DETAILED ACTION
Applicant’s response filed 05/13/2026 has been fully considered. The following rejections and/or objections are either reiterated or newly applied.
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 .
Claim Status
Claim 34 is newly added by Applicant.
Claims 20, 26 and 33 are cancelled by Applicant.
Claims 1-19, 21-25, 27-32 and 34 are currently pending and are herein under examination.
Claims 1-19, 21-25, 27-32 and 34 are rejected.
Priority
The instant application claims domestic benefit as a continuation of U.S. Patent Application No. 17/150,992, filed January 15, 2021, which is a continuation-in-part of U.S. Patent Application No. 15/930,234, filed May 12, 2020, now U.S. Patent No. 11,527,323, which claims priority to U.S. Provisional Application No. 62/983,488, filed February 28, 2020, U.S. Provisional Application No. 62/902,950, filed September 19, 2019, U.S. Provisional Application No. 62/855,750, filed May 31, 2019, and U.S. Provisional Application No. 62/847,859, filed May 14, 2019. The claims to domestic benefit are acknowledged. As such, the effective filing date for claims 1-19, 21-25, 27-32 and 34 is May 14, 2019.
Withdrawn Rejections
35 USC 112(a)
The rejection of claim 33 under 35 USC 112(a) is withdrawn in view of claim cancellation.
35 USC 112(b)
The rejection of claims 1-19 and 21-33 under 35 USC 112(b) is withdrawn in view of claim amendment, except for the rejections maintained below in section 35 USC 112.
35 USC 101
The rejection of claim 26 under 35 USC 101 is withdrawn in view of claim cancellation.
Claim Objections
The objection to claims 1, 3, 9, 19 and 25 are withdrawn in view of claim amendments.
Claim Rejections - 35 USC § 112
35 USC 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-5, 9-15, 18-19, 21-25, 28-32 and 34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
This rejection is either newly recited as necessitated by claim amendment or is maintained from the previous Office action.
Claims dependent from a rejected claim are also rejected, unless otherwise noted.
Claims 2, 4-5, 9 (line 9), 10, 12, 14, 15 (line 9), 18-19 and 21 (line 9) recite “the plurality of cancer origins” which lacks antecedent basis because claim 1 removed the phrase. Provide antecedent basis for the phrase.
Claim 4 recites “the ranked list of cancer origins” which lacks antecedent basis. It appears the recitation should be “the ranked list of cancer types”.
Claim 9 (lines 10 and 12), claim 15 (line 10), and claim 21 (line 10) recite “the subject”. It is unclear if it refers to “the subject” in claim 1, step (d), or if it refers to “a cancer subject” in line 2 of claims 9, 15 and 21. Clarify which subject is being referenced. Amend similarly to claim 3.
Claim 25 recites “the likelihood that a cancer origin in the plurality of cancer origins” which lacks antecedent basis. Provide antecedent basis for the recitation.
Claim 28 recites “the likelihood score assigned to the identified cancer type” which lacks antecedent basis. The antecedent basis for this phrase in claim 1 has been removed. Provide antecedent basis.
Claims 29 and 31 recite “the identified cancer type” which lacks antecedent basis. The antecedent basis for this phrase in claim 1 has been removed. Provide antecedent basis.
Claims 30-32 recite “the additional assay” which lacks antecedent basis. The antecedent basis for this phrase in claim 1 has been removed. Provide antecedent basis. For examination, these claims depend on claim 34 where the additional assay first appears.
Claim 34, line 4, recites “an additional assay”. The use of the word “additional” suggests that a first assay was performed before the “additional assay”. It is unclear if the first assay refers to the data analysis steps recited in claim 1 steps (a)-(d), or to an unspecified assay not recited in the claims. Clarify what the first assay is or amend the phrase to not include the word additional.
Response to Arguments under 35 USC 112(b)
Applicant's arguments filed 05/13/2026 have been fully considered but they are not persuasive.
Applicant argues that because the subject in claim 3 is the same subject that the similar rejection in claim 9 is resolved (pg. 14, para. 5). Applicant’s argument is not persuasive because claim 9 does not depend on claim 3 and still has the issue of uncertainty regarding the subject.
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 1-19, 21-25, 27-32 and 34 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea and a natural phenomenon without significantly more.
Any newly recited portions herein are necessitated by claim amendment.
Step 1:
Step 1 asks whether the claims recite statutory subject matter. In the instant application, claims 1-19, 21-25, 27-32 and 34 recite a method. As such, these claims recite statutory subject matter (Step 1: YES).
