DETAILED ACTION
Applicant's response, filed 12/02/2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
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 .
Priority
This application filed 09/28/2020 is a National Stage entry of PCT/IB2019/052450 with an International Filing Date of 03/26/2019, and claims foreign priority to 102018000004045, filed 03/28/2018. The claims are therefore examined as filed on 03/28/2018, the effective filing date. In future actions, the effective filing date of one or more claims may change, due to amendments to the claims, or further review of the priority application(s).
Claim Status
Claims 17-35 and 37 are pending.
Claims 1-16 and 36 are cancelled.
Claims 17-35 and 37 are examined.
Claims 17-35 and 37 are rejected.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 33-34 and 37 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 33-34 and 37 are considered new matter, because there is no support in the specification for the limitations of manufacturing or administering a drug as a treatment as a result of the claimed analysis. There is no mention of manufacturing a drug in the specification at all, and any mention of administering the drug in the specification is given in the context of data provided for the analysis (known drug information stored and used in creating a model) and not a resulting, active step based on the results of the analysis as claimed. Therefore the specification does not convey that the inventor(s) at the time of filing had possession of the claimed elements.
Response to Arguments under 35 USC § 112(a)
In the reply filed 12/02/2025, Applicant reasserts that claims 33-34 and 37, which are directed towards manufacturing and administering a drug, are implicitly or inherently supported by sections of the specification (remarks pg 18). However, as explained in the previous Office Action, the sections identified by the Applicant [0004-5, 9, 12, and 43] refer to “in silico trials” and computational modeling to characterize drugs. As explained in the rejection above, any mention of administering the drug in the specification is given in the context of data provided for the analysis (known drug information stored and used in creating a model) and not a resulting step based on the analysis as claimed. While such trials and modeling could eventually serve to inform the manufacture and administration of such drugs as the specification indicates, and may be obvious to do as a next step, there is still no actual support in the specification for manufacturing a drug or administering it to a patient based on the analysis - there’s no implicit or inherent step of manufacturing or administering the drug as part of the invention, and a person skilled in the art at the time the application was filed would not have recognized that the inventor was in possession of a method of manufacturing and administering a drug as part of the invention.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 17-35 and 37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of mental processes and mathematical concepts, without significantly more.
The MPEP at MPEP 2106 sets forth steps for identifying eligible subject matter:
(1) Are the claims directed to a process, machine, manufacture or composition of matter?
(2A)(1) Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea?
(2A)(2) Do the claims recite additional elements that integrate the judicial exception into a practical application?
(2B) If the claims recite a judicial exception and do not integrate the judicial exception, do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?
With regard to step (1) (Are the claims directed to a process, machine, manufacture or composition of matter?): Yes. The claims are directed to one of the statutory classes. Claims 17-28, 33-35 and 37 are directed to a process (a method for computational modeling and simulation), and claims 29-32 are directed to a machine product (a system for computational modeling and simulation).
With regard to step (2A)(1) (Do the claims recite a judicially recognized exception?): Yes. The claims recite the abstract ideas of processing data using mental steps and mathematical concepts. Claims that recite nothing more than abstract ideas, natural phenomena, or laws of nature are not eligible for patent protection (see MPEP 2106.04).
Abstract ideas include mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations), certain methods of organizing human activity, and mental processes (including procedures for collecting, observing, evaluating, and organizing information (See MPEP 2106.04(a)(2)). In particular, these abstract ideas include but are not limited to:
Processing information based on modeling data to develop a model (mental process/mathematical concept; the human mind is capable of developing a model using existing data, using data to create a model requires organizing information through mathematical correlations; claims 17, 29)
Executing a simulation (mental process; the human mind is capable of simulating data based on a model; claims 17, 29)
Providing modeling or simulation results (mental process; the human mind is capable of providing data results with or without pen and paper; claims 17, 29)
Obtaining modeling data by selecting from a plurality of models (mental process; the human mind is capable of making a selection and receiving information; claim 21)
Anonymizing, deidentifying or pseudonymizing data (mental process; the human mind is capable of processing a dataset to remove or replace personally identifiable information; claim 22)
Dependent claims 18-20, 23-28 further limit the abstract ideas of the independent claims but do not change their characterization as abstract ideas.
Therefore, the claims recite elements that constitute one or more judicial exceptions.
