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 .
DETAILED ACTION
In the response filed on 13 February 2026, the following has occurred: claims 1, 6-7 and 21 have been amended; claims 5 and 22 have been canceled; claim 23 is newly added.
Now claims 1-4, 6-21 and 23 are pending.
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).
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.
Claims 1-4, 6-21 and 23 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12243628 in view of the teachings of Agarwal, Eun and Afeyan (see specific mapping below but at least Agarwal: paragraph [0175], Table 1, Eun: paragraph [0006] and Afeyan: paragraphs [0068]-[0070]). Although the claims at issue are not identical, they are not patentably distinct from each other because both are directed at methods to identify one or more new indications for a drug using a target pathway using an iterative clustering operation.
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 1-4, 6-21 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 21 and 23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite method, system and non-transitory computer readable media (CRM) for identifying new indications for a drug that targets an IL4/IL3 pathway. The limitations of:
Claim 1, which is representative of claim 21
[… obtaining …] data representing medical records of a plurality of patients; processing , […], the data representing the medical records of the plurality of patients to select a subset of patients, from the plurality of patients, that each have one or more characteristics related to the IL4/IL13 pathway; [… organizing …] the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients, wherein the [… organizing …] comprises: applying an [… organization of data …] operation to feature vectors characterizing the patients in the subset of patients to optimize a clustering function; and identifying the plurality of patient clusters based on a result of applying the [… organization of data …] operation to the feature vectors characterizing the patients in the subset of patients; ranking, […], a plurality of candidate drug indications based on: (i) the plurality of patient [… groupings …], and (ii) one or more reference drug indications associated with the drug and identifying, […], one or more of the plurality of candidate drug indications as new indications for the drug that targets the IL4/IL13 pathway based on the ranking of the plurality of candidate drug indications; and [… providing …] the drug to a patient as a treatment for one of the identified new indications.
Claim 23
identifying that a drug targeting the IL4/IL 13 pathway is indicated for treatment of the disease, wherein the identification is based on a […] process comprising: [… obtaining …], data representing medical records of a plurality of patients; processing, […], the data representing the medical records of the plurality of patients to select a subset of patients, from the plurality of patients, that each have one or more characteristics related to the IL4/IL 13 pathway; [… organizing …], the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients, wherein the [… organizing …] comprises: applying an […] operation to feature vectors characterizing the patients in the subset of patients to optimize a […] function; and identifying the plurality of patient [… groupings …] based on a result of applying the […] operation to the feature vectors characterizing the patients in the subset of patients; ranking, […], a plurality of candidate drug indications based on: (i) the plurality of patient [… groupings …], and (ii) one or more reference drug indications associated with the drug; and identifying, […], one or more of the plurality of candidate drug indications as new indications for the drug that targets the IL4/IL 13 pathway based on the ranking of the plurality of candidate drug indications, wherein the disease is one of the identified new indications; and [… providing …] the drug to a patient as a treatment for one of the identified new indications.
, as drafted, is a method which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with one or more computers (claim 1 and 23), one or more computers and one or more storage devices (claims 21), the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human user interacting one or more computers (claim 1 and 23), one or more computers and one or more storage devices (claims 21), the claim encompasses collection of data, organization of the collected data into clusters and use of the organized data to generate a result for a human user. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more computers (claim 1 and 23), one or more computers and one or more storage devices (claims 21), which implements the abstract idea. The one or more computers (claim 1 and 23), one or more computers and one or more storage devices (claims 21) are recited at a high-level of generality (i.e., a general-purpose computers/ computer component implementing generic computer functions; see Applicant’s specification Figure 5, paragraphs [0066]-[0096]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “receiving”, “iterative clustering” and “administering the drug to a patient”. The “receiving” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “iterative clustering” is recited at a high-level of generality. (i.e., as organizing/grouping data until a criterion is met) and amounts to generally linking the abstract idea to particular technological environment. The “administering the drug to a patient” is recited at a high-level of generality. (i.e., as prescribing a generic off-the shelf drug to a patient) and amounts to generally linking the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more computers (claim 1 and 23), one or more computers and one or more storage devices (claims 21), to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using a generic hardware component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “receiving”, “iterative clustering” and “administering the drug to a patient” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “receiving” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “iterative clustering” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Eun (20210027896): see below but at least paragraph [0063]; Domany (20030059818): paragraph [0010]; El Arab (20100249626): paragraph [0070]; Lipsky (20200090787): paragraph [0072]; iterative clustering of data is well-understood routine and conventional. The “administering the drug to a patient” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Agarwal (2014/0170157): see below but at least paragraph [0056]; Afeyan (20100057368): paragraph [0013]; Beim (20170351806): paragraph [0192]; administration of a drugs to a patient is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-4 and 6-20 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 2 describes applying a dimensionality reduction operation, however this high-level generic language is organization of data and is not an additional element, as such the claim does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 3 and 4 further organize data into results to be used to further organize the data by filtering, such organization of data are not additional elements, as such the claim does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 6-8 further recites organization of data to further organize data into a ranking, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 9 further describes the size of data, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 10 and 11 further describe the label of the drug, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claim 12 further describes selection of a subset of patients, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
Claims 13-20 further describe labels of the characteristics that are used in selection of patients, however does not recite any additional elements are therefore cannot provide a practical application and/or significantly more.
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.
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.
Claim(s) 1-2, 6-9, 12-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2014/0170157 (hereafter “Agarwal”), in view of U.S. Patent Pub. No. 20210027896 (hereafter “Eun”), U.S. Patent Pub. No. 20100057368 (hereafter “Afeyan”).