Step 2A, Prong 1:
Claims that recite statutory subject matter are analyzed under Step 2A, Prong 1 to determine if they recite any concepts that equate to an abstract idea, law of nature or natural phenomena. The instant claims recite the following limitations that equate to one or more categories of judicial exception:
Claim 1 recites “(a) training … classification model using a training dataset comprising a set of normalized mRNA abundance values for a plurality of genes, thereby obtaining a trained classification model, wherein: the training dataset includes (i) a plurality of mRNA abundance values for the plurality of genes from a plurality of tissue samples, each tissue sample being fresh frozen or paraffin-embedded, and (ii) a plurality of initial labels comprising a corresponding initial label for the primary origin of each tissue sample, the plurality of tissue samples collectively includes both primary and metastatic tumors, the plurality of tissue samples comprises 20,000 tissue samples, the classification model is configured to output, for each of at least 60 cancer types, a likelihood that the cancer type is a primary origin of a given cancerous tissue sample, and the at least 60 cancer types includes at least two cancer types from one or more classes of cancer selected from the group consisting of hematological cancers, squamous cancers, endometrial cancers, sarcoma cancers, and neuroendocrine cancers, the training comprising: training a preliminary classification model against the set of normalized mRNA abundance values and the plurality of initial labels, wherein the plurality of initial labels comprises one or more of the classes of cancer selected from the group consisting of hematological cancers, squamous cancers, endometrial cancers, sarcoma cancers, and neuroendocrine cancers, identifying a first cohort of tissue samples, in the plurality of tissue samples, having initial labels of one of the classes of cancer selected from the group consisting of hematological cancers, squamous cancers, endometrial cancers, sarcoma cancers, and neuroendocrine cancers, generating a cluster assignment for the first cohort of tissue samples by unsupervised clustering of the first cohort of tissue samples based on mRNA abundance values for the tissue samples in the first cohort, assigning a first partition label to a portion of tissue samples in the first cohort based on a transcriptionally distinct cluster within the cluster assignment, generating a plurality of updated labels by replacing corresponding initial labels for the portion of tissue samples in the first cohort with the first partition label, and retraining the classification model against the set of normalized mRNA abundance values and the plurality of updated labels; (b) obtaining … a plurality of normalized mRNA abundance values for the plurality of genes from a cancerous tissue sample obtained from the subject; (c) inputting … the plurality of normalized mRNA abundance values from the cancerous tissue sample obtained from the subject into the trained classification model; and (d) receiving … responsive to the inputting (c), an output comprising a respective likelihood score for each cancer type in the at least 60 cancer types, wherein the respective likelihood score represents a probability that the corresponding cancer type is a primary origin of the cancer afflicting the subject;”
Claim 2 recites “wherein the plurality of cancer origins includes leiomyosarcoma, liposarcoma, vascular sarcoma, osteosarcoma, ewing sarcoma, rhabdomyosarcoma, chondrosarcoma, synovial sarcoma, fibrous sarcoma, schwannoma, and carcinosarcoma.”
Claim 3 recites “obtaining the plurality of normalized mRNA abundance values of the plurality of genes from the plurality of sequence reads upon normalization of the plurality of sequence reads for at least GC content and transcript length; and communicating a report that includes the likelihood that each cancer type in the at least 60 cancer types is a primary origin of the cancer afflicting the cancer subject, wherein the report comprises a ranked list of the cancer types, in the at least 60 cancer types, that each have a likelihood satisfying a threshold likelihood.”
Claim 4 recites “wherein the ranked list of cancer origins consists of three respective cancer origins, in the plurality of cancer origins, having a highest likelihood of being the primary origin of the cancer afflicting the subject.”
Claim 5 recites “wherein the report comprises a listing of excluded cancer origins comprising the respective cancer origins, in the plurality of cancer origins, that each have a likelihood that does not satisfy a threshold likelihood.”
Claim 7 recites “wherein the trained classification model comprises multinomial logistic regression.”
Claim 8 recites “wherein results provided by the trained classification model indicate that the cancer subject has a hematological cancer and wherein the report further comprises: when the results provided by the trained classification model further indicate the cancer subject has chronic lymphocytic leukemia, the report further comprises instructions for administering a first therapy tailored for treatment of chronic lymphocytic leukemia; when the results provided by the trained classification model further indicate the cancer subject has acute lymphoblastic leukemia, the report further comprises instructions for administering a second therapy tailored for treatment of acute lymphoblastic leukemia; when the results provided by the trained classification model further indicate the cancer subject has chronic myeloid leukemia, the report further comprises instructions for administering a third therapy tailored for treatment of chronic myeloid leukemia; when the results provided by the trained classification model further indicate the cancer subject has acute myeloid leukemia, the report further comprises instructions for administering a fourth therapy tailored for treatment of acute myeloid leukemia; when the results provided by the trained classification model further indicate the cancer subject has T-cell lymphoma, the report further comprises instructions for administering a fifth therapy tailored for treatment of T-cell lymphoma; when the results provided by the trained classification model further indicate the cancer subject has B-cell lymphoma, the report further comprises instructions for administering a sixth therapy tailored for treatment of B-cell lymphoma; and when the results provided by the trained classification model further indicate the cancer subject has multiple myeloma, the report further comprises instructions for administering a seventh therapy tailored for treatment of multiple myeloma.”
