DETAILED ACTION
Applicant’s response, filed 01 April 2026 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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01 April 2026 has been entered.
Status of Claims
Claims 1-29, 36-103, and 117-118 are cancelled.
Claim 119 is newly added.
Claims 30-35, 104-116, and 119 are pending.
Claims 30-35, 104-116, and 119 are rejected.
Priority
Applicant’s claim for the benefit of a prior-filed application, U.S. Provisional App. Nos. 62/878,095 filed 24 July 2019 and 62/949,025 filed 17 Dec. 2019 under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Accordingly, the effective filing date of the claimed invention is 24 July 2019.
Claim Interpretation
Claim 30 recites “determining, by a trained machine-learning model…, wherein the trained machine-learning model is trained using gene-expression data from brain tumors”. The wherein clause regarding how the trained machine-learning model is trained is interpreted to be a product-by-process limitation defining the process in which the machine learning model was previously trained. However, a step of training the machine learning model using gene-expression data from brain tumors is not required by the claim. "[E]ven though product-by-process claims are limited by and defined by the process, determination of patentability is based on the product itself. The patentability of a product does not depend on its method of production. If the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985) (citations omitted). See MPEP 2113 I.
Claim Rejections - 35 USC § 112(a)
The rejection of claim 118 under 35 U.S.C. 112(a) in the Office action mailed 01 Dec. 2025 has been withdrawn in view of the cancellation of this claim received 01 April 2026.
Claim Rejections - 35 USC § 103
The rejection of claim 118 under 35 U.S.C. 103 in the Office action mailed 01 Dec. 2025 has been withdrawn in view of the cancellation of this claim received 01 April 2026.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 30-35, 104-116, and 119 are rejected under 35 U.S.C. 103 as being unpatentable over Graeber (2019) in view of Spetzler (2018) and Taube (2018). Any newly recited portion is necessitated by claim amendment.
Graeber et al., US 2022/0244263 A1, effectively filed 28 May 2019 (previously cited);
Spetzler et al., US 2018/0045727 A1 (previously cited); and
Taube et al., Implications of the tumor immune microenvironment for staging and therapeutics, 2018, Modern Pathology, 31, pg. 214-234 (previously cited).
Regarding independent claims 30 and 119:
Regarding claim 30¸ Graeber discloses a method for identifying a neuroendocrine tumor in a patient (Abstract) by identifying neuroendocrine features in tumors ([0055]) comprising the following steps:
Graeber discloses accessing expression levels comprising an expression level (i.e. metric) for each of a plurality of biomarker genes, measured from a biological sample of the patient ([0011]-[0013]), wherein the set of genes include SV2A, NCAM1, ITGB6, SH2D3A, and TACSTD2 ([0163], e.g. genes in SCN gene signature; [0008]; [0017], e.g. biomarkers in Table 1 measured; claim 8). These genes are part of a small cel neuroendocrine signature (SCN) and thus associated with neuroendocrine identify.
Graeber determining the gene expression levels’ are different than a to a small cell neuroendocrine (SCN) signature (i.e. a neuronal genetic signature) using a trained machine learning model ([0020], e.g. level of measured markers determined to statistically different compared to control expression levels of biomarkers in a SCN cancer; [0023]-[0024]; [0055]; [0053]-[0054] and [0263-[0265], e.g. prediction of samples with SCN using logistic regression model). Graeber discloses the model distinguishes between cancer types, including lung cancer (LUAD), small cell lung cancer (SCLC), ovary, breast, bladder, and pancreas cancer (i.e. non-brain tumors), that are positive or negative to SCN (i.e. determining that the non-brain tumor is non-neuronal and non-endocrine) ([0016]; [0265]; FIG. 15E-F).
Regarding the process in which the machine learning model was previously trained, as discussed above in claim interpretation, this limitation is a product by process limitation defining the process in which the machine learning model was previously trained. Because the trained machine learning model of Graeber is configured to determine a non-brain tumor is non-neuronal and non-neuroendocrine based on the gene expression data not corresponding to a neuronal genetic signature, the trained machine learning model of Graeber is the same product as the machine-learning model recited in the claims, even if trained by a different process, and thus reads on the limitation. See MPEP 2113 I.
