DETAILED ACTION
This action is made in response to the communication filed on January 31, 2025. This action is made non-final.
Claims 1-20 are pending. Claims 1, 19, and 20 are independent claims.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 and 4-15 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, 9, and 12-16 of U.S. Patent No. 12,243,628 (hereinafter Patent ‘628). Although the claims at issue are not identical, they are not patentably distinct from each other as indicated in the table below:
Present Application
Patent ‘628
Claim 1:
A method performed by one or more computers, the method comprising:
receiving medical record data for a population of patients having characteristics related to a signaling pathway that is targeted by a drug;
processing the medical record data to generate a respective feature array characterizing each patient in the population of patients;
applying an iterative numerical clustering operation to the feature arrays characterizing the patients in the population of patients to generate data identifying a set of patient clusters;
selecting a proper subset of the set of patient clusters for use in identifying new indications for the drug, comprising:
determining, for each of the patient clusters, a stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation;
determining, for each of the patient clusters, a purity of the patient cluster based on a measure of variance between feature arrays of patients included in the patient cluster; and
determining, for each patient cluster in the set of patient clusters, whether to select the patient cluster based at least in part on whether:
(i) the stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation satisfies a first threshold, and (ii) the purity of the patient cluster as determined based on the measure of variance between feature arrays for patients included in the patient cluster satisfies a second threshold;
filtering the set of patient clusters to remove a plurality of patient clusters that are not selected for identifying new indications for the drug; and
processing data characterizing patient clusters that remain in the set of patient clusters after the filtering to identify one or more new indications for the drug.
Claim 1
A computer-implemented method for repurposing a drug by identifying one or more new indications for the drug, comprising:
receiving, by a computer system, data representing medical records of a plurality of patients;
selecting, based on the medical records, a set of patients from the plurality of patients by operations comprising:
determining at least one target signaling pathway associated with the drug; and
determining one or more indicators based on one or more factors corresponding to a diagnosis linked to the target signaling pathway;
determining a plurality of patient characteristics of the set of patients, each patient of the set of patients exhibiting at least one of the plurality of patient characteristics;
grouping, by the computer system, in accordance with the plurality of patient characteristics, the set of patients to generate a plurality of distinct groups, each of the distinct groups including multiple patients of the set of patients, wherein the grouping comprises:
executing a machine learning system configured to perform one or more unsupervised clustering techniques, wherein the one or more unsupervised clustering techniques comprise an iterative clustering operation, comprising:
applying the iterative clustering operation to feature vectors characterizing the patients in the set of patients to optimize a clustering function, wherein the iterative clustering operation comprises a bisecting k-means clustering technique; and
identifying the plurality of distinct groups based on a result of applying the iterative clustering operation to the feature vectors characterizing the patients in the set of patients; and
processing data defining the plurality of distinct groups to identify one or more new indications for the drug, comprising:
selecting, based on one or more group selection criteria, a set of distinct groups of the plurality of distinct groups for use in identifying new indications for the drug, comprising:
determining, for each of the plurality of distinct groups, a stability of the distinct group under perturbations of parameters of the iterative clustering operation;
determining, for each of the plurality of distinct groups, a purity of the distinct group based on a measure of variance between feature vectors of patients included in the distinct group; and
determining, for each of the plurality of distinct groups, whether to select the distinct group based at least in part on whether: (i) the stability of the distinct group under perturbations of parameters of the iterative clustering operation satisfies a first threshold, and (ii) the purity of the distinct group as determined based on the measure of variance between feature vectors of patients included in the distinct group satisfies a second threshold;
wherein fewer than all of the plurality of distinct groups are selected for use in identifying new indications for the drug;
identifying one or more relevant patient characteristics by processing data characterizing only the set of distinct groups that have been selected for use in identifying new indications for the drug; and
identifying at least one of the one or more relevant patient characteristics as a new indication for the drug; and
administering the druq to a patient as a treatment for the new indication, wherein the druq comprises an anti-interleukin-4 receptor alpha (anti-IL-4Rα) antibody, wherein the druq comprises Dupilumab.