Claim 9 recites “obtaining a second set of mRNA abundance values of the plurality of genes from the plurality of sequence reads upon normalization of the plurality of sequence reads for GC content and transcript length; responsive to the obtaining the second set of mRNA abundance values, applying the trained classification model to the second set of mRNA abundance values to provide a likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the subject;”
Claim 10 recites “wherein the trained classification model further provides, for each respective cancer origin in the plurality of cancer origins, a corresponding second indication of whether the respective cancer origin is the primary origin of the cancer, wherein the corresponding second indication is a discrete indication.”
Claim 11 recites “wherein the corresponding second indication is discrete-binary.”
Claim 12 recites “wherein the trained classification model further provides, for each respective cancer origin in the plurality of cancer origins, a corresponding second indication of whether the respective cancer origin is not the primary origin of the cancer, wherein the corresponding second indication is a discrete indication.”
Claim 13 recites “wherein the corresponding second indication is discrete-binary.”
Claim 14 recites “wherein the plurality of cancer origins comprises leiomyosarcoma, liposarcoma, vascular sarcoma, ewing sarcoma, or schwannoma, and the trained classification model assigns a highest likelihood or probability of origin for leiomyosarcoma, liposarcoma, vascular sarcoma, osteosarcoma, ewing sarcoma, schwannoma, or carcinosarcoma with a recall of at least 0.68.”
Claim 15 recites “obtaining a second set of mRNA abundance values of the plurality of genes from the plurality of sequence reads upon normalization of the plurality of sequence reads for GC content and transcript length; responsive to the obtaining the second set of mRNA abundance values, applying the trained classification model to the second set of mRNA abundance values to provide a likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the subject; and communicating a report that includes the likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the cancer subject, wherein the trained classification model determines that the cancer origin of the cancer afflicting the cancer subject is leiomyosarcoma, liposarcoma, vascular sarcoma, osteosarcoma, ewing sarcoma, fibrous sarcoma, schwannoma, or carcinosarcoma, and the report further comprises: providing instructions for administering to the cancer subject a respective therapy tailored for treatment of the leiomyosarcoma, liposarcoma, vascular sarcoma, osteosarcoma, ewing sarcoma, fibrous sarcoma, schwannoma, or carcinosarcoma.”
Claim 16 recites “wherein the plurality of genes is less than 7500 genes.”
Claim 17 recites “wherein the trained classification model detects whether or not the primary origin of the cancer is leiomyosarcoma with a precision of at least 0.76, liposarcoma with a precision of at least 0.88, vascular sarcoma with a precision of at least 0.89, osteosarcoma with a precision of at least 0.57, ewing sarcoma with a precision of at least 0.86, fibrous sarcoma with a precision of at least 0.46, schwannoma with a precision of at least 0.88, and carcinosarcoma with a precision of at least 0.54.”
Claim 18 recites “wherein the trained classification model assigns a highest likelihood or probability of origin to a cancer origin in the plurality of cancer origins with an accuracy of at least 91 percent.”
Claim 19 recites “wherein the plurality of cancer origins comprises leiomyosarcoma, liposarcoma, vascular sarcoma, ewing sarcoma, or schwannoma, and the trained classification model assigns a highest likelihood or probability of origin for leiomyosarcoma, liposarcoma, vascular sarcoma, ewing sarcoma, or schwannoma, with a precision at least 0.76.”
Claim 21 recites “obtaining a second set of mRNA abundance values of the plurality of genes from the plurality of sequence reads upon normalization of the plurality of sequence reads for GC content and transcript length; responsive to the obtaining the second set of mRNA abundance values, applying the trained classification model to the second set of mRNA abundance values to provide a likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the subject; and communicating a report that includes the likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the cancer subject.”
Claim 25 recites “the method further comprising using the likelihood that a cancer origin in the plurality of cancer origins is a primary origin of the cancer of the subject to identify a cancer treatment to administer to the subject.”
Claim 27 recites “wherein the trained classification model comprises a support vector machine.”
