DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-12 are pending.
Priority
This application is a 371 of PCT/JP2021/001265, filed 01/15/2021, and claims foreign priority to application number 2020-006304, filed in Japan on 17 January 2020. The instant application has the effective filing date of 17 January 2020.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/18/2022, 09/30/2022, 01/03/2023, and 03/11/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings submitted on 07/18/2022 have been accepted by the examiner.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Browser executable code was found one or more times at the following locations: [0037], [0038], [0040], [0046], and [0048].
Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract ideas without significantly more, for the reasons detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 1-7, and 10 are directed to a statutory category of invention (method)
Claims 8 and 11 are directed to a statutory category of invention (device)
Claims 9 and 12 are NOT directed to a statutory category (program)
Claims 9 and 12 do not fall within at least one of the four categories of patent eligible subject matter (In re Nuijten, Federal Circuit, 2007) because the claims as instantly recited read on products that do not have a physical or tangible form, such as information (often referred to as "data per se") or a computer program per se (often referred to as "software per se"), as they are claimed as a product without any structural recitations (such as a "means plus function" limitation).
CLAIMS 1-8 and 10-11 [Eligibility Step 1: YES]
CLAIMS 9 and 12 [Eligibility Step 1: NO]
Though claims 9 and 12 are not directed to statutory subject matter, in the interest of compact prosecution, Alice/Mayo Evaluation via MPEP 2143 continues below on all claims.
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: The limitations below are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon).
Recitations of Judicial Exceptions:
Claim 2: The prediction method according to Claim 1, wherein the artificial intelligence model for prediction is trained by means of a set of training data, (mathematical concept, mental process)
wherein the set of training data is data in which (1) already reported adverse event-related information and/or already reported side effect-related information reported for individual known drugs is/are linked with (II) indication data reported for the known drugs. (mental process)
Claim 6: The prediction method according to Claim 1, wherein the set of training data is generated by linking labels indicating indications for the known drugs and information about adverse events reported for the known drugs with labels indicating the names of the known drugs. (mental process)
Claims 10, 11, 12: An estimation method for estimating an action mechanism of a test substance in a living organism, comprising:
hierarchizing the set of data indicating the behavior of a biomarker in one or more organs used in predicting an indication by clustering based on a prediction result about an indication predicted by a prediction method according to Claim 1, (mental process)
performing a pathway analysis on the hierarchized set of data indicating the behavior of a biomarker to acquire information about an action mechanism of the test substance. (mental process)
Generating training data via “linking labels” equates to a mere association of one type of data (drug labels) with another. Hierarchizing, clustering, and performing pathway analyses are also techniques that require no more than mental observations and organizations of data that can be executed using only pen/paper and the human mind. As such, limitations that recite these techniques fall under the mental process grouping of abstract ideas.
Furthermore, the disclosure provides an explicit definition of the term "artificial intelligence model" as a unit of algorithms that can output a result of interest from a set of input data [0041], exemplified by models that use mathematical calculations to characterize data. Therefore, the noted limitation falls under the mathematical concepts grouping of abstract ideas.
As such claims 2, 6, and 10-12 appear to recite judicial exceptions (abstract ideas).
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)).
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Additional elements within the claimed invention include:
Claims 1, 8, and 9:
inputting estimated adverse event-related information estimated from a set of data indicating the behavior of a biomarker in one or more organs collected from non-human animals to which the drug of interest or its equivalent substance has been administered as a test substance into an artificial intelligence model for prediction as test data to predict an indication for the drug of interest or its equivalent substance.
The limitation completes necessary data gathering activities for the claimed invention and are considered insignificant extra-solution activities that do not integrate the judicial exception into practical application per MPEP 2106.05(g).
[Eligibility Step 2A – Prong Two: YES]
Such data gathering activities that selecting and input information, based on types of information and availability of information for further analysis are considered well-known and conventional within the art, as exemplified by Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
[Eligibility Step 2B: NO]
Additional elements that may be categorized differently include:
Claim 3: The prediction method according to Claim 1, wherein the artificial intelligence model for prediction corresponds to one indication.
Claim 4: The prediction method according to Claim 1, wherein the artificial intelligence model for prediction corresponds to multiple indications.
Claim 5: The prediction method according to Claim 1, wherein the estimated adverse event-related information and/or estimated side effect- related information is/are generated using an artificial intelligence model for estimation that is different from the artificial intelligence model for prediction.
Claim 7: The prediction method according to Claim 1, wherein the estimated adverse event-related information and/or estimated side effect- related information correspond(s) to (1) the presence or absence of multiple adverse events and/or side effects, or (2) the occurrence frequencies of multiple adverse events and/or side effects.
The limitations above specify the type, quantity, or format of data to be gathered or generated. It also specifies that one type of analysis technique is “different” from another. Selecting a particular data source or type of data to be manipulated is classified as an insignificant extra-solution activity that does not integrate the judicial exceptions of the claimed invention into practical application per MPEP 2106.05(g).
