DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 March 11, 2026 and June 16, 2026 have been entered. Any rejections or objections not reiterated herein have been withdrawn.
3. Applicant’s election without traverse of hsa.miR.409.3p as the miRNA and CK18 as the EV mRNA in the reply filed on June 16, 2026 is acknowledged.
Claims 2, 6, 8, 13, 17-18, 21-22, and 63-68 are currently pending.
Claim 17 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to non-elected biomarkers, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on June 16, 2026.
The claims have been examined to the extent that the claims read on the elected miRNA (hsa.miR.409.3p) and the elected EV mRNA (CK18). The additionally recited miRNA and EV mRNA have been withdrawn from consideration as being directed to a non-elected subject matter. Prior to allowance of the claim, any non-elected subject matter that is not rejoined with any allowed elected subject matter will be required to be removed from the claims.
Claim Rejections - 35 USC § 101
4. 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 2, 6, 8, 13, 18, 21-22, and 63-68 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception without significantly more. The claims recite a judicial exception that is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim analysis is set forth below.
Step 1: The claims are directed to the statutory category of a process.
Step 2A, prong one: Evaluate Whether the Claim Recites a Judicial Exception
The instant claims recite abstract ideas.
Claim 2 recites the following limitations:
(a) analyzing, by a machine learning model trained to mitigate overfitting, a set of circulating biomarkers
(b) detecting by the machine learning model the presence of the occult metastasis in the subject with an accuracy of greater than 70%
Regarding machine learning models, the specification teaches the following:
[0024] In further embodiments, the disclosed methods comprise applying a machine learning algorithm to the analyzing two or more biomarkers from the biological sample. In some embodiments, the machine learning algorithm comprises Least Absolute Shrinkage Selection Operator (LASSO). In some embodiments, the machine learning algorithm uses one or more classifier models selected from the group consisting of K-Nearest-Neighbors, SVM, linear discriminate analysis, logistic regression, Naive Bayes, and any combination thereof. In some embodiments, the machine learning algorithm distinguishes at least one of the two or more biomarkers from a control.
[0090] In some embodiments, the presently disclosed methods include computational analysis based on a machine learning data analysis. The analysis can comprise a selection step, a training step (e.g. by Least Absolute Shrinkage and Selection Operator (LASSO)), and a validation step using a blinded test set. In some embodiments, various machine learning algorithms can be used. These include but are not limited to K-Nearest-Neighbors, SVM, linear discriminate analysis, logistic regression, and Naive Bayes). In some embodiments, the output results are averaged. In other embodiments, a bootstrapping method can be applied.
Based on these teachings in the specification the broadest reasonable interpretation is that the “machine learning model trained to mitigate overfitting” is a mathematical concept. Thus the analyzing and detecting steps fall within the mathematical concepts grouping of abstract ideas.
Step 2A, prong two: Evaluate Whether the Judicial Exception Is Integrated Into a Practical Application
The claims do NOT recite additional steps or elements that integrate the recited judicial exceptions into a practical application of the exception(s). For example, the claims do not practically apply the judicial exception by including one or more additional elements that the courts have stated integrate the exception into a practical application:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
An additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
An additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
An additional element effects a transformation or reduction of a particular article to a different state or thing; and
An additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological
environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Claim 2 recites that the machine learning model is “trained to mitigate overfitting”. While the claims tell you the desired solution, they do not recite the process by which the invention arrives at the solution. “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome” (MPEP 2106.05(a)). The specification describes a way to overcome overfitting: using ensemble models (para 00120). However, the claims are not limited to this particular approach. So simply saying that the model “is trained to mitigate overfitting” doesn’t tell you anything about the model itself, nor how it’s trained.
Claim 2 recites a step of “treating” the subject with systemic therapy but not with curative intent surgery when the occult metastasis is detected by the machine learning model. It is noted that a claim limitation can integrate a judicial exception by applying or using the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition. However the treatment or prophylaxis limitation must be “particular”, i.e., specifically identified so that it does not encompass all applications of the judicial exceptions. Here the administration step is not particular, and is instead merely instructions to “apply” the exception in a generic way. Thus, the administration step does not integrate the judicial exceptions into a practical application.