Claim 4
The method of claim 1, wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody.
See claim 1
Claim 5
The method of claim 1, wherein the drug comprises Dupilumab
See claim 1
Claim 6
The method of claim 1, further comprising administering the drug to a patient as a treatment for the new indication
See claim 1
Claim 7
The method of claim 1, wherein the iterative numerical clustering operation comprises a bisecting k-means clustering operation
See claim 1
Claim 8
The method of claim 1, wherein applying the iterative numerical clustering operation comprises performing multiple correspondence analysis to reduce dimensionality of the feature arrays characterizing the population of patients.
Claim 2
The method of claim 1, wherein grouping the set of patients comprises performing multiple correspondence analysis to reduce dimensions of the plurality of patient characteristics.
Claim 9
The method of claim 1, wherein selecting the proper subset of the set of patient clusters for use in identifying new indications for the drug further comprises:
determining, for each of the patient clusters, a number of patients that are included in the patient cluster; and
determining, for each patient cluster in the set of patient clusters, whether to select the patient cluster based at least in part on whether the number of patients that are included in the patient cluster satisfies a third threshold
Claim 3
The method of claim 1, wherein selecting the set of distinct groups further comprises:
determining, for each distinct group of the plurality of distinct groups, a feature score for each patient characteristic exhibited by that distinct group; and
comparing the feature score of each distinct group of the plurality of distinct groups to a feature score threshold.
Claim 10
The method of claim 1, wherein the signaling pathway is an IL4/IL13 pathway.
Claim 9
The method of claim 1, wherein the target signaling pathway associated with the drug is an IL4/IL13 pathway, and wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Rα) antibody.
Claim 11
The method of claim 10, wherein each patient in the population of patients is associated with one or more clinical conditions linked to the IL4/IL13 pathway; wherein 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.
Claim 12
he method of claim 11, wherein selecting patients that each have one or more characteristics related to the IL4/IL13 pathway for inclusion in the set of patients comprises:
selecting patients that are each associated with clinical conditions linked to the IL4/IL13 pathway for inclusion in the set of patients,
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.
Claim 12
The method of claim 11, 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.
Claim 13
The method of claim 11, 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.
Claim 13
The method of claim 1, processing data characterizing patient clusters that remain in the set of patient clusters after the filtering to identify one or more new indications for the drug comprises: ranking a plurality of candidate drug indications based on: (i) the patient clusters that remain in the set of patient clusters after the filtering, 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 based on the ranking of the plurality of candidate drug indications.
Claim 14
The method of claim 1, wherein identifying one or more relevant patient characteristics by processing data characterizing only the set of distinct groups that have been selected for use in identifying new indications for the drug comprises:
ranking a plurality of candidate drug indications based on: (i) the set of distinct groups that have been selected for use in identifying new indications for the drug, 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 based on the ranking of the plurality of candidate drug indications.
Claim 14
The method of claim 13, wherein ranking a plurality of candidate drug indications based on: (i) the patient clusters that remain in the set of patient clusters after the filtering, 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 patient clusters that remain in the set of patient clusters after the filtering; 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.
Claim 15
The method of claim 14, wherein ranking the plurality of candidate drug indications based on: (i) the set of distinct groups that have been selected for use in identifying new indications for the drug, 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 set of distinct groups that have been selected for use in identifying new indications for the drug; 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.
Claim 15
The method of claim 14, wherein for each candidate drug indication, determining the co- occurrence 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 patient clusters that remain in the set of patient clusters after the filtering 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.
Claim 16
The method of claim 15, wherein for each candidate drug indication, determining the co-occurrence 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 set of distinct groups that have been selected for use in identifying new indications f or the drug comprises:
determining a number of distinct groups that are associated with both: (i) the candidate drug indication, and (ii) one or more of the reference drug indications.
