DETAILED ACTION
Applicant’s response, filed 24 Nov. 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 Claims
Claims 5-6, 16-21, 29, 31-40 are cancelled.
Claims 1-4, 7-15, 22-28, 30, and 41-45 are pending.
Claim 9 is withdrawn.
Claims 1-4, 7-8, 10-15, 22-28, 30, and 41-45 are rejected.
Claim 41 is objected to.
Priority
The effective filing date of the claimed invention is 30 May 2019.
Specification
The amendments to the specification filed 24 Nov. 2025 have been entered.
The objection to the disclosure in the Office action mailed 22 Aug. 2025 has been withdrawn in view of the amendments to the specification received 24 Nov. 2025.
Claim Objections
Claim 41 is objected to for the following reasons:
Claim 41 recites “obtaining the sample from a subject”, which should be amended to recite “obtaining the sample from [[a]] the subject” to use consistent language with claim 1.
Appropriate correction is required.
Claim Interpretation
Claim 1 recites “training…a PD-L1 status predictive model using a clustering algorithm based on, for each of the plurality of training samples, the respective plurality of molecular data features and the respective PD-L1 protein expression status label”. Claim 28 further limits the PD-L1 status predictive model to be a logistic regression model, a random forest model, or a support vector machine. Therefore, in light of Applicant’s specification in at least para. [0205] and also claim 25, discloses the trained PD-L1 predictive model was trained using a clustering algorithm to determine which labeled features associated with the phenotype of biological relevance, the PD-L1 status predictive is not required to be a clustering algorithm, and instead training the model “using a clustering algorithm” is interpreted to encompass using the clustering for feature selection in the training process of the model.
Claims 2, 8, 11, 14, 28, 39, 30, and 42 use the term “optionally”. Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. See MPEP 2111.04. Wherever the term “optionally” occurs in these claims, the subsequent description is treated as non-limiting.
Claim Rejections - 35 USC § 112(b)
The rejection of claims 1-4, 7-8, 10-15, 22-30, and 41-45 under 35 U.S.C. 112(b) in the Office action mailed 22 Aug. 2025 has been withdrawn in view of claim amendments and cancellations received 24 Nov. 2025.
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.
Claims 30 and 43-35 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. This rejection is newly recited and necessitated by claim amendment.
Claim 30 is indefinite for recitation of “the CDSI report” in line 5. There is insufficient antecedent basis for this limitation in the claim because claim 30 only previously recites “a report”, but does not require the report is a CDSI report. For purpose of examination, the limitation will be interpreted to mean “the report”.
Claim 43, and claims dependent therefrom, are indefinite for recitation of “wherein the NGS run comprises hybrid capture…”. Claim 41, from which claim 43 depends, recites “The…method of claim 1, further comprising one or more of:…carrying out a high throughput, short-read sequencing run of the double-stranded cDNA to generate a data set of sequences”; claim 42, from which claim 43 also depends, further limits the short-read sequencing run of claim 41 to be an NGS run. Claim 1 also recites “performing next generation sequencing on the RNA isolated from that training sample….; performing next generation sequencing on the RNA isolated from the sample”. As a result, it is unclear if “the NGS run” of claim 43 is intended to refer to the high throughput, short-read NGS run of claim 42, the NGS run of the training sample of claim 1, or the NGS run of the sample of claim 1. Clarification is requested via claim amendment. For purpose of examination, claim 43 is interpreted to refer to the high-throughput, short read NGS run of claim 42.
Response to Arguments
Applicant's arguments filed 24 Nov. 2025 regarding 35 U.S.C. 112(b) have been fully considered but they do not pertain to the new grounds of rejection set forth above.
Claim Rejections - 35 USC § 112(d)
The rejection of claims 11-12, 14, and 43-45 under 35 U.S.C. 112(d) in the Office action mailed 22 Aug. 2025 has been withdrawn in view of claim amendments received 24 Nov. 2025.
Claim Rejections - 35 USC § 101
The rejection of claim 29 under 35 U.S.C. 101 in the Office action mailed 22 Aug. 2025 has been withdrawn in view of the cancellation of this claim received 24 Nov. 2025.
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 1-4, 7-8, 10-15, 22-28, 30, and 41-45 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more. Any newly recited portion is necessitated by claim amendment.
The Supreme Court has established a two-step framework for this analysis, wherein a claim does not satisfy § 101 if (1) it is “directed to” a patent-ineligible concept, i.e., a law of nature, natural phenomenon, or abstract idea, and (2), if so, the particular elements of the claim, considered “both individually and as an ordered combination,” do not add enough to “transform the nature of the claim into a patent-eligible application.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1353 (Fed. Cir. 2016) (quoting Alice, 134 S. Ct. at 2355). Applicant is also directed to MPEP 2106.
Step 1: The instantly claimed invention (claim 1 being representative) is directed to a method. Therefore, the instantly claimed invention falls into one of the four statutory categories. [Step 1: YES]
Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in in Prong Two if the recited judicial exception is integrated into a practical application of that exception.
