DETAILED ACTION
This action is made in response the communication filed on November 26, 2024. This action is made non-final.
Claims 1-22 are pending. Claims 1, 2, 10 14, and 16 have been amended and claims 21-22 are newly added by preliminary amendment dated May 30, 2025. Claims 1, 11-14 and 16-18 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 16-20 is rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 6-10 of U.S. Patent No. 12,159,723 (hereinafter ‘723 patent). Although the claims at issue are not identical, they are not patentably distinct from each other as outlined in the table below (similarities in bold).
Present Application
‘723 Patent
Claim 1
A non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor, perform a method comprising:
calculating an aggregated risk factor score representative of each of two or more risk factors associated with a patient’s drug regimen, wherein the two or more risk factors are selected from the group comprising:
1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, 5) competitive inhibition of the active ingredients in the drug regimen, wherein competitive inhibition includes any pharmacokinetic interaction including: interactions between active ingredient inhibitors and active ingredient substrates, interactions between active ingredient inducers and active ingredient substrates, and interactions between active ingredient substrates of the same isoenzyme with different affinities, and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient’s genetic variations; and
combining the risk factor scores calculated for each of said two or more risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event.
Claim 1
A computer-implemented method for determining and reducing a patient's risk of an adverse drug event in a patient diagnosed with pain, wherein the patient has been prescribed a drug regimen that includes at least an opioid for treating pain and a second drug, the method comprising:
calculating, with a calculating module, a quantitative personalized medication total risk score for multi-drug interaction for the patient that is representative of the patient's risk for an adverse drug event by:
(a) aggregating and weighting risk factor scores for risk factors associated with the patient's drug regimen including at least the opioid for treating pain and the second drug onto a common scale to produce aggregated risk factor scores, wherein the risk factors comprise:
(1) Factor 1: number of active ingredients in the patient's drug regimen including at least the opioid for treating pain and the second drug, wherein calculating the aggregated risk factor score representative of the number of active ingredients in the drug regimen comprises importing a data set comprising patient-specific drug regimens, converting said data set into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the risk factor score representative of the number of active ingredients in the drug regimen, wherein the aggregated risk factor score representative of the number of active ingredients in the drug regimen is less than or equal to 12,
(2) Factor 2: anticholinergic burden of the patient's drug regimen including at least the opioid for treating pain and the second drug, wherein calculating the aggregated risk factor score representative of the anticholinergic burden of the drug regimen comprises importing a data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen, wherein the aggregated risk factor score representative of the anticholinergic burden of the drug regimen is less than or equal to 6,
(3) Factor 3: sedative burden of the patient's drug regimen including at least the opioid for treating pain and the second drug, wherein calculating the aggregated risk factor score representative of the sedative burden of the drug regimen comprises importing a data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen, wherein the aggregated risk factor score representative of the sedative burden of the drug regimen is less than or equal to 5,
(4) QT-interval prolongation risk of the patient's drug regimen including at least the opioid for treating pain and the second drug, wherein calculating the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen comprises importing a data set comprising indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen, wherein the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen is less than or equal to 10, and
(5) competitive inhibition of the patient's drug regimen including at least the opioid for treating pain and the second drug, wherein calculating the aggregated risk factor score representative of the competitive inhibition of the drug regimen comprises importing a data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen; and
(b) calculating, with the calculating module, the patient's quantitative personalized medication risk score by combining the aggregated and weighted risk factor scores representative of each of the risk factors associated with the patient's drug regimen including at least the opioid for treating pain and the second drug;
(c) identifying the calculated quantitative personalized medication total risk score as falling below a threshold value and thereby falling within a low-risk group or as falling above the threshold value and thereby falling within a high-risk group;
generating a prognosis for the patient having pain as being within the high-risk group as being a high-risk patient for an adverse drug event if the patient's calculated quantitative personalized medication total risk score is identified as falling within the high-risk group; and