Regarding (Currently Amended) claim 1, Agarwal teaches a method for identifying one or more new indications for using a drug that targets an IL4/IL13 pathway (Agarwal: paragraph [0003], “identify new or unsuspected indications for existing pharmaceuticals”, paragraphs [0008]-[0012], “Methods are provided for repositioning a pharmaceutical… selecting at least one target gene or gene product associated with the treatment of at least one first disease, trait and/or phenotype by said pharmaceutical… identifying at least one second disease, trait and/or phenotype for at least one target gene of pharmaceutical mentioned in (a) using genome-wide associated studies”, paragraph [0018], “The tools include computerized databases”, paragraph [0175], “identify new targets or pathways for therapeutic intervention.”. The Examiner notes in Table 1, IL13 is one of the genes that is targeted pathway and teaches what is required under the broadest reasonable interpretation. Also see, Tables 1-2, paragraph [0058]), the method comprising:
receiving, by one or more computers, data representing medical records of a plurality of patients (Agarwal: Figure 1, paragraphs [0018]-[0019], “The tools include computerized databases… obtain DNA from participants in one of two groups: people with a selected disease, trait and/or phenotype and similar people without that disease trait and/or phenotype. Each person's complete set of DNA, or genome, is surveyed for strategically selected markers of genetic variation, which are called single nucleotide polymorphisms, or SNPs”, paragraphs [0151]-[0154], “To construct a list of GWAS genes associated with disease traits we used the catalog of published GWAS studies from the National Human Genome Research Institute”);
processing, by the one or more computers, the data representing the medical records of the plurality of patients to select a subset of patients, from the plurality of patients, that each have one or more characteristics related to the IL4/IL13 pathway (Agarwal: paragraph [0004], “GWAS associated genes were selected from the GWAS catalog after two filtering steps”, paragraph [0022], “A `population` of subjects may be defined using various criteria… individuals with cancer”, paragraph [0154], “As a first step we eliminated 2,166 associations annotated as not replicated, and an additional 737 associations with p-value> le-7, in an attempt to minimize the inclusion of false positive signals in our analysis. An additional 400 associations were excluded because the associated traits were anthropometric and not relevant in the drug discovery context of our analysis (see Table 1 )”. Also see, paragraph [0018]. The Examiner notes a filtering step reads on what is required under the broadest reasonable interpretation. Additionally, the Examiner notes that “to select a subset of patients” is an intended use of the processing that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the processing); […]; and
ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient [… data …], and (ii) one or more reference drug indications associated with the drug (Agarwal: paragraph [0168], “Selected examples of potential opportunities to reposition a drug for a new disease indication based on the GWAS trait. Examples are ranked from most advanced drug (launched) to less advanced (Preclinical). The associated gene between each GWAS and the drug is shown. The drug indication and the phase of development for each drug are derived from the Pharmaprojects database. In many cases more drugs for the gene are listed in the database at different phases. The GWAS references are from the catalog of GWAS studies”. Also see, Table 1);
identifying, by the one more computers, one or more of the plurality of candidate drug indications as new indications for the drug that targets the IL4/IL13 pathway based on the ranking of the plurality of candidate drug indications (Agarwal: paragraph [0003], “identify new or unsuspected indications for existing pharmaceuticals”, paragraphs [0149]-[0151], “new therapeutic indications are provided for drugs and biotherapeutics according to Table 1… these analyses suggest new translational applications for GWAS-identified genes as both theoretical and practical drug targets”, paragraph [0168], “Selected examples of potential opportunities to reposition a drug for a new disease indication based on the GWAS trait”, paragraph [0175], “identify new targets or pathways for therapeutic intervention”, paragraph [0245], “Based on the methods provided herein new therapeutic indications are provided for drugs and biotherapeutics according to Table 1”. Also see, paragraph [0168]. The Examiner notes in Table 1, IL13 is one of the genes that is targeted pathway and teaches what is required under the broadest reasonable interpretation. Also see, Tables 1-2, paragraph [0058]); and
administering the drug to a patient as a treatment for one of the identified new indications ((Agarwal: paragraphs [0003]-[0006], “identify new or unsuspected indications for existing pharmaceuticals… Methods are provided for treating… disease in a human in need thereof, comprising administering to said human at least one compound”. Also see, Table 1).