As illustrated in the table above, while the terminology is slightly different, claims 1-3, 9, and 12-16 of patent ‘628 contain all the limitations of claims 1 and 4-15 of the present application.
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 1-20 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 1-18 recite a method of identifying one or more new indications for a drug, which is within the statutory category of a process. Claim 19 recites a system for identifying one or more new indications for a drug, which is within the statutory class of a machine. Claim 20 recites a non-transitory computer readable storage media performing instructions for identifying one or more new indications for a drug, which is within the statutory class of a manufacture.
Claims are eligible for patent protection under § 101 if they are in one of the four statutory categories and not directed to a judicial exception to patentability. Alice Corp. v. CLS Bank Int'l, 573 U.S. ___ (2014). Claims 1-20, each considered as a whole and as an ordered combination, are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
MPEP 2106 Step 2A – Prong 1:
The bolded limitations of:
Claims 1, 19, and 20 (claim 1 being representative)
receiving medical record data for a population of patients having characteristics related to a signaling pathway that is targeted by a drug; processing the medical record data to generate a respective feature array characterizing each patient in the population of patients; applying an iterative numerical clustering operation to the feature arrays characterizing the patients in the population of patients to generate data identifying a set of patient clusters; selecting a proper subset of the set of patient clusters for use in identifying new indications for the drug, comprising: determining, for each of the patient clusters, a stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation; determining, for each of the patient clusters, a purity of the patient cluster based on a measure of variance between feature arrays of patients included in the patient cluster; and determining, for each patient cluster in the set of patient clusters, whether to select the patient cluster based at least in part on whether: (i) the stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation satisfies a first threshold, and (ii) the purity of the patient cluster as determined based on the measure of variance between feature arrays for patients included in the patient cluster satisfies a second threshold; filtering the set of patient clusters to remove a plurality of patient clusters that are not selected for identifying new indications for the drug; and processing data characterizing patient clusters that remain in the set of patient clusters after the filtering to identify one or more new indications for the drug.
as presently drafted, under the broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). For example, but for the noted computer elements, the claim encompasses a person following rules or instructions to analyze and process data in the manner described in the abstract idea, such as analyzing relevant patient characteristics for creating patient groups for a particular drug trial. The examiner further notes that “methods of organizing human activity” includes a person’s interaction with a computer (see October 2019 Update: Subject Matter Eligibility at Pg. 5). If the claim limitation, under its broadest reasonable interpretation, covers managing persona 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.
Additionally, under the broadest reasonable interpretation, these limitations are process steps that cover mental processes including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper but for the recitation of a generic computer component, such as those a clinician may perform for analyzing patient data. If a claim, under its broadest reasonable interpretation, covers a mental process but for the recitation of generic computer components, then it falls within the "Mental Process" grouping of abstract ideas. A person would readily be able to perform this process either mentally or with the assistance of pen and paper. See MPEP § 2106.04(a)(2).
MPEP 2106 Step 2A – Prong 2:
This judicial exception is not integrated into a practical application because there are no meaningful limitations that transform the exception into a patent eligible application. The additional elements merely amount to instructions to apply the exception using generic computer components (“computer”, “storage device”, “non-transitory computer readable media”—all recited at a high level of generality). Although they have and execute instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(I) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application). Accordingly, the additional elements do not integrate the abstract idea into a practical application because they do not impose meaningful limits on practicing the abstract idea. Therefore, the claims are directed to an abstract idea.
The claims only manipulate abstract data elements as part of performing the abstract idea. They do not set forth improvements to another technological field or the functioning of the computer itself and instead use computer elements as tools in a conventional way to improve the functioning of the abstract idea identified above. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. None of the additional elements recited "offers a meaningful limitation beyond generally linking 'the use of the [method] to a particular technological environment,' that is, implementation via computers." Alice Corp., slip op. at 16 (citing Bilski v. Kappos, 561 U.S. 610, 611 (U.S. 2010)).