adjusting the high-risk patient's drug regimen including at least the opioid for treating pain and the second drug to decrease the patient's calculated quantitative personalized medication total risk score such that the high-risk patient's risk of an adverse drug event is reduced; and such that treatment of the patient's pain is improved by performing one or more steps of:
(a) reordering which of the opioid for treating pain and the second drug is taken first by the patient;
(b) changing timing of when the opioid for treating pain and/or the second drug are taken by the patient;
(c) changing time of day when the opioid for treating pain and/or the second drug are taken by the patient;
(d) replacing the opioid for treating pain and/or the second drug of the patient's drug regimen with one or more alternate drugs of the same class and/or category as the opioid for treating pain and/or the second drug;
(e) reducing a dosage of the opioid for treating pain and/or the second drug from an initial dosage to a reduced dosage;
(f) increasing a dosage of the opioid for treating pain and/or the second drug from an initial dosage to an increased dosage;
(g) adding at least a third drug to the patient's drug regimen including at least the opioid for treating pain and the second drug; and
(h) replacing the opioid for treating pain and/or the second drug of the patient's drug regimen including at least the opioid for treating pain and the second drug with one or more alternate drugs of a different class and/or category as the opioid for treating pain and/or the second drug; and
administering to the high-risk patient the adjusted drug regimen to decrease the patient's calculated quantitative personalized medication total risk score such that the high-risk patient's risk of an adverse drug event is reduced; and such that treatment of the patient's pain is improved.
Claim 16
A method of reducing a risk of an adverse drug event in a patient, wherein the patient has been prescribed a drug regimen that includes at least a first drug and a second drug, the method comprising:
calculating a quantitative personalized medication risk score that is representative of the patient's risk for an adverse drug event by combining aggregated risk factor scores representative of each of two or more risk factors associated with the patient's drug regimen, wherein the two or more risk factors are selected from the group consisting of:
1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the drug regimen, 3) sedative burden of the drug regimen, 4) QT-interval prolongation risk of the drug regimen, and 5) competitive inhibition of the drug regimen; and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations; and
adjusting the patient's drug regimen by performing one or more steps of:(a) removing the first drug and/or the second drug from the patient's drug regimen; (b) reordering which of the first drug and the second drug is taken first by the patient; (c) changing the timing of when the first drug and/or the second drug are taken by the patient; (d) changing time of day when the first drug and/or the second drug are taken by the patient; (e) replacing the first drug and/or the second drug of the patient's drug regimen with one or more alternate drugs of the same class and/or category as the first drug and/or the second drug; (f) reducing the dosage of the first drug and/or the second drug from an initial dosage to a reduced dosage; (g) increasing the dosage of the first drug and/or the second drug from an initial dosage to an increased dosage; (h) performing a surgical procedure; and (i) adding at least a third drug to the patient's drug regimen
Claim 6
A method of reducing a risk of an adverse drug event in a patient having hypertension, wherein the patient has been prescribed a drug regimen that includes at least an antihypertensive and a second drug, the method comprising:
calculating a quantitative personalized medication risk score that is representative of the patient's risk for an adverse drug event by:
aggregating and weighting risk factor scores for risk factors associated with the patient's drug regimen including at least the antihypertensive for treating hypertension and the second drug onto a common scale to produce aggregated risk factor scores, wherein the risk factors comprise:
1) number of active ingredients in the drug regimen, wherein calculating the aggregated risk factor score representative of the number of active ingredients in the drug regimen comprises importing a data set comprising patient-specific drug regimens, converting said data set into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the risk factor score representative of the number of active ingredients in the drug regimen, wherein the aggregated risk factor score representative of the number of active ingredients in the drug regimen is less than or equal to 12;
2) anticholinergic burden of the drug regimen, wherein calculating the aggregated risk factor score representative of the anticholinergic burden of the patient's drug regimen comprises importing a data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen, wherein the aggregated risk factor score representative of the anticholinergic burden of the drug regimen is less than or equal to 6;
3) sedative burden of the drug regimen, wherein calculating the aggregated risk factor score representative of the sedative burden of the drug regimen comprises importing a data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen, wherein the aggregated risk factor score representative of the sedative burden of the drug regimen is less than or equal to 5;