Claim 28 recites “the method further comprising altering a course of treatment for the subject based on the likelihood score assigned to the identified cancer type.”
Claim 29 recites “wherein the identified cancer type is breast cancer and the method further comprises altering the course of treatment from platinum chemotherapy to a breast cancer therapy.”
Claim 30 recites “wherein the additional assay is performed on an organoid derived from the subject.”
Claim 31 recites “wherein the additional assay determines a sensitivity of the subject to a drug for the identified cancer type.”
Claim 32 recites “wherein the additional assay is a methylation status assessing assay that determines a genomic methylation pattern of the subject.”
Claim 34 recites “(e) originating … on the basis of a likelihood score assigned to an identified cancer type in the plurality of cancer types by the trained classification model, a request to perform an additional assay for the subject to further evaluate the subject;”
Limitations reciting a mental process.
Claims 1, 3, 7-9, 14-15, 21, 25 and 27-29 recite limitations that are recited at such a high level of generality that they equate to a mental process because they are similar to the concepts of collecting information, analyzing it, and displaying certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom (830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)), which the courts have identified as concepts that can be practically performed in the human mind. The paragraphs below discuss the limitations in these claims that recite a mental process under their broadest reasonable interpretation (BRI).
Claim 1 step (a) limitations of “training a classification model using a training dataset” and “the classification model is configured to output, for each of at least 60 cancer types, a likelihood that the cancer type is a primary origin” includes training a model such as multinomial logistic regression, as recited in claim 7, using a cross-entropy loss function and a stochastic gradient descent function. The input into the model includes numerical values representing mRNA abundance values, wherein the output is a number (i.e., likelihood). Step (a) also recites training a preliminary model and retraining the model, which as described above include mathematical calculations and equations to output a likelihood. Step (a) also recites identifying a cohort, assigning a first partition label and generating a plurality of updated labels, which all require analyzing data, writing down information, and making mental determinations.
Claim 1 step (b) includes accessing the plurality of mRNA abundance values from a database and writing down the values for each gene, which were previously normalized using the plurality of scaling factors. Claim 1 step (c) includes inputting values into the trained multinomial logistic regression using pen and paper. Claim 1 step (d) includes determining the output by using the model, then writing down the output of the model.
Claims 9, 15 and 21 recite “applying the trained classification model to the second set of mRNA abundance values to provide a likelihood” which includes performing the operations of the model, which may be a multinominal logistic regression, using more mRNA abundance values to calculate a probability, wherein a human is capable of performing such calculations on pen and paper. Claim 14 recites assigning a highest likelihood or probability using the trained classification model which includes performing calculations using a multinomial logistic regression. Claim 25 includes making a determination but does not include actively administering the treatment to the subject. Claims 28-29 include determining a future treatment for the subject, wherein altering a course of treatment does not necessitate actively administering treatment to the subject.
Limitations reciting a mathematical concept.
Claims 1, 3, 7-15, 17-19, 21 and 27 recite limitations that equate to a mathematical concept because these limitations are similar to the concepts of organizing and manipulating information through mathematical correlations in Digitech Image Techs., LLC v Electronics for Imaging, Inc. (758 F.3d 1344, 111 U.S.P.Q.2d 1717 (Fed. Cir. 2014)), which the courts have identified as mathematical concepts. The paragraphs below discuss the limitations in these claims that recite a mathematical concept under their BRI.
Claim 1 step (a) recites training a model, training a preliminary model, and retraining a model, which may be a multinomial logistic regression as recited in claim 7, includes performing calculations of a cross-entropy loss function and a stochastic gradient descent function. Claim 1 step (a) recites generating a cluster assignment which includes using UMAP or PCA as recited in para. [480].
Claims 9, 15 and 21 recite “applying the trained classification model to the second set of mRNA abundance values to provide a likelihood” includes performing calculations of a multinomial logistic regression to output probabilities, which equates to mathematical function and calculation.
Claims 10-13 includes mathematical relationships because they are similar to organizing information and manipulating information through mathematical correlations in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). They also recite a mathematical relationship because they include outputting discrete binary indications from a function such as 0 or 1.
Claim 14 of assigning a highest likelihood or probability using the trained classification model includes performing calculations using a multinomial logistic regression. Claim 27 includes training an SVM by using quadratic programming.
Limitations reciting a natural phenomenon.
Claims 1, 3, 9, 15 and 21 recite a natural phenomenon because they correlate expression levels of specific genes to a primary origin of cancer. These limitations are similar to a correlation between the presence of myeloperoxidase in a bodily sample (such as blood or plasma) and cardiovascular disease risk, Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017), which the courts have established as natural phenomena.
