Detailed Notice
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
Claims 1-19 are pending.
Claims 1-19 are rejected.
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 1-19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,475,984. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-19 are similar to claims 1-17 of U.S. Patent No. 12,475,984 as shown below:
Application No. 19/364,699
U.S. Patent No. 12,475,984
A method of providing interactive clinical decision support for drug dosage reduction, comprising:
generating an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival;
receiving periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determining, using one or more machine learning algorithms, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient;
recommending at least one medication or therapy for the indicated side effect; and
determining a prescription adjustment based on the revised glide path.
1. A method of treating withdrawal symptoms associated with reduced dosage of a drug, comprising:
providing a patient with access to a prescription support application configured to:
generate, an initial non-linear glide path of recommended dosages starting at an initial dosage of the drug associated with the withdrawal symptoms of the patient and ending at a goal dosage at an estimated time of arrival based on a guideline for the drug;
receive, from a patient interface, periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determine, using one or more machine learning algorithms, a revised glide path starting at a current dosage of the drug associated with the withdrawal symptoms and ending at the goal dosage at a new estimated time of arrival based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient, wherein determining the revised glide path comprises:
estimating a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determining that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
recommending at least one medication or therapy for the indicated side effect;
determining a prescription adjustment of the drug for the patient based on the revised glide path; and
administering doses of the drug to the patient according to the adjusted prescription based on the revised glide path until reaching the goal dosage,
wherein the withdrawal symptoms are associated with a diagnosed Opioid Use Disorder, wherein the Opioid Use Disorder is specified for an opioid pain medication previously prescribed to the patient, wherein the drug is the opioid pain medication previously prescribed to the patient, and wherein the drug comprises a synthetic opioid other than methadone.
The method of claim 1, wherein generating the initial non-linear glide path of recommended dosages comprises:
determining a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug;
estimating a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes; and
adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability.
1… determine, using one or more machine learning algorithms, a revised glide path starting at a current dosage of the drug associated with the withdrawal symptoms and ending at the goal dosage at a new estimated time of arrival based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient, wherein determining the revised glide path comprises:
estimating a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determining that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
recommending at least one medication or therapy for the indicated side effect;
determining a prescription adjustment of the drug for the patient based on the revised glide path; and
administering doses of the drug to the patient according to the adjusted prescription based on the revised glide path until reaching the goal dosage,
wherein the withdrawal symptoms are associated with a diagnosed Opioid Use Disorder, wherein the Opioid Use Disorder is specified for an opioid pain medication previously prescribed to the patient, wherein the drug is the opioid pain medication previously prescribed to the patient, and wherein the drug comprises a synthetic opioid other than methadone.
2. The method of claim 1, wherein generating the initial non-linear glide path of recommended dosages comprises:
adjusting the initial non-linear glide path using a second machine-learning model trained to select adjustments that improve the success probability.
3. The method of claim 1, wherein determining the revised glide path, using one or more machine learning algorithms, comprises determining that a predicted success probability of the initial glide path based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient is less than a threshold.
1… estimating a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determining that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
4. The method of claim 3, wherein determining the revised glide path, using one or more machine learning algorithms, comprises adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the predicted success probability.
3. The method of claim 1, wherein determining the revised glide path, using one or more machine learning algorithms, comprises adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the estimated success probability.
5. The method of claim 1, wherein determining, using one or more machine learning algorithms, the revised glide path comprises adjusting the estimated time of arrival.
4. The method of claim 1, wherein determining, using one or more machine learning algorithms, the revised glide path comprises adjusting the estimated time of arrival.
6. The method of claim 1, wherein determining the prescription adjustment based on the revised glide path comprises: determining a current supply of prescribed medication that can satisfy doses according to the revised glide path; and recommending prescribing additional doses for unsatisfied doses of the revised glide path.
5. The method of claim 1, wherein determining the prescription adjustment based on the revised glide path comprises:
determining a current supply of prescribed medication that can satisfy doses to the revised glide path; and
recommending prescribing additional doses for unsatisfied doses of the revised glide path.
7. The method of claim 1, wherein recommending the at least one medication or therapy for the indicated side effect comprises using an artificial neural network trained to select from a set of treatments based on the indicated side effect, the drug, and the patient record.
6. The method of claim 1, wherein recommending the at least one medication or therapy for the indicated side effect comprises using an artificial neural network trained to select from a set of treatments based on the indicated side effect, the drug, and the patient record.
8. The method of claim 1, wherein the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
7. The method of claim 1, wherein the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
9. The method of claim 1, wherein the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor.
8. The method of claim 1, wherein the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor.
10. A system for providing interactive clinical decision support for drug dosage reduction, comprising:
a memory storing computer-executable instructions; and
a processor configured to execute the computer-executable instructions to:
generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival;
receive periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determine, using one or more machine learning algorithms, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient;
recommend at least one medication or therapy for the indicated side effect; and
determine a prescription adjustment based on the revised glide path.
9. A system for treating withdrawal symptoms associated with a reduced dosage of a drug, comprising:
a memory storing computer-executable instructions; and
a processor configured to execute the computer-executable instructions to:
generate an initial non-linear glide path of recommended dosages starting at an initial dosage of the drug associated with the withdrawal symptoms of a patient and ending at a goal dosage at an estimated time of arrival based on a guideline for the drug;
receive, from a patient interface, periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determine, using one or more machine learning algorithms, a revised glide path starting at a current dosage of the drug associated with the withdrawal symptoms and ending at the goal dosage at a new estimated time of arrival based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient, wherein to determine the revised glide path, the processor is configured to:
estimate a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determine that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
recommend at least one medication or therapy for the indicated side effect; and
determine a prescription adjustment of the drug for the patient based on the revised glide path,
wherein the drug is a drug being used by the patient based on a previous prescription, wherein the prescription adjustment for the patient is a number of doses of the drug for administration to the patient until reaching the goal dosage, wherein the withdrawal symptoms are associated with a diagnosed Opioid Use Disorder, wherein the Opioid Use Disorder is specified for an opioid pain medication previously prescribed to the patient, wherein the drug is the opioid pain medication previously prescribed to the patient, and wherein the drug comprises a synthetic opioid other than methadone.
11. The system of claim 10, wherein the processor is configured to execute the instructions to: determine a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug;
estimate a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes;
and adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability.