Regardless, it is noted that Graeber discloses the cancer of a subject being determined to be a SCN cancer or not could be glioma (i.e. a brain tumor) ([0016]; [0146]) and further discloses scoring glioma cells according to their SCN-gene expression score ([0196]; FIG. 5A). Therefore, while the example of training a classifier for lung and prostate cancers in Graeber does not explicitly utilize a brain tumor sample ([0053]-[0054]; [0265]), given Graeber discloses the method can be used to determine if glioma comprises a SCN cancer, it would have been obvious to one of ordinary skill in the art that using the method to classify a glioma as one of the several cancer types would involve training the model using gene expression data of brain tumor (glioma), in addition to the expression data of lung an prostate cancers.
Graeber discloses selecting a cancer treatment including an immunotherapy of checkpoint inhibitors ([0063]-[0064]). Graeber discloses the patient has not been previously treated (i.e. the therapy is an initial use) ([0098]).
Regarding claim 119, Graeber discloses a method for identifying a neuroendocrine tumor in a patient (Abstract) by identifying neuroendocrine features in tumors ([0055]) comprising the following steps:
Graeber discloses accessing expression levels comprising an expression level (i.e. metric) for each of a plurality of biomarker genes, measured from a biological sample of the patient ([0011]-[0013]), wherein the set of genes include SV2A, NCAM1, ITGB6, SH2D3A, and TACSTD2 ([0163], e.g. genes in SCN gene signature; [0008]; [0017], e.g. biomarkers in Table 1 measured; claim 8). Graeber discloses that any one or more biomarkers from Table 1 may be measured ([0008]), such that Graber discloses only measuring any one or more of SV2A, NCAM1, ITGB6, SH2D3A, and TACSTD2 above (i.e. the set of genes consists of one or more of SV2A, NCAM1, etc.).
Graeber determining the gene expression levels’ are different than a to a small cell neuroendocrine (SCN) signature (i.e. a neuronal genetic signature) using a trained machine learning model ([0020], e.g. level of measured markers determined to statistically different compared to control expression levels of biomarkers in a SCN cancer; [0023]-[0024]; [0055]; [0053]-[0054] and [0263-[0265], e.g. prediction of samples with SCN using logistic regression model). Graeber discloses the model distinguishes between cancer types, including lung cancer (LUAD), small cell lung cancer (SCLC), ovary, breast, bladder, and pancreas cancer, that are positive or negative to SCN (i.e. determining that the tumor does not correspond to a neuronal genetic signature) ([0016]; [0265]; FIG. 15E-F).
Graeber discloses selecting a cancer treatment including an immunotherapy of checkpoint inhibitors ([0063]-[0064]). Graeber discloses the patient has not been previously treated (i.e. the therapy is an initial use) ([0098]).
Regarding claims 30 and 119 Graeber does not disclose the following limitations:
Graeber does not explicitly disclose the identification of the therapy approach is based on the determination of the tumor as non-neuronal and non-neuroendocrine, and then administering an effective amount of the checkpoint blockade therapy to the subject.
However, Graeber does disclose that, in addition to distinct cancer types like SRBCTs, blood cancers, or small cell and non-small cell histologies seen in biopsies, the SCN phenotype exists along an expression signature-defined spectrum which influences therapeutic vulnerabilities in individual cancers, and thus screening for and targeting the SCN phenotype has clinical benefit ([0204]-[0205]), generally disclosing that SCN status informs treatment decisions.