Step 2B: Evaluate Whether the Claim Provides an Inventive Concept
Claim 2 recites that the machine learning model is “trained to mitigate overfitting”. This does not amount to significantly more because it simply appends well understood, routine, and conventional activities previously known in the art, specified at a high level of generality, to the judicial exception. For example the prior of Yang (Current Bioinformatics, 5 (4) 296-308, 2010) teaches a review of ensemble methods in bioinformatics which mitigate overfitting (page 1).
For the reasons set forth above the claims are not directed to patent eligible subject matter.
Response To Arguments-35 USC 101
5. In the response the Applicants traversed the rejection under 35 USC 101.
Regarding Step 2A, Prong One, the Applicants argue that the claims do not recite concepts that can be performed in the human mind and the claims do not recite a law of nature.
These arguments have been fully considered. It is noted that the rejection has been modified. As discussed above, the broadest reasonable interpretation is that the “machine learning model trained to mitigate overfitting” is a mathematical concept. Thus the “analyzing” and “detecting” which are performed by the machine learning model fall within the mathematical concepts grouping of abstract ideas.
Regarding Step 2A, Prong Two, the Applicants argue that claims recite additional elements that integrate the judicial exceptions into a practical application. The Applicants argue that the recited machine learning model “trained to mitigate overfitting” places limits on how the machine learning model functions and integrates the judicial exception into a practical application.
This argument has been fully considered but is not persuasive. As discussed above, Claim 2 recites that the machine learning model is “trained to mitigate overfitting”. While the claims tell you the desired solution, they do not recite the process by which the invention arrives at the solution. “An important consideration in determining whether a claim improves technology is the extent to which the claim covers a particular solution to a problem or a particular way to achieve a desired outcome, as opposed to merely claiming the idea of a solution or outcome” (MPEP 2106.05(a)). The specification describes a way to overcome overfitting: using ensemble models (para 00120). However, the claims are not limited to this particular approach. So simply saying that the model “is trained to mitigate overfitting” doesn’t tell you anything about the model itself, nor how it’s trained. Therefore this fails to provide integration.
Additionally the Applicants argue that the additional elements, in combination with the alleged judicial exception, provide the claimed improvement of detecting occult metastases in early stages of disease.
This argument has been fully considered but is not persuasive. The step of the “detecting” by the machine learning model the presence of the occult metastasis is one of the judicial exceptions recited in the claim. If the judicial exception itself is the purported improvement, such as in the instant case, then the claim is directed to the judicial exception, not an eligible improvement to a technology or technical field through integration of the judicial exception.
Further the Applicants argue that the set of circulating biomarkers are not a judicial exception because they do not recite a law of nature.
This argument has been fully considered. It is noted that the rejection has been modified to state that the claims recite an abstract idea judicial exception. In particular the machine learning model recited in the claims is a mathematical concept. Thus the step of “analyzing” and “detecting” the recited biomarkers are performed by the machine learning model and fall within the mathematical concepts grouping of abstract ideas.
Regarding Step 2B the Applicants argue that the claims provide an inventive concept.
They argue that the additional elements (circulating biomarkers) in combination with the judicial exception provide an improvement in accurately detecting occult metastases.
This argument has been fully considered but is not persuasive. Claim 2 recites that the machine learning model is trained to mitigate overfitting. This does not amount to significantly more because it simply appends well understood, routine, and conventional activities previously known in the art, specified at a high level of generality, to the judicial exception. For example the prior of Yang (Current Bioinformatics, 5 (4) 296-308, 2010) teaches a review of ensemble methods in bioinformatics which mitigate overfitting (page 1). The rejection is maintained.
Claim Rejections - 35 USC § 112(a)
6. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2, 6, 8, 13, 18, 21-22, and 63-68 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention.
Scope of the Claims/Nature of the Invention
The claims are drawn to a non-invasive method of detecting an occult metastasis in a subject. The claims broadly encompass being able detect occult metastasis of greater than 200 different known cancer types. Only claims 8 and 67-68 are limited to pancreatic cancer.
The claims recite a first step of analyzing, by a machine learning model trained to mitigate overfitting, a set of circulating biomarkers comprising:
(i) extra-cellular vesicle (EV) miRNA hsa.miR.409.3p,
(ii) EV mRNA- CK18,
(iii) a circulating tumor DNA (ctDNA) comprising a mutated KRAS DNA was mutation KRASG12D, KRASG12V, or KRASG12R, and
(iv) protein biomarker cancer antigen 19-9 (CA19-9).