9… determine, using one or more machine learning algorithms, a revised glide path starting at a current dosage of the drug associated with the withdrawal symptoms and ending at the goal dosage at a new estimated time of arrival based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient, wherein to determine the revised glide path, the processor is configured to:
estimate a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determine that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
recommend at least one medication or therapy for the indicated side effect; and
determine a prescription adjustment of the drug for the patient based on the revised glide path,
wherein the drug is a drug being used by the patient based on a previous prescription, wherein the prescription adjustment for the patient is a number of doses of the drug for administration to the patient until reaching the goal dosage, wherein the withdrawal symptoms are associated with a diagnosed Opioid Use Disorder, wherein the Opioid Use Disorder is specified for an opioid pain medication previously prescribed to the patient, wherein the drug is the opioid pain medication previously prescribed to the patient, and wherein the drug comprises a synthetic opioid other than methadone.
10. The system of claim 9, wherein the processor is configured to execute the instructions to:
adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability.
12. The system of claim 10, wherein the processor is configured to execute the instructions to determine that a predicted success probability of the initial glide path based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient is less than a threshold.
9… estimate a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determine that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
13. The system of claim 12, wherein the processor is configured to execute the instructions to adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the predicted success probability.
11. The system of claim 9, wherein the processor is configured to execute the instructions to adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the estimated success probability.
14. The system of claim 10, wherein the processor is configured to execute the instructions to adjust the estimated time of arrival.
12. The system of claim 9, wherein the processor is configured to execute the instructions to adjust the estimated time of arrival.
15. The system of claim 10, wherein the processor is configured to execute the instructions to: determine a current supply of prescribed medication that can satisfy doses according to the revised glide path; and recommend prescribing additional doses for unsatisfied doses of the revised glide path.
13. The system of claim 9, wherein the processor is configured to execute the instructions to:
determine a current supply of prescribed medication that can satisfy doses according to the revised glide path; and
recommend prescribing additional doses for unsatisfied doses of the revised glide path.
16. The system of claim 10, wherein the processor is configured to execute the instructions to use an artificial neural network trained to select from a set of treatments based on the indicated side effect, the drug, and the patient record.
14. The system of claim 9, wherein the processor is configured to execute the instructions to use an artificial neural network trained to select from a set of treatments based on the indicated side effect, the drug, and the patient record.
17. The system of claim 10, wherein the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
15. The system of claim 9, wherein the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
18. The system of claim 10, wherein the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor.
16. The system of claim 9, wherein the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor.
19. A non-transitory computer readable medium storing computer-executable instructions that when executed by a processor cause the processor to:
generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival;
receive periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determine, using one or more machine learning algorithms, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient;
recommend at least one medication or therapy for the indicated side effect; and
determine a prescription adjustment based on the revised glide path.
17. A non-transitory computer readable medium storing computer-executable instructions that when executed by a processor cause the processor to:
generate an initial non-linear glide path of recommended dosages for treating withdrawal symptoms of a patient starting at an initial dosage of a drug associated with the withdrawal symptoms of the patient and ending at a goal dosage at an estimated time of arrival based on a guideline for the drug;
receive, from a patient interface, periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect;
determine, using one or more machine learning algorithms, a revised glide path starting at a current dosage of the drug prescribed to the patient and ending at the goal dosage at a new estimated time of arrival based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient, wherein the instructions to determine the revised glide path comprise instructions to:
estimate a success probability of the initial non-linear glide path for the patient by applying a machine-learning model trained on labeled past outcomes of glide paths for other patients with withdrawal symptoms for the drug to the data record for the patient, the at least one drug withdrawal scale score, and the at least one anxiety scale score for the patient; and
determine that the estimated success probability of the initial non-linear glide path is less than a threshold probability;
recommend at least one medication or therapy of the indicated side effect;
determine a prescription adjustment of the drug for the patient based on the revised glide path; and
issue an updated prescription for administration of doses of the drug to the patient based on the revised glide path until reaching the goal dosage,
wherein the withdrawal symptoms are associated with a diagnosed Opioid Use Disorder, wherein the Opioid Use Disorder is specified for an opioid pain medication previously prescribed to the patient, wherein the drug is the opioid pain medication previously prescribed to the patient, and wherein the drug comprises a synthetic opioid other than methadone.
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 1-19 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.
Step 1:
In the instant case, claims 1-9 are directed toward a method (i.e., process), claims 10-18 are directed toward a system (i.e., machine), and claim 19 is directed toward a non-transitory computer-readable medium (i.e., manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A—Prong 1:
Independent claims 1, 10, and 19 recites steps that, under their broadest reasonable interpretations, cover performance of the limitations of a mental process but for the recitation of generic computer components.
Claim 1 recites: “A method of providing interactive clinical decision support for drug dosage reduction, comprising: generating an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival; receiving periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect; determining, using one or more machine learning algorithms, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient; recommending at least one medication or therapy for the indicated side effect; and determining a prescription adjustment based on the revised glide path”.
The limitations of generating an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival; receiving periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect; determining, a revised glide path based on a data record for the patient, the at least the drug withdrawal scale score and the at least one anxiety scale score for the patient; recommending at least one medication or therapy for the indicated side effect; and determining a prescription adjustment based on the revised glide path, given the broadest reasonable interpretation, cover the abstract idea of a mental process because a doctor/pharmacist/etc. would perform mentally (in the mind) or via pen and paper, but instead automates the process via a computer model, e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements”, and will be discussed in further detail below.
Further, the abstract idea of claims 10 and 19 are identical as the abstract idea of claim 1. This limitation, given the broadest reasonable interpretation, also falls under the abstract idea of a mental process because it recites steps a doctor/pharmacist/etc. would perform mentally (in the mind) or via pen and paper.
Step 2A—Prong 2:
Independent claims 1, 10, and 19 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
Amount to mere instructions to apply an exception—for example, the recitation of “machine learning algorithms”, “memory”, “processor”, and “non-transitory computer readable medium”, which amount to merely invoking a computer as a tool to perform the abstract idea, e.g. see FIG. 1, FIG. 7, [0009], [0032], [0040], and [0069], of the present specification, and see further MPEP 2106.05(f);
Generally linking the abstract idea to a particular technological environment or field of use, for example, “using one or more machine learning algorithms”, “ a memory storing computer-executable instructions; and a processor configured to execute the computer-executable instructions to”, and “non-transitory computer readable medium storing computer-executable instructions that when executed by a processor cause the processor to”, which amounts to limiting the abstract idea to the field of technology/the environment of computers, see MPEP 2106.05(h); and/or
Merely acquiring information for further analysis by the system and the particular manner of acquisition is not described or shown to be important, for example, “receiving periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect”, which amounts to insignificant extra-solution activity in the form of mere data gathering because it merely functions tangentially to the main idea of the invention and serves only to bring in the data necessary for the inventions main analysis, see MPEP 2106.05(g).
Step 2B:
The independent claims do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the elements other than the abstract idea), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, which even when reevaluated under the considerations of Step 2B of the analysis, do not amount to “significantly more” than the abstract idea.