Furthermore, Taube overviews the implications of the immune microenvironment in cancer therapeutics, including checkpoint inhibitors such as anti-PD-/PD-L1, and discloses characterizing the tumor immune microenvironment enables the identification of therapeutic strategies to guide first-line treatment algorithms (Abstract). Taube discloses various cancers in which checkpoint inhibitors are a first line of treatment either alone or in combination therapies (pg. 218, col. 2, para. 2; Figure 3). Taube discloses prognostic indicators of a response to checkpoint inhibitors involving immunoscores which quantify immune infiltration within the central region and invasive margin of a broad range of tumors, wherein patients with higher immunoscores (e.g. immune infiltration) are recommended for checkpoint inhibitor therapy (pg. 218, col. 1, para. 2-3; FIG. 4). Taube then explains that many solid tumor types, such as neuroendocrine tumors (e.g. such as SCN in Graeber) are not classically recognized as immune infiltrated, and thus if an immune checkpoint blockade were to be effective, it could be used in a combination treatment after treatment of some other inhibitor that first incites an immune response prior to treatment with the checkpoint inhibitor (pg. 227, col. 2, para. 2), rather than using a checkpoint inhibitor as an initial first-line of treatment. This demonstrates that neuroendocrine tumors, such as SCN, generally have low immune infiltration, or immunoscores and do not respond well to immune checkpoint inhibitors as an initial treatment. Last, Taube discloses the administration of checkpoint inhibitors to cancer patients results in responses to treatment between 2 and 12 weeks and show durable responses (pg. 215, col. 2, para. 2 to pg. 216, col. 1, para. 1)
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 Graeber to have further identified the therapy approach based on the determination of the tumor as non-neuronal and non-neuroendocrine (e.g. not having a low immunoscore), and then administering an effective amount of the checkpoint blockade therapy to the subject, as shown by Taube, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Graeber and Taube to provide durable treatment responses to cancer patients who do not have tumors with low immune infiltration, as shown by Taube (pg. 215, col. 2, para. 2 to pg. 216, col. 1, para. 1). This modification would have had a reasonable expectation of success given Graeber discloses determining various cancer types do not have the SCN signature, including bladder cancer, head and neck squamous cell cancer, and lung cancer, ([0016]), which Taube discloses can be treated with checkpoint inhibitors (Table 4), and thus the method of Taube is applicable to Graeber.
Graeber further does not disclose outputting an indication that the subject is amenable to the therapy approach.
However, Spetzler discloses methods and systems, including a computer system with a processor coupled to memory (claim 83) and non-transitory computer readable medium ([0310]) for molecular profiling of cancer via a clinical decision support system for personalized medicine (Abstract). Spetzler discloses the method involves generating a report that identifies a molecular profile for the patient that includes a list describing the expected benefit of the plurality of treatment options based on the assessed characteristics (i.e. outputting an indication the subject is amenable to a therapy approach) ([0006]; [0350]; [0360] FIG. 6). Spetzler additionally discloses clinical responses to immune checkpoint inhibitor therapy ranges by tumor type, and provides an example in which expression data is used to identify a subset of cancer cases as candidates for immune checkpoint inhibitor therapy ([0531]).
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 Graeber to have further utilized the gene expression data to output an indication that the subject is amenable to the therapy approach, as shown by Spetzler, discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Graeber and Spetzler in order to provide more informed and effective treatment options, resulting in improved patient care and enhanced treatment outcomes, as shown by Spetzler ([0006]). This modification would have had a reasonable expectation of success because both Graeber and Spetzler analyze gene expression data of a patient to identify cancer and cancer treatments, and thus the method of Spetzler is applicable to Graeber.
Regarding the dependent claims:
Regarding claim 31, Graeber discloses the cancer treatment may be used alone ([0063]), which demonstrates the therapy approach does not include the use of chemotherapy.
Regarding claim 32¸ Graeber discloses determining the gene expression levels’ are different than a to a small cell neuroendocrine (SCN) signature ([0020]; [0023]-[0024]; [0055]) by classifying the expression data of the patient using a classifier trained on a training set of SCN positive cases and a second subset of small cell lung cancer (SCLC) samples to assign the patient to the SCN class or non-SCN class, wherein the SCN and non-SCN classes are differentiated by expression levels of one or more genes ( [0263], e.g. gene expression profiles used)
Regarding claim 33, Graeber further discloses determining the SCN signature by training a logistic regression classifier using training data ([0263]-[0265]), wherein the training data includes the following:
Graeber discloses the training data includes gene expression values of for each gene in the SCN signature ([0263]).
Graeber discloses the training data includes a set of samples as part of a SCN class (i.e. a first subset with a first label being indicative of a tumor having a neuronal property) and a set of samples as part of a non-SCN class (i.e. a second subset with a second label being indicative of a tumor not having the neuronal property) ([0265]).
Regarding claims 34-35, Graeber discloses the set of genes includes SV2A, NCAM1, ITGB6, SH2D3A, and TACSTD2 ([0163], e.g. genes in SCN gene signature; [0008]; [0017], e.g. biomarkers in Table 1 measured; claim 8).