At the levels of abstraction described above, the claims do not readily lend themselves to a finding that they are directed to a nonabstract idea. Therefore, the analysis proceeds to step 2B. See BASCOM Global Internet v. AT&T Mobility LLC, 827 F.3d 1341, 1349 (Fed. Cir. 2016) ("The Enfish claims, understood in light of their specific limitations, were unambiguously directed to an improvement in computer capabilities. Here, in contrast, the claims and their specific limitations do not readily lend themselves to a step-one finding that they are directed to a nonabstract idea. We therefore defer our consideration of the specific claim limitations’ narrowing effect for step two.") (citations omitted).
MPEP 2106 Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the same reasons as presented in Step 2A Prong 2. Moreover, the additional elements recited are known and conventional generic computing elements (“computer”, “storage device”, “non-transitory computer readable media”—see Specification Figs. 1,5, [0027]-[0030] describing the various components as general purpose, common, standard, known to one of ordinary skill, and at a high level of generality, and in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy the statutory disclosure requirements). Therefore, these additional elements amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept that amounts to significantly more. See MPEP 2106.05(f).
The Federal Circuit has recognized that "an invocation of already-available computers that are not themselves plausibly asserted to be an advance, for use in carrying out improved mathematical calculations, amounts to a recitation of what is 'well-understood, routine, [and] conventional.'" SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 1023 (Fed. Cir. 2018) (alteration in original) (citing Mayo v. Prometheus, 566 U.S. 66, 73 (2012)). Apart from the instructions to implement the abstract idea, they only serve to perform well-understood functions (e.g., receiving, translating, and displaying data—see Specification above as well as Alice Corp.; Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307 (Fed. Cir. 2016); and Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334 (Fed. Cir. 2015) covering the well-known nature of these computer functions).
Dependent Claims
The limitations of dependent but for those addressed below merely set forth further refinements of the abstract idea without changing the analysis already presented. Claim 2 merely recites performing operations in parallel, Claims 3, 16, 17, and 18 merely recite the size/amount/type of data to be utilized, Claims 4-5 and 10-12 merely recite the type of drug or pathway utilized, Claims 7-8 merely recite a type of algorithm utilized, Claim 9 merely recites selecting a patient cluster based on a threshold, Claims 13-15 merely recite ranking the patient clusters based on a type of identified data, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) and/or mental process.
Claim 6 further refines the abstract idea described in the independent claim and merely recite administering the drug as a treatment. Although this limitation indicates that a treatment is to be administered, it does not provide any information as to how the patient is to be treated, or what the treatment is. This limitation is recited at such a high level of generality and is at best the equivalent of merely adding the words “apply it” to the judicial exception These additional elements are considered to “apply it” under both the practical application and significantly more analysis, as detailed in the analysis above.
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.
Claim(s) 1, 3, 6-9, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das et al. (USPPN: 2019/0295703; hereinafter Das) in further view of Tedaldi et al. (USPPN: 2020/0336397; hereinafter Tedaldi).
As to claim 1, Das teaches A method performed by one or more computers, the method comprising:
receiving medical record data for a population of patients having characteristics related to a signaling pathway that is targeted by a drug (e.g., see [0005], [0014], [0037]-[0038], [0063] wherein patient health record data of patients having criteria that align with the mechanisms of a drug are retrieved);
processing the medical record data to generate a respective feature array characterizing each patient in the population of patients (e.g., see [0049]-[0050] wherein the data is processed to identify features which are most important for classification or prediction of the task);
applying an iterative numerical clustering operation to the feature arrays characterizing the patients in the population of patients to generate data identifying a set of patient clusters (e.g., see Fig. 4, [0071], [0077] teaching the classifier resulting from the training is continuously and constantly retrained/updated as new data becomes available and patient data is inputted to identify patients positive or negative for the targeted pathway);
selecting a proper subset of the set of patient clusters for use in identifying new indications for the drug (e.g., see Fig. 4, [0048], [0078] wherein a patient identified as positive for the targeted pathway can be selected for a given trial, including for new indications of the drug), comprising:
filtering the set of patient clusters to remove a plurality of patient clusters that are not selected for identifying new indications for the drug (e.g., see Fig. 4, [0056] wherein patients determined to be negative for the target pathway are not included in the trial); and
processing data characterizing patient clusters that remain in the set of patient clusters after the filtering to identify one or more new indications for the drug (e.g., see [0051], [0071], [0072], [0078] wherein physiological pathways can be identified from the selected patient group, including identifying new indications).