4) QT-interval prolongation risk of the drug regimen, wherein calculating the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen comprises importing a data set comprising indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen, wherein the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen is less than or equal to 10; and
5) competitive inhibition of the drug regimen, wherein calculating the aggregated risk factor score representative of the competitive inhibition of the drug regimen comprises importing a data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen, wherein the aggregated risk factor score representative of the competitive inhibition of the drug regimen in the drug regimen is less than or equal to 20; and
calculating, with the calculating module, the patient's quantitative personalized medication risk score by combining the aggregated and weighted risk factor scores representative of each of the risk factors associated with the patient's drug regimen including at least the antihypertensive and the second drug;
identifying the calculated quantitative personalized medication total risk score as falling below a threshold value and thereby falling within a low-risk group or as falling above the threshold value and thereby falling within a high-risk group;
generating a prognosis for the patient having pain as being within the high-risk group as being a high-risk patient for an adverse drug event if the patient's calculated quantitative personalized medication total risk score is identified as falling within the high-risk group; and
adjusting the high-risk patient's drug regimen including at least the antihypertensive for treating hypertension and the second drug to decrease the patient's calculated quantitative personalized medication total risk score such that the high-risk patient's risk of an adverse drug event is reduced; and such that treatment of the patient's hypertension is improved by performing one or more steps of:
(a) reordering which of the antihypertensive for treating hypertension and the second drug is taken first by the patient;
(b)changing timing of when the antihypertensive for treating hypertension and/or the second drug are taken by the patient;
(c)changing time of day when the antihypertensive for treating hypertension first drug and/or the second drug are taken by the patient;
(d)replacing the antihypertensive for treating hypertension and/or the second drug of the patient's drug regimen with one or more alternate drugs of the same class and/or category as the antihypertensive for treating hypertension and/or the second drug;
(e) reducing the dosage of the antihypertensive for treating hypertension and/or the second drug from an initial dosage to a reduced dosage;
(f) increasing the dosage of the antihypertensive for treating hypertension and/or the second drug from an initial dosage to an increased dosage;
(g)replacing the antihypertensive for treating hypertension and/or the second drug of the patient's drug regimen including at least the antihypertensive for treating hypertension and the second drug with one or more alternate drugs of a different class and/or category as the antihypertensive for treating hypertension and/or the second drug; and
(h) adding at least a third drug to the patient's drug regimen including at least the antihypertensive for treating hypertension and the second drug; and
administering to the high-risk patient the adjusted drug regimen to decrease the patient's calculated quantitative personalized medication total risk score such that the high-risk patient's risk of an adverse drug event is reduced; and such that treatment of the patient's hypertension is improved.
Claim 17
wherein calculating the quantitative personalized medication risk score comprises executing instructions stored on a non-transitory computer-readable medium
Claim 7
wherein calculating the quantitative personalized medication risk score comprises executing instructions stored on a non-transitory computer-readable medium
Claim 8
using a computing device to execute the instructions stored on the non-transitory computer-readable medium.
Claim 8
using a computing device to execute the instructions stored on the non-transitory computer-readable medium.
Claim 19
comparing the patient's quantitative personalized medication risk score for the drug regimen to quantitative personalized medication risk scores of a patient population for said drug regimen.
Claim 9
comparing the patient's quantitative personalized medication risk score for the drug regimen to quantitative personalized medication risk scores of a patient population for said drug regimen.
Claim 20
wherein adjusting the patient's drug regimen causes the quantitative personalized medication risk score to decrease.
Claim 10
wherein adjusting the patient's drug regimen causes the quantitative personalized medication risk score to decrease.
As can be seen from the table above, claims 1 and 16-20, are broader recitations of the claims 1 and 16-20 of the ‘723 patent. Notably, claim 1 of the present application requires only two or more risk factors, and is thus taught by claim 1 of the ‘723 patent. As such, claims 1 and 6-10 of the ‘723 patent contain every limitation of Claims 1 and 16-20 of the present application and a double patenting rejection is appropriate.
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 1-22 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 1-22 recite methods of assessing a risk of a drug regimen, which is within the statutory category of a process.