Step 2A, Prong 1: Under the MPEP § 2106.04, the Step 2A (Prong 1) analysis requires determining whether a claim recites an abstract idea, law of nature, or natural phenomenon.
Claim 1 recites the following steps which fall under the mathematical concepts and/or mental processes groupings of abstract ideas:
for each of a plurality of training samples:…generate a training RNA expression data set comprising a respective plurality of molecular data features corresponding to one or more RNA transcripts of one or more proteins;
…determining a PD-L1 protein expression label for that training sample…, wherein the PD-L1 protein expression status label indicates a positive PD-L1 status, a negative PD-L1 status, or an equivocal PD-L1 status;
….generate an unlabeled RNA expression data set;
training…a PD-L1 status predictive model using a clustering algorithm based on, for each of the plurality of training samples, the respective plurality of molecular data features and the respective PD-L1 protein expression status label;
generating...a predicted PD-L1 protein expression status of the sample by processing the unlabeled RNA expression data set corresponding to the sample using the PD-L1 status predictive model that was trained.
The identified claim limitations falls into one of the groups of abstract ideas of mathematical concepts and/or mental processes for the following reasons. The steps of generating a training RNA expression data set comprising molecular data features and generating an unlabeled RNA expression data set recite a mental process because the steps involve analyzing and organizing generated RNA expression data into a data set, which can be practically performed in the mind aided with pen and paper. Similarly, the step of determining a PD-L1 protein expression status label can be practically performed in the mind by comparing a PD-L1 protein expression determined by the IHC, RPPA, or FISH to a threshold to determine of the PD-L1 status is positive, negative, or equivocal. The step of predicting a PD-L1 protein expression status using a trained PD-L1 model recites a mental process because the limitation encompasses inputting numerical values relating to the expressions of the sample into a trained linear regression classifier and performing weighted addition to determine a model output indicating a probability of a positive PD-L1 status, for example. Other than reciting the limitation is performed by a processor, nothing in the claims precludes the step from being practically performed in the mind. See MPEP 2106.04(a)(2) III.
Furthermore, the limitations of training a PD-L1 status predictive model using a clustering algorithm and generating the predicted PD-L1 protein expression status using the trained model also recite a mathematical concept because in light of the specification, at para. [0231], [0242], and dependent claims, the limitation amounts to a textual equivalent of performing mathematical calculations (e.g. weighted addition) as discussed above. For example, training via a clustering algorithm amounts to a textual equivalent of calculating distances between data points, such as in k-means clustering as described at para. [0242] of Applicant’s specification, and applying a trained model encompasses applying a linear regression model, as described in para. [0231] of Applicant’s specification, which amounts to a textual equivalent of performing weighted addition. See MPEP 2106.04(a)(2) I.
Last, claim 1 further recites the law of nature of a natural correlation between PD-L1 protein expression and RNA expression features of one or more proteins, similar to a correlation between variations in non-coding regions of DNA and allele presence in coding regions of DNA, Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1375, 118 USPQ2d 1541, 1545 (Fed. Cir. 2016). See MPEP 2106.04(b) I.
Dependent claims 2-4, 7-8, 10, 22, 24-28, 30, and 41 further recite an abstract idea and/or are part of the abstract idea and law of nature of claim 1 above. Dependent claim 2 further limits the mental process of generating the training RNA expression data set to include a labeled cancer type. Dependent claims 3-4, 7-8, 10, and 22 further limit the mental process of generating the training data set, and therefore these claims are part of the abstract idea of claim 1. Claim 13 further recites the mental process and mathematical concept of normalizing RNA-seq data. Claims 22-23 further limit the mental process of generating the training data set and unlabeled data set to include clinical data. Claim 24 further recites the mental process and mathematical concepts of training the PD-L1 status predictive model using expression data and training the PD-L1 status predictive model to select labeled features predetermined to have an association with a phenotype of relevance. Claim 25 further recites the mental process and mathematical concept of using a clustering algorithm (e.g. calculating distances between data points) to determine which labeled features associated with the phenotype. Dependent claim 26 further limits the phenotype of claim 25 and thus is part of the abstract idea of claim 25. Dependent claim 27 further limits the mental process and mathematical concept of training the model in claim 24 to select labeled features from one or more genes from the recited list of genes. Dependent claim 28 further limits the predictive model to be at least a logistic regression model, which is part of the mental process and mathematical concept of claim 1 above. Dependent claim 30 further recites the mental process of generating a report including a subject’s identity and the predicted PD-L1 status, which involves analyzing and organizing information via pen and paper. Dependent claim 41 further recites the abstract idea of aligning the data set of sequences to a reference genome (i.e. performing data comparisons). Therefore, claims 1-4, 7-8, 10-15, 22-28, 30, and 41-45 recite an abstract idea and law of nature. [Step 2A, Prong 1: YES]
Step 2A: Prong 2: Under the MPEP § 2106.04, the Step 2A, Prong 2 analysis requires identifying whether there are any additional elements recited in the claim beyond the judicial exception(s), and evaluating those additional elements to determine whether they integrate the exception into a practical application of the exception. This judicial exception is not integrated into a practical application for the following reasons.
