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 .
Information Disclosure Statement
The IDS filed 7/9/2026 and 12/11/2023 have been considered by the Examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority under 35 U.S.C. 119(a)-(d) to EP201305807.6 filed 6/11/2021.
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 15-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Step 1: Process, Machine, Manufacture or Composition
Claims 15-26 are drawn to a method, so a process.
Claim 27 is drawn to a non-transitory computer storage medium, so a manufacture.
Claim 28 is drawn to a processing circuit with a processing unit, so a machine.
Step 2A Prong One: Identification of an Abstract Idea
The claim(s) recite(s):
1. obtaining a population pharmacological model, said model describing a variability in drug exposure within a patient population receiving known doses of said drug.
This step reads on organizing data of drug exposure, such as therapeutic effect or undesirable effect of a drug, into a model which reads on a process that can be done as a mental process or math. The step is therefore an abstract idea.
2. for at least one parameter relating to a source of said variability within the patient population, sampling a plurality of values of said parameter from a distribution conditional on individual patient characteristics and/or at least one measurement related to a drug exposure of the at least one patient.
This step can be performed by the human mind by selecting a plurality of parameters. The step is therefore an abstract idea.
3. forming sets of values, one set of values comprising one sampled value of each of said parameter.
This step can be performed by the human mind by organizing data into sets. The step is therefore an abstract idea.
4. by using the population pharmacological model, calculating, for each set of values, a corresponding drug exposure with given dose regimens.
This step reads on a process of calculating by using correlated data or math, which can be performed by the human mind or with math. The step is therefore an abstract idea.
5. determining the effect of the drug on at least one patient, with at least one specific dose regimen, based at least on the calculated drug exposures.
This step can be performed by evaluating the results of calculated information. The step is therefore an abstract idea.
Claims 16-26 are drawn to limitations that further recite the abstract idea steps.
Step 2A Prong Two: Consideration of Practical Application
The claims result in a step of determining the effect of the drug on a patient, which is an abstract idea step. The claims do not recite any additional elements that integrate the abstract idea into a practical application.
This judicial exception is not integrated into a practical application because the claims do not meet any of the following criteria:
An additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than
a drafting effort designed to monopolize the exception.
Step 2B: Consideration of Additional Elements and Significantly More
The claimed method also recites "additional elements" that are not limitations drawn to an abstract idea. The recited additional elements are drawn to:
1. a processing circuit with a processing unit (claim 28) which reads on a generic computer.
2. a non-transitory computer readable medium (claim 27).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the processing circuit and computer readable medium is a recitation of generic computer structure that serves to perform generic computer functions that are well-understood, routine, and conventional activities previously known to the pertinent industry. Viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea recited in the instantly presented claims into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are 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(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims under 35 U.S.C. 103(a), the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were made absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and invention dates of each claim that was not commonly owned at the time a later invention was made in order for the examiner to consider the applicability of 35 U.S.C. 103(c) and potential 35 U.S.C. 102(e), (f) or (g) prior art under 35 U.S.C. 103(a).
Claims 15-28 are rejected under 35 U.S.C. 103(a) as being unpatentable over Mould et al. (US 2019/0171962).
Mould et al. teach models that account for between-subject variability which describe responses for a patient population or a response for a typical patient having certain characteristics accounted for as covariates in the model (i.e. obtaining a population pharmacological model, said model describing a variability in drug exposure within a patient population receiving known doses of a drug), as in claims 15 and 27-28.
Mould et al. teach observing a patient’s specific response to the initial dosing regimen wherein the observed responses of a specific patient are used to refine the models and related forecasts, to effectively personalize the models (par. 0034); Mould et al. also teach patient factors accounted for by the models, such as weight, age, race, laboratory test results, etc., to be treated as continuous functions (par. 0038)(i.e. for one parameter, sampling a plurality of values of said parameter from a distribution conditional on individual patient characteristics or at least one measurement related to a drug exposure of one patient), as in claims 15 and 27-28.
Mould et al. teach patient-specific dosing (i.e. sets of values) based on mathematical models describing responses for a population of patients and for observed response data for the specific patient to be treated (par. 0042)(i.e. forming sets of values comprising one sampled value of each of said parameter), as in claims 15 and 27-28.
Mould et al. teach models that describe the expected impact of specific patient characteristics on response (par. 0045)(i.e. the effect of the drug). Mould et al. suggest that the impact is determined based on model that describes outcomes of a give dose (i.e. based at least on the calculated drug exposure), as in claims 15 and 27-28.
It would be obvious to one of ordinary skill in the art to use the models of Mould et al. that model outcomes for a given dose for determining the effect of a particular dose on a patient, as taught by Mould et al. Mould et al. teach a combination of elements that would yield a predictable result for determining an effect of a drug on a patient based on previously calculated drug exposures, as in claims 15 and 27-28.
Mould et al. teach determining dose adjustment in a patient (par. 0097)(i.e. which suggests that the patient was submitted to a former does regiment) and determining a dosage regime forecasted to provide improved results (par. 0097)(i.e. determining the effect of the drug based on measurement), as in claim 16.
Mould et al. teach maintenance of a trough blood concentration level above a therapeutic threshold (par. 0061)(i.e. comparing calculated drug exposure to a threshold) and comparing forecasts of patient response to various dosing regimens to create a set of satisfactory ding regiments (pa. 0061), as in claim 17.
Mould et al. teach patient response as a function of dose and regimen (par. 0097) to determine the direct maintained concentration within the patient (i.e. calculated drug exposure to corresponding level of magnitude of effect of said drug and forecasting to find a better result for the patient treated (par. 0097)(i.e. determine the effect of the drug based on linked levels of magnitude), as in claim 18.
Mould et al. teach Bayesian forecasting to forecast a patient response (par. 006 and Figure 2, item 162)(i.e. a score representative of effect based on calculated drug exposures), as in claim 19.
Mould et al. teach population based on an “average” patient (par. 0007)(i.e. general population), as in claim 20.
Mould et al. teach dosing regimes with medication characteristics and treatment objectives (par. 0071) and an initial dosing regimen (par. 0073)(i.e. instantaneous exposure value), as in claim 21.
Mould et al. teach dosing regimes over a 1, 2 and 4 week dose interval (i.e. exposure across predetermined time interval)(par. 0071), as in claim 22.
Mould et al. teach a “sampling distribution” which is the distribution of the observed data conditional on its parameters (par. 0081) and Bayesian updating by sampling parameters from the prior distribution (par. 0081)(i.e. using direct sampling frm an explicit conditional distribution of values of said parameter), as in claim 23.
Mould et al. teach models including Markov Chain Monte Carlo (par. 0051-0052), as in claim 24.
Mould et al. teach a system that receives a proposed dosing regimen and projects the associated expected response (par. 0087)(i.e. calculating corresponding additional drug exposure with additional dose regimen) and also selected and tests proposed dosing regimens to optimize the dosing regimen (par. 0087)(i.e. determining the corresponding effect of the drug with additional dose regimen based on additional exposures), as in claim 25.
Mould et al. teach determining a desired goal (i.e. corresponding determined effect) from Bayesian forecasting of dosing regimens including doses and intervals (i.e. in view of determining a personalized dose regimen of the drug of a patient), as in claim 26.
Mould et al. teach that their system may be computerized, as for claims 27-28.
E-mail communication Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anna Skibinsky whose telephone number is (571) 272-4373. The examiner can normally be reached on 12 pm - 8:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ram Shukla can be reached on (571) 272-7035. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Anna Skibinsky/
Primary Examiner, AU 1635
