DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are examined.
Claims 1-20 are rejected.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 10/05/2022, 10/06/2022, and 02/06/2024 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category?
Claims 1-19 recite a series of steps and, therefore, are process. (Step 1, YES).
Claim 20 recites a computing device having processor to execute instructions, which is a machine and /or manufacturer and falls within one of the statutory categories of invention. (Step 1, YES).
Step 2A - Prong 1: Judicial Exception Recited?
Claim 1 recites using of a patient HIF-PHI model to determine the dose for the hydroxylase inhibitor (HIF) stabilizer treatment. The claim describes gathering patient data and generating personalized model parameters (plurality of virtual patient avatars) and determining one or more HIF-PHI treatment schemes for administration of HIF-PHI dosages based on the plurality of HIF-PHI models. The steps (generating, determining) recited in the claim can be performed mentally, falls in to an abstract idea category of mental step (observation, evaluation, judgment, opinion).
Claim 2 recites comparison of historical data with the data of second patient and generate a patient HIF-PHI model for the second patient and determine HIF-PHI dosage for the second patient. The above recited activities can be performed in human mind or by a human using a pen and paper (observation, evaluation, judgment, opinion), therefore mental step.
Claim 4 recites generating the plurality of HIF-PHI models by inserting the set of personalized model parameters into the mathematical model for hydroxylase inhibitor (HIF) stabilizer treatment, which can be performed in human mind or by a human using a pen and paper (observation, evaluation, judgment, opinion), therefore mental step.
Claims 6-7 recite determining and generating one or more HIF-PHI treatment schemes based on simulating the plurality of virtual trials, which involves observation, evaluation, judgment, opinion, thus mental step can be performed in the human mind or by a human using a pen and paper.
Claim 9 recites a set of personalized model parameters of a HIF-PHI bioavailability parameter, a red blood cell (RBC) lifespan parameter, and a hemoglobin set point parameter, and generate the plurality of virtual patient avatars, which involves mental steps of observation, evaluation, judgment, opinion, thus mental step.
Claim 10 recites generating the plurality of virtual patient avatars to determine the basal EPO synthesis rate parameter the HIF signal threshold parameter, and the hepcidin decay rate parameter for each of the plurality of virtual patient avatars. This is a mental step because the concept involves evaluation, judgment, opinion., which can be performed in the human mind or by a human using a pen and paper.
Claims 11-13 recite the use of mathematical equation/calculation for the plurality of HIF-PHI models, which is a mathematical step.
Claim 14 recites to determine a next HIF-PHI dosage for the patient, by comparing historical and current patient data thereby identifying a patient HIF-PHI model for the patient based on the previous HIF-PHI dosage. The described steps can be performed in the human mind or by a human using a pen and paper (evaluation, judgment, opinion), therefore mental step.
Claim 15 describes administering the next HIF-PHI dosage including display of the next HIF-PHI dosage on a display device, which does not involve mathematical equation/calculation, not this can be performed in the mind of human, thus not a mental or mathematical step.
Claim 16-17 recite determining the next HIF-PHI dosage for the patient is based on using set parameters, which fall in to the concept group of observation, evaluation, judgment, opinion, therefore mental step.
Claim 18-19 recites selecting lowest amount of HIF-PHI dosage based on the data from a set of HIF-PHI dosages. The claim describes imputing a plurality of next HIF-PHI dosages into the HIF-PHI model to determining a subset of next HIF-PHI dosages. The steps involve data selection and comparison, which fall in the group of observation, evaluation, judgment, opinion, therefore mental step.
Claim 20 recites a computing device with processor used to identify and generate HIF-PHI models for the plurality of virtual patient avatar. The claim further describes of determining one or more HIF-PHI treatment schemes for administration of HIF-PHI dosages based on the plurality of HIF-PHI models. The steps recited being performed in a computer environment, or is merely using a computer as a tool to perform the concept. In this situation, the claim is considered to recite a mental process.
(Step 2A-Prong 2, Yes).
Step 2A - Prong 2: Integrated into a Practical Application?
Claims determined to recite a judicial exception under Step 2A, Prong 1 are further analyzed to find if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2).