With regard to step (2A)(2) (Do the claims recite additional elements that integrate the judicial exception into a practical application?): Claims 17-28, 33-35 and 37 recite the additional element of storing data on a computational platform, and receiving data with a user interface. Claim 22 recites the additional limitation of using a distributed cloud computing platform for displaying data that has been anonymized, deidentified or pseudonymized. Claims 29-32 also recite the additional element of a system, comprising a computational platform with a user interface for performing the steps of claim 17, with claim 32 specifying that the system is implemented by means of a distributed cloud computing platform. Claims 33 also recites the additional limitation of manufacturing at least one drug, and claims 34 and 37 recites administering a drug.
While the claims recite additional elements related to the use of computers, they do not provide any specific details by which the computer platform or user interface performs or carries out the judicial exception listed in step (2A)(1), nor do they provide any details of how specific structures of the computer are used to implement these functions. The judicial exception is therefore not integrated into a practical application because the generically recited computer elements do not add a meaningful limitation to the abstract idea, as they amount to simply implementing the abstract idea on a computer (see MPEP 2106.05(f)). Similarly, the additional elements of manufacturing a drug and administering a drug are insignificant extrasolution activities that does not add a meaningful limitation to the claims (see MPEP 2106.05(g)) – specifically, because these limitations are very broad (encompassing all possible drugs for all possible conditions), without any particular details on what the drug could be or what condition it would be treating. In order for a "treatment" step to integrate a judicial exception into a practical application, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition through the use of the judicial exception (see MPEP 2106.04(d)(2)). Claim 37, while specifying an analgesic as a drug, does not specify any particular patient (not necessarily the patient referred to in the model as a cancer patient with chronic pain), and it is unclear how the model would be impacting the act of administering the drug – the judicial exception (the analysis/ in-silico trails) appears to have no bearing on this step. Because the claims do not recite any additional elements that integrate the judicial exception into a practical application, the claims as a whole are directed to an abstract idea.
With regard to step (2B) (Do the claims recite additional elements that provide an inventive concept and amount to significantly more than the judicial exception?): No. The claims recite an abstract idea with additional elements; however, these additional elements are general computer elements added to abstract ideas, and non-particular instructions to apply the abstract idea by linking it to a field of use or extrasolution activity (see MPEP 2106.05(f-h)). General computer elements used to perform an abstract idea do not provide an inventive concept, and similarly, non-particular instructions to manufacture or administer a treatment do not provide an inventive concept. Non-particular instructions to implement or administer a non-specific treatment are also considered well-understood, routine and conventional activities (see MPEP 2106.05(d), which indicates that limitations such as “Immunizing a patient against a disease”, from Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1063, 100 USPQ2d 1492, 1497 (Fed. Cir. 2011) are recognized as routine and conventional activities). Further, the use of a distributed cloud computing platform, particularly in the healthcare field, is well-known, routine and conventional (see pg 10 col 2 par 3, and Figs 1 and 3 of GRIEBEL 2015, as previously cited). The claims therefore do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As a result, the claims as a whole do not provide an inventive concept.
Response to Arguments – Rejections Under 35 USC § 101
In the reply filed 12/02/2025, Applicant asserts that the arrangement of steps recited in claim 17 amount to an inventive concept (remarks pg 24). However, the current independent claims recite the generic additional elements of storing data, receiving data, and processing the data by conventional and well-known computer elements, without providing any improvement over other systems/methods. These elements do not provide an inventive concept or amount to significantly more than the judicial exception.
The Applicant also asserts that claim 22 is integrated into a practical application because it actively recites anonymizing/de-identifying/pseudonymizing data and then displaying/reporting data via a cloud computing platform (remarks pg 25). While this is an active step as asserted, the limitation of anonymizing/de-identifying/pseudonymizing data is still a mental process, because such a process can be as simple as removing personally identifiable information from a dataset or replacing it – such generic data manipulation is a mental process. Similarly, while it is acknowledged that use of a cloud computing platform is indeed considered an additional element, it still does not integrate the judicial exception into a practical application or provide an inventive concept, as it simply links the mental process to a field of use that is well-known, routine and conventional (see GRIEBEL 2015).