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 1-22, 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 and 11-13 (claim 1 being representative)
calculating an aggregated risk factor score representative of each of two or more risk factors associated with a patient's drug regimen, wherein the two or more risk factors are selected from the group comprising: 1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, and 5) competitive inhibition of the active ingredients in the drug regimen, wherein competitive inhibition includes any pharmacokinetic interaction including: interactions between active ingredient inhibitors and active ingredient substrates, interactions between active ingredient inducers and active ingredient substrates, and interactions between active ingredient substrates of the same isoenzyme with different affinities, and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations; and combining the aggregated risk factor scores calculated for each of said two or more risk factors to provide a quantitative personalized medication risk score that is representative of the patient's risk for an adverse drug event
Claim 14
determining a patient’s risk of an adverse drug event based at least on the patient’s drug regimen, comprising: a database containing two or more of the following data sets related to the patient's risk factors: (1) number of active ingredients in the drug regimen, (2) anticholinergic burden of the active ingredients in the drug regimen, (3) sedative burden of the active ingredients in the drug regimen, (4) QT-interval prolongation risk of the active ingredients in the drug regimen, and (5) competitive inhibition of the active ingredients in the drug regimen, and (6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations; and a calculating module, which applies algorithms to said two or more data sets and calculates a quantitative personalized medication risk score that is representative of the patient's risk for an adverse drug event.
Claims 16-18 (claim 16 being representative)
A method of reducing a risk of an adverse drug event in a patient, wherein the patient has been prescribed a drug regimen that includes at least a first drug and a second drug, the method comprising: calculating a quantitative personalized medication risk score that is representative
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of the patient's risk for an adverse drug event by combining aggregated risk factor scores representative of each of two or more risk factors associated with the patient's drug regimen, wherein the two or more risk factors are selected from the group consisting of:
1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the drug regimen, 3) sedative burden of the drug regimen, 4) QT-interval prolongation risk of the drug regimen, and 5) competitive inhibition of the drug regimen; and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations; and adjusting the patient's drug regimen by performing one or more steps of:(a) removing the first drug and/or the second drug from the patient's drug regimen; (b) reordering which of the first drug and the second drug is taken first by the patient; (c) changing the timing of when the first drug and/or the second drug are taken by the patient; (d) changing time of day when the first drug and/or the second drug are taken by the patient; (e) replacing the first drug and/or the second drug of the patient's drug regimen with one or more alternate drugs of the same class and/or category as the first drug and/or the second drug; (f) reducing the dosage of the first drug and/or the second drug from an initial dosage to a reduced dosage; (g) increasing the dosage of the first drug and/or the second drug from an initial dosage to an increased dosage; (h) performing a surgical procedure; and (i) adding at least a third drug to the patient's drug regimen.
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 assess and process data in the manner described in the abstract idea, such as a person assesses various patient drug data and patient characteristics to determine the patient’s overall risk of an adverse event in their medication regimen. 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.
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 (“a non-transitory computer-readable medium” and "a database” 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 (“a non-transitory computer-readable medium” and "a database” see Specification Fig. 17, [0161], [00166] 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, 3, 5-10, 15, 19. and 21-22 merely recites calculating the risk using various patient/medication data, claim 4 merely recites visually providing the risk score, claim 19 merely recites further adjusting the regime to reduce the risk, which covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions).
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, 3-20, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Von Schweber et al. (USPPN: 2010/0223068; hereinafter Von Schweber) in further view of Hoffman et al. (USPPN: 2014/0358576; hereinafter Hoffman).