The claims are further examined as a whole including the judicial exceptions recited in step 1 for practical application.
Claims 1-19 recite additional elements “ obtaining patient data, administering HIF-PHI dosage” in addition to judicial exceptions. The claims viewed individually, and as a whole, in combination with recited judicial exception and additional elements integrate into a practical application. Thus, the claims are eligible at step 2A .
Claim 20 recites a device with no practical treatment, but merely displaying information associated with a patient HIF-PHI model. Therefore, claim 20 is rejected under 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4, 6-20 are rejected under 35 USC 103 as being unpatentable over Fuertinger et al. (US2019/0019570 A1, publication date: Jan17, 2019) in view of Thijssen et al. (US2019/10319478 B2, publication date: Jun11, 2019) and further in view of Bailey et al. (BMC Nephrology, 2019, vol20, page1-12).
The claims are drawn to a computer-implemented method determining a next hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) dosage for a first patient using a patient HIF-PHI model (predictive models/virtual patient avatars).
Regarding claim 1, Fuertinger et al. teaches generating a plurality of patient avatars configured to model a health condition associated with a population of patients using data obtained from real-world patients ([0008], [0068]) as in claim 1 teaching generating a plurality of virtual patient avatars based on the population patient data.
Regarding claim 1, Fuertinger et al. teaches plurality of models (for instance, disease models, treatment models, pharmaceutical interaction modules, and/or the like) for virtual avatars based on patient data [0066]. Fuertinger et al. discloses using individual patient characteristics for personalized patient avatars [0067] as in claim 1 teaching limitation of determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized patient data.
Regarding claim 1, Fuertinger et al. teaches determination treatment plan based on patient data of drug dose based on patient parameters [0071] as in claim 1 teaching limitation determining one or more HIF-PHI treatment schemes for HIF-PHI dosages based on patient parameters.
Regarding claim 1, Fuertinger et al. further teaches ESA drug dose adjustments based hemoglobin (Hgb) data and treatment protocol [0088] as in claim 1 teaching of the next HIF-PHI dosage for the first patient based on using the one or more determined HIF-PHI treatment schemes and a hematocrit and/or hemoglobin concentration for the first patient.
Regarding claim 1, Thijssen et al. teaches patient's hematocrit and / or hemoglobin concentration at a predetermined time (abstract) as in claim 1 teaching of a hematocrit and/or hemoglobin concentration for the first patient.
Regarding claim 1, Bailey et al. teaches evaluation of HIF-PHI medication dosage to treat anemia patients as in to obtain data as in claim 1 teaching prescribing HIF-PHI dosages to patients.
Claim 2 recites obtaining patient data of previous HIF-PHI dosage and hemoglobin measurement.
Regarding claim 2, Thijssen et al. teaches obtaining patient parameters (hemoglobin concentration associated with ESA dosage) required for input into a model for predicting the
patient ' s hematocrit and / or hemoglobin concentration at a predetermined time (abstract) as in claim 2 teaching obtaining individualized patient data of previous HIF-PHI dosage and a hemoglobin measurement.
Regarding claim 2, Thijssen et al. teaches determination of patient's hematocrit and / or hemoglobin concentration based on initial drug dose (abstract) as in claim 2 teaching of based on the previous HIF- PHI dosage, the hemoglobin measurement for the second patient.
Regarding claim 2, Thijssen et al. teaches a method of adjusting a patient's undesired hematocrit and / or hemoglobin concentration to a value within a desired range by using a patient model (abstract) as in claim 2 teaching adjust the hemoglobin concentration to be within the patient threshold.
Regarding claim 2, Thijssen et al. teaches inputting patient parameters into a model to predict next dose to bring the patient’s hematocrit and / or hemoglobin concentration are at undesired level to desired level, stabilizing treatment (abstract) as in claim 2 teaching a mathematical model for hydroxylase inhibitor (HIF) stabilizer treatment .
Regarding claim 2, Thijssen et al. further teaches the administering next dose of anemia drug to patient based the prediction of model to adjust the patient's hematocrit and / or hemoglobin concentration to the desired range (abstract) as in claim 2 teaching of administering the next HIF-PHI dosage to the patient to adjust the hemoglobin concentration to be within the patient threshold.