While Das teaches selecting a patient cluster, Das fails to teach the selecting comprising determining, for each of the patient clusters, a stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation; determining, for each of the patient clusters, a purity of the patient cluster based on a measure of variance between feature arrays of patients included in the patient cluster; and determining, for each patient cluster in the set of patient clusters, whether to select the patient cluster based at least in part on whether: (i) the stability of the patient cluster under perturbations of parameters of the iterative numerical clustering operation satisfies a first threshold, and (ii) the purity of the patient cluster as determined based on the measure of variance between feature arrays for patients included in the patient cluster satisfies a second threshold.
However, in the same field of endeavor machine learning classifiers, Tedaldi teaches determining, for each of the [patient] clusters, a stability of the [patient] cluster under perturbations of parameters of the iterative numerical clustering operation (e.g., see [0077]-[0079] teaching clustering using successive iterations of the clustering wherein the clusters remain stable); determining, for each of the [patient] clusters, a purity of the [patient] cluster based on a measure of variance between feature arrays of [patients] included in the [patient] cluster (e.g., see [0070], [0083], [0084] teaching clustering using the purity/homogeneity of the cluster); and determining, for each [patient] cluster in the set of [patient] clusters, whether to select the [patient] cluster based at least in part on whether: (i) the stability of the [patient] cluster under perturbations of parameters of the iterative numerical clustering operation satisfies a first threshold, and (ii) the purity of the [patient] cluster as determined based on the measure of variance between feature arrays for [patients] included in the [patient] cluster satisfies a second threshold (e.g., see [0070], [0083]-[0085], [0099], [0108] teaching a cluster is established balancing concepts stability and purity, wherein the homogeneity of the cluster is compared to a maximum v-measure (i.e., second threshold) as well as the number or percentage of data remain within the cluster across iterations (i.e., stability) to a desired threshold (i.e., first threshold). Notably, Das is relied upon for explicitly teaching the clusters being “patient” clusters).
Accordingly, it would have been obvious to modify Das in view of Tedaldi before the effective date of the application. One would have been motivated to make the modification in order to prevent misclassification of data in the absence of ground truth labels (e.g., see [0004], [0077] of Tedaldi).
As to the claim 3, the rejection of claim 1 is incorporated. Das further teaches wherein the population of patients includes at least 94 million patients (e.g., see [0038], [0074] wherein the records are collected and trained from tens of thousands of patients and hospitals). Notably, the claim limitation of “wherein the population of patients includes at least 94 million patients” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of the size of the patient population. Therefore, Das, having taught large patient data sets, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any number of the patient population to as a simple substitution. As such, it would have been obvious at the effective date to substitute the patient population of the prior art with at least 94 million patients because the results would have been predictable for training the data with sufficient data). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
As to the claim 6, the rejection of claim 1 is incorporated. Das further teaches further comprising administering the drug to a patient as a treatment for the new indication (e.g., see Abstract, [0078] wherein the drug can be used for treatment of the new indication).
As to the claim 7, the rejection of claim 1 is incorporated. Tedaldi further teaches wherein the iterative numerical clustering operation comprises a bisecting k-means clustering operation (e.g., see [0037] teaching a plurality of clustering techniques including k-means, mean-shift, etc.).