Agarwal may not explicitly teach (underlined below for clarity):
clustering the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients,
wherein the clustering comprises: applying an iterative clustering operation to feature vectors characterizing the patients in the subset of patients to optimize a clustering function; and
identifying the plurality of patient clusters based on a result of applying the iterative clustering operation to the feature vectors characterizing the patients in the subset of patients;
Eun teaches clustering the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients (Eun: Figures 1, 4-6, paragraph [0006], “a system for identifying and characterizing distinct progression pathways of each of various diseases… generates numerical vectors in a continuous vector space that each represent substantially the entire medical history of a patient. This machine-learning engine is further provided within a unique pipeline that includes a clustering engine that identifies clusters of patients with similar patient journeys using the numerical vectors, and a cluster profiling engine that identifies distinguishing features of each cluster”, paragraphs [0052]-[0053], “Clustering engine 246 operates on the single vector representations of the patient medical histories to identify clusters of the patients in the pre-identified cohort that have similar patient journeys… Once the clusters of patients have been identified, and a cluster label is generated by clustering engine 246 for each cluster of patients, cluster profiling engine 248 operates on the single vector representations to identify distinguishing medical events for each cluster”, paragraph [0067], “a graphical representation is provided that indicates the relative numbers of patients in each of Cluster 1, Cluster 2, Cluster 3, Cluster 4, and Cluster 5 (each having been identified by clustering engine 246)”),
wherein the clustering comprises: applying an iterative clustering operation to feature vectors characterizing the patients in the subset of patients to optimize a clustering function (Eun: paragraph [0063], “the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset. In order to propagate the cluster labels to the rest of the patients in the cohort, clustering engine 246 operates again on the single vector representations (prior to the dimensionality reduction step) for the other patients in the cohort but using the known cluster labels, and iteratively and populates the cluster labels for the subset, using the existing labels to enlarge the clusters (e.g., by assigning more of the patients in the cohort to that cluster) in an iterative manner. In this way, the cluster labels can be generated, without supervision, for the cohort, while still allowing processing of large sets of patient data. As indicated by the dashed arrows in FIG. 4, mapping server 130 may iteratively cycle through operations 402, 404, and 406 to further define the clusters as desired. In the second solution, a neural-network based dimensionality reduction method is employed so that batches/chunks of the patient dataset could be fed into the server over a set number of iterations. This solution allows the dimensionality reduction and subsequent clustering operation of the entire dataset, without having to propagate the cluster labels and enlarge clusters”, paragraph [0090], “medical-event embedding engine 240 provides vector representations of individual medical events”); and
identifying the plurality of patient clusters based on a result of applying the iterative clustering operation to the feature vectors characterizing the patients in the subset of patients (Eun: Figures 1, 4-6, paragraph [0064], “Mapping server 130 may then (e.g., by operation of cluster profiling engine 248 on the single vector representations, the medical-event vectors from clustering engine 246, and/or the medical records themselves) perform a cluster profiling operation 408 that identifies differentiating medical events for each identified cluster. Identification of these differentiating medical events allows mapping server 130 to generating information, for display, that illustrates why a cluster of patient medical histories is distinct from other clusters”, paragraph [0069], “cluster profiling engine 248 identifies the distinguishing features of each cluster, after the clusters are identified, rather than the clusters being forced to conform to pre-determined cluster labels”, paragraph [0067], “a graphical representation is provided that indicates the relative numbers of patients in each of Cluster 1, Cluster 2, Cluster 3, Cluster 4, and Cluster 5 (each having been identified by clustering engine 246). The Examiner notes the identified clusters are processed which in combination with the teachings of Agarwal teach what is required under the broadest reasonable interpretation);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using an interactive clustering technique to process data as taught by Eun within the determination of novel identifications for a specific pathway as taught by Agarwal with the motivation of “allows the dimensionality reduction and subsequent clustering operation of the entire dataset, without having to propagate the cluster labels and enlarge clusters” (Eun: paragraph [0063]).
Agarwal and Eun may not explicitly teach (underlined below for clarity):
ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient clusters, and (ii) one or more reference drug indications associated with the drug;
Afeyan teaches ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient clusters, and (ii) one or more reference drug indications associated with the drug (Afeyan: paragraphs [0016]-[0020], “data characterizing all of the biomolecules from each subject (in each column) are clustered. Thus, composite values indicative of the biochemical profile from each individual are grouped by similarity… Such maps can be used to reveal subtypes of disease and to group individual subjects based on similarity of their biochemistry, as opposed to just their presenting clinical symptoms… generating an informative study set by clustering biomolecules or subjects according to an algorithm”, paragraphs [0068]-[0070], “These data analysis techniques are well known and are often embodied in data analysis software which determine Euclidean distance, correlation distance (Pearson Correlation or rank correlation), Manhattan distance, weighted harmonic distance, Chebychev distance, or principal component score distance… Comparison of patterns may also be used to evaluate drugs or rank drug candidates based on toxicity, potency (dosage), bioavailability, duration of action, and the frequency or severity of a side effect when compared to an appropriate reference”);
One of ordinary skill in the art before the effective filing date would have found it obvious to rank data using clustered data labels as taught by Afeyan within the ranking and clustering as taught by Agarwal and Eun with the motivation of “improvement in drug performance based upon the efficacy and side effects of the drugs in patients” (Afeyan: paragraph [0081]).
Regarding (Original) claim 2, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein clustering the subset of patients to generate the plurality of patient clusters further comprises: applying a dimensionality reduction operation to reduce a dimensionality of the feature vectors characterizing the patients in the subset of patients (Eun: paragraphs [0062]-[0063], “mapping server 130 may perform one or more dimensionality reduction operations on the single vector representations. For example, non-linear dimensionality reduction operations (e.g., spectrum embedding and auto-encoding operations) may be performed on the single vector representations to reduce the dimensionality of the single vector representations from hundreds of dimensions to a few dimensions”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Currently Amended) claim 6, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein the one or more reference indications associated with the drug comprise one or more of: asthma, atopic dermatitis, or immunoglobin E allergy (Agarwal: Table 1, “asthma”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Currently Amended) claim 7, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein ranking the plurality of candidate drug indications based on: (i) the plurality of patient clusters, and (ii) one or more reference drug indications associated with the drug comprises: determining, for each candidate drug indication, a co-occurrence score that defines a frequency of co-occurrence of: (i) the candidate drug indication, and (ii) the one or more reference drug indications, among the plurality of patient clusters; and determining the ranking of the plurality