Additionally, the additional elements (i.e., “receiving periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect”), add extra solution activity, which comprises limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in a particular field as demonstrated by:
Relevant court decisions (See MPEP 2106.05(d)(II)):
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) (“Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink.” (emphasis added)).
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Dependent Claims Analysis:
Dependent claims 2-9 and 11-18 include other limitations, as well as specific step of data to be processed, received, and applied, but these only serve to further limit the abstract idea and do not add any additional elements, and hence are nonetheless directed towards fundamentally the same abstract idea (mental process) as independent claims 1, 10, and 19.
Additionally, dependent claims 2-9 and 11-18 include other limitations, but as stated above, the limitations recited by these claims do not include any additional elements beyond those already recited in independent claims 1, 10, and 19, and hence also do not integrate the aforementioned abstract idea into a practical application because the additional elements amount to merely invoking a computer as a tool to perform the abstract idea, generally link (“apply it”) the abstract idea to a particular technological environment or field of use, and/or amounts to insignificant extra-solution activity in the form of mere data gathering.
Furthermore, dependent claims 2-9 and 11-18 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 10, and 19, and hence do not amount to “significantly more” than the abstract idea.
Therefore, whether taken individually or as an ordered combination, claims 1-19 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
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) 1-6, 8-15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Jenkins (US 20160357924 A1), in view of Jenkins, (US 20160356763 A1), hereinafter Jenkins 2 and Pessala et al. (US 20140352693 A1), hereinafter Pessala.
Regarding claim 1, Jenkins teaches a method of providing interactive clinical decision support for drug dosage reduction (Jenkins, Abstract), comprising: receiving periodic patient monitoring (Jenkins, [0193]: “The graphical display of the performance anxiety ignition threshold curve for a patient can give insight into the susceptibility to the performance anxiety, and could be used to monitor the effectiveness of medication and cognitive therapies aimed at treating this condition”) including at least one drug withdrawal scale score (Jenkins, FIG. 6D, FIG. 17, [0030]: “showing display of a first projected curve giving the effect site concentration that is estimated to result in medication withdrawal as a function of time”, [0042]: “FIG. 17 is an exemplary scatter plot showing anxiety scores plotted against cognitive-objective stress assessment scores for a particular patient and showing two outlying values consistent with early withdrawal;”), at least one anxiety scale score (Jenkins, FIG. 5, [0026]: “a scale for scoring the current level of anxiety”, [0094]: “Input information received by the receiving module 702 may include dose and time of medication administration, information about other medication dosages and substance use, cognitive-objective stress scores, anxiety or depression scores input using a numeric or visual analog scale, galvanic skin conductance or other real-time physiologic clinical indicators obtained from sensors”), and at least one indicated side effect (Jenkins, [0115]: “In one aspect, information from the receiving module 702 is inputted into the pharmacokinetic module 703 which estimates effect-site concentrations of the medication. Information from the pharmacokinetic module 703 is provided to the pharmacodynamics module 704 which models the effect of the medication at the effect site”, [0135]: “In step 2070, the estimated time to medication withdrawal is determined using a pharmacokinetic model, patient dosage history data store 2010, and the estimated minimum effect site concentration required to prevent withdrawal symptoms (determined at step 2060). For example, the time may be estimated as the time to reach minimum effector site concentration required to prevent withdrawal symptoms if dosage is reduced by a predetermined fraction (RD) and dosage history is continued in the future. The dosage reduction factor RD, for example, may be expressed as a fraction of the current daily dose, or an absolute value in reduction. In step 2080, the database of patient information for the initial population model of mediation tolerance/dependence is updated to include the new information from the current patient”); determining, using one or more machine learning algorithms, a revised glide path based on a data record for the patient (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence” and [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”), the at least the drug withdrawal scale score (Jenkins, FIG. 6D, FIG. 17, [0030]: “showing display of a first projected curve giving the effect site concentration that is estimated to result in medication withdrawal as a function of time”, [0042]: “FIG. 17 is an exemplary scatter plot showing anxiety scores plotted against cognitive-objective stress assessment scores for a particular patient and showing two outlying values consistent with early withdrawal;”) and the at least one anxiety scale score for the patient (Jenkins, FIG. 5, [0026]: “a scale for scoring the current level of anxiety”, [0094]: “Input information received by the receiving module 702 may include dose and time of medication administration, information about other medication dosages and substance use, cognitive-objective stress scores, anxiety or depression scores input using a numeric or visual analog scale, galvanic skin conductance or other real-time physiologic clinical indicators obtained from sensors”, and [0192]); recommending at least one medication or therapy for the indicated side effect (Jenkins, [0020]: “FIGS. 1A and 1B are exemplary schematic diagrams illustrating systems for recommending dose and time of medication administrating, according to certain exemplary aspects”, [0034]: “FIG. 9 is an exemplary diagram showing display of a scale for objective, cognitive scoring of the stressfulness of a current situation; a scale for scoring the current level of anxiety; display of buttons for entering medication dose and time; display of a conservative estimate of the time until medication dependence for a case in which dependence is determined to have already occurred; a display of a time until safe de-escalation following a de-escalation regimen; and a display of the minimum time until the next dose should be taken following the de-escalation recommendation”, [0039]: “FIG. 14 is an exemplary graph showing display of a suggested conservative de-escalation dosing regimen determined to return a patient to a state of medication independence, and daily dose from the preceding 16 days, showing initial incompliance with the recommended regimen and a subsequent overcorrection leading to potential withdrawal symptoms but reaching the initially targeted de-escalation goal”, and [0126]: “However, by following a de-escalation regimen which is faster than the recommended de-escalation regimen, the patient's daily dosing (as determined at step 602) may at time fall below the initial target curve initially displayed at step 626 indicating to the patient an increased risk of withdrawal symptoms during these periods when the actual daily dose falls below the recommended daily dose for the de-escalation regimen”); and determining a prescription adjustment based on the revised glide path (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence”, [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”, and [0245]: “In some embodiments, the individual ingested doses are recorded by an estimation system which employs a pharmacokinetic model that is used to estimates the steady state effect site concentration of the medication. In such embodiments, the model is used to estimate the effect of one or more missed doses and the timing of the missed doses on the effect site concentration of the medication. The pharmacokinetic model can be used to determine an adjusted medication schedule that compensates for missed or delayed doses. For example, after a patient misses several doses of a medication, the estimated effect site concentration may be so low that a full or partial loading dose of a medication can be recommended to reestablish therapeutic levels of the medication”).
Jenkins does not teach generating an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival.
However, Jenkins 2 teaches generating an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Jenkins and Jenkins 2 do not teach ending at a goal dosage at an estimated time of arrival.