Regarding claim 3, Fuertinger et al. discloses application programming interfaces (APIs) and/or graphical user interfaces (GUIs) to read, write, and/or otherwise access information, such as via display [0043] as in claim 3 administering the next HIF-PHI dosage causing display of the next HIF-PHI dosage on a display device.
Regarding claim 4, Fuertinger et al. teaches mathematical models for adjusted treatment anemia treatment methods by using a set of parameters ([0070], [0083]) as in claim 4 teaching mathematical model for hydroxylase inhibitor (HIF) stabilizer treatment.
Claim 6 recites limitation of determining one or more prescribed HIF-PHI treatment schemes (options) for virtual patient avatar.
Regarding claim 6, Fuertinger et al. teaches a virtual clinical trial to simulate a course of treatment for the plurality of avatars, the virtual clinical trial to include at least one module associated with at least one event (abstract, [0006]) as in claim 6 teaching of simulating a plurality of virtual trials using the plurality of HIF-PHI treatment schemes.
Claim 7 recites limitation of virtual trial on simulated patients in determining one or more adjusted HIF-PHI treatment protocol based on patient population.
Regarding claim 7, Fuertinger et al. teaches treatment protocol for anemia in virtual clinical trial based on patient data [0007] as in claim 7 teaching of treatment plans for anemia based on patient data.
Regarding claim 8, Fuertinger et al. discloses application programming interfaces (APIs) and/or graphical user interfaces (GUIs) to read, write, and/or otherwise access information, such as via display [0043] as in claim 8 administering the next HIF-PHI dosage causing display of the next HIF-PHI dosage on a display device.
Claim 9 recites limitation of personalized model parameters comprising HIF-PHI bioavailability.
Regarding claim 9, Fuertinger et al. discloses using individual patient characteristics (parameter) for personalized patient avatars ([0067], [0082-0083]) and as in claim 9 limitation of determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized patient data.
Regarding claim 9, Fuertinger et al. further teaches generating patient avatars based on a red blood cell (RBC) lifespan parameter, and other parameters [0086] as in claim 9 teaching generating the plurality of virtual patient avatars and bioavailability parameter.
Claim 10 also recites limitation of personalized model parameters, and virtual patient avatars based on a set of model parameters.
With respect to claim 10, Fuertinger et al. teaches individual model parameters of erythropoiesis and created virtual patient avatars based on model parameters [0083] as in claim 10 teaching generating the plurality of virtual patient avatars determining the EPO synthesis rate parameter, the HIF signal threshold parameter, and the hepcidin decay rate parameter for each plurality of virtual patient avatars.
Fuertinger et al. does not teach hepcidin decay and HIF signal threshold parameter.
Thijssen et al. teaches parameters of erythropoiesis and regulation of hepcidin (col 6, line 33-68).
Claims 11-13 recite a mathematical models for virtual patient avatars with physiological parameters.
Regarding claim 11-13, Fuertinger et al. teaches physiology-based mathematical model virtual patient avatars ([0028],[0066],[0070],[0083],[0086]) with parameters including RBC Lifespan, erythropoietin levels, life of administered drug complex as in claims 11-13 teachings plurality of models for virtual patient avatars.
Claim 14 recites limitation of using a HIF-PHI model to adjust a patient's hematocrit and/or hemoglobin concentration based on individual patient data from a previous HIF-PHI dosage and a hemoglobin measurement.
Regarding claim 14, Thijssen et al. teaches a method of adjusting a patient's erythropoiesis stimulating agent (ESA) regimen includes hematocrit and / or hemoglobin concentration to a desired range using a model (section: summary of invention) as in claim 14 teaching method of adjusting a patient's hematocrit and/or hemoglobin concentration using a patient hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model.
Regarding claim 14, Thijssen et al. teaches obtaining patient parameters required for input into a model range at a predetermined time (section: summary of invention) as in claim 14 teaching of obtaining individualized patient data for the patient.
Regarding claim 14, Thijssen et al. further teaches a stabilizer treatment, if the patient ' s hematocrit and / or hemoglobin concentration is not in the desired range at the predetermined time, the method includes employing the model with one or more different ESA drug administration regimens until the model predicts that the patient ' s hematocrit and / or hemoglobin concentration will be in the desired range (col1, section: summary of invention) as in claim 14 teaching of stabilizer treatment model.