As to claim 1, Von Schweber teaches A non-transitory computer-readable medium with instructions stored thereon, that when executed by a processor (e.g., see Abstract, Fig. 8, [0128] teaching a method/apparatus/machine-readable medium), perform a method comprising:
calculating an aggregated risk factor score representative of each of two or more risk factors associated with a patient’s drug regimen (e.g., see Abstract, Fig. 1, [0040]-[0051] teaching various regime risk evaluation methods including aggregating a risk of two or more components of the regimen), wherein the two or more risk factors are selected from the group comprising:
number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, 5) competitive inhibition of the active ingredients in the drug regimen, wherein competitive inhibition includes any pharmacokinetic interaction including: interactions between active ingredient inhibitors and active ingredient substrates, interactions between active ingredient inducers and active ingredient substrates, and interactions between active ingredient substrates of the same isoenzyme with different affinities, and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient’s genetic variations (e.g., see Fig. 1, [0035], [0096], [0102] teaching calculating aggregated risk factor of multiple risk factors associated with a patient’s drug regimen); and
While Von Schweber teaches combining risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event (e.g., see Fig. 1) and further teaches calculating multiple aggregated risk factor scores, Von Schweber fails to explicitly teach combining the aggregated risk factor scores calculated for each of said two or more risk factors.
However, in the same field of endeavor of assessing patient risks associated with medical products, Hoffman teaches combining the aggregated risk factor scores calculated for each of said two or more risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event (e.g., see [0089] teaching calculating an overall risk score of a patient of all medications the patient is using).
Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
As to claim 3, the rejection of claim 1 is incorporated. Von Schweber fails to teach combining the aggregated risk factor scores calculated for each of said two or more risk factors to further provide a data set representative of a patient population’s risk of an adverse drug event.
However, in the same field of endeavor of assessing patient risks associated with medical products, Hoffman teaches combining the aggregated risk factor scores calculated for each of said two or more risk factors to further provide a data set representative of a patient population’s risk of an adverse drug event (e.g., see [0016], [0089] teaching calculating an overall risk score of all medications a patient is using, the patient being for a particular patient, patient group, or population).
Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
As to claim 4, the rejection of claim 1 is incorporated. Von Schweber further teaches providing the quantitative personalized medication risk score as a visual representation of a relative risk of each of said risk factors with respect to each other (e.g., see Fig. 1 wherein a personalized risk score is provided as a visual representation displaying relative risks with respect to one another).
As to claim 5, the rejection of claim 1 is incorporated. Von Schweber further teaches wherein calculating the aggregated risk factor score representative of the number of active ingredients in the drug regimen comprises importing a data set comprising patient-specific drug regimens, converting said data into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the risk factor score representative of the number of active ingredients in the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. Nonetheless, e.g., see [0010], [0096], [0103] wherein the aggregated risk factor score comprises importing data of patient-specific drug regimens, identifying any level of composition of the regimen, including individual ingredients, and assigning a risk score).
As to claim 6, the rejection of claim 1 is incorporated. Von Schweber-Hoffman further teaches wherein calculating the aggregated risk factor score representative of the anticholinergic burden of the active ingredients in the drug regimen comprises importing a data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0096] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated, including those impacting the nervous system).
As to claim 7, the rejection of claim 1 is incorporated. Von Schweber-Hoffman further teaches wherein calculating the aggregated risk factor score representative of the sedative burden of the active ingredients in the drug regimen comprises importing a data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0037], [0096], [0102] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event, including drowsiness. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated, including barbiturates (i.e., sedatives)).
As to claim 8, the rejection of claim 1 is incorporated. Von Schweber-Hoffman further teaches wherein calculating the aggregated risk factor score representative of the QT-interval prolongation risk of the active ingredients in the drug regimen comprises importing a data set comprising indices of QT-interval prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0096], [0100] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event, including heart palpitations. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated).
As to claim 9, the rejection of claim 1 is incorporated. Von-Schweber-Hoffman further teaches wherein calculating the aggregated risk factor score representative of the competitive inhibition of the active ingredients in the drug regimen comprises importing a data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective active ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0037], [0096] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event, including drowsiness. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated, including inhibitors).