The Applicant also asserts that the amended claim 37 integrates the judicial exception by effecting a particular treatment for a medical condition (remarks pg 25). However, as explained in the rejection above, in order for a "treatment" step to integrate a judicial exception into a practical application, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition through the use of the judicial exception (see MPEP 2106.04(d)(2)). Claim 37, while specifying an analgesic as a drug, does not specify any particular patient – the claim recites that model information comprises a relationship between levels of an analgesic drug in systemic circulation and its effect on reducing chronic pain in a cancer patient and comprising characterizing behavior of the drug, but then simply states administering a drug to “a patient” based on the characterization and patient data. The wording of the claim indicates that behavior of the drug is not necessarily characterized in a specific type of patient, or that the drug is administered to a specific type of patient that matches that of the model. As such, it is currently unclear how the abstract idea (the creation/simulation of the model) would be impacting the act of administering the drug – this is necessary for such a treatment step to integrate the judicial exception.
The Applicant also asserts that the distributed cloud computing platform of claim 32 is not conventional or routine in the field of the invention as shown in GRIEBEL, as the field is “not just healthcare, but computational modeling and simulation related to drugs in healthcare” (remarks pg 26). However, the simulation and modeling aspects of the invention are not part of the step 2B analysis – rather, the additional elements, such as cloud computing, are analyzed to determine if they were well-known, routine and conventional at the time the invention was filed. GRIBEL in particular reviews multiple publications in cloud computing, clearly outlining that in 2015, before the effective filing date of the invention, that cloud computing was a fast growing area of development in healthcare – the use of it was clearly well-known, routine and conventional, even if it was not as polished compared to present day use. As such, the claims do not amount to significantly more than the judicial exception.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim Rejection
Claims 17-21 and 23-30 are rejected under 35 U.S.C. 103 as being unpatentable over SPANAKIS 2013 “Exploitation of patient avatars towards stratified medicine through the development of in silico clinical trials approaches” (as previously cited) and JAMEI 2009 “The Simcyp Population-based ADME Simulator” (as previously cited).
Claim Interpretation and Scope and Contents of Prior Art
Claims 17 and 29 recite a method and a system, respectively, for computational modeling and simulation to characterize and optimize a drug, by first 1) storing digital modeling data comprising biological, pharmacological, genetic, physiological, pharmacokinetic, pharmacodynamic and clinical data of an individual where the drug may interact with the individual, and 2) storing digital modeling data comprising biological, pharmacological, genetic, physiological, pharmacokinetic, pharmacodynamic and clinical data of an animal where the drug may interact with the animal. With respect to this limitation, SPANAKIS teaches computational modeling and simulation to improve drug research (Introduction), including the use of pharmacokinetic/ pharmacodynamic modeling data (including clinical physiology and genetic data) for both humans and other animal species (pg 1 col 2 section II, Fig 1/Table 1, Fig 4). SPANAKIS does not teach storing this data in separate steps/sets; however, JAMEI expands upon the Simcyp simulator described in SPANAKIS; the simulator system can combine different categories of information or different sets of data into one data repository (Abstract, Fig 2, pg 214 col 2).
Claims 17 and 29 also recite the limitations of 3) storing digital modeling data for properties of a chemical or biological or drug compound related to the researched drug, and 4) storing digital modeling data representing chemical, chemical- physical, biological, physiological, genetic, clinical, pharmacological, pharmacodynamic and pharmacokinetic data related to a disease or therapeutics related to the researched drug. With respect to this limitation, SPANAKIS also teaches using modeling data of drug properties that includes data related to disease/therapeutics and biochemical, pathophysiological, and genetic features (pg 1 col 2 section II, Fig 2), and JAMEI further teaches using data generated during preclinical drug discovery and development and relevant physicochemical attributes of the drug and dosage forms (pg 215 col 1 par 1).
Claims 17 and 29 also recite the limitation of 5) providing a user interface via computer platform that connects to the Internet, allows user to interact with the computer platform and contains software, and 6) receiving selection, definition, or setting information comprising information related to a pharmacometrics/physiological model, and a chemical/pharmacological/biological system model entered by the user by means of the user interface based on the previously stored data, wherein the information comprises parameters aiming to elaborate the models; information of a screening/optimization model comprising parameters aiming to customize computational model algorithms using artificial intelligence; and information on selection/setting of input and output simulation parameters . With respect to this limitation, SPANAKIS teaches an in silico approach using “Simcyp,” a PB/PK/PD simulation platform that connects to the internet and involves selection or setting information entered by a user (pg 3 section IV). JAMEI expands on Simcyp, further teaching that Simcyp has a user interface connected though different environments, including a web browser (pg 214 col 1), and allows for a user to enter drug specific data or in vivo conditions and parameters to elaborate the models (pg 215 col 2 par 1, Fig 4). JAMEI also teaches defining settings that include an optimization tool to operate on the data (pg 217 col 2), and selecting/setting information on input and output parameters, (Fig 4, pg 218 section 3.3 and 3.4). JAMEI also teaches that the main structure of the simulator is based in Microsoft foundation classes (pg 212 col 2 last par, Fig 3) and the interface was developed using Microsoft ASP.NET pg 214 par 1); as Microsoft is known as one of many software provider with processing solutions based on artificial intelligence (evidenced on pg 12 of the specification) as one of ordinary skill in the art would be able to integrate Microsoft’s own AI into these processes, as there is an obvious expectation of success.