Dependent claims 2-4, 7-8, 10, 13, 22-28, and 30 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements of claim 1 include:
isolating RNA from that training sample;
performing next generation sequencing on the RNA isolated from that training sample;
performing immunohistochemistry (IHC), reverse phase protein array (RPPA), or fluorescence in situ hybridization (FISH) on that training sample;
isolating RNA from the sample of the subject;
performing next generation sequencing on the RNA isolated from the sample;
via one or more processors;
The additional elements of claims 11-12 and 14-15 include:
wherein the at least one training RNA expression data set comprises mRNA expression data (claim 11);
wherein the mRNA expression data is RNA-seq data (claim 12);
wherein the unlabeled RNA expression data set comprises mRNA expression data (claim 14);
wherein the mRNA expression data is RNA-seq data (claim 15).
The additional elements of one or more processors are generic computer components. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Furthermore, the steps relating to isolating RNA from training samples, performing next generation sequencing on the isolated RNA, and performing IHC, RPPA, or FISH on the training sample only serves to collect data to generate the training set for use by the abstract idea of training the machine learning model, which amounts to insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g). Similarly, the step of isolating RNA from the sample of the subject and performing next generation sequencing on the isolated RNA of the sample only serves to generate the input RNA expression data for the trained model, which also amounts to insignificant extra-solution activity that does not integrate the recited judicial exception into a practical application. See MPEP 2106.05(g).
The additional elements of claim 41 include:
obtaining the sample from a subject;
isolating mRNA from cells of the sample;
fragmenting the mRNA, producing double-stranded cfDNA based on one or more mRNA fragments; carrying out a high-throughput, short-read sequencing run of the double-stranded cDNA to generate a data set of sequences; or… [aligning, which part of the abstract idea as discussed above].
The additional element of claim 42-45 include:
wherein the high throughput, short-read sequencing run of the double-stranded cDNA is a next generation sequencing (NGS) run (claim 42);
wherein the NGS run comprises hybrid capture, and wherein the hybrid capture includes use of biotinylated probes which bind to specific target nucleotide sequences (claim 43);
wherein at least one of the specific target nucleotide sequences encodes PD-L1, PD-1, or a combination thereof (claim 44); and
wherein at least one of the specific target nucleotide sequences encodes 4-1BB, TIM-3, or other immune checkpoint blockade molecules (claim 45).
Regarding the additional elements of claim 41, these do not integrate the recited judicial exception into a practical application because they are not required by the claims. Claim 41 requires one of the recited steps, which includes the step of “aligning…” (a mental process), such that none of the additional elements are required. Claims 42-45 further limit the optional short-read sequencing step of claim 41, and thus also is not required. Furthermore, even if the steps were required by the claims, the limitation(s) would only serve to collect sequencing data for use by the abstract idea, specifically to generate expression data for input into the predictive model, which is insignificant extra-solution activity that does not integrate the judicial exception into a practical application. See MPEP 2106.05(g).
Therefore, the additionally recited elements amount to insignificant extra-solution activity and, as such, the claims as a whole do no integrate the abstract idea into practical application. Thus, claims 1-4, 7-8, 10-15, 22-28, 30, and 41-45 are directed to an abstract idea and law of nature. [Step 2A, Prong 2: NO]
Step 2B: In the second step it is determined whether the claimed subject matter includes additional elements that amount to significantly more than the judicial exception. See MPEP § 2106.05.
The claims do not include any additional steps appended to the judicial exception that are sufficient to amount to significantly more than the judicial exception. The additional elements of the claims are outlined above.
First, the additional elements of one or more processors are conventional computer components. The courts have found the use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit).
The additional elements of claims 1, 11-12, and 14-15 encompass performing RNA-seq on isolated RNA from training samples and a test sample to generate mRNA expression data, and performing IHC, RPPA, or FISH to generate PD-L1 protein expression data. First, Applicant’s specification at para. [0103] states that “many techniques for RNA isolation, for example, mRNA isolation, from a tissue sample are known in the art “, at para. [0130]-[0137] describes commercially-available library preparation systems that fragment RNA and reverse-transcribe it to cDNA, and at para. [0119] that sequencing data may be acquired by any methodology known in the art. Wargo et al. (Monitoring immune responses in the tumor microenvironment, 2016, Current Opinion in Immunology, 41, pg. 23-31; newly cited) reviews immune monitoring in the tumor microenvironment and discloses current immune monitoring strategies (Abstract; Figure 1-2). Wargo discloses current immune monitoring strategies include both genomic analysis and RNA-seq for profiling the transcriptome of cancer tissue in addition to immunohistochemistry (IHC) for detecting molecular and immune markers (Figure 2). Wargo discloses that the use of IHC for detecting markers such as PD-L1 is becoming a pervasive monitoring strategy (pg. 26, col. 1, para. 2 to col. 2, para. 2), and that in addition to assessing immune markers, there is increasing use of genomic profiling of tumor tissue and RNA sequencing assays (pg. 27, col. 1, para. 3-4 and col. 2, para. 3). Even considering the additional elements in combination, the use of both RNA-sequencing and IHC in assessing expression of tumors is well-understood, routine, and conventional as demonstrated by Wargo, and furthermore, the combination of these additional elements only serve as necessary data gathering for the abstract idea. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978). See MPEP 2106.05(g).