However, Pessala teaches ending at a goal dosage at an estimated time of arrival (Pessala, [0016]-[0017]: “The graph may include a trend part for past values of the control parameter which target is to be obtained. The trend allows the operator to better understand the estimated continued path towards reaching the target, i.e. to understand the context of the development of the control parameter towards the selected anesthetic target value”, [0031]: “the processing unit is configured to calculate a time estimate until at least one of the target values is reached, wherein the processing unit is preferably configured to update the time estimate continuously until the at least one target value is reached”, and [0036]-[0037]: “the processing unit is configured to calculate an estimated duration or end time from a current time when at least one of the targets is reached and to preferably calculate an estimated path to reach the at least one target”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins and Jenkins 2 to incorporate the teachings of Pessala and account for estimated times until a set medication target is obtained (Pessala, Abstract and [0008]-[0010]).
Regarding claim 2, Jenkins does not teach generating the initial non-linear glide path of recommended dosages comprises: determining a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug; estimating a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes; and adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability.
However, Jenkins 2 teaches generating the initial non-linear glide path of recommended dosages comprises: determining a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence” and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”); estimating a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence” and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”); and adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability (Jenkins 2, FIG. 36, [0066]: “FIG. 36 is an exemplary flow diagram showing a method of improving population and patient-customized models of medication tolerance/dependence using statistical and machine learning methods”, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”, and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 3, Jenkins further teaches determining the revised glide path (Jenkins, [0183]), using one or more machine learning algorithms (Jenkins, [0212]-[0213]), comprises determining that a predicted success probability of the initial glide path based on a data record for the patient, the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient is less than a threshold (Jenkins, [0186]: “By dynamically recalculating the de-escalation curve, the patient is given ongoing feedback about compliance and can make decisions toward more successful de-escalation outcomes. In embodiments, the medication prescriber can also enter parameters that influence and shape the suggested de-escalation regimen, for example either encouraging strict compliance to the initial de-escalation curve or encouraging patient-selected, but prescriber-limited periods of more aggressive de-escalation toward meeting an initial de-escalation target or a more aggressive target that is developed during the de-escalation regimen” and [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”).
Regarding claim 4, Jenkins further teaches determining the revised glide path, using one or more machine learning algorithms, comprises adjusting the initial glide path using a second machine-learning model trained to select adjustments that improve the predicted success probability (Jenkins, [0076]: “In certain aspects, using a matching method, computing device 508 may include units, modules, means, and circuitry for various steps described herein for recommending a drug dose and timing and for controlling a single-dose administration device”, [0097]: “FIG. 1C illustrates a method for recommending and controlling dose and timing of medication administration according to certain aspects. The term “medication,” as used herein, may refer to any drug that can be administered to a patient. In an embodiment, the route is oral administration, for example ingestion of a pill or capsule. In embodiments, the drug may be an anxiolytic or anti-anxiety medication, for example alprazolam; an analgesic such as oxycodone, an antidepressant, or an antibiotic”, and [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”).
Regarding claim 5, Jenkins further teaches determining, using one or more machine learning algorithms, a glide path (Jenkins, [0076]: “In certain aspects, using a matching method, computing device 508 may include units, modules, means, and circuitry for various steps described herein for recommending a drug dose and timing and for controlling a single-dose administration device”, [0097]: “FIG. 1C illustrates a method for recommending and controlling dose and timing of medication administration according to certain aspects. The term “medication,” as used herein, may refer to any drug that can be administered to a patient. In an embodiment, the route is oral administration, for example ingestion of a pill or capsule. In embodiments, the drug may be an anxiolytic or anti-anxiety medication, for example alprazolam; an analgesic such as oxycodone, an antidepressant, or an antibiotic”, [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”, and [0244]: “In some embodiments, the individual ingested doses are recorded by an estimation system which employs a pharmacokinetic model that is used to estimates the steady state effect site concentration of the medication. In such embodiments, the model is used to estimate the effect of one or more missed doses and the timing of the missed doses on the effect site concentration of the medication”).
Jenkins and Jenkins 2 teach a glide path but do not teach adjusting the estimated time of arrival.
However, Pessala teaches adjusting the estimated time of arrival (Pessala, [0016]-[0017]: “The graph may include a trend part for past values of the control parameter which target is to be obtained. The trend allows the operator to better understand the estimated continued path towards reaching the target, i.e. to understand the context of the development of the control parameter towards the selected anesthetic target value”, [0031]: “the processing unit is configured to calculate a time estimate until at least one of the target values is reached, wherein the processing unit is preferably configured to update the time estimate continuously until the at least one target value is reached”, and [0036]-[0037]: “the processing unit is configured to calculate an estimated duration or end time from a current time when at least one of the targets is reached and to preferably calculate an estimated path to reach the at least one target”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins and Jenkins 2 to incorporate the teachings of Pessala and account for estimated times until a set medication target is obtained (Pessala, Abstract and [0008]-[0010]).
Regarding claim 6, Jenkins further teaches determining the prescription adjustment based on the revised glide path comprises: determining a current supply of prescribed medication that can satisfy doses according to the revised glide path (Jenkins, [0016]: “sending a signal to a single-dose medication administration dispenser being in a locked state when a request for a dose of medication is received and when the conservative estimate of the time is greater than a predetermined amount, the signal unlocking a single dose of the medication from the single-dose medication dispenser”, [0204]: “recalling how many tablets have been scored, the patient often has no good estimate of the amount of medication taken and the amount of medication remaining”, and [0242]: “A threshold function for tracer detection can be employed which increases the sensitivity and specificity of the detection, but may provide little information about the actual amount of the tracer/medication taken”);
Jenkins does not teach recommending prescribing additional doses for unsatisfied doses of the revised glide path.