of candidate drug indications based at least in part on the co-occurrence scores for the plurality of candidate drug indications (Agarwal: paragraph [0168], “Selected examples of potential opportunities to reposition a drug for a new disease indication based on the GWAS trait. Examples are ranked from most advanced drug (launched) to less advanced (Preclinical). The associated gene between each GWAS and the drug is shown. The drug indication and the phase of development for each drug are derived from the Pharmaprojects database. In many cases more drugs for the gene are listed in the database at different phases. The GWAS references are from the catalog of GWAS studies”. Also see, Table 1; Eun: paragraphs [0065]-[0067], “The cluster profiling operation may include determining, for each medical event for each of the patients in the cohort, the number of occurrences in a given cluster, normalized by the total number medical events in the cluster (e.g., in order to adjust for the varying lengths of medical histories from cluster to cluster). The computed frequencies of each medical event (e.g., the term frequency, TF) can be obtained from the inverse of the weighted numbers of the events to represent the weight or the importance of the medical events in the cluster… In this way, the inverse frequencies are applied to penalize a medical event that appears in most or all of the clusters”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 8, Agarwal, Eun and Afeyan teach the limitations of claim 7, and further teach wherein for each candidate drug indication, determining the cooccurrence score that defines the frequency of co-occurrence of: (i) the candidate drug indication, and (ii) the one or more reference drug indications, among the plurality of patient clusters comprises: determining a number of patient clusters that are associated with both: (i) the candidate drug indication, and (ii) one or more of the reference drug indications (Eun: paragraphs [0065]-[0067], “The cluster profiling operation may include determining, for each medical event for each of the patients in the cohort, the number of occurrences in a given cluster, normalized by the total number medical events in the cluster (e.g., in order to adjust for the varying lengths of medical histories from cluster to cluster). The computed frequencies of each medical event (e.g., the term frequency, TF) can be obtained from the inverse of the weighted numbers of the events to represent the weight or the importance of the medical events in the cluster… These frequencies may then be further adjusted by multiplying each frequency with the inverse frequencies of the same medical event in other clusters (e.g., inverse cluster frequency, ICF)”, paragraphs [0100]-[0103], “the cluster profiling engine 248 determines a normalized number of occurrences of each medical event in a given one of the clusters, normalized by a total number of medical events in that cluster.”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 9, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein the plurality of patients comprise at least 1 million patients; or wherein each patient in the subset of patients is associated with a respective feature vector comprising at least 1000 features characterizing the patient; or wherein the plurality of patient clusters comprises at least 100 clusters (Eun: paragraph [00033], “1 million psoriasis patients”, paragraph [0057], “hundreds or thousands of parameters”. Also see, paragraph [0075]).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 12, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein processing the data representing the medical records of the plurality of patients to select a subset of patients that each have one or more characteristics related to the IL4/IL13 pathway comprises: selecting a subset of patients that each have one or more characteristics associated with clinical conditions linked to the IL4/IL13 pathway (Agarwal: paragraph [0019], “obtain DNA from participants in one of two groups: people with a selected disease, trait and/or phenotype and similar people without that disease trait and/or phenotype”, paragraph [0022], “A `population` of subjects may be defined using various criteria… individuals with cancer”, paragraph [0153], “allow selection of patients”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 13, Agarwal, Eun and Afeyan teach the limitations of claim 12, and further teach wherein the clinical conditions linked to the IL4/IL13 pathway comprise one or more of: eosinophilic esophagitis, eosinophilic granulomatosis with polyangiitis (Churg-Strauss Syndrome), anaphylaxis, allergic conjunctivitis, urticaria, thyroiditis, pancreatitis, amyloidosis, or basal cell carcinoma (Agarwal: Table 1, “Conjunctivitis… Amyloidosis… Pancreatitis… Urticaria”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 14, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein characteristics related to the IL4/IL13 pathway comprise one or more of: a diagnosis linked to the IL4/IL13 pathway, a medication linked to the IL4/IL13 pathway, a lab test linked to the IL4/IL13 pathway, or a procedure linked to the IL4/IL13 pathway (Agarwal: paragraphs [00008]-[0010], “at least one first disease, trait and/or phenotype”, paragraph [0030], “identify patients most suited to therapy with particular pharmaceutical agents (this is often termed "pharmacogenomics")”, paragraph [0037], “the testing of a biological sample from a subject to determine the subject's genotype”).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 15, Agarwal, Eun and Afeyan teach the limitations of claim 14, and further teach wherein medications linked to the IL4/IL13 pathway comprise one or more of: IL-4 inhibitors, IL-5 inhibitors, IL-13 inhibitors, medications targeting downstream pathway modulators, medications targeting upstream pathway modulators, medications targeting pathway determining interleukin targets, medications targeting downstream interleukin targets, or medications targeting proinflammatory targets (Agarwal: paragraph [0006], “administering to said human at least one compound selected from the group consisting of: an inhibitor and/or antagonist”, paragraph [0092], “Janus kinase inhibitor”, paragraph [0097], “the inhibitor blocks downstream cellular signaling (JAK-STAT) leading to suppression of proliferation and induction of apoptosis”, paragraph [0124], “the antagonist is a monoclonal antibody to IL-13”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 16, Agarwal, Eun and Afeyan teach the limitations of claim 15, and further teach wherein the medications targeting downstream pathway modulators comprise one or more of: JAK inhibitors, STAT inhibitors, GATA inhibitors, or IL-25 inhibitors (Agarwal: paragraph [0092], “Janus kinase inhibitor”, paragraph [0097], “the inhibitor blocks downstream cellular signaling (JAK-STAT) leading to suppression of proliferation and induction of apoptosis”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 17, Agarwal, Eun and Afeyan teach the limitations of claim 15, and further teach the medications targeting upstream pathway modulators comprise IL-2 inhibitors (Agarwal: paragraph [0006], “administering to said human at least one compound selected from the group consisting of: an inhibitor and/or antagonist”, paragraph [0114], “Aldesleukin is a man-made protein that has the same actions as native human interleukin-2 (IL-2)”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 18, Agarwal, Eun and Afeyan teach the limitations of claim 15, and further teach wherein the medications targeting pathway determining interleukin targets comprise one or more of: IL-1 inhibitors, IL-6 inhibitors, IL-12 inhibitors, IL-21 inhibitors, or IL-23 inhibitors (Agarwal: paragraph [0006], “IL-23 receptor inhibitor”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 19, Agarwal, Eun and Afeyan teach the limitations of claim 15, and further teach wherein the medications targeting downstream interleukin targets comprise one or more of: IL-9 inhibitors, IL-17 inhibitors, or IL-22 inhibitors (Agarwal: paragraph [0006], “administering to said human at least one compound selected from the group consisting of: an inhibitor and/or antagonist”, paragraph [0163], “IL-17”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
Regarding (Original) claim 20, Agarwal, Eun and Afeyan teach the limitations of claim 15, and further teach wherein the medications targeting proinflammatory targets comprise one or more of: IFN inhibitors, TGFb inhibitors, or TNFa inhibitors (Agarwal: paragraph [0086], “TNF inhibitors”. Additionally, the Examiner notes are the claim limits an alternative of its parent claims markush group, the prior art need not disclose every alternative to be encompassed by the claim limitation).