Regarding the additional elements recited in claims 41-45, these steps are not required by the claims as discussed above, and therefore cannot provide significantly more. Regardless, that is noted that the specification at para. [0103] states that “many techniques for RNA isolation, for example, mRNA isolation, from a tissue sample are known in the art “, at para. [0130]-[0137] describes commercially-available library preparation systems that fragment RNA and reverse-transcribe it to cDNA, and at para. [0119] that sequencing data may be acquired by any methodology known in the art. Therefore, performing RNA sequencing on cDNA generated from isolated RNA is well-understood, routine, and conventional.
Therefore, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself. [Step 2B: NO]
Therefore, the instantly rejected claims are not drawn to eligible subject matter as they are directed to an abstract idea and law of nature without significantly more. For additional guidance, applicant is directed generally to applicant is directed generally to the MPEP § 2106.
Response to Arguments
Applicant's arguments filed 24 Nov. 2025 regarding 35 U.S.C. 101 have been fully considered but they are not persuasive.
Applicant remarks the claims integrate the asserted abstract idea into a practical application by providing an improvement to a technology, namely clinical diagnostic testing, and specifically testing for predicted PD-L1 protein expression status in cancer samples without the costs of previously existing PD-L1 diagnostic assays (Applicant’s remarks at pg. 13, para. 3). Applicant remarks that the current diagnostic technologies of IHC, FISH, or RPPA may be used to detect and treatment-related molecule, and the specification identifies technical problems with existing IHC, FISH, and RPPA diagnostic technologies of requiring time, trained technicians, equipment, and antibodies, all of which can be expensive, that IHC straining, FISH, and RPPA assays require ten slices of tumor tissues such that the amount of cancer cells available for testing is limited, and therefore, there is a need for systems and method to predict the PD-L1 status of cancer cells beyond those used in the art (Applicant’s remarks at pg. 13, para. 3 to 14, para. 1). Applicant remarks the claims solve these technical problems by providing a diagnostic modality that predicts PD-L1 protein expression status from RNA sequencing data, and thus the claims improve clinical diagnostic technology by enabling PD-L1 protein expression status from RNA sequencing data by generating a training set using RNA sequencing data and protein-level assays on training samples and training a model, which eliminates the need to perform separate, expensive, protein-level assays on each patient sample for routine clinical testing, which overcomes the cost, time, subjectivity, and tissue consumption limitations of existing protein-detection methods (Applicant’s remarks at pg. 14, para. 2 to pg. 15, para. 2). Applicant remarks the claims demonstrate integration by reciting the additional elements relating to generating the training set, including isolating RNA, performing sequencing, and performing IHC, RPPA, or FISH (Applicant’s remarks at pg. 15, para. 3-4).
This argument is not persuasive. It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. Furthermore, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. See MPEP 2106.05(a) II.
In the instant case, the additional elements of claim 1 include: isolating RNA from that training sample, performing next generation sequencing on the RNA isolated from that training sample, performing immunohistochemistry (IHC), reverse phase protein array (RPPA), or fluorescence in situ hybridization (FISH) on that training sample, isolating RNA from the sample of the subject, and performing next generation sequencing on the RNA isolated from the sample. As explained in the above rejection, these additional elements only serve to collect the necessary data for use by the abstract idea of training the PD-L1 expression status model and applying the model, which amounts to insignificant extra solution activity that does not integrate the recited judicial exception into a practical application. The claims merely use conventional assays to generate the necessary data used by the abstract idea. As explained by the Supreme Court, the addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional. Parker v. Flook, 437 U.S. 584, 588-89, 198 USPQ 193, 196 (1978).
Instead, the alleged improvement in being able predict a PD-L1 protein expression status from RNA expression data amounts to an improvement in the abstract idea (i.e. the training and application of the model to predict a PD-L1 status using expression data) rather than an improvement in any particular technology. The claims do not serve to improve any particular assay, such as RNA sequencing, IHC, FISH, or RPPA.
It is further noted that Applicant asserts that predicting a PD-L1 protein status using next-generation sequencing on RNA from a sample is an improvement over simply performing IHC, FISH, or RPPA on the sample due to these protein assays being time consuming, expensive, and requiring skilled technicians. However, performing next-generation sequencing also requires skilled technicians, is time consuming, and is expensive. Therefore, it is not apparent that determining a PD-L1 protein status by performing RNA-sequencing is clearly an improvement over determining a PD-L1 protein status by performing IHC. For example, Warg (newly cited above) discloses the caveat that performing RNA sequencing “is somewhat costly and requires significant bioinformatic input for analysis” (pg. 27, col. 2, para. 3), highlighting the need for skilled technicians in addition to expensive costs.