As to the claim 8, the rejection of claim 1 is incorporated. Das-Tedaldi further teaches wherein applying the iterative numerical clustering operation comprises performing multiple correspondence analysis to reduce dimensionality of the feature arrays characterizing the population of patients (e.g., see [0050] of Das wherein the dimensionality or number of features is reduced. See also [0102] of Tedaldi wherein the data is reduced to a lower dimensional representation).
As to the claim 9, the rejection of claim 1 is incorporated. Das further teaches wherein selecting the proper subset of the set of patient clusters for use in identifying new indications for the drug further comprises: determining, for each of the patient clusters, a number of patients that are included in the patient cluster; and determining, for each patient cluster in the set of patient clusters, whether to select the patient cluster based at least in part on whether the number of patients that are included in the patient cluster satisfies a third threshold (e.g., see [0071] wherein the system can determine a needed trial size to expand or decrease the number of patients participating in the trial).
As to the claim 16, the rejection of claim 1 is incorporated. Das further teaches wherein the medical record data for the population of patients includes data characterizing diagnoses, lab tests, procedures, medication prescriptions, and biomarker measurements for patients in the population of patients (e.g., see [0035] wherein the patient data can include diagnosis and treatment information, medication data, lab test results, vital sign and physiological measurement data, etc.).
As to the claim 17, the rejection of claim 1 is incorporated. Das further teaches wherein the set of patient clusters comprises at least 500 patient clusters (e.g., see [0038], [0074] wherein the records are collected and trained from tens of thousands of patients and hospitals). Notably, the claim limitation of “wherein the set of patient clusters comprises at least 500 patient clusters” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the particular number of patient clusters exists. Therefore, Das, having taught large patient data sets, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any number of patient clusters to as a simple substitution. As such, it would have been obvious at the effective date to substitute the patient clusters of the prior art with at least 500 patient clusters because the results would have been predictable for training the data with sufficient data). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
As to the claim 18, the rejection of claim 1 is incorporated. Das further teaches wherein for a plurality of patients in the population of patients, the feature array characterizing the patient comprises at least 2700 features (e.g., see [0049] teaching a plurality of features for characterizing the patient population). Notably, the claim limitation of “characterizing the patient comprises at least 2700 features” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the particular number of features exists. Therefore, Das, having taught large patient data sets and numerous features for training the model, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any number of features to as a simple substitution. As such, it would have been obvious at the effective date to substitute the features of the prior art with at least 2700 features because the results would have been predictable for training the data with sufficient data). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
As to claim 19, the claim is directed to the system implementing the method of claim 1 and further recites one or more computers and one or more storage devices (e.g., see Fig. 2 of Tedaldi) and is similarly rejected.
As to claim 20, the claim is directed to the non-transitory computer storage media implementing the method of claim 1 (e.g., see Fig. 2 of Tedaldi) and is similarly rejected
Claim(s) 2 and 13-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das and Tedaldi, as applied above, and in further view of Vivero (USPPN: 2016/0196398; hereinafter Vivero).
As to the claim 2, the rejection of claim 1 is incorporated. While Tedaldi teaches performing operations to determine the stability and purity of the patient clusters (e.g., see rejection above), Tedaldi fails to explicitly teach wherein are performed substantially in parallel using parallel processing techniques. However, it would have at least been obvious to perform operations in parallel as a there are a finite number of ways to execute tasks: separately/sequentially or simultaneously/parallel. See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, Vivero additionally teaches operations may be performed in parallel (e.g., see [0095]). Accordingly, it would have been obvious to perform the operation in parallel in order to efficiently perform operations.
As to the claim 13, the rejection of claim 1 is incorporated. Das teaches identifying one or more of the plurality of candidate drug indications as new indications for the drug (e.g., see [0078] wherein new indications of the drugs can be determined).