The motivation to combine is the same as that in claim 1, incorporated herein.
REGARDING CLAIM(S) 21
Claim(s) 21 is analogous to Claim(s) 1, thus Claim(s) 21 is similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1.
Regarding (New) claim 23, Agarwal teaches a method of treating a patient having a disease involving an IL4/IL13 pathway, the method comprising: identifying that a drug targeting the IL4/IL 13 pathway is indicated for treatment of the disease (Agarwal: paragraphs [0003]-[0006], “identify new or unsuspected indications for existing pharmaceuticals… Methods are provided for treating… disease in a human in need thereof, comprising administering to said human at least one compound”, paragraphs [0008]-[0012], “Methods are provided for repositioning a pharmaceutical… selecting at least one target gene or gene product associated with the treatment of at least one first disease, trait and/or phenotype by said pharmaceutical… identifying at least one second disease, trait and/or phenotype for at least one target gene of pharmaceutical mentioned in (a) using genome-wide associated studies”, paragraph [0018], “The tools include computerized databases”, paragraph [0175], “identify new targets or pathways for therapeutic intervention.”. The Examiner notes in Table 1, IL13 is one of the genes that is targeted pathway and teaches what is required under the broadest reasonable interpretation. Also see, Tables 1-2, paragraph [0058]), wherein the identification is based on a computer-implemented process comprising:
receiving, by one or more computers, data representing medical records of a plurality of patients (Agarwal: Figure 1, paragraphs [0018]-[0019], “The tools include computerized databases… obtain DNA from participants in one of two groups: people with a selected disease, trait and/or phenotype and similar people without that disease trait and/or phenotype. Each person's complete set of DNA, or genome, is surveyed for strategically selected markers of genetic variation, which are called single nucleotide polymorphisms, or SNPs”, paragraphs [0151]-[0154], “To construct a list of GWAS genes associated with disease traits we used the catalog of published GWAS studies from the National Human Genome Research Institute”);
processing, by the one or more computers, the data representing the medical records of the plurality of patients to select a subset of patients, from the plurality of patients, that each have one or more characteristics related to the IL4/IL 13 pathway (Agarwal: paragraph [0004], “GWAS associated genes were selected from the GWAS catalog after two filtering steps”, paragraph [0022], “A `population` of subjects may be defined using various criteria… individuals with cancer”, paragraph [0154], “As a first step we eliminated 2,166 associations annotated as not replicated, and an additional 737 associations with p-value> le-7, in an attempt to minimize the inclusion of false positive signals in our analysis. An additional 400 associations were excluded because the associated traits were anthropometric and not relevant in the drug discovery context of our analysis (see Table 1 )”. Also see, paragraph [0018]. The Examiner notes a filtering step reads on what is required under the broadest reasonable interpretation. Additionally, the Examiner notes that “to select a subset of patients” is an intended use of the processing that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the processing); […];
ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient [… data …], and (ii) one or more reference drug indications associated with the drug (Agarwal: paragraph [0168], “Selected examples of potential opportunities to reposition a drug for a new disease indication based on the GWAS trait. Examples are ranked from most advanced drug (launched) to less advanced (Preclinical). The associated gene between each GWAS and the drug is shown. The drug indication and the phase of development for each drug are derived from the Pharmaprojects database. In many cases more drugs for the gene are listed in the database at different phases. The GWAS references are from the catalog of GWAS studies”. Also see, Table 1); and
identifying, by the one or more computers, one or more of the plurality of candidate drug indications as new indications for the drug that targets the IL4/IL 13 pathway based on the ranking of the plurality of candidate drug indications, wherein the disease is one of the identified new indications (Agarwal: paragraph [0003], “identify new or unsuspected indications for existing pharmaceuticals”, paragraphs [0149]-[0151], “new therapeutic indications are provided for drugs and biotherapeutics according to Table 1… these analyses suggest new translational applications for GWAS-identified genes as both theoretical and practical drug targets”, paragraph [0168], “Selected examples of potential opportunities to reposition a drug for a new disease indication based on the GWAS trait”, paragraph [0175], “identify new targets or pathways for therapeutic intervention”, paragraph [0245], “Based on the methods provided herein new therapeutic indications are provided for drugs and biotherapeutics according to Table 1”. Also see, paragraph [0168]. The Examiner notes in Table 1, IL13 is one of the genes that is targeted pathway and teaches what is required under the broadest reasonable interpretation. Also see, Tables 1-2, paragraph [0058]); and
administering the drug to a patient as a treatment for one of the identified new indications ((Agarwal: paragraphs [0003]-[0006], “identify new or unsuspected indications for existing pharmaceuticals… Methods are provided for treating… disease in a human in need thereof, comprising administering to said human at least one compound”. Also see, Table 1).