Applicant remarks that the claims include additional elements that amount to significantly more than the judicial exception because the claims recite a specific multi-modal diagnostic approach that combines physical laboratory techniques with computational analysis in a manner that is not well-understood, routine, and conventional in the field of clinical cancer diagnostics (Applicant’s remarks at pg. 15, para. 6 to pg. 16, para. 1). Applicant remarks that the steps of generating the training data set (1), generating the unlabeled RNA expression data set (2), training the PD-L1 status model (3) and generating the predicted PD-L1 protein expression status (4) is not a well-understood, routine, and conventional combination (Applicant’s remarks at pg. 15, para. 2 to pg. 16, para. 2). Applicant further overviews the identified problem in the specification already discussed above, and states that the existence of these problems demonstrates the claimed solution is not well-understood, routine, and conventional, and thus the claimed invention includes additional elements that are not conventional (Applicant’s remarks at pg. 16, para. 4 to pg. 17, para. 1).
This argument is not persuasive. Under Step 2B, the additional elements are evaluated to determine whether they are well-understood, routine, and conventional. See MPEP 2106.05(d). However, the conventionality of the additional elements in combination with the judicial exception is not considered under step 2B. As a result, whether the organization of the generated RNA sequencing data and protein expression data into training data sets with molecular features, how the generated data is being used in training of the PD-L1 predictive model, and the application of the predictive model are well-understood, routine, and conventional is not considered. The additional elements relating to isolating RNA from samples, performing next generation sequencing on the isolated RNA, and performing IHC, RPPA, or FISH on a sample are well-understood, routine, and conventional as applied in the above rejection, in view of the newly cited reference, Wargo.
Claim Rejections - 35 USC § 102
The rejection of claims 1, 14-15, 28-29, and 41-45 under 35 U.S.C. 102(a)(1) as being anticipated by Vitiello (2019) in the Office action mailed 22 Aug. 2025 has been withdrawn in view of claim amendments and cancellations received 24 Nov. 2025. However, after further consideration, a new grounds of rejection is set forth below under 35 U.S.C. 103 in view of the amendments.
Claim Rejections - 35 USC § 103
The rejection of claims 2-4, 7-8, 10-13, 22-27, and 30 under 35 U.S.C. 103 as being unpatentable over Vitiello (2019) in view of Bloom (2019) in the Office action mailed 22 Aug. 2025 has been withdrawn in view of claim amendments received 24 Nov. 2025. However, after further consideration, a new grounds of rejection is set forth below under 35 U.S.C. 103 in view of the amendments.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4, 7-8, 10-15, 22-28, 30, and 41-45 are rejected under 35 U.S.C. 103 as being unpatentable over Vitiello (2019) in view of Shimoji (2016) and Bloom (2019). This rejection is newly recited and necessitated by claim amendment.
Cited references:
Vitiello et al., Differential immune profiles distinguish the mutational subtypes of gastrointestinal stromal tumor, 14 Feb. 2019, J. Clin Invest, 129(5), pg. 1863-1877; previously cited;
Shimoji et al., Clinical and pathological features of lung cancer expression programmed cell death ligand 1 (PD-L1), 2016, Lung Cancer, pg. 69-75; previously cited; and
Bloom et al., WO 2019/014647; previously cited;
Regarding claim 1¸ Vitiello discloses a method for identifying high-PD-L1 expression tumors (Abstract) comprising the following steps:
Vitiello discloses generating training data set comprising mRNA expression levels from a plurality of training samples (pg. 1871, col. 1 , para. 2 and 4 to col. 2, para. 2; Figure 6), wherein generating the training data asset comprises:
Vitiello discloses extracting RNA from human tumors (pg. 1874, col. 2, para. 6).
Vitiello discloses performing next generation sequencing, specifically RNA-seq, on the extracted RNA to determine RNA expression data features for the training set samples (pg. 1871, col. 1, para. 2 and 4; pg. 1874, col. 2, para. 7). Vitiello discloses the training data includes molecular features including expression levels of CD27, PDCD1LG2, BTLA, CD40, CXCL14, and MICB derived from the RNA expression data (pg. 1871, col. 1, para. 5 to col. 2, para. 2).
Vitiello determining a high PD-L1 expression status label (i.e. positive PD-L1 status) and low PD-L1 expression status label (i.e. negative PD-L1 status) for each training sample (pg. 1871, col. 2, para. 1-2; Figure 7).
Vitiello discloses isolating RNA from a patient specimen in a test set and determining mRNA expression data (i.e. unlabeled data, given this is the test set) from the patient specimen (i.e. the sample of the subject) by performing next generation sequencing (Fig. 7; pg. 1863, col. 2, para. 3, e.g. RNA-seq from 75 patient specimens; pg. 1871, col. 2, para. 1-2, e.g. testing cohort).