The claims state that the set of circulating biomarkers is obtained from a processed sample from the subject.
The claims state that one or more of the biomarkers do not correlate with the presence of the occult metastases. However the claims do not set forth which biomarkers (i-iv) correlate with the presence of occult metastasis of any cancer and which biomarkers (i-iv) do not correlate with the presence of occult metastases of any cancer.
The claims state that the correlation between the circulating biomarkers EV miRNA hsa.miR.409.3p or EV mRNA CK18 and the protein biomarker CA19-9 is less than 0.6.
The claims recite a second step of detecting by the machine learning model the presence of the occult metastasis in the subject with an accuracy of greater than 70%.
The claims recite a third step of treating the subject with systemic therapy but not with curative intent surgery when the occult metastasis is detected by the machine learning model.
The nature of the invention requires a reliable correlation between a set of circulating biomarkers comprising (i-iv) and occult metastasis of ANY type of cancer.
Teachings in the Specification and Examples
The specification (Example 6) imaging is a widely used but imperfect technique for detecting metastases and determining whether a PDAC patient's disease is sufficiently localized for consideration of curative-intent surgery. The model disclosed herein was tested to assess if it can identify a biomarker panel that, in conjunction with imaging, could better stage PDAC patients by distinguishing metastatic from non-metastatic disease. To train the model, 20 PDAC patients, originally staged by imaging, were selected which included 9 patients with no detectable metastasis (M0; including 7 resectable and 2 locally advanced), and 11 patients with metastasis (M1). Since some patients originally identified as M0 may have had occult metastases below the level of imaging detection, a chart review was conducted and retrospectively the M0 patients were re-stratified into two groups: 1) M0s: those with no evidence of metastatic disease intraoperatively or within 4 months of follow-up and 2) Occult metastases: those who had metastases detected intraoperatively or had metastatic recurrence within 4 months of blood draw. This stratification resulted in the training set of 8 M0 and 12 M1 (11 with imaging-confirmed metastases and one with occult metastases).
Using LASSO, a biomarker panel of 4 markers, including EV-miR.1299, EV-GAPDH, circulating mutant KRAS allele fraction, and CA19-9 was selected as having the highest Accuracy (A=91%; FIG. 4B, C).
To further evaluate the panel's ability to identify occult metastatic disease, the approach to an independent blinded test set of 35 subjects with PDAC was applied as part of a clinical workflow starting with standard of care diagnostic imaging and followed by liquid biopsy. Twelve of 35 patients were identified by imaging alone as having metastases, were classified as Ml, and had no further evaluation. The remaining 23 patients were determined by baseline imaging to have no detectable metastases (MO-imaging). Upon retrospective chart review, 15 of 23 had no evidence of metastases within 4 months (median time to metastases). Eight of 23 patients were determined to have had occult metastases. The liquid biopsy workflow correctly identified 6 of 8 patients as having metastatic disease, and 13 of 15 patients as being metastasis-free. Thus, by comparing the liquid biopsy prediction to the true state of the patients, the ptest had an accuracy of detecting distant metastasis of A =83% (19/23) with sensitivity of 75% and specificity of 87% (AUC=0. 8), which compares favorably to the accuracy of imaging alone (A=65% (15/23); P<0.01. FIG. 4F) among 23 patients originally identified as M0 by imaging.
State of the Art and the Unpredictability of the Art
While methods of measuring EV-miRNA, EV-mRNA, ctDNA, and protein biomarkers are known in the art, methods of correlating EV-miRNA, EV-mRNA, ctDNA, and protein biomarkers with a phenotype such as occult metastasis are highly unpredictable. The unpredictability will be discussed below.
The specification discloses biomarkers (EV-miR.1299, EV-GAPDH, circulating mutant KRAS allele fraction, and CA19-9) that can be used to detect occult metastasis of PDAC. However the claims are directed to a different set of biomarkers (EV-miR.409.3p, EV-CK18, circulating mutant KRAS mutations at amino acid 12, and CA19-9). There is no evidence in the specification that shows that the presently claimed set of biomarkers will work and it would be highly unpredictable if the findings between EV-miR.1299, EV-GAPDH, circulating mutant KRAS allele fraction, and CA19-9 and occult metastasis of PDAC could be extrapolated to the presently claimed set of biomarkers. Even if the presently claimed set could detect occult metastasis, it is highly unpredictable if the accuracy would be greater than 70%.