Agarwal may not explicitly teach (underlined below for clarity):
clustering, by the one or more computers, the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients, wherein the clustering comprises:
applying an iterative clustering operation to feature vectors characterizing the patients in the subset of patients to optimize a clustering function; and
identifying the plurality of patient clusters based on a result of applying the iterative clustering operation to the feature vectors characterizing the patients in the subset of patients;
Eun teaches clustering, by the one or more computers, the subset of patients to generate a plurality of patient clusters, wherein each of the patient clusters includes multiple patients of the subset of patients (Eun: Figures 1, 4-6, paragraph [0006], “a system for identifying and characterizing distinct progression pathways of each of various diseases… generates numerical vectors in a continuous vector space that each represent substantially the entire medical history of a patient. This machine-learning engine is further provided within a unique pipeline that includes a clustering engine that identifies clusters of patients with similar patient journeys using the numerical vectors, and a cluster profiling engine that identifies distinguishing features of each cluster”, paragraphs [0052]-[0053], “Clustering engine 246 operates on the single vector representations of the patient medical histories to identify clusters of the patients in the pre-identified cohort that have similar patient journeys… Once the clusters of patients have been identified, and a cluster label is generated by clustering engine 246 for each cluster of patients, cluster profiling engine 248 operates on the single vector representations to identify distinguishing medical events for each cluster”, paragraph [0067], “a graphical representation is provided that indicates the relative numbers of patients in each of Cluster 1, Cluster 2, Cluster 3, Cluster 4, and Cluster 5 (each having been identified by clustering engine 246)”), wherein the clustering comprises:
applying an iterative clustering operation to feature vectors characterizing the patients in the subset of patients to optimize a clustering function (Eun: paragraph [0063], “the clustering operation 406 can include selecting a subset (e.g., one third or another fraction) of the patient cohort 304 and generating (e.g., with clustering engine 246) clusters and associated cluster labels for the subset. In order to propagate the cluster labels to the rest of the patients in the cohort, clustering engine 246 operates again on the single vector representations (prior to the dimensionality reduction step) for the other patients in the cohort but using the known cluster labels, and iteratively and populates the cluster labels for the subset, using the existing labels to enlarge the clusters (e.g., by assigning more of the patients in the cohort to that cluster) in an iterative manner. In this way, the cluster labels can be generated, without supervision, for the cohort, while still allowing processing of large sets of patient data. As indicated by the dashed arrows in FIG. 4, mapping server 130 may iteratively cycle through operations 402, 404, and 406 to further define the clusters as desired. In the second solution, a neural-network based dimensionality reduction method is employed so that batches/chunks of the patient dataset could be fed into the server over a set number of iterations. This solution allows the dimensionality reduction and subsequent clustering operation of the entire dataset, without having to propagate the cluster labels and enlarge clusters”, paragraph [0090], “medical-event embedding engine 240 provides vector representations of individual medical events”); and
identifying the plurality of patient clusters based on a result of applying the iterative clustering operation to the feature vectors characterizing the patients in the subset of patients (Eun: Figures 1, 4-6, paragraph [0064], “Mapping server 130 may then (e.g., by operation of cluster profiling engine 248 on the single vector representations, the medical-event vectors from clustering engine 246, and/or the medical records themselves) perform a cluster profiling operation 408 that identifies differentiating medical events for each identified cluster. Identification of these differentiating medical events allows mapping server 130 to generating information, for display, that illustrates why a cluster of patient medical histories is distinct from other clusters”, paragraph [0069], “cluster profiling engine 248 identifies the distinguishing features of each cluster, after the clusters are identified, rather than the clusters being forced to conform to pre-determined cluster labels”, paragraph [0067], “a graphical representation is provided that indicates the relative numbers of patients in each of Cluster 1, Cluster 2, Cluster 3, Cluster 4, and Cluster 5 (each having been identified by clustering engine 246). The Examiner notes the identified clusters are processed which in combination with the teachings of Agarwal teach what is required under the broadest reasonable interpretation);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using an interactive clustering technique to process data as taught by Eun within the determination of novel identifications for a specific pathway as taught by Agarwal with the motivation of “allows the dimensionality reduction and subsequent clustering operation of the entire dataset, without having to propagate the cluster labels and enlarge clusters” (Eun: paragraph [0063]).
Agarwal and Eun may not explicitly teach (underlined below for clarity):
ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient clusters, and (ii) one or more reference drug indications associated with the drug;
Afeyan teaches ranking, by the one or more computers, a plurality of candidate drug indications based on: (i) the plurality of patient clusters, and (ii) one or more reference drug indications associated with the drug (Afeyan: paragraphs [0016]-[0020], “data characterizing all of the biomolecules from each subject (in each column) are clustered. Thus, composite values indicative of the biochemical profile from each individual are grouped by similarity… Such maps can be used to reveal subtypes of disease and to group individual subjects based on similarity of their biochemistry, as opposed to just their presenting clinical symptoms… generating an informative study set by clustering biomolecules or subjects according to an algorithm”, paragraphs [0068]-[0070], “These data analysis techniques are well known and are often embodied in data analysis software which determine Euclidean distance, correlation distance (Pearson Correlation or rank correlation), Manhattan distance, weighted harmonic distance, Chebychev distance, or principal component score distance… Comparison of patterns may also be used to evaluate drugs or rank drug candidates based on toxicity, potency (dosage), bioavailability, duration of action, and the frequency or severity of a side effect when compared to an appropriate reference”);
One of ordinary skill in the art before the effective filing date would have found it obvious to rank data using clustered data labels as taught by Afeyan within the ranking and clustering as taught by Agarwal and Eun with the motivation of “improvement in drug performance based upon the efficacy and side effects of the drugs in patients” (Afeyan: paragraph [0081]).