Claims 17 and 29 also teach the limitation of 7) processing the information to develop a pharmacometrics or physiological model based on the stored digital modeling data, 8) processing the information to develop a chemical, pharmacological or biological model based on the stored digital modeling data, 9) processing the information to develop a screening or optimization model based on the data comprising search algorithms to identify properties of compounds, targets or diseases relevant to the researched drug, and 10) processing the information to define input setting data for computation simulation software. With respect to these limitations, SPANAKIS also teaches processing user information and previous data to generate and fuse pharmacokinetic & pharmacodynamic (PB/PK/PD) models (pg 3 section IV, Fig 4) and models that identify properties of compounds (example of warfarin in pg 3 section IV) where a screening/optimization process based on the data is used to determine personalized treatments (Fig 4).
Claims 17 and 29 recite the limitations of 11) executing a computational simulation based on the input data and previous model data to obtain output data of the model/simulation, 12) processing the output data to report to the user and where the data contains information to describe compounds related to the researched drug and 13) providing the modeling or simulation results by means of the user interface. With respect to this limitation, SPANAKIS teaches that using the PB/PK/PD simulation platform of Simcyp, previous data can be applied and combined to link several models using patient avatars (pg 3 section IV, Fig 4); Simcyp and similar platforms process and provide output reports from simulations by a user interface (Fig 5). JAMEI further teaches that complete sets of drug compound files, population library files and study designs can be modified, loaded, saved and shared among users (pg 216 col 2 par 1) and that all details of a batch simulation, including the input and output settings, are saved in profiles that can be shared and distributed among users (pg 219 col 1 par 3).
Claim 18 recites the limitation of the user interface involving providing user selectable templates associated with types of simulations, where templates comprise input parameters that can be selected for the simulation and associated with permitted values appropriate for the simulation, where the parameters can be set, and with selectable output parameters with quantiles requested as an output result, and displaying and reporting options that can be selected by the user to choose the format of the results. With respect to this limitation, JAMEI teaches that the Simcyp simulator includes a user interface where the user can define sets of data for one or many simulations or specify changes in one or a group of parameters, the output can be customized, the user can override variability setting or restrict simulations to an individual vary parameters (pg 219 section 3.5). JAMEI further teaches outputs can be selected that include specific parameters or a time-course of parameter changes as a profile (pg 218 section 3.3 and 3.4).
Claim 19 recites the limitation of selecting/setting information entered by the user comprising parameters to define the models, customizer algorithms, select patients, or set up the simulation. With respect to this limitation, SPANAKIS teaches setting up a simulation using Simcyp for three defined patients and setting treatment parameters so that the treatment is specifically 10 mg/24h of S-warfarin discontinued after the 10th day of being administered (pg 3 section IV par 2).
Claim 20 recites the limitation of the selection/setting information comprising initial and boundary conditions to perform computational modeling or simulation, or parameters related to the numerical method used by the simulation software. With respect to this limitation, SPANAKIS teaches setting up a simulation using Simcyp and specifying conditions in which to perform the simulation, where parameters are related to the numerical method used by the software (Table 1, pg 3 section IV par 2, Fig 5).
Claim 21 recites the limitation of obtaining digital modeling data for a pharmacometrics, physiological, chemical, pharmacological or biological model or of a screening or optimization model by selecting models stored on a digital library of the computational platform or pre-loaded by the user on the computational platform, and obtaining digital modeling data comprising biological, pharmacological, genetic, physiological, pharmacokinetic, pharmacodynamic and clinical data of an individual stored or preloaded on the platform. With respect to this limitation, SPANAKIS teaches integrating pharmacokinetic & pharmacodynamic models in silico (Abstract), loading patient data to produce a virtual patient profile (Table 1) and selecting model data and patient data to be integrated/simulated for the development of personalized treatments (Fig 4, pg 3 section IV).