Limitations reciting organizing human activity.
Claims 3, 15, 21 and 34 recite limitations that equate to organizing human activity because they are similar to managing personal behavior or relationships or interactions between people (See MPEP 210604(a)(2).II.C). Claim 34 recites originating a request to perform an additional assay which equates to a healthcare provider verbally requesting that a lab technician perform an assay. The following language recites an intended use: “to perform an additional assay for the subject to further evaluate the subject”. Claims 3, 15 and 21 recite communicating a report which includes a healthcare provider reading the report to a subject.
Limitations included in the recited judicial exception.
The following limitations in claim 1 step (a) are included in the recited judicial exception in claim 1 step (a) of training the classification model: “the training dataset includes (i) … (ii)”, “the plurality of tissue samples collectively”, “the plurality of tissue samples comprises”, and “the set of at least 60 cancer types includes”. Claims 2, 4-5, 14, 16 and 19 further limit the plurality of cancer origins, the ranked list in the report, the report, and the plurality of genes are included in the judicial exception. Claims 14 and 17-19 assign likelihoods with a specific accuracy, recall, and precision value further limit the judicial exception of training the classification mode. Claim 15 recites “the report further comprises: providing instructions for administering” which is included in the judicial exception of communicating the report. Claims 30-32 are being interpreted to depend on claim 34, but are not required to be performed because they further limit the additional assay which recites an intended use.
As such, claims 1-19, 21-25, 27-32 and 34 recite an abstract idea and a natural phenomenon (Step 2A, Prong 1: YES).
Additional Elements:
Once limitations have been identified that recite a judicial exception, the claims are evaluated for additional elements. The additional elements are then analyzed under Step 2A, Prong 2 then Step 2B. The instant claims recite the following additional elements:
Claim 1 recites “A method implemented at a computer system that includes one or more processors and system memory, for identifying a primary origin of a cancer in a subject and monitoring the subject, the method comprising: “(a) … by the computer system, computer-based …; (b) … by the computer system …; (c) … using the computer system …; (d) … by the computer system …; (e) … by the computer system …; (f) obtaining, by the computer system, responsive to the request, a determination that the additional assay has been performed on the subject, thereby monitoring the subject.”
Claim 3 recites “sequencing a plurality of RNA molecules from the cancerous tissue of the cancer subject, thereby obtaining a plurality of sequence reads of RNA from the cancerous tissue;”
Claim 9 recites “sequencing a plurality of RNA molecules from a cancerous tissue of a cancer subject, thereby obtaining a plurality of sequence reads of RNA from the cancerous tissue; communicating a report that includes the likelihood that each cancer origin in the plurality of cancer origins is a primary origin of the cancer afflicting the subject over a network; and wherein the report further comprises instructions for administering to the cancer subject an anti-cancer agent 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, nilotinib, denosumab, abiraterone acetate, promacta, imatinib, everolimus, palbociclib, erlotinib, and bortezomib.”
Claims 15 and 21 recite “sequencing a plurality of RNA molecules from a cancerous tissue of a cancer subject, thereby obtaining a plurality of sequence reads of RNA from the cancerous tissue;”
Claim 22 recites “wherein the sequencing is whole exome sequencing.”
Claim 23 recites “wherein the sequencing is targeted panel sequencing using a plurality of probes.”
Claim 24 recites “wherein the plurality of probes includes probes for at least 300 genes.”
Claim 34 recites “(e) by the computer system; and (f) obtaining, by the computer system, responsive to the request, a determination that the additional assay has been performed on the subject, thereby monitoring the subject.”
These above recited additional elements are analyzed below under both Step 2A, Prong 2 and Step 2B:
Step 2A, Prong 2:
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). The judicial exception is not integrated into a practical application because the claims do not recite additional elements that reflect an improvement to a computer, technology, or technical field (MPEP § 2106.04(d)(1) and 2106.5(a)), require a particular treatment or prophylaxis for a disease or medical condition (MPEP § 2106.04(d)(2)), implement the recited judicial exception with a particular machine that is integral to the claim (MPEP § 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP § 2106.05(c)), nor provide some other meaningful limitation (MPEP § 2106.05(e)). Rather, the claims include limitations that equate to an equivalent of the words “apply it” and/or to instructions to implement an abstract idea on a computer (MPEP § 2106.05(f)), insignificant extra-solution activity (MPEP § 2106.05(g)), and field of use limitations (MPEP § 2106.05(h)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claim 1 recites “a method implemented at a computer system that includes one or more processors and system memory”, “by the computer system”, and “computer-based”. Nothing in the claims require anything other than a generic computer. Thus, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which the courts have established does not render an abstract idea eligible in Alice Corp. 573 U.S. at 223, 110 USPQ2d at 1983.