Claim(s) 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2014/0170157 (hereafter “Agarwal”), U.S. Patent Pub. No. 20210027896 (hereafter “Eun”) and U.S. Patent Pub. No. 20100057368 (hereafter “Afeyan”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20120232788 (hereafter “Diao”).
Regarding (Original) claim 3, Agarwal, Eun and Afeyan teach the limitations of claim 1, and further teach wherein processing the data defining the plurality of patient clusters from the subset of patients having one or more characteristics related to the IL4/IL13 pathway to identify one or more new indications for the drug that targets the IL4/IL13 pathway comprises: filtering the plurality of patient clusters, based on one or more cluster selection criteria, […]; determining whether to remove the patient cluster from the plurality of patient clusters […] (Agarwal: paragraph [0004], “GWAS associated genes were selected from the GWAS catalog after two filtering steps”, paragraph [0022], “A `population` of subjects may be defined using various criteria… individuals with cancer”, paragraph [0154], “As a first step we eliminated 2,166 associations annotated as not replicated, and an additional 737 associations with p-value> le-7, in an attempt to minimize the inclusion of false positive signals in our analysis. An additional 400 associations were excluded because the associated traits were anthropometric and not relevant in the drug discovery context of our analysis (see Table 1 )”; Eun: paragraph [0003], “inclusion and exclusion criteria”, paragraph [0059], “based on other exclusion criteria such as lab tests or procedures that indicate a different diagnosis”).
Agarwal, Eun and Afeyan may not explicitly teach (underlined below for clarity): comprising, for each patient cluster of the plurality of patient clusters: determining a stability of the patient cluster under perturbations of parameters of the iterative clustering operation; and determining whether to remove the patient cluster from the plurality of patient clusters based at least in part on the stability of the patient cluster.
Diao teaches comprising, for each patient cluster of the plurality of patient clusters: determining a stability of the patient cluster under perturbations of parameters of the iterative clustering operation; and determining whether to remove the patient cluster from the plurality of patient clusters based at least in part on the stability of the patient cluster (Diao: Figures 10-12, paragraph [0102], “One measure of stability is to compare the differences between the numbers of clusters generated for different pre-set values of the expected number 1018 of the clusters 402. Stability of the results across different runs can be an asset of a clustering method”, paragraph [0156], “The evaluation module 1408 can evaluate the clustering performance 1202 of FIG. 12 to adjust the user-defined percentile 1014. Clustering performance can include any one of purity, NMI, RI, F1 score, precision, and recall”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using stability to for cluster processing as taught by Diao with the filtering as taught by the combination of Agarwal, Eun and Afeyan with the motivation of “reduce costs, improve efficiencies and performance, and meet competitive pressures” (Diao: paragraph [0006]).
Regarding (Original) claim 4, Agarwal, Eun, Afeyan and Diao teach the limitations of claim 3, and further teach wherein filtering the plurality of patient clusters further comprises, for each patient cluster of the plurality of patient clusters: determining a purity of the patient cluster based on a measure of variance between feature vectors of patients included in the patient cluster (Diao: Figures 10-12, paragraphs [0094]-[0095], “Purity can be an external evaluation criterion for cluster quality, measuring whether the clusters 402 of FIG. 4 contain one single class”, paragraph [0156], “The evaluation module 1408 can evaluate the clustering performance 1202 of FIG. 12 to adjust the user-defined percentile 1014. Clustering performance can include any one of purity, NMI, RI, F1 score, precision, and recall”); and
determining whether to remove the patient cluster from the plurality of patient clusters based at least in part on the purity of the patient cluster (Agarwal: paragraph [0004], “GWAS associated genes were selected from the GWAS catalog after two filtering steps”, paragraph [0022], “A `population` of subjects may be defined using various criteria… individuals with cancer”, paragraph [0154], “As a first step we eliminated 2,166 associations annotated as not replicated, and an additional 737 associations with p-value> le-7, in an attempt to minimize the inclusion of false positive signals in our analysis. An additional 400 associations were excluded because the associated traits were anthropometric and not relevant in the drug discovery context of our analysis (see Table 1 )”; Eun: paragraph [0003], “inclusion and exclusion criteria”, paragraph [0059], “based on other exclusion criteria such as lab tests or procedures that indicate a different diagnosis”).
The motivation to combine is the same as that in claim 3, incorporated herein.
Claim(s) 10 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2014/0170157 (hereafter “Agarwal”), U.S. Patent Pub. No. 20210027896 (hereafter “Eun”) and U.S. Patent Pub. No. 20100057368 (hereafter “Afeyan”) as applied to claim 1 above, and further in view of U.S. Patent Pub. No. 20180221582 (hereafter “Klemm”).
Regarding (Original) claim 10, Agarwal, Eun and Afeyan teach the limitations of claim 1, but may not explicitly teach wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody.
Klemm teaches wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody (Klemm: paragraph [0096], “information indicative of the type of the dispensed drug, which is determined based on the drug indication code portion 67, may be stored in association with the dose information.”, paragraph [0105], “anti IL-4 mAb (e.g., Dupilumab)”).
It would have been prima facie obvious to one of ordinary skill in the art at the time of the invention was made to combine the noted features of use of Dupilumab as taught by Klemm within teaching of identification of new indications for drugs that target the IL4/IL3 pathway as taught by Agarwal and Eun since the combination of the two references is merely simple substitution of one known element for another producing a predictable result (KSR rationale B). Since each individual element and its function are shown in the prior art, albeit shown in separate references, the difference between the claimed subject matter and the prior art rests not on any individual element or function but in the very combination itself—that is, in the substitution of the use of Dupilumab as taught by Klemm for the identification of new indications for drugs that target the IL4/IL3 pathway as taught by Agarwal and Eun. Thus, the simple substitution of one known element for another producing a predictable result renders the claim obvious.