Vitiello discloses training a random forest model to predict PD-L1 protein expression status (pg. 1875, col. 1, para. 4 to col. 2, para. 1; Fig. 6-7). Vitiello discloses the training iteratively uses an algorithm to identify the top features associated with the PD-L1 protein expression label from the full set of initial RNA expression features (i.e. the molecular data features) of the training samples (pg. 1871, col. 1, para. 3; Fig. 6)
Vitiello discloses generating a predicted PD-L1 protein expression status of the test sample by processing the RNA expression data of the test sample (pg. 1871, col. 2, para. 2; Figure 7C-F, e.g. testing set by machine learning). Vitiello discloses the prediction is generated using (1) a machine learning model trained using the mRNA expression training data (Figure 7a-b, e.g. random forest modeling trained by training set).
Further regarding claim 1, Vitiello discloses the machine learning was performed using a software package, which necessarily requires the training and generating steps were performed by a computer (i.e. via one or more processors) (pg. 1875, col. 1, para. 4).
Regarding claim 3, Vitiello further discloses the expression data sets comprise expression data for Gastrointestinal Stromal tumors (GIST) (i.e. a single cancer type) (Fig. 7).
Regarding claim 7, Vitiello further discloses the expression data sets comprise expression data for Gastrointestinal Stromal tumors (GIST) (i.e. a stomach cancer) (Fig. 7; pg. 1863, col. 1, para. 1).
Regarding claim 8, Vitiello further discloses the expression data sets comprise expression data for mutational subtypes of Gastrointestinal Stromal tumors (GIST) (i.e. a stomach cancer subtype) (Fig. 7; Abstract; pg. 1863, col. 1, para. 1).
Regarding claim 11, Vitiello discloses the training RNA expression data is mRNA expression data (pg. 1871, col.1 , para. 2-4).
Regarding claim 12, Vitiello further discloses the mRNA expression data is determined by RNA-seq (pg. 1871, col. 1, para. 2 and 4).
Regarding claim 13, Vitiello discloses the mRNA expression data determined by RNA-seq is normalized (pg. 1875, col. 2, para. 2; Figure 7).
Regarding claim 14, Vitiello discloses the expression data is mRNA expression data (pg. 1871, col. 1, para. 2 and 4).
Regarding claim 15, Vitiello discloses the mRNA expression data is determined by RNA-seq (pg. 1871, col. 1, para. 2 and 4).
Further regarding claim 24¸ Vitiello discloses training the PD-L1 status predictive model using expression data labeled as PD-L1 high or PD-L1 low (pg. 1875, col. 2, para. 2).
Vitiello further discloses training the PD-L1 model to identify the top 6 features associated with PD-L1 status (i.e. features pre-determined to have an association with a phenotype of biological relevance (pg. 1875, col. 1, para. 4 to col. 2, para. 2; FIG. 7, e.g. top 6 features identified by random forest modeling).
Regarding claim 26, Vitiello discloses the phenotype of biological relevance is PD-L1 high expression and PD-L1 low expression status (i.e. a PD-L1 protein expression status) (Fig. 7; pg. 1875, col. 2, para. 2).
Regarding claim 27, Vitiello discloses that all 117 immune features were used to initially create the model and training reduced the features, such that training started with all 117 features (pg. 1871, col. 1, para. 3), wherein the immune features included TIGIT (i.e. expression data for at least one gene from the group consisting of…TIGIT) (pg. 1865, col. 2, para. 4 to pg. 1866, col. 1, para. 1, e.g. differential expression on 117 immune features, which include TIGIT).
Regarding claim 28, Vitiello discloses the PD-L1-status predictive model is a random forest model (Fig. 7; pg. 1871, col. 2, para. 2).
Regarding claim 41, Vitiello discloses aligning the sequencing reads to a human genome (pg. 1874, col. 2, para. 6 to pg. 1875, col. 1, para. 1).
Regarding claim 42, the embodiment of claim 41 where the aligning is required is examined herein. Therefore, the step of carrying out the high throughput short-read sequencing is not required. Regardless, it is noted that Vitiello discloses the sequencing is next-generation sequencing RNA-seq (pg. 1874, col. 2, para. 6).
Regarding claims 43-45, these limitations only serve to further limit the alternative embodiment of claims 41-42 that is not required by the claim. As discussed above, the embodiment of claim 41 where the aligning is required is examined herein. Therefore, the step of carrying out the high throughput short read sequencing that is an NGS run with hybrid capture using biotinylated probes targeting specific sequences is not required, and claims 43-45 are rejected for the reasons discussed above for claim 41.
Further regarding claims 1-2, 4, 10, 22-23, 25, and 30, Vitiello does not disclose the following:
Regarding claim 1, Vitiello does not disclose the PD-L1 protein expression label for each training sample was determined by performing immunohistochemistry (IHC), reverse phrase protein array (RPPA), or fluorescent in situ hybridization (FISH) on the training sample.
Instead Vitiello determines labels of a high PD-L1 expression status label (i.e. positive PD-L1 status) and low PD-L1 expression status label (i.e. negative PD-L1 status) for each training sample based on the PD-L1 expression being above or below a median PD-L1 read count (i.e. the labels are based on the RNA expression data from RNA-seq) (pg. 1871, col. 2, para. 1-2; pg. 1875, col. 2, para. 2, e.g. label based on median read count). However, Vitiello does disclose that PD-L1 mRNA expression correlated with PD-L1 protein expression in the cohort (pg. 1871, col. 2, para. 2).