However, Jenkins 2 teaches recommending prescribing additional doses for unsatisfied doses of the revised glide path (Jenkins 2, FIG. 12-13, [0121]: “The display of these two curves, together with the actual daily doses provides a clear graphical portrayal of the patients past adherence to the curve and the effect of the patient's compliance with the regimen on the future shape of the de-escalation curve and the effect of compliance on the targeted estimated time until the patient is no longer dependent on the medication”, [0123]: “At step 622, the processing circuitry does not delay unlocking the single-dose medication container when it is time for a next dose. The single-dose medication container is paired to the dispenser control module 707. If, on the other hand, it is determined that the patient is not in substantial compliance, resulting in a “no” at step 630, the control flow proceeds to step 620 in which the dispenser control module 707 may cause a delay in the unlocking of a single dose-medication dispenser that is paired to the dispenser control module 707”, [0126]: “However, by following a de-escalation regimen which is faster than the recommended de-escalation regimen, the patient's daily dosing (as determined at step 602) may at time fall below the initial target curve initially displayed at step 626 indicating to the patient an increased risk of withdrawal symptoms during these periods when the actual daily dose falls below the recommended daily dose for the de-escalation regimen”, [0184]: “FIG. 12 shows that the patient's daily doses closely followed the suggested de-escalation curve. However, on the fourth day of the actual de-escalation regimen, the patient' daily dose fell slightly below the daily dosage estimated by the model to potentially result in signs or symptoms of medication withdrawal at that time. In exemplary embodiments, the de-escalation regimen is provided as a guideline. If the patient keeps daily dosages consistently above the de-escalation guideline then the approach to successful de-escalation is delayed. If, on the other hand, the patient allows daily dosages to fall significantly below the de-escalation curve, then signs or symptoms of medication withdrawal may be expected during those periods. The solid dark circles illustrated in FIG. 12 fall below the suggested de-escalation curve and indicate to a patient that an increased risk of withdrawal signs of symptoms may occur during the associated dosing periods. Using exemplary models of medication tolerance /withdrawal, the expected severity of the withdrawal signs or symptoms can be in some proportion to the vertical displacement of the actual dose from the suggested de-escalation curve. The graphical portrayal of a personalized suggested or prescribed de-escalation regimen shown in FIG. 11 and FIG. 12 can provide a patient with a conservative guideline to follow, and ongoing feedback revealing the patient's compliance with the regime”, and [0185]: “FIG. 13 shows the initial suggested de-escalation regimen for the same patient, but in this case the patient fails to follow the suggested de-escalation regimen. The first three hatched circles portray the same pre-de-escalation regimen daily dosages for the same patient as portrayed in FIG. 11 and FIG. 12. However, in this case the patient did not follow the suggested dosing guideline but instead showed substantial incompliance with the initially suggested de-escalation curve, labeled 1305. In this case, the patient only made modest reductions in daily medication dosages during the de-escalation regimen. In response, the system 500 re-calculated a new de-escalation curve 1307 for the patient to follow. The new de-escalation curve 1307 produces a longer time period to de-escalation than the initially suggested de-escalation curve 1305. In the case of the recalculated de-escalation curve, the corresponding de-escalation target is several days farther into the future and is not shown since it is beyond the time axis on the graphical display of FIG. 13”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 8, Jenkins does not teach the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
However, Jenkins 2 teaches the initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase (Jenkins 2, FIG. 6B, FIG. 6C, FIG. 6D, [0021]-[0023], and [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 9 Jenkins further teaches the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor (Jenkins, [0235]: “Medications such as benzodiazepines and opioid narcotics have a high potential for abuse, tolerance, dependence and even addiction. The methods and systems described herein, for managing the dosing of such medications using personalized models of medication tolerance and dependence, in conjunction with an optional locked, paired, controlled single-dose medication dispenser is applicable to medications such as benzodiazepines, opioid narcotics, and other medications that patients tend to over use”).
Regarding claim 10, Jenkins teaches a system for providing interactive clinical decision support for drug dosage reduction, comprising: a memory storing computer-executable instructions; and a processor configured to execute the computer-executable instructions to (Jenkins, [0084]: “Memory 514 and database 510 may include cache memory, long term memory such as a hard drive, and/or external memory, for example, including random access memory (RAM), read only memory (ROM), dynamic RAM (DRAM), synchronous DRAM (SD-RAM), flash memory, volatile memory, non-volatile memory, cache memory, buffer, short term memory unit, long term memory unit, or other suitable memory units or storage units. Memory 506 and database 510 may store instructions (e.g., software 516) and data to execute embodiments of the aforementioned methods, steps and functionality (e.g., in long-term memory, such as a hard drive)”): receive periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect (Jenkins, [0193]: “The graphical display of the performance anxiety ignition threshold curve for a patient can give insight into the susceptibility to the performance anxiety, and could be used to monitor the effectiveness of medication and cognitive therapies aimed at treating this condition”); determine, using one or more machine learning algorithms, a revised glide path based on a data record for the patient (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence” and [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”), the at least the drug withdrawal scale score (Jenkins, FIG. 6D, FIG. 17, [0030]: “showing display of a first projected curve giving the effect site concentration that is estimated to result in medication withdrawal as a function of time”, [0042]: “FIG. 17 is an exemplary scatter plot showing anxiety scores plotted against cognitive-objective stress assessment scores for a particular patient and showing two outlying values consistent with early withdrawal;”) and the at least one anxiety scale score for the patient (Jenkins, FIG. 5, [0026]: “a scale for scoring the current level of anxiety”, [0094]: “Input information received by the receiving module 702 may include dose and time of medication administration, information about other medication dosages and substance use, cognitive-objective stress scores, anxiety or depression scores input using a numeric or visual analog scale, galvanic skin conductance or other real-time physiologic clinical indicators obtained from sensors”, and [0192]); recommend at least one medication or therapy for the indicated side effect (Jenkins, [0020]: “FIGS. 1A and 1B are exemplary schematic diagrams illustrating systems for recommending dose and time of medication administrating, according to certain exemplary aspects”, [0034]: “FIG. 9 is an exemplary diagram showing display of a scale for objective, cognitive scoring of the stressfulness of a current situation; a scale for scoring the current level of anxiety; display of buttons for entering medication dose and time; display of a conservative estimate of the time until medication dependence for a case in which dependence is determined to have already occurred; a display of a time until safe de-escalation following a de-escalation regimen; and a display of the minimum time until the next dose should be taken following the de-escalation recommendation”, [0039]: “FIG. 14 is an exemplary graph showing display of a suggested conservative de-escalation dosing regimen determined to return a patient to a state of medication independence, and daily dose from the preceding 16 days, showing initial incompliance with the recommended regimen and a subsequent overcorrection leading to potential withdrawal symptoms but reaching the initially targeted de-escalation goal”, and [0126]: “However, by following a de-escalation regimen which is faster than the recommended de-escalation regimen, the patient's daily dosing (as determined at step 602) may at time fall below the initial target curve initially displayed at step 626 indicating to the patient an increased risk of withdrawal symptoms during these periods when the actual daily dose falls below the recommended daily dose for the de-escalation regimen”); and determine a prescription adjustment based on the revised glide path (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence”, [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”, and [0245]: “In some embodiments, the individual ingested doses are recorded by an estimation system which employs a pharmacokinetic model that is used to estimates the steady state effect site concentration of the medication. In such embodiments, the model is used to estimate the effect of one or more missed doses and the timing of the missed doses on the effect site concentration of the medication. The pharmacokinetic model can be used to determine an adjusted medication schedule that compensates for missed or delayed doses. For example, after a patient misses several doses of a medication, the estimated effect site concentration may be so low that a full or partial loading dose of a medication can be recommended to reestablish therapeutic levels of the medication”).