[Eligibility Step 2A – Prong Two: YES]
Considering the analysis techniques are well-known, routine, and conventional in the art per Vamathevan et al. (Nature Reviews; Vol. 18; 2019), that reviews distinct methods and applications of predictive models in the drug discovery pipeline; and selecting information based on the types and information and availability of information for collection and analysis are also routine, well known, and conventional per Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), the recited elements further lack inventive concept when evaluated as a whole claimed invention.
[Eligibility Step 2B: NO]
As such, claims 1-12 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention for the following reasons.
Claim 6 recites “the set of training data”, “the known drugs”, and “the names ” wherein there is insufficient antecedent basis for the terms.
To overcome the rejection, please amend to “a set of training data” “a known drug”, and “names of the known drugs” and/or clarify if the claim is intended to be dependent on claim 2, which has antecedent basis for the term “a set of training data”.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 5, 8, and 9 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tran et al. (IDS reference, filed on 09/30/2022; U.S. Patent Application Publication; cite no. 2; 2019).
Claims 1, 8, and 9 are directed to methods and programs that obtain adverse event information, from biomarker behavior data, and input it into an algorithmic (artificial intelligence) model.
Claim 5 is directed to the estimated adverse event information being obtained by an algorithmic prediction model separate from the one that received adverse event information data as input.
Tran et. al teaches a system to provide information on a drug substance for a subject, comprising: a network unit to receive gene or DNA sequencing data; and a processor running: code to match genomic biomarker(s) from gene or DNA sequencing for a population with historical information for a population on drug structure, dosage, clinical variability and risk for adverse events for the drug substance, the computer constructing side effect features for each drug, and applying a classifier to the features to predict one or more adverse drug interactions, the computer generating one or more indicia for the drug substance; and code to apply the indicia to the subject DNA to provide personalized medicine (claim 1).
Regarding claims 1, 8, and 9 Tran et al. teaches that FIG. 4 shows a deep learning machine using deep convolutionary neural networks [0050] and that the system of FIG. 4 receives data on adverse events strongly associated with indications for which the indication and the adverse event have a known causative relationship [0051].
Regarding claim 5, Tran et al. further teaches that an embodiment of the present invention uses an algorithm to infer unreported adverse drug events [0057]. This algorithm is separate from the algorithm previously described.
Claims 1 and 9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by D-iOrgans (IDS reference, filed on 07/18/2022; Non-Patent Literature; cite no. 1; 2017).
Claims 1 and 9 are directed to the methods and computer programs, as previously described.
D-iOrgans describes a database and analysis tool that can enable the discovery of drug repositioning candidates and hidden side effect risks. D-iOrgans teaches a website that uses genetic expression comprehensive analysis and artificial intelligence analysis to provide raw data, research data (page 1, column 1), and a treasure trove of unknown medical effects, such as drug repositioning candidates and hidden side effect risk information (page 1, column 1) to researchers.
D-iOrgans further teaches that its database compiles the effects of drugs on nearly all organs in the full bodies of model animals, such as mice (page 1, column 1).
Therefore, D-iOrgans anticipates a method and computer program that inputs adverse-event related information, in the form of side effect information, derived from the administering a drug of interest to mice organs, into an data model that uses artificial intelligence.
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-9 are rejected under U.S.C 103 as unpatentable over D-iOrgans (IDS reference, filed on 07/18/2022; Non-Patent Literature; cite no. 1; 2017) in view of Zhang et al. (IDS reference, filed on 07/18/2022; Non-Patent Literature; cite no. 6; 2013).
Claims 1, 8, and 9 are directed to methods, devices, and programs that obtain adverse event information, from biomarker behavior data, and input the adverse event information into an algorithmic model (artificial intelligence).
D-iOrgans teaches inputting adverse risk information into an algorithmic model, as described previously. D-iOrgans further teaches using the platform in order to seek out the potential/risk of not yet known side effects of drugs (page 2, column 1), search for indicators to measure bio-markers that narrow down eligible patients of drugs (page 2, column 1), find new treatable diseases of drugs (page 2, column 1), or find new effects and actions of mechanisms of drugs (page 2, column 1). However, D-iOrgans does not teach the particularities of completing these processes, as recited in claims 2-7 and 10-12.
Claim 2 is directed to the training the algorithm with a set of training data. The set of training data includes reported adverse event and/or side effect information linked with indication data for the known drugs. Claim 6 is directed to further linking this set of training data with drug name information.
Zhang et al. explores the associations between drug side-effects and therapeutic indications by inputting analogous variables into predictive algorithms.