Regarding claim 104, Graeber discloses the neuronal genetic signature comprises genes that are used to determine of a sample has a tumor with a SCN or neuroendocrine features (i.e. the first class of tumors ) or non-neuronal (the second class of tumors) [0055]. It is noted that the limitation regarding how the set of genes was previously identified is a product by process limitation, and step of identifying the set of genes as informative of assignment is not required by the claims. See MPEP 2113.
Regarding claim 105, Graeber discloses the signature distinguishes between a SCN, or small cell neuroendocrine tumor (i.e. developed from cells of the neuroendocrine system) and a non-neuronal tumor (i.e. the first class is a neuroendocrine tumor) ([0055]).
Regarding claim 106¸ Graeber further discloses that the expression data for each of the samples that are part of the non-SCN class included expression data from SCLC samples (i.e. non-neuronal and neuroendocrine derived from a respective type of tissue) and expression data for each samples part of the SCN class include pan-cancer SCN SCLC cases confirmed by pathology (i.e. a neuroendocrine tumor derived from the same respective type of tissue) ([0265]).
Regarding claim 107, Graeber discloses training the model includes identifying the set of genes [0053]-[0054], comprising the following.
Graeber discloses using a gene expression profile for each of a first tumor class with a label indicative of the tumor having a neuronal property (i.e. having the neuronal genetic signature) and a second non-SCN class (i.e. tumors not having the neuronal genetic signature) ([0263]; [0265]), wherein the gene expression profile includes, for each gene, an expression level (i.e. an expression-metric statistic) indicating a level (i.e. a degree) to which the gene is expressed for the given class ([0020]).
Graeber discloses each gene in the determined set of genes has a signature weight greater than a predefined threshold ([0013]-[0015]), wherein the signature weight reflects a difference between the expression levels of the respective gene between the two classes (i.e. between the first and second expression-metric statistic ([0184], e.g. weights determined by PCA between SCN cases and non-SCN tumors).
Regarding claims 108 Graeber discloses measuring the set of genes including SV2A, NCAM1, ITGB6, SH2D3A, TACSTD2, C19orf33, SFN, RND2, PHLDA3, OTX2, and TBC1D2 ([0163], e.g. genes in SCN gene signature; [0008]; [0017], e.g. biomarkers in Table 1 measured; claim 8), and that the genes are measured in a tumor sample ([0025]; claims 1 and 15)
Graeber further discloses using the measured expression levels are used to determine whether the subject has the SCN cancer ([0020]) and ultimately determine whether the patient would benefit from an immune checkpoint inhibitor ([0027]; [0065]), as shown by Graeber in view of Taube and Spetzler as applied to claim 30 above.
Regarding claims 109-116, Graeber discloses the set of genes measured includes SV2A, NCAM1, ITGB6, SH2D3A, TACSTD2, C19orf33, SFN, RND2, PHLDA3, OTX2, and TBC1D2 (i.e. ten or more genes in Tables 2, 3, and 4) ([0163], e.g. genes in SCN gene signature; [0008]; [0017], e.g. biomarkers in Table 1 measured; claim 8).
Therefore, the invention is prima facie obvious.
Response to Arguments
Applicant's arguments filed 01 April 2026 regarding 35 U.S.C. 103 have been fully considered but they are not persuasive.
Applicant remarks that none of the recited references teach “determining, by a trained machine-learning model, that the non-brain tumor is non-neuronal and non-neuroendocrine…, wherein the machine-learning model is trained using gene-expression data from brain tumors”, and that Graber teaches training a model on lung tumors and prostate tumors for classifying tumors across tissue types, but does not teach the model is trained using gene-expression data from brain tumors (Applicant’s remarks at pg. 8, para. 2 to pg. 9, para. 1).
This argument is not persuasive. As discussed above in claim interpretation, claim 30 does not require a step of training the machine-learning model using gene-expression data from brain tumors, and instead the limitation only serves to define the process in which the machine-learning model of claim 30 was previously trained. MPEP 2113 I. states if the product in the product-by-process claim is the same as or obvious from a product of the prior art, the claim is unpatentable even though the prior product was made by a different process." In re Thorpe, 777 F.2d 695, 698, 227 USPQ 964, 966 (Fed. Cir. 1985) (citations omitted). Because the trained machine learning model of Graeber is configured to determine a non-brain tumor is non-neuronal and non-neuroendocrine based on the gene expression data not corresponding to a neuronal genetic signature, the trained machine learning model of Graeber is the same product as the machine-learning model recited in the claims, even if trained by a different process, and thus reads on the limitation.