Jenkins does not teach generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival.
However, Jenkins 2 teaches generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Jenkins and Jenkins 2 do not teach ending at a goal dosage at an estimated time of arrival.
However, Pessala teaches ending at a goal dosage at an estimated time of arrival (Pessala, [0016]-[0017]: “The graph may include a trend part for past values of the control parameter which target is to be obtained. The trend allows the operator to better understand the estimated continued path towards reaching the target, i.e. to understand the context of the development of the control parameter towards the selected anesthetic target value”, [0031]: “the processing unit is configured to calculate a time estimate until at least one of the target values is reached, wherein the processing unit is preferably configured to update the time estimate continuously until the at least one target value is reached”, and [0036]-[0037]: “the processing unit is configured to calculate an estimated duration or end time from a current time when at least one of the targets is reached and to preferably calculate an estimated path to reach the at least one target”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins and Jenkins 2 to incorporate the teachings of Pessala and account for estimated times until a set medication target is obtained (Pessala, Abstract and [0008]-[0010]).
Regarding claim 11 Jenkins does not teach the processor is configured to execute the instructions to: determine a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug; estimate a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes; and adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability.
However, Jenkins 2 teaches the processor is configured to execute the instructions to: determine a baseline glide path from the initial dosage to the goal dosage based on a guideline for the drug (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence” and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”); estimate a success probability of the baseline glide path for the patient using a machine-learning model trained on labeled past patient outcomes (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence” and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”); and adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the success probability (Jenkins 2, FIG. 36, [0066]: “FIG. 36 is an exemplary flow diagram showing a method of improving population and patient-customized models of medication tolerance/dependence using statistical and machine learning methods”, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”, and [0212]: “Exemplary embodiments of the present method can incorporate such homeostatic models of medication tolerance as a baseline or population model of medication tolerance (e.g., in step 610 in FIG. 1C) which can then be personalized to a patient, for example using the method of FIG. 3 and/or FIG. 35D, and improved with additional data using statistical and machine learning methods, as shown in FIG. 36”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 12 Jenkins further teaches the processor is configured to execute the instructions to determine that a predicted success probability of the initial glide path based on a data record for the patient (Jenkins, [0183] and [0212]-[0213]), the at least one drug withdrawal scale score and the at least one anxiety scale score for the patient is less than a threshold (Jenkins, [0186]: “By dynamically recalculating the de-escalation curve, the patient is given ongoing feedback about compliance and can make decisions toward more successful de-escalation outcomes. In embodiments, the medication prescriber can also enter parameters that influence and shape the suggested de-escalation regimen, for example either encouraging strict compliance to the initial de-escalation curve or encouraging patient-selected, but prescriber-limited periods of more aggressive de-escalation toward meeting an initial de-escalation target or a more aggressive target that is developed during the de-escalation regimen” and [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”)..
Regarding claim 13 Jenkins further teaches the processor is configured to execute the instructions to adjust the initial glide path using a second machine-learning model trained to select adjustments that improve the predicted success probability (Jenkins, [0076]: “In certain aspects, using a matching method, computing device 508 may include units, modules, means, and circuitry for various steps described herein for recommending a drug dose and timing and for controlling a single-dose administration device”, [0097]: “FIG. 1C illustrates a method for recommending and controlling dose and timing of medication administration according to certain aspects. The term “medication,” as used herein, may refer to any drug that can be administered to a patient. In an embodiment, the route is oral administration, for example ingestion of a pill or capsule. In embodiments, the drug may be an anxiolytic or anti-anxiety medication, for example alprazolam; an analgesic such as oxycodone, an antidepressant, or an antibiotic”, and [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”).
Regarding claim 14 Jenkins further teaches the processor is configured to execute the instructions (Jenkins, [0076]: “In certain aspects, using a matching method, computing device 508 may include units, modules, means, and circuitry for various steps described herein for recommending a drug dose and timing and for controlling a single-dose administration device”, [0097]: “FIG. 1C illustrates a method for recommending and controlling dose and timing of medication administration according to certain aspects. The term “medication,” as used herein, may refer to any drug that can be administered to a patient. In an embodiment, the route is oral administration, for example ingestion of a pill or capsule. In embodiments, the drug may be an anxiolytic or anti-anxiety medication, for example alprazolam; an analgesic such as oxycodone, an antidepressant, or an antibiotic”, [0125]: “The information representing a discrepancy between the anxiety score and the COS score may also be weighed based on its frequency of occurrence as well as the timing of occurrence. For example, in embodiments, a consistently recurring discrepancy between COS score and anxiety score at a time when the estimated effect site concentration (e.g., as determined at step 604 of FIG. 1C) is close to, but not below a threshold level that is conservatively expected to produce withdrawal symptom in the initial can be taken as evidence of withdrawal symptoms and the time constants of the initial population model of medication tolerance (e.g., using the receptor induction model of FIG. 35B) can be adjusted so that that the model applied to the patient conservatively predicts an increased probability of withdrawal symptoms under the same conditions”, and [0244]: “In some embodiments, the individual ingested doses are recorded by an estimation system which employs a pharmacokinetic model that is used to estimates the steady state effect site concentration of the medication. In such embodiments, the model is used to estimate the effect of one or more missed doses and the timing of the missed doses on the effect site concentration of the medication”).
Jenkins and Jenkins 2 do not teach adjust the estimated time of arrival.
However, Pessala teaches adjust the estimated time of arrival (Pessala, [0016]-[0017]: “The graph may include a trend part for past values of the control parameter which target is to be obtained. The trend allows the operator to better understand the estimated continued path towards reaching the target, i.e. to understand the context of the development of the control parameter towards the selected anesthetic target value”, [0031]: “the processing unit is configured to calculate a time estimate until at least one of the target values is reached, wherein the processing unit is preferably configured to update the time estimate continuously until the at least one target value is reached”, and [0036]-[0037]: “the processing unit is configured to calculate an estimated duration or end time from a current time when at least one of the targets is reached and to preferably calculate an estimated path to reach the at least one target”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins and Jenkins 2 to incorporate the teachings of Pessala and account for estimated times until a set medication target is obtained (Pessala, Abstract and [0008]-[0010]).
Regarding claim 15 Jenkins further teaches the processor is configured to execute the instructions to: determine a current supply of prescribed medication that can satisfy doses according to the revised glide path (Jenkins, [0016]: “sending a signal to a single-dose medication administration dispenser being in a locked state when a request for a dose of medication is received and when the conservative estimate of the time is greater than a predetermined amount, the signal unlocking a single dose of the medication from the single-dose medication dispenser”, [0204]: “recalling how many tablets have been scored, the patient often has no good estimate of the amount of medication taken and the amount of medication remaining”, and [0242]: “A threshold function for tracer detection can be employed which increases the sensitivity and specificity of the detection, but may provide little information about the actual amount of the tracer/medication taken”).