Regarding claims 2 and 6, Zhang et al. teaches obtaining side effect keywords from the SIDER database, which contains information about marketed medicines and their recorded adverse drug reaction (page 1569, column 1) and drugs’ known uses were obtained through extracting treatment relationships between drugs and diseases from the National Drug File Reference Terminology (page 1569, column 2)… which constructed 3,250 treatment relationships between 799 drugs and 719 diseases (page 1570, column 1).
Zhang et al. further teaches building on a confirmation of strong correlation between drug indications and side-effects, by compiling a list of relationships among all known drug-disease and drug-side-effect to build disease-side-effect profiles and identify statistically significant relationships between drug side-effects and therapeutic indications (page 1569, column 1). These disease-side-effect profiles represent side effect information linked with known indication data and drug names.
Though Zhang et al. does not teach using these profiles as a set of algorithmic model training data, Zhang et al. does teach that these strong relationships can be used to provide repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension), as well as adverse-effect watch lists (e.g., drugs for heart failure possibly cause impotence) (page 1569, column 1).
Therefore, Zhang et al. teaches that the profiles are a well-suited variable for several drug repositioning tasks and provides sufficient motivation for one of ordinary skill in the art to link the profiles, using the technique described, for the purpose of constructing a set of training data used to predict therapeutic indications.
Claim 3 is directed to the algorithm predicting one indication for a drug/substance of interest. Claim 4 is directed to the algorithm predicting multiple indications for a drug/substance of interest.
Zhang et al. teaches the result of the prediction algorithm includes 10 therapeutic indications (diseases), ranked by correlation to a particular side-effect (page 1575, column 1). Therefore, the model can predict one or more therapeutic indications.
Claim 5 is directed to the estimated adverse event information being obtained by an algorithmic prediction model separate from the one that received adverse event information data as input.
Zhang et al. teaches that for the side effect prediction task, protein target features are used (page 1571, column 1) as input; and for the therapeutic indication prediction task, several sources of side effect data are used as input (page 1571, column 1).
Zhang et al. further teaches that for indication prediction, we constructed a classifier for predicting whether a given drug x treat a particular disease or not, and repeat this process for all 719 diseases; and for side-effect prediction, we constructed a classifier for predicting whether a given drug x has a side-effect or not, and repeat this process for all 1385 side-effects (page 1570, column 1).
Zhang et al. further shows that the averaged ROC curves of 10 runs of the cross-validation for the therapeutic prediction task (page 1573, figure 4) is separate and distinct from the averaged ROC curves of 10 runs of the cross-validation for the drug side-effect prediction task (page 1573, figure 5).
Therefore, Zhang et al. trains and tests two separate algorithmic prediction models using different combinations of input data and classifier constructions.
Claim 7 is directed to the adverse event/side effect information including at least one of the following: presence/absence or frequency of events.
Regarding claim 7, Zhang et al. teaches that each drug was represented by a 1385-dimensional binary profile whose elements encode for the presence or absence of each of the side-effect keywords by 1 or 0, respectively (page 1569, column 1).
As such, D-iOrgans introduced the base product of a robust database with artificial intelligence analysis tools for the purpose of elucidating new therapeutic indications and adverse event-related information. However, it did not disclose an exact method for one to use the tool to achieve the intended results. Zhang et al. discloses a methodology, within the same field, that one of ordinary skill in the art could apply as a method of use for the base product to yield predictable results and result in an improved system.
Claims 10-12 are rejected under U.S.C 103 as unpatentable over D-iOrgans in view of Zhang et al., as applied to claims 1-9 above, and in further view of Iorio et al. (Proc Natl Acad Sci USA; Vol. 107: 33; 2010).
Claims 10-12 are directed to methods, devices, and programs that rank and organize the biomarker behavior information via clustering, based of the drug indication prediction results. The method further includes performing a pathway analysis on the organized data to derive information about a drug or substance’s mechanism of action.
D-iOrgans in view of Zhang et al. teach linking and inputting adverse event related information into predictive algorithms, as described above.
Zhang et al. further teaches that if drugs treating a disease share the same side-effects, this may be manifestation of some underlying mechanism-of-action (MOA) linking the indicated disease and the side-effect (page 1568, column 1) and that some side-effects are physiologically linked to hypertension and the mechanism of action (MOA) can be explained. Zhang et al. further teaches that for example, some hypertension drugs may result in a sudden drop in blood pressure when a person stands up, thus the side-effect postural hypotension happens; and some hypertension drugs (e.g., β-blockers) hits α-adrenergic receptors protein target in penile tissue, which will cause side-effect impotence (page 1574, column 1). Therefore, Zhang et al. teaches performing a pathway analysis to determine the mechanism of action of according to the protein-target binding behavior, based on the results of the indication prediction.
However, neither Zhang et al. nor D-iOrgans teach organizing the biomarker behavior data via clustering, as a prerequisite to completing the pathway analysis.