SPANAKIS does not teach the limitation of claim 23 of the computation modeling/simulation comprising modeling or simulations that search for biological targets, chemical compounds, biological compounds or drugs. However, JAMEI teaches that the Simcyp simulator can be used to find drug interactions/biological targets for compounds (Table 1) and compounds can be searched/selected from a large data storage for simulations (pg 216 col 1).
Claim 24 recites the limitation of modeling/simulations for characterizing or optimizing at least one drug, or modeling/simulation of the behavior of a drug on a population of patients. With respect to this limitation, SPANAKIS teaches setting up a simulation using Simcyp for three defined patients and setting treatment parameters so that the treatment is specifically 10 mg/24h of S-warfarin discontinued after the 10th day of being administered (pg 3 section IV par 2).
Claim 25 recites the limitation of the computational modeling/simulations comprise modeling/simulations for the analysis and prediction of drug behavior on a population of animals. With respect to this limitation, SPANAKIS teaches simulations for predicting drug behavior, and that simulations/models can be done using any patient profile (Table 1, pg 3 section IV). One of ordinary skill in the art would recognize that simulating/modeling effects of drugs on non-human animals as obvious.
Claim 26 recites the limitation of the computational modeling/simulation comprising modeling/simulation for the design or development of a chemical compound or for analysis and prediction of the chemical -physical/pharmacological/biological properties of chemical compounds. With respect to this limitation, SPANAKIS teaches setting up a simulation using Simcyp for simulating the effect of S-warfarin on virtual patients (modeling for analysis of biological properties; pg 3 section IV par 2, Fig 5).
Claim 27 recites the limitation of the computational modeling/simulation comprising modeling/simulation for the analysis/identification/characterization of biological targets for research, safety, or efficacy evaluations. With respect to this limitation, SPANAKIS teaches that the in silico clinical trials can be used in identifying novel biomarkers for personalized diagnosis (Abstract), and JAMEI further teaches modeling interactions with biological targets (Fig 1).
SPANAKIS does not teach the limitation of claim 28 of the computational modeling/simulation comprising search algorithms operating on the digital modeling data, however JAMEI teaches that within Simcyp, compound modeling data can be searched/selected from a large data storage for simulations (pg 216 col 1).
Claim 30 recites the limitation of the software programs on the computation platform comprising interface management programs, computational modeling/simulation programs configured to perform the modeling/simulation on a computer, and software processing programs for processing information for the various models, processing user entered information for input to the modeling/simulation programs, and for processing the output data to be reported to a user. SPANAKIS does not discuss additional software details for the Simcyp simulator, however JAMEI teaches that the Simcyp simulator includes user interface software for processing inputs (pg 219 section 3.5), simulation programs for performing modeling/simulations (Abstract), software for processing the models (Fig 2, pg 212 col 1), and software for processing the output data for a user (pg 218 section 3.4 Outputs).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in simulated drug clinical trials would have been motivated to combine the teachings of SPANAKIS and JAMEI to achieve the claimed invention, because drug simulation programs such as Simcyp can be used to model and analyze drug candidates in various types of patients in silico to assist in preclinical drug discovery (JAMEI Abstract), and because Simcyp is already being used in SPANAKIS in integrating pharmacokinetic & pharmacodynamic models as part of in silico clinical trials. A person of ordinary skill would reasonably expect success from combining these teachings, as both SPANAKIS and JAMEI teach the use of Simcyp for performing in silico trails and drug analysis. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over SPANAKIS in view of JAMEI as applied to claims 17-21 and 23-30 above, and further in view of AZZOUZI 2016 “Process Integration and Design Optimization technologies for modelling improvement” (as previously cited).
Claim Interpretation and Scope and Contents of Prior Art
SPANAKIS in view of JAMEI teaches the limitations of claims 17-21 and 23-30 above.