Regarding claim 14, Thijssen et al.’s teaching includes administering ESA to the patient with the ESA drug administration based on the previous ESA drug dose and the patient ' s hematocrit and / or hemoglobin concentration within the desired range (col1, section: summary of invention) as in claim 14 teaching of administering the next HIF-PHI dosage to the patient to adjust the hematocrit and/or the hemoglobin concentration to be within the patient threshold.
Regarding claim 15, Fuertinger et al. discloses application programming interfaces (APIs) and/or graphical user interfaces (GUIs) to read, write, and/or otherwise access information, such as via display [0043] as in claim 15 teaching administering the next HIF-PHI dosage causing display of the next HIF-PHI dosage on a display device.
Claim 16 recites limitation of a set model parameters (a red blood cell (RBC) lifespan parameter, and a hemoglobin set point) and determining the next HIF-PHI dosage for the patient is based on these model parameters.
Regarding claim 16, Thijssen et al. teaches, the patient parameters can include the starting hematocrit and / or hemoglobin concentration in the patient ' s blood , the total blood volume of the patient , the lifespan of red blood cells (RBCs ) of the patient , the mean corpuscular volume of the RBCs , and the rate of neocytolysis in the patient's blood (col1, section: summary of invention) as in claim 16 teaching of model parameters.
Claim 17 recites further limitation of model parameters (a basal erythropoietin (EPO) synthesis rate parameter, a HIF signal threshold parameter, and a hepcidin decay rate parameter) and determining the next HIF-PHI dosage based on these model parameters
Regarding claim 17, Thijssen et al. teaches, a modeling system that employs the patient parameters (lifespan of red blood cells (RBCs), erythropoietin (EPO) synthesis, hepcidin regulation, rate of neocytolysis) and determines the next ESA dosage based on model parameters (col1 and 2, section: summary of invention) as in claim 17 teaching limitation of model parameters and determining the next HIF-PHI dosage.
Claim 18 also recites limitation of inputting next HIF-PHI dosages into the HIF-PHI model and simulating expected output of hematocrit or hemoglobin concentrations of patients. Determining the next HIF-PHI dosages based on an output, being within the patient threshold (range).
With respect claim 18, Thijssen et al. teaches comparison of two different anemia algorithms over 200 days includes inputting multiple ESA dosages to receive multiple model outputs and determine the next drug dosage based on the model output (col1 and 2, section: summary of invention) as in claim 18 teaching of determine the next HIF-PHI dosage based on an output.
Claim 19 recites inputting plurality of drug dosages to model to predict hematocrit or hemoglobin concentration.
Regarding claim 19, Thijssen et al. teaches a model with one or different ESA dosages until the model predicts that the patient's hematocrit and / or hemoglobin concentration will be in the desired range [section: summary of invention, col 1 and 2) as in claim 19 teaching inputting a plurality of next HIF-PHI dosages into the HIF-PHI model to determine outputs indicating expected hematocrit or hemoglobin concentration.
With respect to claim 19, Thijssen et al. teaches a model with low and high ESA group dosages with outputs [Fig 11A] as in claim 19 teaching of subset of HIF-PHI dosages based on out puts.
Thijssen et al. teaches prescribing ESA dosages based data from previous low and high ESA dosages (section: summary of invention, col 1 and 2) as in claim 19 next HIF-PHI dosage from the subset of next HIF-PHI dosages.
Claim 20 recites a computing device to display information associated with a patient hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) model.
Regarding claim 20, Fuertinger et al. teaches application programming interfaces (APIs) and/or graphical user interfaces (GUIs) to read, write, and/or otherwise access information, such as via display [0043] as in claim 20 teaching display information associated with a patient (HIF-PHI) model.
Regarding claim 20, Fuertinger et al. teaches a computer-readable medium having processor-executable instruction ([0040-0042], [0106]) as in claim 20 teaching of computer-readable medium having processor-executable instructions.