As to claim 10, the rejection of claim 1 is incorporated. Von-Schweber-Hoffman further teaches wherein calculating each of the aggregated risk factor scores comprises: importing a first data set comprising patient-specific drug regimens, converting said data set into respective active ingredients, quantifying the number of active ingredients each patient-specific regimen contains, and assigning the aggregated risk factor score representative of the number of active ingredients in the drug regimen; importing a second data set comprising indices of anticholinergic burden, associating the respective active ingredients with their clinically determined anticholinergic value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the anticholinergic burden of the drug regimen; importing a third data set comprising indices of sedation effects, associating the respective active ingredients with their clinically determined sedation value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the sedative burden of the drug regimen; importing a fourth data set comprising indices of QT-prolongation risk, associating the respective active ingredients with their clinically determined QT-risk value, quantifying the value for the entire respective regimen, and assigning the aggregated risk factor score representative of the QT-interval prolongation risk of the drug regimen; and importing a fifth data set comprising metabolic pathways and extent of metabolism for each active ingredient, associating the respective ingredients with competitive inhibition values based on shared pathways, quantifying the competitive inhibition value for the entire respective regimen, and assigning the aggregated risk factor score representative of the competitive inhibition of the drug regimen; and importing a sixth data set comprising information regarding the patient's genetic variations affecting drug metabolism for each active ingredient, associating the respective ingredient with their clinically determined pharmacogenomic risk value, quantifying the pharmacogenomic risk value for the entire respective regimen, and assigning the aggregated risk factor score representative of the pharmacogenomic risk of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0037], [0096], [0102] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event, including drowsiness, heart palpitations, etc. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated, including those impacting the nervous system, inhibitors, sedatives, etc.).
As to claim 11, the claim is directed to a processor (e.g., see Fig. 8 of Von Schweber) implementing the instructions of non-transitory medium of claim 1, and is similarly rejected.
As to claim 12, the claim is directed to client device (e.g., see Fig. 8 of Von Schweber) comprising the processor of claim 11, and further recites a communication infrastructure, a memory, a user interface and a communication interface (e.g., see Fig. 8), and is similarly rejected.
As to claim 13, the claim is directed to a system comprising one or more computing devices (e.g., see Fig. 8 of Von Schweber) comprising the processor of claim 11, and is similarly rejected.
As to claim 14, Von Schweber teaches A computer implemented system for determining a patient’s risk of an adverse drug event based on the patient’s drug regimen (e.g., see Abstract, Fig. 8, [0128] teaching a method/apparatus/machine-readable medium), comprising:
a database containing two or more of the following data sets related to the patient’s risk factors: number of active ingredients in the drug regimen, 2) anticholinergic burden of the active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, 5) competitive inhibition of the active ingredients in the drug regimen, wherein competitive inhibition includes any pharmacokinetic interaction including: interactions between active ingredient inhibitors and active ingredient substrates, interactions between active ingredient inducers and active ingredient substrates, and interactions between active ingredient substrates of the same isoenzyme with different affinities, and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient’s genetic variations (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber, having taught a plurality of different risk factor scores that can be calculated using data from a database, then it meets the claimed limitation. e.g., see Fig. 1, [0010], [0096], [0124], [0125] teaching a plurality of databases for relational drug data, wherein any level of composition of the regimen can be applied, including drowsiness, heart palpitations, plurality of ingredients, etc.); and
While Von Schweber teaches a calculating module, which applies algorithms and calculates a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event (e.g., see Fig. 1, [0097]-[0099]) and further teaches calculating multiple aggregated risk factor scores, Von Schweber fails to explicitly teach the algorithm to said two or more data sets.
However, in the same field of endeavor of assessing patient risks associated with medical products, Hoffman teaches two or more data sets (e.g., see [0089] teaching calculating an overall risk score of a patient of all medications the patient is using).
Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
As to claim 15, the rejection of claim 14 is incorporated. Hoffman further teaches wherein the calculating module calculates the quantitative personalized medication risk score based on aggregated risk factor scores representative of each of the two or more data sets (e.g., see [0089] teaching calculating an overall risk score of a patient of all medications the patient is using).
Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
As to claim 16, Von Schweber teaches A method of reducing a risk of an adverse drug event in a patient, wherein the patient has been prescribed a drug regimen that includes at least a first drug and a second drug (e.g.,see Abstract, Figs. 1, 3), the method comprising:
calculating a quantitative personalized medication risk score that is representative of the patient's risk for an adverse drug event by combining aggregated risk factor scores associated with the patient's drug regimen (e.g., see Fig. 1, [0035], [0096], [0102] teaching calculating aggregated risk factor of multiple risk factors associated with a patient’s drug regimen),
wherein the two or more risk factors are selected from the group consisting of: 1) number of active ingredients in the drug regimen, 2) anticholinergic burden of the drug regimen, 3) sedative burden of the drug regimen, 4) QT-interval prolongation risk of the drug regimen, and 5) competitive inhibition of the drug regimen; and 6) pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber, having taught a plurality of different risk factor scores that can be calculated using data from a database, then it meets the claimed limitation. e.g., see [0010], [0037], [0096], [0102] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event at any level of composition of the regimen, including drowsiness, heart palpitations, plurality of ingredients, etc.); and
adjusting the patient's drug regimen by performing one or more steps of: (a) removing the first drug and/or the second drug from the patient's drug regimen; (b) reordering which of the first drug and the second drug is taken first by the patient; (c) changing the timing of when the first drug and/or the second drug are taken by the patient; (d) changing time of day when the first drug and/or the second drug are taken by the patient; (e) replacing the first drug and/or the second drug of the patient's drug regimen with one or more alternate drugs of the same class and/or category as the first drug and/or the second drug; (f) reducing the dosage of the first drug and/or the second drug from an initial dosage to a reduced dosage; (g) increasing the dosage of the first drug and/or the second drug from an initial dosage to an increased dosage; (h) performing a surgical procedure; and (i) adding at least a third drug to the patient's drug regimen (e.g., see Fig. 3, [0011], [0103] wherein in response to the identified risk, modifications to the patient’s regimen can be implemented including modification of the medication and/or dose/duration/frequency, etc.).
While Von Schweber teaches combining risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event (e.g., see Fig. 1) and further teaches calculating multiple aggregated risk factor scores, Von Schweber fails to explicitly teach combining the aggregated risk factor scores calculated for each of said two or more risk factors.
However, in the same field of endeavor of assessing patient risks associated with medical products, Hoffman teaches combining the aggregated risk factor scores calculated for each of said two or more risk factors to provide a quantitative personalized medication risk score that is representative of the patient’s risk for an adverse drug event (e.g., see [0089] teaching calculating an overall risk score of a patient of all medications the patient is using).
Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
As to claim 17, the claim is directed to the non- transitory computer-readable medium (e.g., see Fig. 8) implementing the method of claim 16 and is similarly rejected.
As to claim 18, the claim is directed to a computing device (e.g., see Fig. 8) implementing the instructions of the computer-readable medium of claim 17 and is similarly rejected.
As to claim 19, the rejection of claim 16 is incorporated. Hoffman further teaches comparing the patient's quantitative personalized medication risk score for the drug regimen to quantitative personalized medication risk scores of a patient population for said drug regimen (e.g., see [0016], [0041], [0077] wherein a risk score can be calculated for a patient, patient group, or patient population and the factoring risks are compared to weightings assigned to similar data points).
As to claim 20, the rejection of claim 16 is incorporated. Von Schweber further wherein adjusting the patient's drug regimen causes the quantitative personalized medication risk score to decrease (e.g., see [0103] wherein the modification can reduce the combined risk).
As to claim 22, the rejection of claim 10 is incorporated. Von Schweber teaches wherein the step of importing the first data set comprises associating each of the respective active ingredients with its respective risk of one or more side effects by utilizing data from the FDA Adverse Event Reporting System, quantifying the associated respective risk of one or more side effects of each respective active ingredient in each patient-specific regimen, and assigning an aggregated risk score based on a combined associated respective risk of one or more side effects of each respective active ingredient (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber, having taught a plurality of different risk factor scores that can be calculated using data from a database, then it meets the claimed limitation. e.g., see [0010], [0037], [0096], [0102] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event at any level of composition of the regimen).
Nonetheless, for the purposes of compact prosecution and in the same filed of endeavor of assessing patient risk score associated with medical products, Hoffman explicitly teaches the database being from an FDA Adverse Event Reporting system (e.g., see [0010], [0011], [0031], [0062], [0075]-[0077] wherein the system uses available information for predicting risks associated with use of a drug/medication from numerous sources including the FDA Adverse Event Reporting System). Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman).