Regarding (Original) claim 11, Agarwal, Eun, Afeyan and Klemm teach the limitations of claim 10, and further teach wherein the drug comprises Dupilumab (Klemm: paragraph [0096], “information indicative of the type of the dispensed drug, which is determined based on the drug indication code portion 67, may be stored in association with the dose information.”, paragraph [0105], “anti IL-4 mAb (e.g., Dupilumab)”).
The motivation to combine is the same as that in claim 10, incorporated herein.
Response to Arguments
Applicant's arguments filed on 13 February 2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed herein below in the order in which they appear in the response filed on 13 February 2026.
Rejections under 35 U.S.C. § 101
Regarding claims 1-22, the Examiner has considered the Applicant’s arguments but does not find them persuasive. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
Applicant respectfully disagrees with these assertions, and maintains that the claims recite limitations that cannot be construed as organizing human activity… These limitations describe specific operations performed by computers for identifying biological correlations through machine learning… Because the claims recite a specific technical process for data processing that falls outside the enumerated grouping of "Organizing Human Activity," the claims are not directed to an abstract idea under Step 2A, Prong 1… The present claims provide an improvement in the technical field of drug discovery and repurposing, specifically for drugs targeting the IL4/IL13 pathway. This is achieved by providing a specific computational method that utilizes iterative clustering and co-occurrence ranking to identify new clinical indications that would otherwise be missed by conventional methods or manual analysis. As described in the specification, conventional methods for drug repurposing, such as literature mining or simple association studies, suffer from limitations including… The claimed invention improves upon this by using a specific "real-world data driven protocol" involving unsupervised machine learning to cluster patients based on distinct characteristics. The claimed method employs a specific technical solution-iterative clustering of feature vectors and ranking based on co-occurrence with reference indications-to solve the technical problem of efficiently processing large datasets ( e.g., "94 million records") to find hidden biological correlations…. the presently claimed invention solves the technical problem of efficiently processing highvolume, high-dimensional real-world data to identify and validate obscured biological relationships that are otherwise computationally prohibitive to detect using conventional association methods.
The Examiner respectfully disagrees.
It is respectfully submitted, the claims under the broadest reasonable interpretation amount to collection and organization of data to provide an output for a human user to use in deciding how a care provider treats their patient, via human interaction with generic computer components, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”, the claims are directed toward organization of data to provide a human user an output and is directed toward an abstract idea.
Applicant’s claimed amendments do not solve a technical problem rooted in computer hardware recited in their specification, the argued paragraphs at best describe a human activity problem a care provider evaluating a drug for treatment of their patients, it is not a technical problem rooted in computer hardware technology, to discover drugs, this is a doctor treatment problem, at best this is merely application of the abstract idea on generic computer components which are not particular, they are generic off-the shelf hardware (see Applicant’s Specification Figure 5, paragraphs [0066]-[0096]), and which as stated in 2106.05(f)(2) “claiming the improved speed or efficiency inherent with applying the abstract idea on a computer” does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA). Finally, with respect to the amended limitation, administration of a generic off-the-shelf drug with no particular details of the drug, is not a particular treatment, and therefore cannot provide a practical application of the abstract idea.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-22, the Examiner has considered the applicant’s arguments; however, the arguments are not persuasive as addressed herein. Any arguments inadvertently not addressed are unpersuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the applied references, either applied independently or in combination, fail to teach or suggest, at least… Some of the above-quoted features were similarly recited in previously presented claim 5. Regarding claim 5, the Office cited paragraph [0168] and Table 1 of Agarwal as allegedly teaching… Applicant respectfully disagrees and submits that Agarwal fails to teach ranking the candidate drug indications and identifying the new indications as claimed… That is, the cited portions of Agarwal fail to teach at least the above-quoted features of amended claim 1… The Office further cited paragraphs [0065]- [0067] of Eun as allegedly teaching the above-quoted features of claim 7. However, the quoted portions of Eun describe that… As such, the quoted portions of Eun describe calculating the term frequency or importance of a single medical event within a cluster, which does not result in determining a co-occurrence score between two indications-a candidate indication and a reference indication-across a plurality of clusters. Eun fails to teach using a known "reference indication" as an anchor to validate a new "candidate indication" based on how frequently they appear together.
The Examiner respectfully disagrees.
It is respectfully submitted, that it is the combination of Eun within the teachings of Agarwal which teach the argued limitation, in particular Agarwal explicitly teaches ranking of data (see above but at least paragraph [0168]) in which reference indications are used in combination with patient data, although Agarwal does not explicitly recite use of clustered data, use of clustered data is explicitly taught by Eun, one of ordinary skill in the art would find it prima facie obvious to use the clustered data within the ranking as taught by Agarwal with the motivation of “allows the dimensionality reduction and subsequent clustering operation of the entire dataset, without having to propagate the cluster labels and enlarge clusters” (Eun: paragraph [0063]). Nevertheless, in view of the amendment to the claim, and arguendo, newly applied Afeyan explicitly shows clustering and ranking of data in the same application and would be prima facie obvious to combine within the teachings of Agarwal and Eun with the motivation of “improvement in drug performance based upon the efficacy and side effects of the drugs in patients” (Afeyan: paragraph [0081]).
With respect to claim 7, Eun explicitly teaches that the frequencies are used in combination with the inverse frequencies to “penalize a medical event that appears in most or all of the clusters” (Eun: paragraphs [0065]-[0067]) and teaches what is required under the broadest reasonable interpretation of a co-occurrence score, in combination with the ranking as taught by both Agarwal and newly applied Afeyan.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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