Claim 31 recites the limitation of the computational platform comprising a PIDO software program, configured to manage the flow process of software programs included in the platform and optimize simulations. SPANAKIS does not teach inclusion of a PIDO program as in claim 31, however AZZOUZI teaches improving systems modelling by using PIDO technologies (Abstract) to automate and manage setup and execution of digital simulation and analysis (pg 1 section II).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in simulations and modeling would have been motivated to combine the teachings of SPANAKIS in view of JAMEI and AZZOUZI to achieve the claimed invention, because using a PIDO program has the benefits of allowing for automation and management of setup and execution of digital simulations, integrating analysis results, and optimization by iterating analysis across a range of parameter values (pg 1-2, section II). A person of ordinary skill would reasonably expect success from combining these teachings, as SPANAKIS, JAMEI, and AZZOUZI teach methods of simulation and modeling, and because a PIDO-type program can be applied to the simulation methods of SPANAKIS and JAMEI. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 22 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over SPANAKIS in view of JAMEI as applied to claims 17-21 and 23-30 above, and further in view of CALABRESE 2015 “Cloud Computing In Healthcare And Biomedicine” (as previously cited).
Claim Interpretation and Scope and Contents of Prior Art
SPANAKIS in view of JAMEI teaches the limitations of claims 17-21 and 23-30 above.
Claim 22 recites the limitation of anonymizing, de-identifying or pseudonymizing the digital modeling data of a real individual, and displaying and or reporting the data via a distributed cloud computing platform. With respect to this limitation, SPANAKIS teaches generating virtual patients from the data of real patients for simulations (Abstract) but does not teach anonymizing real patient data and displaying/reporting it via a distributed cloud computing platform. However, CALABRESE teaches distributed cloud computing in healthcare (Abstract) and that anonymity, in which patient data is made anonymous when stored on the cloud, is a security and privacy requirement for cloud-based applications (pg 15 par 1). CALABRESE further teaches that data can be displayed or reported through such applications (pg 8).
Claim 32 recites implementing the system by means of a distributed cloud computing platform. SPANAKIS does not teach a distributed cloud computing platform, however CALABRESE teaches that cloud computing is useful for healthcare, biomedicine and bioinformatics solutions in healthcare (Abstract).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in computing technologies would have been motivated to combine the teachings of SPANAKIS in view of JAMEI and CALABRESE to achieve the claimed invention, because using cloud computing allows for large scalable computing and storage, data sharing, and increased access to resources and applications (Abstract), and because anonymizing data is a security and privacy requirement for cloud-based applications (pg 15 par 1). A person of ordinary skill would reasonably expect success from combining these teachings, as SPANAKIS, JAMEI and CALABRESE teach methods of using computer technologies in healthcare and biomedicine applications, and cloud computing can be added to the simulation methods of SPANAKIS. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claims 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over SPANAKIS in view of JAMEI as applied to claims 17-21 and 23-30 above, and further in view of MADDISON 2013 “The pharmacokinetics and pharmacodynamics of single dose (R)- and (S)-warfarin administered separately and together: relationship to VKORC1 genotype” (as previously cited).
Claim Interpretation and Scope and Contents of Prior Art
SPANAKIS in view of JAMEI teaches the limitations of claims 17-21 and 23-30 above.
Claim 33 recites manufacturing at least one drug, claim 34 recites administering the drug, and claim 35 recites that the real and/or virtual animals are one or more real animals. With respect to these limitations, SPANAKIS teaches conducting in silico clinical trials of drugs, for example S-warfarin, using Simcyp (Fig 5), but does not teach the manufacture of a drug or administering it to a real animal. However, MADDISON teaches determining the pharmacokinetics and pharmacodynamics of S-warfarin in particular, by synthesizing it (pg 210 col 1 last paragraph), and then administering it to real people (pg 210 col 2, par 1).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in computing technologies would have been motivated to combine the teachings of SPANAKIS in view of JAMEI and MADDISON to achieve the claimed invention, because after conducting in silico trails, synthesizing and administering a drug in real clinical trials is a logical next step in drug development, and is necessary to determine potential problems such as drug-drug interactions (pg 209 col 1). A person of ordinary skill would reasonably expect success from combining these teachings, as SPANAKIS and JAMEI teach analysis of drug pharmacokinetic/pharmacodynamic models and MADDISON teaches the logical next step of administering/testing a drug in a clinical setting. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Claim Rejection
Claim 37 are rejected under 35 U.S.C. 103 as being unpatentable over SPANAKIS in view of JAMEI as applied to claims 17-21 and 23-30 above, and further in view of LLOYD 2017 “Pharmacogenomics and Patient Treatment Parameters to Opioid Treatment in Chronic Pain: A Focus on Morphine, Oxycodone, Tramadol, and Fentanyl” (as previously cited).