Claim(s) 2 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Von Schweber and Hoffman, as applied above, and in further view of Knowlton (USPPN: 2015/0178465; hereinafter Knowlton).
As to claim 2, the rejection of claim 1 is incorporated. Von Schweber-Hoffman further teach wherein the method comprises calculating the risk factor score representative of [six] or more risk factors associated with the patient’s drug regiment within a patient population, wherein the risk factors comprise:
number of active ingredients in the drug regimen, 3) sedative burden of the active ingredients in the drug regimen, 4) QT-interval prolongation risk of the active ingredients in the drug regimen, 6) competitive inhibition of the active ingredients in the drug regimen (e.g., see [0035], [0037], [0096], [0100], [0107]-[0110] of Von Schweber teaching calculating risk scores for a plurality of different risk factors for a regimen at any level of composition, including the risk of drowsiness (i.e., sedation), heart palpitations (i.e, QT-interval prolongation), and the active ingredients.
While Von Schweber teaches calculating a risk score for a plurality of different risk factors, Von Schweber fails to explicitly teach wherein the risk factor comprises: anticholinergic burden of the active ingredients in the drug regimen, and inhibition of the active ingredients in the drug regimen (e.g., see [0089], Table 1 of Hoffman wherein an aggregate risk score is calculated from a plurality of individual risks, including inhibitors and anticholingertic. Accordingly, it would have been obvious to modify Von Schweber in view of Hoffman before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0018], [0019] of Hoffman)).
While Von Schweber-Hoffman teach calculating a risk factor score representative of a plurality of risk factors, Von Schweber-Hoffman fail to teach the risk factor comprising pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient’s genetic variations.
However, in the same field of endeavor of medication risk mitigation, Knowlton teaches the risk factor comprising pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient’s genetic variations (e.g., see [0017]-[0033], [0051] teaching a plurality of components for determining a medication risk, including pharmacogenomic data, sedative burdens, anticholinergic burden, drug-drug interactions, etc.). Accordingly, it would have been obvious to modify Von Schweber-Hoffman in view of Knowlton before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0011], [0035] of Knowlton)).
As to claim 21, the rejection of claim 1 is incorporated. Von Schweber-Hoffman teach wherein calculating the aggregated risk factor score representative of the pharmacogenomic interactions of the active ingredients in the drug regimen based on the patient's genetic variations comprises importing a sixth data set comprising information regarding the patient's genetic variations affecting drug metabolism for each active ingredient, associating the respective ingredient with their clinically determined pharmacogenomic risk value, quantifying the pharmacogenomic risk value for the entire respective regimen, and assigning the aggregated risk factor score representative of the pharmacogenomic risk of the drug regimen (Notably, the particular type of risk factor is interpreted as non-functional descriptive language as they are not functionally required in the claimed method. See MPEP 2111.05. The function described in the claimed method would be performed regardless of the particular type of risk factor score calculated. Therefore, Von Schweber and Hoffman, having taught a plurality of different risk factor scores that can be calculated, then it meets the claimed limitation. e.g., see [0010], [0037], [0096], [0102] wherein aggregated risk factor score comprising importing data from various databases to associated a probable risk of an adverse event, including drowsiness, heart palpitations, etc. See also [0062], [0074], Table 1, Table 2 teaching a plurality of risk scores that can be calculated, including those impacting the nervous system, inhibitors, sedatives, etc.).
Nonetheless, for the purposes of compact prosecution and in the same field of endeavor of medication risk mitigation, Knowlton teaches pharmacogenomic risk (e.g, see [0021], [0035] wherein a pharmacogenomic risk of a drug regimen is assessed).
Accordingly, it would have been obvious to modify Von Schweber-Hoffman in view of Knowlton before the effective date of the present invention with a reasonable expectation of success. One would have been motivated to make the modification in order to produce a probability safety risk score comprising the plurality of potential risks to a patient (e.g., see [0011], [0035] of Knowlton)).
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
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/STELLA HIGGS/Primary Examiner, Art Unit 3681