The claims broadly encompass a method of detecting occult metastasis of ANY type of cancer. It is noted that there are greater than 200 different types of cancer. The specification discloses biomarkers (EV-miR.1299, EV-GAPDH, circulating mutant KRAS allele fraction, and CA19-9) that can be used to detect occult metastasis of PDAC. It is known in the art that different cancers have different expression patterns and different mutations. In the absence of evidence to the contrary, it is highly unpredictable if the findings between EV-miR.1299, EV-GAPDH, circulating mutant KRAS allele fraction, and CA19-9 and occult metastasis of PDAC could be extrapolated to any of the other greater than 200 different cancers known in the art. Further as noted above, there is no evidence in the specification that shows that the presently claimed set of biomarkers will work for PDAC, let alone any other cancer.
The claims are directed to a set of biomarkers (EV-miR.409.3p, EV-CK18, circulating mutant KRAS mutations at amino acid 12, and CA19-9). The claims state that one or more of these biomarkers do not correlate with the presence of occult cancer. However this assertion is not supported by any evidence on the record. There appears to be no examples in the specification where the four claimed biomarkers were detected in patients with known metastasis and occult metastasis that shows that at least one of these biomarkers is NOT correlated with occult metastasis. It is highly unpredictable if ANY of these biomarker(s) meet this requirement for greater than 200 different cancers encompassed by the claims.
The claims additionally state that the correlation between (i) EV miRNA has.miR.409.3p and CA19-9 protein and (ii) EV mRNA CK18 and CA19-9 is less than 0.6. Figure 2D shows a Pearsons correlation coefficient between each circulating biomarker but the Figure is not clearly legible and does not appear to support this assertion. It is highly unpredictable if the correlation between these markers is less than 0.6.
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Quantity of Experimentation:
The quantity of experimentation necessary is great, on the order of many man-years, and then with little if any reasonable expectation of successfully enabling the full scope of the claims.
In order to practice the breadth of the claimed invention one of skill in the art would first have to recruit patients with occult metastasis of a representative number of the different cancer types encompassed by the claims and controls. Then additional experimentation would need to be performed to measure EV-miR.409.3p, EV-CK18, circulating mutant KRAS mutations at amino acid 12, and CA19-9 protein in samples obtained from those patients. Then all the data would need to be analyzed to determine if this set of biomarkers meets the claimed requirements set forth above and can be used to detect a occult metastasis of a representative number of cancers. The specification has merely provided an invitation for further experimentation. The results of such experimentation are highly unpredictable.
The amount of experimentation that would be required to practice the full scope of the claimed invention and the amount of time and cost this experimentation would take supports the position that such experimentation is undue. Attention is directed to Wyeth v. Abbott Laboratories 107 USPQ2d 1273, 1275, 1276 (Fed. Cir. June 2013):
Claims are not enabled when, at the effective filing date of the patent, one of ordinary skill in the art could not practice their full scope without undue experimentation. MagSil Corp. v. Hitachi Global Storage Techs., Inc., 687 F.3d 1377, 1380-81 [103 USPQ2d 1769] (Fed. Cir. 2012).
