cited art of record discloses this limitation.
Applicant's arguments (Last Paragraph on Page 29 to Last Paragraph on Page 40 of the Amendment filed May 18, 2026) regarding the rejection of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 under 35 U.S.C. § 101 have been fully considered and are unpersuasive. Thus, the rejections of claims 1 – 9, 20 – 21, 24, 26 – 28, and 30 – 34 have been maintained.
In the Amendment (Second Paragraph on Page 34), Applicant argued that independent claims 1, 9, and 34 recite a technical solution to a technical problem disclosed in the specification (Paragraph [0028]). The Office respectfully disagrees.
In the Amendment (Second Paragraph on Page 34), Applicant argued that the technical solution recited in the claims is “a multi-task model that processes abundance values from a biological sample and generates both a predicted effect of a pharmaceutical agent and, for each cell-type variable, a corresponding cell-type classification.” MPEP 2106.05(a) states “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome.” None of claims 1, 9, and 34 recites detail on any particular multi-task model; nor do they provide detail on how the multi-task model processes abundance values from a biological sample and generates both a predicted effect of a pharmaceutical agent and, for each cell-type variable, a corresponding cell-type classification.
Conclusion
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/V.C.I./Examiner, Art Unit 3686
/DEVIN C HEIN/Examiner, Art Unit 3686
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