Das-Tedaldi fail to teach processing data characterizing patient clusters that remain in the set of patient clusters after the filtering to identify one or more new indications for the drug comprises: ranking a plurality of candidate drug indications based on: (i) the patient clusters that remain in the set of patient clusters after the filtering, and (ii) one or more reference drug indications associated with the drug.
However, in the same field of endeavor of managing patient health, Vievero teaches ranking a plurality of candidate drug indications based on: (i) the patient clusters that remain in the set of patient clusters after the filtering, and (ii) one or more reference drug indications associated with the drug (e.g., see [0053] wherein expected events are determined, e.g., ranked, by their frequency of co-occurrence in the patient cohort, which is related to a treatment)
Accordingly, it would have been obvious to one with ordinary skill in the art to modify the combination to include ranking, as taught by Vivero, because Vivero teaches that ranking in this manner can assist in identifying and classifying patient cohorts.
As to the claim 14, the rejection of claim 13 is incorporated. Vivero further teaches wherein ranking a plurality of candidate drug indications based on: (i) the patient clusters that remain in the set of patient clusters after the filtering, 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 patient clusters that remain in the set of patient clusters after the filtering; 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 (e.g., see [0053] the frequency of co-occurrence is determined for the patient and the patient’s cohort, so the portion of the cohort (e.g., group) that includes the co-occurring event is determined in the process)
Accordingly, it would have been obvious to one with ordinary skill in the art to modify the combination to include the co-occurrence factor, as taught by Vivero, because Vivero teaches that considering co-occurrence can help control for overall frequency (e.g., see [0053] of Vivero).
As to the claim 15, the rejection of claim 14 is incorporated. Vivero further teaches wherein for each candidate drug indication, determining the co- occurrence 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 patient clusters that remain in the set of patient clusters after the filtering 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 (e.g., see [0053], [0071] wherein expected events are determined by their frequency of co-occurrence with other events such as specific medical condition and type of medical treatment/encounter).
Accordingly, it would have been obvious to one with ordinary skill in the art to modify the combination to include ranking, as taught by Vivero, because Vivero teaches that ranking in this manner can assist in identifying and classifying patient cohorts.
Claim(s) 4, 5, and 10-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Das and Tadaldi, as applied above, and in further view of Kostic et al. (USPPN: 2015/0017176; hereinafter Kostic).
As to the claim 4, the rejection of claim 1 is incorporated. While Das teaches numerous drugs, Das fails to teach wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody. Notably, the claim limitation of “the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the claimed drug exists. Therefore, Das, having taught numerous drugs the system/method can be used, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any type of drug to as a simple substitution. As such, it would have been obvious at the effective date to substitute the drugs of the prior art with any specific drug because the results would have been predictable for easily determining a patient cluster based on a specific drug). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, in the same field of endeavor of treating patients, Kostic teaches wherein the drug comprises an anti-interleukin-4 receptor alpha (anti-IL-4Ra) antibody (e.g., see Abstract, [0009] teaching treatment using a drug comprising an anti-interleukin-4 receptor alpha antibody). Accordingly, it would have been obvious to modify Das in view of Kostic before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification to treat, prevent or reduce the severity of a particular disease/ailment (e.g., see Abstract).
As to the claim 5, the rejection of claim 1 is incorporated. While Das teaches numerous drugs, Das fails to teach wherein the drug comprises Dupilumab. Notably, the claim limitation of “the drug comprises Dupilumab” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless of whether the claimed drug exists. Therefore, Das, having taught numerous drugs the system/method can be used, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any type of drug to as a simple substitution. As such, it would have been obvious at the effective date to substitute the drugs of the prior art with any specific drug because the results would have been predictable for easily determining a patient cluster based on a specific drug). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, in the same field of endeavor of treating patients, Kostic teaches wherein the drug comprises Dupilumab (e.g., see [0009] teaching treatment using a drug comprising Dupilumab). Accordingly, it would have been obvious to modify Das in view of Kostic before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification to treat, prevent or reduce the severity of a particular disease/ailment (e.g., see Abstract).