The remaining question is whether having to synthesize and screen each of at least tens of thousands of candidate compounds constitutes undue experimentation. We hold that it does. Undue experimentation is a matter of degree. Chiron Corp. v. Genentech, Inc., 363 F.3d 1247, 1253 [70 USPQ2d 1321] (Fed. Cir. 2004) (internal quotation omitted). Even “a considerable amount of experimentation is permissible,” as long as it is “merely routine” or the specification “provides a reasonable amount of guidance” regarding the direction of experimentation. Johns Hopkins Univ. v. CellPro, Inc., 152 F.3d 1342, 1360-61 [47 USPQ2d 1705] (Fed. Cir. 1998) (internal quotation omitted). Yet, routine experimentation is “not without bounds.” Cephalon, Inc. v. Watson Pharm., Inc., 707 F.3d 1330, 1339 [105 USPQ2d 1817] (Fed. Cir. 2013). (Emphasis added)
In Cephalon, although we ultimately reversed a finding of nonenablement, we noted that the defendant had not established that required experimentation “would be excessive, e.g., that it would involve testing for an unreasonable length of time.” 707 F.3d at 1339 (citing White Consol. Indus., Inc. v. Vega Servo-Control, Inc., 713 F.2d 788, 791 [218 USPQ 961] (Fed. Cir. 1983)). Finally, in In re Vaeck, we affirmed the PTO's nonenablement rejection of claims reciting heterologous gene expression in as many as 150 genera of cyanobacteria. 947 F.2d 488, 495-96 [20 USPQ2d 1438] (Fed. Cir. 1991). The specification disclosed only nine genera, despite cyanobacteria being a “diverse and relatively poorly understood group of microorganisms,” with unpredictable heterologous gene expression. Id. at 496. (Emphasis added)
Additionally, attention is directed to Cephalon at 1823, citing White Consol. Indus., Inc. v. Vega Servo-Control, Inc., 218 USPQ 961, that work that would require 18 months to 2 years so to enable the full scope of an invention, even if routine, would constitute undue experimentation. As stated therein:
Permissible experimentation is, nevertheless, not without bounds. This court has held that experimentation was unreasonable, for example, where it was found that eighteen months to two years’ work was required to practice the patented invention. See, e.g., White Consol. Indus., Inc. v. Vega Servo-Control, Inc., 713 F.2d 788, 791 [218 USPQ 961] Fed. Cir.1983). (Emphasis added)
Attention is also directed to MPEP 2164.06(b) and In re Vaeck, 20 USPQ2d 1438, 1445 (Fed. Cir. 1991).
Where, as here, a claimed genus represents a diverse and relatively poorly understood group of microorganisms, the required level of disclosure will be greater than, for example, the disclosure of an invention involving a “predictable” factor such as a mechanical or electrical element. See Fisher, 427 F.2d at 839, 166 USPQ at 24.
In view of such legal precedence, the aspect of having to work for so many years just to provide the starting materials for minute fraction of the scope of the claimed invention is deemed to constitute both an unreasonable length of time and undue experimentation.
Conclusions:
Herein, although the level of skill in the art is high, given the lack of disclosure in the specification and in the prior art and the unpredictability of the art, it would require undue experimentation for one of skill in the art to make and use the invention as broadly claimed.
Response To Arguments
7. In the response the Applicants traversed the rejection under 35 USC 112(a) Enablement. In the response the Applicant state that they have amended claim 2 to specify individual biomarkers. They state that they have amended claim 2 to clarify that the correlation between the circulating biomarkers EV miRNA or EV mRNA and the protein biomarker is less than 0.6. As discussed above, these biomarker panels are described in the application with examples, words, in tables, and in figures, which collectively show that these groups of circulating biomarkers can be used in diagnosing and staging PDAC (distinguishing PDAC patients from normal controls), and in distinguishing PDAC patients with occult metastases from non-metastatic PDAC patients. The most informative EV miRNAs for biomarker panel development are described in paragraph [00122] and shown in Figure 6C. The informative EV mRNAs for biomarker panel development are described in paragraph [00122]. Even though several of the EV miRNA and EV mRNA biomarker were not differentially expressed between normal controls and PDAC patients (Figure 7), and were weakly or not at all correlated with the circulating biomarker protein CA19-9, when combined with other circulating biomarkers, these EV miRNA and EV mRNA biomarkers could nonetheless: distinguish PDAC patients from non-cancer controls (Figures 3G and 3H), and distinguish metastatic from non-metastatic PDAC (Figures 4F and 4G). The Applicants argue that based on the detailed description of the claimed methods in the application with examples, words, tables, and figures, one of ordinary skill in the art would be able to practice the claimed methods without undue experimentation.
This argument has been fully considered but is not persuasive. The rejection has been modified to address the claims as amended. The claims have been examined to the extent that the read on a biomarker panel comprising hsa.miR.409.3p, CK18, KRAS DNA with the G12D, G12V, or G12R mutation, and CA19-9 protein. This is NOT the panel exemplified in the specification. It is highly unpredictable if this panel would be able to detect occult metastasis of ANY cancer or even PDAC. Further the claims state that one or more of hsa.miR.409.3p, CK18, KRAS DNA with the G12D, G12V, or G12R mutation, and CA19-9 protein does NOT correlate with the presence of occult metastases. This is highly unpredictable and not supported by any evidence in the specification. There appears to be no examples in the specification where the four claimed biomarkers were detected in patients with known metastasis and occult metastasis that shows that at least one of these biomarkers is NOT correlated with occult metastasis. The showing in Fig 7 that the claimed markers were not differentially expressed in PDAC compared to normal controls does not mean that the claimed markers are not differentially expressed in occult metastasis of PDAC as compared to known metastasis of PDAC. Finally the rejection is maintained because it is highly unpredictable if (i) EV miRNA has.miR.409.3p and CA19-9 protein and (ii) EV mRNA CK18 and CA19-9 is less than 0.6. Further experimentation would be necessary. The rejection is maintained.