Claim 34 step (f) includes insignificant, extra-solution activity because it does not impose meaningful limitations on the claim (MPEP 2106.05(g)(2)) and includes mere instructions to apply an exception because it only recites the idea of a solution (MPEP 2106.05(f)(1)). Step (f) confirms that an assay performed outside the metes and bounds of the claim was performed. Although the assay is based on a likelihood score of a particular cancer type and is intended to further evaluate the subject, the assay is not specified and is performed outside the metes and bounds of the instant invention. Thus, there appears to be a nominal relationship between an unspecified assay intended to “further evaluate the subject” based on a likelihood score assigned to one out of sixty possible cancer types. Claim 1 step (f) also equates to invoking a computer as a tool to perform an existing process because its BRI includes receiving data at a computer (MPEP 2106.05(f)(2)).
Claims 3, 9, 15 and 21 recite sequencing a plurality of RNA molecules from a cancerous tissue and obtaining a second set of mRNA abundance values. These limitations equate to insignificant, extra-solution activity of mere data gathering, which does not integrate a judicial exception into a practical application (MPEP § 2106.05(g)(3)). These limitations gather data necessary to perform the recited judicial exception of applying the trained classification model or communicating the report.
Claim 9 communicates a report over a network. This equates to insignificant, extra-solution activity of data outputting because it outputs the results of the judicial exception and because there is no affirmative step of administrating the therapy. Claims 22-24 further limit the sequencing in claim 21 and also equate to insignificant, extra-solution activity of mere data gathering.
Claim 6 equates to mere instructions to implement an abstract idea on a generic computer. The trained neural network performs the abstract idea in claim 1 step (c) of “inputting … the plurality of mRNA abundance values into the trained classification model” and step (d) of “receiving … responsive to the inputting (c), an output”. The trained neural network is used to generally apply the abstract idea without placing any limitations on how the trained neural network functions. Rather, this limitation only recites the outcome of applying steps (c) and (d), but does not include any details about how the “applying” is accomplished. See MPEP 2106.05(f). The recitation of “wherein the trained classification model comprises a neural network” merely indicates a field of use or technological environment in which the judicial exception is performed. Although these additional elements limit the identified judicial exceptions, these limitations merely confine the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h).
As such, claims 1-19, 21-25, 27-32 and 34 are directed to an abstract idea and a natural phenomenon (Step 2A, Prong 2: NO).
Step 2B:
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these claims recite additional elements that equate to instructions to apply the recited exception in a generic way and/or in a generic computing environment (MPEP § 2106.05(f)) and to well-understood, routine and conventional (WURC) limitations (MPEP § 2106.05(d)). The paragraphs below discuss the additional elements recited above in the instant claims.
Claim 1 recites “a method implemented at a computer system that includes one or more processors and system memory”, “by the computer system”, and “computer-based”. Nothing in the claims require anything other than a generic computer. Therefore, these limitations equate to instructions to implement an abstract idea on a generic computing environment, which the courts have established does not provide an inventive concept in Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
Claim 33 step (f) and claim 9 communicates a report over a network. These limitations equate to receiving/transmitting data over a network, which the courts have established as WURC limitation of a generic computer in buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014).
Claim 6 recites “wherein the trained classification model comprises a neural network”, which equates to instructions to “apply” the abstract idea, which cannot provide an inventive concept. See MPEP 2106.05(f). See above in section Step 2A, Prong 2 for further explanation.
Claims 1, 3, 8-9, 15 and 21-24 when viewed in combination equate to WURC and conventional limitations. Claim 1 recites generic computer components and claim 34 step (f) and claim 9 recite transmitting/receiving data over a network which are WURC functions/components. These generic computer functions/components are WURC when viewed in combination with claims 2, 9, 15 and 21-24 for sequencing RNA from cancer patients, obtaining mRNA abundance levels as normalization for GC content and transcript length, and using various sequencing techniques as taught below by the instant specification, Cieslik et al. (“Cieslik”; Genome research 25, no. 9 (2015): 1372-1381; previously cited on PTO892 mailed 07/14/2025), Nakagawa et al. (“Nakagawa”; Oncogene 34, no. 49 (2015): 5943-5950; previously cited on PTO892 mailed 07/14/2025), and Priedigkeit et al. (“Priedigkeit”; JCI insight 2, no. 17 (2017): e95703; previously cited on PTO892 mailed 07/14/2025).