Furthermore, Shimoji overviews clinical and pathological features of cancer expression PD-L1 (Abstract), and discloses obtaining mRNA expression measurements and immunohistochemical (IHC) measurements of PD-L1 expression from cancer cells (pg. 70, col. 1, para. 4 and col. 2, para. 3-5). Shimoji discloses there is a strong positive correlation between mRNA expression of PD-L1 and IHC measurements of PD-L1 expression in cancer cells (Fig. 1B, pg. 71, col. 1, para. 2), and further discloses that IHC measurements of PD-L1 expression can be a determination of whether a tumor is PD-L1 positive or negative (pg. 74, Table 3).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the mRNA expression based PD-L1 labels of Vitiello with the IHC determined PD-L1 expression labels of Shimoji, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Vitiello and Shimoji based on the simple substitution of the mRNA expression PD-L1 labels of Vitiello with the IHC protein PD-L1 expression labels of Shimoji, given that each of Vitiello and Shimoji disclose that there is a strong positive correlation between mRNA PD-L1 expression and PD-L1 protein expression (Vitiello: pg. 1871, col. 2, para. 2; Shimoji: pg. 71, col. 1, para. 2; Fig. 1B) and thus one of ordinary skill in the art would have recognized that the result of substituting the mRNA expression labels with IHC protein expression labels would have predictably categorized PD-L1 into a positive or negative status, as shown by Shimoji (pg. 74, Table 3).
Regarding claims 1 and 25, while Vitiello discloses performing feature selection to identify the top features associated with PD-L1 status, as discussed above, Vitiello does not disclose the training uses a clustering algorithm to identify these features associated with the PD-L1 expression status (i.e. also a phenotype of biological relevance).
However, Bloom discloses a method for generating an immune-oncology profile including expression of immune escape genes from a biological sample (Abstract), which comprises obtaining RNA from a sample and measuring gene expression of the RNA molecules of a plurality of genes ([0003]), inputting gene expression features into a classifier to predict a response to an immune therapy ([0003]; [0024]). Bloom further discloses analyzing the gene expression features using feature selection techniques in which relevant features are selected for including in the final classifier ([0125]). Bloom discloses the selecting the features of predictive value include a principal component analysis (PCA) ([0119]), which is a clustering algorithm (evidenced by Applicant’s specification at para. [0242]).
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Vitiello to have performed the feature selection using a clustering algorithm of PCA, as shown by Bloom ([0119]; [0125]). One of ordinary skill in the art would have been motivated to combine the methods of Vitiello and Bloom based on the simple substitution of the feature selection performing during training the Random Forest classifier of Vitiello (Fig. 7) with the feature selection performed by PCA of Bloom. One of ordinary skill in the art recognizes that various feature selection algorithms are available in the art and serve to identify a subset of features for use in a classifier, as described by Bloom ([0119]; [0125]), and thus one of ordinary skill in the art would have predictably expected the results of the substitution to have resulted in identifying top features using PCA which are then used in the random forest classifier of Vitiello.
Further regarding claim 2, Vitiello further does not disclose each labeled expression data set comprises a labeled cancer type, as recited in claim 2.
Regarding claim 4, Vitiello further does not disclose the labeled expression data sets comprise expression data for samples of 2 or more different cancer types.
Regarding claim 10¸ Vitiello does not disclose the plurality of labeled expression data comprises expression data for lung adenocarcinoma, melanoma, renal cell carcinoma, bladder cancer, mesothelioma, and lung small cell cancer.
Regarding claims 22-23, Vitiello further does not disclose the labeled expression data and the unlabeled RNA expression data set comprises data from images, image features, clinical data, epigenic data, pharmacogenetic data, or a combination thereof, of the subject.
However, regarding claims 2, 4, 10, and 22-23 as discussed above, Bloom discloses a method for generating an immune-oncology profile including expression of immune escape genes from a biological sample (Abstract), which comprises inputting gene expression features into a classifier to predict a response to an immune therapy, including tumor progression ([0003]; [0024]; claim 2). Bloom further discloses the classifier is trained on a training data set having cell type percentages, gene expression data, and mutational burden (i.e. clinical data) ([0116]; [0121]-[0122]), wherein the cell types include at least one tumor cell type ([0003]), demonstrating multiple tumor cell types (i.e. labels for multiple cancer types) can be used. Bloom further discloses the cancer types include non-small cell lung cancers including adenocarcinomas, melanoma, kidney (i.e. renal cell) cancer, bladder cancer, mesothelioma, and small cell lung cancer ([0109]), such that the expression data includes data from these cancer types. Bloom further discloses the accuracy of classification can be improved by combining two or more feature spaces in a classifier rather than using a single feature space ([0120]), and further discloses the method can be used to diagnose or treat particular cancers ([0109]).