Iorio et al. describes a method of discovering drug mode of action and drug repositioning from transcriptional responses.
Iorio et al. teaches partitioning drugs into groups of densely interconnected nodes (i.e., communities) that are significantly enriched for compounds with similar MoA, or acting on the same pathway, and can be used to identify the compound-targeted biological pathways (page 14621, column 1) Iorio et. al teaches that the groups are based on known direct target genes and Anatomical Therapeutic Chemical (ATC) codes (14622, column 1), which have the drugs’ therapeutic and chemical profiles (14623, column 1).
Iorio et al. further teaches checking if compounds in the same community share common biological pathways via a Fuzzy-Logic-based approach to identify a common set of genes that was consistently up-, or, down-regulated within a prototype ranked list (PRL) and performing a GO enrichment analysis on the common set of genes (page 14623, column 1).
Iorio et al. further teaches that it is possible to find previously unrecognized MoAs of well-characterized drugs by simply looking for the drugs neighboring a drug of interest; and that by analyzing the PRLs associated to each drug in the network, one may identify the drug communities that consistently up-, or down-regulate a given set of genes, thus hinting to drug classes able to modulate a specific pathway of interest (page 14625, column 2).
As such, Iorio et al. describes a method of organizing, ranking, and performing pathway analyses on both biomarker behavior (gene regulation) and drug data, based on their therapeutic indications. Iorio et al. further teaches that a major limitation of its approach is the limited number of compounds in the network (page 14625, column 2), and therefore makes their tool publicly available online where other researchers can easily search for a compound of interest or query the transcriptional responses of a unique compound (14626, column 1).
Therefore, Iorio et al. also provides sufficient motivation for one of ordinary skill in the art to integrate its tool into an indication prediction (drug repositioning) pipeline, such as D-iOrgans and Zhang et al., in order to enhance the ability to find previously unrecognized MoAs and thus more drug repositioning candidates.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
Rejection 1:
Claims 1, 2, 6, 8, and 9 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 16 and 19-21 of U.S. Application No. 17/769,516 (reference).
Claims 1, 8, and 9 are directed to methods, devices, and programs that input adverse event information derived from data regarding biomarker behavior into an algorithmic model that predicts an indication of a drug/substance of interest. The biomarker behavior data is generated as a result of administering a drug/substance to at least one animal organ, as previously described.
Claims 16 and 19-21 (reference) are directed to methods, devices, and programs for predicting an indication for a test substance in humans. The method includes acquiring adverse event and biomarker dynamics information. The biomarker dynamics data must also be generated as a result of administering a drug/substance to at least one animal organ.
The claims further require linking labels indicated the known drugs with their respective known adverse event information to generate a set of training data, as recited in claims 2 and 6 of the instant application.
As presented, the independent claims of the application have identical effect and function of using a model to predict substance indications using biomarker behavior, adverse-effect, and known drug label data. The applications might differ slightly in scope as the reference application predicts indications for test substances in humans whereas the instant application does not place limits on the applicable organism for the predicted indication. However, it would be obvious to one of ordinary skill in the art to apply the technique of the instant application to predict indications for test substances in humans as the disclosure highlights the utility of the claimed invention as a means of increasing clinical trial efficiency for the safe development of human pharmaceuticals [0003].
Although the claims at issue are not identical, they are not patentably distinct from each other. As such claims 1, 2, 6, 8, and 9 are provisionally rejected. The rejection is provisional as the co-pending claims have not yet been patented.
Rejection 2:
Claims 1 and 8-12 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 13, 14, and 17 of U.S. Patent No. 11,676,684 (reference).
Claims 1, 8, and 9 are directed to the methods, devices, and programs as previously described. Claims 10-12 are directed to further analyzing the biomarker behavior data of the claims via ranking and clustering in order to estimate a mechanism of action.
Claims 1, 13, 14, and 17 (reference) are directed to methods and devices of predicting one or more actions of a test substance in humans. The methods include measuring and comparing biomarker dynamic data, generated as a result of administering a test substance to non-human animal organs, via inputting the information into an algorithmic prediction model.
As presented, the claims of both inventions have the same effect and function, but differ in scope, as the reference claims provide much more detail on how the biomarker behavior information is measured, ranked, and analyzed. The reference claims also input this data into an algorithm to estimate a mechanism of action. However, as the algorithm is only generically claimed and the instant claims do not limit how the MOA is estimated, the automation and detail of the reference claims is encompassed within the scope of the instant claims.
Although the claims at issue are not identical, they are not patentably distinct from each other. As such, claims 1 and 8-12 are rejected.
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Conclusion
No claims are currently allowed.
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure: Jia et al. "Coegna, a novel tool for co-expressed gene-set enrichment analysis, applied to drug repositioning and drug mode of action discovery." BMC Genomics; Vol. 17 (414); 2016.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571) 272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-1113. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687