Regarding claim 20, Fuertinger et al. teaches obtain plurality patient data (ESA dosages and hemoglobin concentration) from real-world patients ([0008], [0068]) as in claim 20 teaching obtaining population patient data (HIF-PHI dosages and hemoglobin measurements) associated with a plurality of patients.
Regarding claim 20, Fuertinger et al. teaches generating a plurality of patient avatars configured to model a health condition associated with a population of patients, using data obtained from real-world patients ([0008], [0068]) as in claim 1 teaching generating a plurality of virtual patient avatars based on the population patient data.
Regarding claim 20, Fuertinger et al. teaches plurality of model (for instance, disease models, treatment models, pharmaceutical interaction modules, and/or the like) for virtual avatars based on patient data [0066] as in claim 20 teaching plurality of virtual patient avatars based on the population patient data.
Fuertinger et al. further discloses using individual patient characteristics for personalized patient avatars [0067] as in claim 20 teaching determining a plurality of HIF-PHI models for the plurality of virtual patient avatars based on the set of personalized patient data.
Regarding claim 20, Fuertinger et al. teaches determination treatment plan based on patient data of drug dose based on patient parameters [0071] as in claim 1 teaching limitation determining one or more HIF-PHI treatment schemes for HIF-PHI dosages based on patient parameters.
Regarding claim 20, Fuertinger et al. teaches accessing information associated with models using application programming interfaces (APIs) and/or graphical user interfaces (GUIs) to read, write, such as via display [0043] as in claim 20 teaching displaying information (next HIF-PHI dosage, treatment scheme) associated with a patient (HIF-PHI) model.
Fuertinger et al. teaches use of ESA models, and Erythropoiesis-Stimulating Agents (ESA) for anemia treatment. Fuertinger et al. does not show specifically HIF-PHI models and Hypoxia-Inducible Factor Prolyl-Hydroxylase Inhibitors (HIF-PHIs) for anemia treatment.
Fuertinger et al. teaches generation of plurality of patient avatars, which are mathematical models configured to model a health condition associated with a population of patients and simulate a course of treatment for the plurality of avatars. The health condition includes anemia, and the treatment protocol comprising hemoglobin levels or erythropoiesis stimulating agents (ESA) dosage, or a combination thereof.
Thijssen et al. teachings are directed to a method of modeling erythropoiesis and its management. The modeling includes adjusting a patient's undesired hematocrit and / or hemoglobin concentration to a value within a desired range at a predetermined time with an ESA regimen.
Fuertinger and Thijssen does not teach,
use of hypoxia-inducible factor prolyl hydroxylase inhibitors (HIF-PHIs)
medication, and patient HIF-PHI model but teaches ESA-medication and patient ESA-model.
Determination of hematocrit concentration and regulation of hepcidin.
Thijssen et al. teaches patient's hematocrit and / or hemoglobin concentration at a predetermined time and regulation of hepcidin.
Bailey et al. teaches use of HIF-PHI medication to treat anemia and determines hemoglobin response in the hemodialysis patient.
It would have been obvious to one of ordinary skill in the art at the time of the inventions was made to modify Fuertinger with Thijssen and Bailey to include hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI) class of drugs in the model, determine hematocrit and / or hemoglobin concentration and regulate hepcidin to arrive at the claimed invention.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Rostoker et al. (PLoS ONE 9(12): e115096, published date:dec.14, 2014).
Claim 5 is dependent to claim 4 and is directed to the limitation of anemia treatment schemes indicating decision tree based on hemoglobin concentrations.
Regarding claim 5, Rostoker et al. teaches use of decision-tree and iron content to treat hemodialysis patients (abstract) as in claim 5 teaching of a decision tree indicating different HIF-PHI dosages based on hemoglobin concentrations.
Rostoker et al. does not teach hemoglobin concentrations. However, Thijssen et al. teaches anemia patient parameters that includes hematocrit and / or hemoglobin concentration (abstract).
Determination of hemoglobin concentrations in the anemia treatment plan is a common clinical practice. Therefore, it would have been obvious to one of ordinary skill in the art at the time of the inventions was made to modify Rostoker with Thijssen by including determination of hemoglobin concentrations to arrive at the claimed invention.
Conclusion
Claims 1-20 are not allowed.
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/PKM/
Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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