It would have been further prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Vitiello to have utilized training (i.e. labeled) expression data including labeled tumor types and expression data from the recited cancer types, and additionally using clinical data as features in the model, as shown by Bloom, discussed above. One of ordinary skill in the art would have been motivated to further combine the methods of Vitiello and Bloom in order to improve the accuracy of classification in diagnosing cancer types by combining multiple feature spaces, including tumor cell types and clinical data, as shown by Bloom ([0120]). This modification would have had a reasonable expectation of success given both Vitiello and Bloom utilize gene expression data in a trained classifier to make a clinical prediction, and thus the data of Bloom is applicable to the method of Vitiello.
Last, regarding claim 30, Vitiello does not disclose generating a report including at least an identify of the subject and the predicted PD-L1 protein expression status.
However, Bloom further discloses a user interface for providing reports of risk stratification analysis of the nucleic acid sequencing of a sample and an output profile ([0011], e.g. output profile as report with graphical elements; [0139]; Fig. 7), wherein the sample is a patient [0059]. Bloom discloses the output profile (i.e. report) can include a predicted response to a procedure from the classifier, and treatment recommendations can be made based on the output profile ([0011]), and the report can be used to provide a procedure to the patient ([0014]), which makes obvious the profile would include an identify of the patient.
It would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the method of Vitiello to have generated a clinical decision support information report including an identity of the subject and classifier prediction, as suggested and shown by Bloom, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Vitiello and Bloom in order to provide information that can be used to provide treatment recommendations to a patient, as shown by Bloom ([0011]; [0014]), given Vitiello discloses PD-L1 status can be used to guide immunotherapy (pg. 1874, col. 2, para. 1-4). This modification would have had a reasonable expectation of success given both Vitiello and Bloom utilize a classifier on gene expression data of patients to make a prediction, and thus the output report of Bloom is applicable to the data of Vitiello.
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 24 Nov. 2025 regarding 35 U.S.C. 102/103 have been fully considered but they are not persuasive.
Applicant remarks that Vitiello does not teach the combination of elements of “for each of a plurality of training samples: isolating…, performing next generation sequencing…to generate a training RNA expression data set…., and determining a PD-L1 protein expression status label for that training sample by performing immunohistochemistry…, isolating RNA from the sample of the subject…and performing next generation sequencing on the RNA isolated from the sample”, and instead Vitiello describes a study of 75 human gastrointestinal stromal tumors (GISTs) revealing that PDGFRA-mutant GISTs have higher immune cell numbers, increased catalytic activity, and distinct chemokine profiles while machine learning-based immune profiles offer insights into potential immunotherapy strategies for GIST subtypes (Applicant’s remarks at pg. 17, para. 2 to pg. 18, para. 3).
This argument is not persuasive. First, it is acknowledged that Vitiello does not disclose the limitation of determining a PD-L1 protein expression status label as claimed as part of the generating training data, as set forth in the above rejection. Instead, this limitation is obvious in view of Shimoji, as applied in the new grounds of rejection set forth above in the 103 rejection.
However, Vitiello is not silent on the remaining steps of generating a training data set as alleged by Applicant, and as applied in the newly recited 103 rejection set forth above. Vitiello does disclose a study of gastrointestinal tumors. In this study, Vitiello analyzes the tumors as claimed to generate a training set by performing RNA-seq on RNA isolated from the tumor samples to determine mRNA expression features and generates a PD-L1 expression status label using the mRNA expression data (pg. 1874, col. 2, para. 5 to pg. 1875, col. 1, para. 1; pg. 1875, col. 2, para. 2). Vitiello performs this same process to generate the mRNA expression data for tumors of the test set (i.e. the unlabeled data) (pg. 1871, col. 1, para. 2). Furthermore Vitiello trains a machine learning model using the mRNA expression features of the training set to predict a PD-L1 expression label of high or low (i.e. positive or negative) of unlabeled training data (pg. 1871, col. 1, para. 2 to col. 2, para. 1; Fig. 6-7). While Vitiello does also disclose the various aspects discussed at pg. 17, para. 4 of Applicant’s remarks, Applicant does not explain why the various paragraphs actually cited in the previous and above rejection do not disclose the claimed features.
Applicant remarks that dependent claims 14, 15, 28, and 41-45 depend from claim 1 and therefore the rejection should be withdrawn (Applicant’s remarks at pg. 18, para. 3).
This argument is not persuasive for the same reasons discussed above for claim 1, as applied to the new grounds of rejection set forth under 35 U.S.C. 103.
Applicant remarks that Bloom also fails to disclose the claimed combination of steps relating to generating the training set, and therefore Vitiello and Bloom fail to teach or suggest these elements, and the rejection of claims 2-4, 7, 8, 10-13, 22-27, and 30 should be withdrawn (Applicant’s remarks at pg. 18, para. 4 to pg. 19, para. 4).
This argument is not persuasive for the same reason discussed above for claim 1, given Bloom is not relied upon to disclose the steps of generating the training set of claim 1. Instead, Bloom is relied upon to disclose the clustering algorithm of claim 1.
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.
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685