Regardless, it is noted that Graeber discloses the cancer of a subject being analyzed as a potential SCN cancer, could be a glioma (i.e. a brain tumor) ([0016]; [0146]) and further discloses scoring glioma cells according to their SCN-gene expression score ([0196]; FIG. 5A). Therefore, while the example of training a classifier for lung and prostate cancers in Graeber does not explicitly utilize a brain tumor sample ([0053]-[0054]; [0265]), given Graeber discloses the method can be used to determine if glioma comprises a SCN cancer, this clearly suggests or makes obvious that using the method to classify a glioma as one of the several cancer types would involve training the model using gene expression data of brain tumor (glioma) in addition to the other cancer types.
Applicant remarks that Spetzler and Taube fail to cure the deficiencies of Graeber because they are silent regarding training a machine learning model on any tumor types (Applicant’s remarks at pg. 9, para. 2).
This argument is not persuasive because Spetzler and Taube are not relied upon for disclosing the machine-learning model, and for the reasons discussed above regarding Graeber.
Applicant remarks that new claim 119 is non-obvious over Greaber, Spetzler, and Taube because these references do not disclose or suggest the set of genes consists of one or more of the recited set of genes, and that Graeber lists many hundreds of genes in Table 1 which may be used for classification and states that preferred biomarkers are those having “an absolute value…greater than 0.025”, and of the several hundred genes, none of the recited genes have an absolute value of greater than 0.025 in Graeber (Applicant’s remarks at pg. 9, para. 4 to pg. 10, para. 2).
This argument is not persuasive. Regarding Applicant’s argument that none of the recited genes in claim 119 are considered a preferred biomarker in Graeber due, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Labs., Inc. 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir. 1989). See MPEP 2123 I. Therefore, simply because Graeber teaches a preferred biomarker has an absolute value of greater than 0.025 does not negate the fact that Graeber teaches that any one or more biomarkers from Table 1 may be used (including non-preferred biomarkers).
Furthermore, while it is acknowledged that Graeber includes a large list of genes in Table 1, MPEP 2131.02 II states a genus does not always anticipate a claim to a species within the genus. However, when the species is clearly named, the species claim is anticipated no matter how many other species are additionally named. See Ex parte A, 17 USPQ2d 1716 (Bd. Pat. App. & Inter. 1990). MPEP 2131.02 II. further states a reference disclosure can anticipate a claim when the reference describes the limitations but "'d[oes] not expressly spell out' the limitations as arranged or combined as in the claim, if a person of skill in the art, reading the reference, would ‘at once envisage’ the claimed arrangement or combination." Kennametal, Inc. v. Ingersoll Cutting Tool Co., 780 F.3d 1376, 1381, 114 USPQ2d 1250, 1254 (Fed. Cir. 2015)
Graeber explicitly discloses that the level of expression of one or more biomarkers from Table 1 may be used for classification, and that the method can comprise measuring the expression of exactly 1, 2, 3, 4, etc. biomarkers from Table 1 ([0008]; [0017]). Therefore, Graeber explicitly discloses that exactly 1 biomarker from Table 1 can be used, which one of ordinary skill in the art could at one envisage that any of the listed genes, including SV2A, NCAM1, ITGB6, SH2D3A, or TACSTD2, could be used as the biomarker for classification, regardless of how many different genes could be used as the biomarker or biomarkers.
Applicant remarks that Spetzler and Taube fail to cure the deficiencies of Graeber because neither reference teaches or suggests the set of genes consists of one or more of the recited set of genes (Applicant’s remarks at pg. 9, para. 2).
This argument is not persuasive because Spetzler and Taube are not relied upon for disclosing the set of genes, and for the reasons discussed above regarding Graeber.
Conclusion
No claims are allowed.
Claims 30-35, 104-116, and 119 are patent eligible for the reasons discussed in the Office action mailed 01 Dec. 2025 at para. [018]-[019].
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/KAITLYN L MINCHELLA/Primary Examiner, Art Unit 1685
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