As to the claim 10, the rejection of claim 1 is incorporated. While Das teaches a target pathway, Das fails to teach wherein the signaling pathway is an IL4/IL13 pathway. Notably, the claim limitation of “wherein the signaling pathway is an IL4/IL13 pathway” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless the particular pathway. Therefore, Das, having taught numerous drugs and targeted pathways the system/method can be used, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any type of pathway to as a simple substitution. As such, it would have been obvious at the effective date to substitute the generic targeted pathway of the prior art with any specific pathway because the results would have been predictable for easily determining a patient cluster based on a specific drug). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, in the same field of endeavor of treating patients, Kostic teaches wherein the signaling pathway is an IL4/IL13 pathway (e.g., see [0009] teaching treatment using a drug targeting IL-4 and/or IL-13). Accordingly, it would have been obvious to modify Das in view of Kostic before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification to treat, prevent or reduce the severity of a particular disease/ailment (e.g., see Abstract).
As to the claim 11, the rejection of claim 10 is incorporated. While Das teaches wherein each patient in the population of patients is associated with one or more clinical conditions (e.g., see Abstract, rejection above), Das fails to teach the one or more clinical conditions linked to the IL4/IL13 pathway; wherein 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. Notably, the claim limitation of “one or more clinical conditions linked to the IL4/IL13 pathway; wherein 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” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless the particular condition. Therefore, Das, having taught numerous drugs and targeted pathways the system/method can be used, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any type of condition to as a simple substitution. As such, it would have been obvious at the effective date to substitute the condition of the prior art with any specific condition because the results would have been predictable for easily determining a patient cluster based on a specific drug). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, in the same field of endeavor of treating patients, Kostic teaches one or more clinical conditions linked to the IL4/IL13 pathway; wherein 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 (e.g., see Abstract, [0008], [0024] wherein the condition includes eosinophilic esophagitis, allergic conjunctivitis, anaphylaxis, etc.). Accordingly, it would have been obvious to modify Das in view of Kostic before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification to treat, prevent or reduce the severity of a particular disease/ailment (e.g., see Abstract).
As to the claim 12, the rejection of claim 11 is incorporated. While Das teaches wherein the characteristics related to the pathway comprise one or more of: a diagnosis, a medication, a lab test, or a procedure (e.g., see [0035] wherein the patient data include diagnosis, medication, lab test, medical history), Das fails to teach the 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. Notably, the claim limitation of “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” is interpreted as nonfunctional descriptive information as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed the same regardless the particular condition. Therefore, Das, having taught numerous drugs and targeted pathways the system/method can be used, teaches the claimed limitation. Furthermore, it would have been obvious to substitute any type of condition to as a simple substitution. As such, it would have been obvious at the effective date to substitute the condition of the prior art with any specific condition because the results would have been predictable for easily determining a patient cluster based on a specific drug). See KSR Int’l v. Teleflex Inc., 127 S. Ct. 1727, 1740-41, 82 USPQ2d 1385, 1396 (2007); and MPEP 2143.
Nonetheless, in the same field of endeavor of treating patients, Kostic teaches IL4/IL13 pathway (e.g., see [0009] teaching treatment using a drug targeting IL-4 and/or IL-13). Accordingly, it would have been obvious to modify Das in view of Kostic before the effective date of the application with a reasonable expectation of success. One would have been motivated to make the modification to treat, prevent or reduce the severity of a particular disease/ailment (e.g., see Abstract).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain.” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co. v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert. denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to STELLA HIGGS whose telephone number is (571)270-5891. The examiner can normally be reached Monday-Friday: 9-5PM.
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, Peter Choi can be reached at (469) 295-9171. 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.
/STELLA HIGGS/Primary Examiner, Art Unit 3681
Prosecution Insights