The specification in paras. [113-114] discloses commercially available next-generation sequencing (NGS) techniques. Paras. [123-125] disclose NGS techniques that use probes from commercially available products such as IDT xGen Exome Research Panel. Paras. [115-116] disclose standard sequencing methods and bioinformatics techniques to derive gene expression data from microarrays and RNA-seq that include normalization by GC content and transcript length. Paras. [117-120] detail specific steps of the RNA-seq bioinformatic pipeline, which includes use of software packages, indicating that standard RNA-seq methods are WURC when viewed in combination with a generic computer.
Cieslik discloses an exome-capture transcriptome protocol that provides accurate and unbiased estimates of RNA abundance (abstract). The protocol was performed on a cohort of prostate cancer patients and on a patient with a highly malignment solitary fibrous tumor (abstract; pg. 1378, col. 2, last para – pg. 1379). Cieslik discloses using computer-implemented software such as STAR 2.3 and SAMtools (pg. 1379, col. 1, para. 4), indicating that sequencing RNA molecules from cancer tissue with whole exome sequencing is WURC.
Priedigkeit discloses an exome-capture RNA sequencing method of breast cancers and matched decalcified bone metastases (abstract). Primary breast tumors and matched bone metastases were obtained, sequenced, and had RNA expression measured (pg. 10, para. 4 – pg. 11, para. 3). Supplementary table 6 shows over 600 differentially expressed genes. Since the method is exome-capture, there are at least 300 probes to capture these individual genes. Computer-implemented software such as Salmon and DESeq2 were used, indicating that targeted pane sequencing with at least 300 probes is WURC when viewed in combination with a generic computer.
Nakagawa discloses a review on whole exome sequencing in cancer (abstract). Nakagawa discloses that TCGA, ICGC, and COSMIC databases contain over a million whole exome sequenced cancer samples (pg. 56944, col. 1, para. 3 – col. 2, para. 2). Nakagawa also discloses that the data is typically analyzed with computer-implemented bioinformatic/computational approaches (pg. 5943, col. 1, para. 2; Figure 2), indicating that sequencing cancer samples using whole exome sequencing is WURC when viewed in combination with a generic computer.
When these additional elements are considered individually and in combination, they do not provide an inventive concept because they equate to WURC functions/components of a generic computer in combination with a generic RNA-seq pipeline on cancer samples. Therefore, these additional elements do not transform the claimed judicial exception into a patent-eligible application of the judicial exception and do not amount to significantly more than the judicial exception itself (Step 2B: No).
As such, claims 1-19, 21-25, 27-32 and 34 are not patent eligible.
Response to Arguments under 35 USC 101
Applicant's arguments filed 05/13/2026 have been fully considered but they are not persuasive.
Applicant argues the following limitations improve training of a tumor of unknown origin classifier: training a preliminary classifier, identifying a first cohort, generating a cluster assignment, assigning a first partition label, generating a plurality of updated labels, and retraining the classification model (pg. 15, para. 2 – pg. 16, para. 2). Applicant’s argument is not persuasive because:
As described the rejection above, these limitations recite either a mental process or mathematical concept. When evaluating improvements to technology, MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement”. The additional elements alone or in combination with the judicial exception can provide a practical application. However, claim 1 recites generic computer component/functions and equate to mere instructions to apply the abstract idea on a generic computer.
Applicant argues claim 1 is eligible because it improves how a classification model itself is trained, similar to Ex parte Desjardins Appeal No. 2024-000567 (hereinafter “Desjardins”), by using the same limitations recited in the response directly above (pg. 16, para. 3 – pg. 17, para. 3) (pg. 18, last para. – pg. 19, para. 1). Applicant also references USPTO MEMO from 5 Dec. 2025 and states that there is a change in training architecture by updating labels that control how the model learns, resulting in improvements in data sets, data structures, and machine learning training (pg. 19, para. 2-3). Applicant’s argument is not persuasive because:
Instant claim 1 is distinct from Desjardins because the limitation in Desjardins that conferred the improvement to training itself recited an additional element. The alleged improvement in the instant claims is derived from the newly recited training process, which recites a judicial exception. MPEP 2106.05(a)(II) recites “an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” As such, the alleged improvement in the training of a tumor origin classification model is an improvement in the abstract idea itself and not in training itself.
Furthermore, the training process itself is not improved. Rather, the classification model is presented with new data. This is similar to Ex Parte Wang Appeal no. 2024-001155 where the PTAB decided “[t]he question on this record, therefore, is whether Appellant’s claim 1 does more than apply established methods of machine learning to a new data environment––it does not. Cf. Recentive, 134 F.4th at 1211. An abstract idea does not become nonabstract by limiting the invention to a particular field of use or technological environment, nor does the application of existing technology to a novel database create patent eligibility. Id. at 1213 14” (pg. 31, para. 2 of Wang). Similar to Wang, the instant claims provide improved data for the classification model to be trained on which improves its predictions. However, nothing about the training process itself has been improved. Retraining a pre-trained model is not a new concept, for example, fine-tuning.