Jenkins does not teach recommending prescribing additional doses for unsatisfied doses of the revised glide path.
However, Jenkins 2 teaches recommending prescribing additional doses for unsatisfied doses of the revised glide path (Jenkins 2, FIG. 12-13, [0121]: “The display of these two curves, together with the actual daily doses provides a clear graphical portrayal of the patients past adherence to the curve and the effect of the patient's compliance with the regimen on the future shape of the de-escalation curve and the effect of compliance on the targeted estimated time until the patient is no longer dependent on the medication”, [0123]: “At step 622, the processing circuitry does not delay unlocking the single-dose medication container when it is time for a next dose. The single-dose medication container is paired to the dispenser control module 707. If, on the other hand, it is determined that the patient is not in substantial compliance, resulting in a “no” at step 630, the control flow proceeds to step 620 in which the dispenser control module 707 may cause a delay in the unlocking of a single dose-medication dispenser that is paired to the dispenser control module 707”, [0126]: “However, by following a de-escalation regimen which is faster than the recommended de-escalation regimen, the patient's daily dosing (as determined at step 602) may at time fall below the initial target curve initially displayed at step 626 indicating to the patient an increased risk of withdrawal symptoms during these periods when the actual daily dose falls below the recommended daily dose for the de-escalation regimen”, [0184]: “FIG. 12 shows that the patient's daily doses closely followed the suggested de-escalation curve. However, on the fourth day of the actual de-escalation regimen, the patient' daily dose fell slightly below the daily dosage estimated by the model to potentially result in signs or symptoms of medication withdrawal at that time. In exemplary embodiments, the de-escalation regimen is provided as a guideline. If the patient keeps daily dosages consistently above the de-escalation guideline then the approach to successful de-escalation is delayed. If, on the other hand, the patient allows daily dosages to fall significantly below the de-escalation curve, then signs or symptoms of medication withdrawal may be expected during those periods. The solid dark circles illustrated in FIG. 12 fall below the suggested de-escalation curve and indicate to a patient that an increased risk of withdrawal signs of symptoms may occur during the associated dosing periods. Using exemplary models of medication tolerance /withdrawal, the expected severity of the withdrawal signs or symptoms can be in some proportion to the vertical displacement of the actual dose from the suggested de-escalation curve. The graphical portrayal of a personalized suggested or prescribed de-escalation regimen shown in FIG. 11 and FIG. 12 can provide a patient with a conservative guideline to follow, and ongoing feedback revealing the patient's compliance with the regime”, and [0185]: “FIG. 13 shows the initial suggested de-escalation regimen for the same patient, but in this case the patient fails to follow the suggested de-escalation regimen. The first three hatched circles portray the same pre-de-escalation regimen daily dosages for the same patient as portrayed in FIG. 11 and FIG. 12. However, in this case the patient did not follow the suggested dosing guideline but instead showed substantial incompliance with the initially suggested de-escalation curve, labeled 1305. In this case, the patient only made modest reductions in daily medication dosages during the de-escalation regimen. In response, the system 500 re-calculated a new de-escalation curve 1307 for the patient to follow. The new de-escalation curve 1307 produces a longer time period to de-escalation than the initially suggested de-escalation curve 1305. In the case of the recalculated de-escalation curve, the corresponding de-escalation target is several days farther into the future and is not shown since it is beyond the time axis on the graphical display of FIG. 13”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 17 Jenkins does not teach initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase.
However, Jenkins 2 teaches initial non-linear glide path includes an initial linear phase, a gradual adjustment phase, and a soft landing phase (Jenkins 2, FIG. 6B, FIG. 6C, FIG. 6D, [0021]-[0023], and [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Regarding claim 18 Jenkins further teaches the drug is one of: an opioid, a benzodiazepine, a non-benzodiazepine sleep medication, an antidepressant, or a proton pump inhibitor (Jenkins, [0235]: “Medications such as benzodiazepines and opioid narcotics have a high potential for abuse, tolerance, dependence and even addiction. The methods and systems described herein, for managing the dosing of such medications using personalized models of medication tolerance and dependence, in conjunction with an optional locked, paired, controlled single-dose medication dispenser is applicable to medications such as benzodiazepines, opioid narcotics, and other medications that patients tend to over use”).
Regarding claim 19 Jenkins teaches a non-transitory computer readable medium storing computer-executable instructions that when executed by a processor cause the processor to: receive periodic patient monitoring including at least one drug withdrawal scale score, at least one anxiety scale score, and at least one indicated side effect (Jenkins, [0193]: “The graphical display of the performance anxiety ignition threshold curve for a patient can give insight into the susceptibility to the performance anxiety, and could be used to monitor the effectiveness of medication and cognitive therapies aimed at treating this condition”); determine, using one or more machine learning algorithms, a revised glide path based on a data record for the patient (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence” and [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”), the at least the drug withdrawal scale score (Jenkins, FIG. 6D, FIG. 17, [0030]: “showing display of a first projected curve giving the effect site concentration that is estimated to result in medication withdrawal as a function of time”, [0042]: “FIG. 17 is an exemplary scatter plot showing anxiety scores plotted against cognitive-objective stress assessment scores for a particular patient and showing two outlying values consistent with early withdrawal;”) and the at least one anxiety scale score for the patient (Jenkins, FIG. 5, [0026]: “a scale for scoring the current level of anxiety”, [0094]: “Input information received by the receiving module 702 may include dose and time of medication administration, information about other medication dosages and substance use, cognitive-objective stress scores, anxiety or depression scores input using a numeric or visual analog scale, galvanic skin conductance or other real-time physiologic clinical indicators obtained from sensors”, and [0192]); recommend at least one medication or therapy for the indicated side effect (Jenkins, [0020]: “FIGS. 1A and 1B are exemplary schematic diagrams illustrating systems for recommending dose and time of medication administrating, according to certain exemplary aspects”, [0034]: “FIG. 9 is an exemplary diagram showing display of a scale for objective, cognitive scoring of the stressfulness of a current situation; a scale for scoring the current level of anxiety; display of buttons for entering medication dose and time; display of a conservative estimate of the time until medication dependence for a case in which dependence is determined to have already occurred; a display of a time until safe de-escalation following a de-escalation regimen; and a display of the minimum time until the next dose should be taken following the de-escalation recommendation”, [0039]: “FIG. 14 is an exemplary graph showing display of a suggested conservative de-escalation dosing regimen determined to return a patient to a state of medication independence, and daily dose from the preceding 16 days, showing initial incompliance with the recommended regimen and a subsequent overcorrection leading to potential withdrawal symptoms but reaching the initially targeted de-escalation goal”, and [0126]: “However, by following a de-escalation regimen which is faster than the recommended de-escalation regimen, the patient's daily dosing (as determined at step 602) may at time fall below the initial target curve initially displayed at step 626 indicating to the patient an increased risk of withdrawal symptoms during these periods when the actual daily dose falls below the recommended daily dose for the de-escalation regimen”); and determine a prescription adjustment based on the revised glide path (Jenkins, [0127]: “Embodiments may include various gamification motivators such as motivating the patient to follow a safe “glide slope” toward a medication “soft landing” which achieves lower daily doses and a lower risk of medication tolerance and dependence”, [0187]: “The suggested de-escalation can be portrayed and used by the patient as a conservative estimate of a safe “glide path” to be used to approach a specific daily dosage at which the risk of medication tolerance/dependence is reduced”, and [0245]: “In some embodiments, the individual ingested doses are recorded by an estimation system which employs a pharmacokinetic model that is used to estimates the steady state effect site concentration of the medication. In such embodiments, the model is used to estimate the effect of one or more missed doses and the timing of the missed doses on the effect site concentration of the medication. The pharmacokinetic model can be used to determine an adjusted medication schedule that compensates for missed or delayed doses. For example, after a patient misses several doses of a medication, the estimated effect site concentration may be so low that a full or partial loading dose of a medication can be recommended to reestablish therapeutic levels of the medication”).