Improper Markush Grouping Rejection
8. Claims 2, 6, 8, 13, 18, 21, 22, and 63-68 are rejected on the basis that it contains an improper Markush grouping of alternatives. See In re Harnisch, 631 F.2d 716, 721-22 (CCPA 1980) and Ex parte Hozumi, 3 USPQ2d 1059, 1060 (Bd. Pat. App. & Int. 1984). A Markush grouping is proper if the alternatives defined by the Markush group (i.e., alternatives from which a selection is to be made in the context of a combination or process, or alternative chemical compounds as a whole) share a “single structural similarity” and a common use. A Markush grouping meets these requirements in two situations. First, a Markush grouping is proper if the alternatives are all members of the same recognized physical or chemical class or the same art-recognized class, and are disclosed in the specification or known in the art to be functionally equivalent and have a common use. Second, where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the alternatives do not belong to a recognized class as set forth above, the members of the Markush grouping may be considered to share a “single structural similarity” and common use where the alternatives share both a substantial structural feature and a common use that flows from the substantial structural feature. See MPEP § 2117.
The claims recite the following Markush groups:
-an extra-cellular vesicle (EV) miRNA selected from the group consisting of hsa.miR.103b, hsa.miR.23a.3p, hsa.miR.409.3p, hsa.miR.224.5p and hsa.miR.1299
-an EV mRNA selected from the group consisting of CD63, CK18, GAPDH,H3F3A, KRAS, and ODC1
These Markush groupings are improper because the alternatives defined by the Markush grouping do not share both a single structural similarity and a common use for the following reasons:
MPEP 2117(II) states that “A Markush claim may be rejected under judicially approved “improper Markush grouping” principles when the claim contains an improper grouping of alternatively useable members. A Markush claim contains an “improper Markush grouping” if either: (1) the members of the Markush group do not share a “single structural similarity” or (2) the members do not share a common use. Supplementary Guidelines at 7166 (citing In re Harnisch, 631 F.2d 716, 721-22, 206 USPQ 300, 305 (CCPA 1980)).
MPEP 2117(II) further state that alternatives (1) share a “single structural similarity” when they belong to the same recognized physical or chemical class or to the same art-recognized class and (2) share a common function or use when they are disclosed in the specification or known in the art to be functionally equivalent in the context of the claimed invention.
MPEP § 2117(II)(A) states that “A recognized physical class, a recognized chemical class, or an art-recognized class is a class wherein “there is an expectation from the knowledge in the art that members of the class will behave in the same way in the context of the claimed invention. In other words, each member could be substituted one for the other, with the expectation that the same intended result would be achieved”. Herein the members of the Markush grouping are all miRNA or mRNA. These do not belong to the same recognized physical or chemical class or to the same art-recognized class because there is no expectation from the art that each of the recited miRNA or mRNA would function in the same way in the claimed method. It is only in the context of this specification that it was disclosed that all members of this group may behave in the same way in the context of the claimed invention.
MPEP § 2117(II)(B) states that “Where a Markush grouping describes alternative chemical compounds, whether by words or chemical formulas, and the alternatives do not belong to a recognized class as explained in subsection IIA above, the members of the Markush grouping may still be considered to be proper where the alternatives share a substantial structure feature that is essential to a common use. Again the members of the Markush grouping are all miRNA or mRNA. While they are all made up of nucleic acids, the structure of comprising nucleic acids is not essential to any asserted common use.
To overcome this rejection, Applicant may set forth each alternative (or grouping of patentably indistinct alternatives) within an improper Markush grouping in a series of independent or dependent claims and/or present convincing arguments that the group members recited in the alternative within a single claim in fact share a single structural similarity as well as a common use.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA HANEY whose telephone number is (571)272-8668. The examiner can normally be reached Monday-Friday, 8:15am-4:45pm EST.
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/AMANDA HANEY/ Primary Examiner, Art Unit 1682
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