The training data is not a data structure with a particular structure such as the self-referential table in Enfish. Thus, it is unclear where the improvement in data structure is coming from.
Applicant argues that certain limitations are not mental processes and further limit any alleged judicial exception into a practical application, and the claims reflect the disclosed improvement in the specification (pg. 17, para. 4 – pg. 18, para. 4). Applicant’s argument is not persuasive because:
Assigning a partition label to a portion of samples and replacing initial labels with the partition labels are mental processes as they require analyzing output of a clustering process and writing down labels for samples. Even if the following limitations did not recite a mental process, which examiner does not concede, they would still recite at least a mathematical concept as described in the rejection above: training a model using the training dataset, retraining the model using abundance values, generating likelihoods, and generating cluster assignments. Integration into a practical application is based on the additional elements themselves or how the additional elements use or interact with the exception (MPEP 2106.04(d)(III)). As of record, the only additional elements in claim 1 are generic computer components/functions and equate to mere instructions to apply the abstract idea on a generic computer, which does not integrate into a practical application (MPEP 2106.05(f)).
As discussed in response above, the newly recited limitations further defining the training in claim 1 recite a judicial exception. MPEP 2106.05(a) recites “the judicial exception alone cannot provide the improvement.”
Applicant compares instant claims to Ex Parte Carmody Appeal No. 2025-002843 (hereinafter “Carmody”) arguing for improvement in training of models (pg. 19, para. 4 – pg. 20, para. 1). Applicant’s argument is not persuasive because:
The improvement in Carmody was a result of specific models, training datasets, and architecture that enabled automated orchestration. It was not just the specific training datasets, but rather the training dataset in combination with the model architecture such that each model in the plurality of modular plug-and-play tactic-specific models were trained separately and independently without affecting other models. This is not the same as the instant claims. The instant claims do not recite a particular model architecture nor training an ensemble of models independently and separately.
Furthermore, the training process in claim 1 recites a judicial exception, and the judicial exception alone cannot provide the improvement (MPEP 2106.05(a)).
Applicant’s remarks regarding product-by-process limitations are noted (pg. 20, para. 2). The set of normalized mRNA abundance values for a plurality of genes in claim 1 still recites a product by process because there is no active step of normalizing. However, the other product-by-process limitations are no longer being interpreted as such.
Applicant argues claim 1 is not directed to a natural phenomenon because it does not preempt a natural correlation between gene expression and cancer origin. Rather, biological relationships are used by a classifier in a specific technological process (pg. 20, para. 3). Applicant’s argument is not persuasive because:
Although claim 1 may not preempt all forms of detecting cancer origin using mRNA abundance, it is still ineligible after evaluation under the Alice/Mayo test. MPEP 2106.04.I recites “[w]hile preemption is the concern underlying the judicial exceptions, it is not a standalone test for determining eligibility … Instead, questions of preemption are inherent in and resolved by the two-part framework from Alice Corp. and Mayo … It is necessary to evaluate eligibility using the Alice/Mayo test, because while a preemptive claim may be ineligible, the absence of complete preemption does not demonstrate that a claim is eligible.” Relating mRNA abundance to cancer origin is a natural relationship found between a cancer’s origin based on its unique gene expression pattern.
Applicant argues the claims do not equate to mere instructions to apply an abstract idea on a generic computer (pg. 20, last para.). Applicant’s argument is not persuasive because:
The limitations listed by Applicant recite a judicial exception which are performed by the computer system in claim 1. As such, these limitations equate to mere instructions to implement an abstract idea on a generic computer, which does not provide a practical application or significantly more (MPEP 2106.05(f)). The limitation of obtaining a determination by the computer system in claim 34 invokes a computer as a tool to perform a generic computer function of receiving data, which does not provide a practical application or significantly more (MPEP 2106.05(f)(2)).
Applicant argues that Examiner has not shown under Step 2B that claims are WURC (pg. 21, para. 2). Applicant’s argument is not persuasive because:
Only additional elements under Step 2B are examined for whether they are WURC. See MPEP 2106.05. Applicant has listed only limitations that recite a judicial exception. As such, they have not been evaluated under Step 2B.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Noah A. Auger whose telephone number is (703)756-4518. The examiner can normally be reached M-F 7:30-4:30 EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-9047. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.A.A./Examiner, Art Unit 1687
/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
Prosecution Insights