Jenkins does not teach generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient and ending at a goal dosage at an estimated time of arrival.
However, Jenkins 2 teaches generate an initial non-linear glide path of recommended dosages starting at an initial dosage of a drug for a patient (Jenkins 2, [0171]: “The graphical portrayal of the non-linear approach to medication dependence can educate patients as to the hazard of dose escalation. By using accurate dosage history together with the method described herein of employing a reasonable and conservative model of medication tolerance/dependence that is adapted to the individual patient, the graphical portrayal can be a useful daily warning of the approach to medication dependence. The graphical portrayal can motivate the patient to reduce daily doses to synergistically modulate both the upper medication tolerance curve and the lower daily dose curve in a way that delays or ultimately prevents the intersection of the two curves, and therefore prevents dependence”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins to incorporate the teachings of Jenkins 2 and account for determines that the patient has taken the dose of medication when the first-pass tracer substance is detected using the sensor at a time after a scheduled medication administration and develop tolerance and dependence to medications if the effect-site is exposed to the drug at sufficient concentrations over sufficient time (Jenkins 2, Abstract and [0003]-[0005]).
Jenkins and Jenkins 2 do not teach ending at a goal dosage at an estimated time of arrival.
However, Pessala teaches ending at a goal dosage at an estimated time of arrival (Pessala, [0016]-[0017]: “The graph may include a trend part for past values of the control parameter which target is to be obtained. The trend allows the operator to better understand the estimated continued path towards reaching the target, i.e. to understand the context of the development of the control parameter towards the selected anesthetic target value”, [0031]: “the processing unit is configured to calculate a time estimate until at least one of the target values is reached, wherein the processing unit is preferably configured to update the time estimate continuously until the at least one target value is reached”, and [0036]-[0037]: “the processing unit is configured to calculate an estimated duration or end time from a current time when at least one of the targets is reached and to preferably calculate an estimated path to reach the at least one target”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins and Jenkins 2 to incorporate the teachings of Pessala and account for estimated times until a set medication target is obtained (Pessala, Abstract and [0008]-[0010]).
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Jenkins, Jenkins 2, and Pessala, in view of Katabi et al. (US 20200155038 A1), hereinafter Katabi.
Regarding claim 7, Jenkins teaches recommending the at least one medication or therapy for the indicated side effect and select from a set of treatments based on the indicated side effect, the drug, and the patient record (Jenkins [0119]: “the modeling of changes in both pharmacokinetic (e.g., at step 608) and pharmacodynamics (e.g., at step 610) parameters that occur with increasing exposure to a medication can be implemented into a single tolerance estimation module (e.g., tolerance module 705) which can include discrete models of pharmacokinetic tolerance, pharmacodynamics tolerance and/or combined physiologic homeostatic models of tolerance, as well as machine learning methods applied to individual patient and population data received at step 602 by the receiving module 702 and may employ domain specific, knowledge-based and/or rule-based expert system methods and other clinical decision support modelling methods which may also incorporate neural network or other machine learning techniques as would be understood by one of ordinary skill in the art”).
Jenkins, Jenkins 2, and Pessala does not teach artificial neural network.
However, Katabi teaches artificial neural network (Katabi, [0013]: “The model may include an artificial neural network” and [0033]: “In some examples, the therapy response model 110 is implemented as a computer and/or dedicated hardware implemented artificial neural network, such a deep convolutional or a recurrent neural network”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins, Jenkins 2, and Pessala to incorporate the teachings of Katabi and account for available interventions to improve medication compliance range from behavioral and educational interventions to integrated care interventions, patient decision aids, packaging and daily reminders (Katabi, [0006]).
Regarding claim 16 Jenkins further teaches the processor is configured to execute the instructions… trained to select from a set of treatments based on the indicated side effect, the drug, and the patient record (Jenkins [0119]: “the modeling of changes in both pharmacokinetic (e.g., at step 608) and pharmacodynamics (e.g., at step 610) parameters that occur with increasing exposure to a medication can be implemented into a single tolerance estimation module (e.g., tolerance module 705) which can include discrete models of pharmacokinetic tolerance, pharmacodynamics tolerance and/or combined physiologic homeostatic models of tolerance, as well as machine learning methods applied to individual patient and population data received at step 602 by the receiving module 702 and may employ domain specific, knowledge-based and/or rule-based expert system methods and other clinical decision support modelling methods which may also incorporate neural network or other machine learning techniques as would be understood by one of ordinary skill in the art”).
Jenkins, Jenkins 2, and Pessala does not teach artificial neural network.
However, Katabi teaches artificial neural network (Katabi, [0013]: “The model may include an artificial neural network” and [0033]: “In some examples, the therapy response model 110 is implemented as a computer and/or dedicated hardware implemented artificial neural network, such a deep convolutional or a recurrent neural network”).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Jenkins, Jenkins 2, and Pessala to incorporate the teachings of Katabi and account for available interventions to improve medication compliance range from behavioral and educational interventions to integrated care interventions, patient decision aids, packaging and daily reminders (Katabi, [0006]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHAEL SOJIN STONE whose telephone number is (571)272-8798. The examiner can normally be reached Monday-Friday 7 AM - 7 PM (EST).
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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.
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/R.S.S./Examiner, Art Unit 3681
/PETER H CHOI/Supervisory Patent Examiner, Art Unit 3681