Claim Interpretation and Scope and Contents of Prior Art
SPANAKIS in view of JAMEI teaches the limitations of claims 17-21 and 23-30 above.
Claim 37 recites the limitation wherein the information on selection/definition/setting of a pharmacometrics or physiological model comprises a relationship between the levels of an analgesic drug in systemic circulation and its effect on reducing chronic pain in a cancer patient, characterizing behavior of said analgesic drug, and administering said drug to a patient based on the characterization and an age, weight, and gender of the patient. With respect to these limitations, SPANAKIS teaches using pharmacokinetic & pharmacodynamic modeling for drug behavior characterization (pg 2 col2 and pg 3) administering drugs based on personal patient data or to specific populations (pg 2 col 1 Fig 4), and linking population characteristics with physiological data, such as age and weight, that play a role in the absorption, distribution, metabolism and excretion (ADME) processes of drugs (Fig 2), but does not specifically teach modeling the relationship between the levels of an analgesic drug in systemic circulation and its effect on reducing chronic pain in a cancer patient, and administering the drug based on characterization and age, weight, and gender.
However, LLYOD reviews studies on the pharmacogenetics and effects of levels/dose of analgesics in chronic pain patients (Abstract), including those with cancer (pg 2370 col 1 last par), based on patient factors that include age, body mass index, and gender (pg 2377 col 2 par 2). One of ordinary skill in the art would be able to input all of these factors/parameters into the model and simulation of SPANAKIS, and administer the analgesic based on the results, as the methods of SPANAKIS can be applied using known drug data and personal demographic data to develop personalized treatments, for any combination of drug and patient data (Fig 4).
Resolving Ordinary Skill in the Art and Obviousness Rationale
A teaching, suggestion, or motivation in the prior art would have led one of ordinary skill in the art to modify or combine the prior art to arrive at the claimed invention. Specifically, a person of ordinary skill in computing technologies would have been motivated to combine the teachings of SPANAKIS in view of JAMEI and LLOYD to achieve the claimed invention, because analgesics such as opioids are commonly used for treating pain in patients (Abstract), and because patient demographics may correlate to drug effectiveness or side effects (pg 2377 col 2 par 2). A person of ordinary skill would reasonably expect success from combining these teachings, as any known drug and patient data, including analgesics and cancer patients with specific demographics, can be included in the methods of SPANAKIS to produce an accurate model or simulation. Therefore, the claims at issue would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention as there is both a reason to modify or combine the prior art, and a reasonable expectation of success (see MPEP 2143.02 (I)).
Response to Arguments – Rejections Under 35 USC § 103
In reply filed 12/02/2025, Applicant asserts that limitations 6-9 are not disclosed or suggested by the cited references, particularly the limitations of information that includes parameters suitable for developing the models. Applicant has also stated that the Office has acknowledged that limitations 6-9 are not disclosed or suggested by the reference SPANAKIS, instead relying on JAMEI (remarks pg 20-22). This is incorrect, as SPANAKIS is relied upon to teach the PB/PK/PD simulation platform, while JAMEI is relied upon to expand on this approach. SPANAKIS teaches the limitations of elements 6-9 of the information/parameters aiming to elaborate or develop the model, as the whole purpose of the platform is to generate physiologically-based pharmacokinetic & pharmacodynamic (PB/PK/PD) models (Abstract) from information entered by a user, and JAMEI expands on this by explaining that a user may enter drug specific data or in vivo conditions and parameters to elaborate the models (pg 215 col 2 par 1, Fig 4). The combined references clearly teach that the information provided comprise parameters aiming to elaborate/develop the models.
The Applicant also asserts that “JAMEI fails to disclose or suggest utilizing user-input parameters suitable for model development starting from basic models” and that the cited sections of JAMEI are directed to drug discovery development, not model development. However, in allowing the user to define model settings, and set/select input information, JAMEI is customizing and developing the models. JAMEI specifically states that the code of the simulator was structured so that only one code based existed that could easily be adapted to suit the end requirement (pg 213 col 2), showing that the algorithms/programs are customizable. While JAMEI may have the primary goal of drug development, it also clearly uses model development and customization to realize this goal (just as drug development is ultimately the end goal of the present invention).
The Applicant also asserts that the claims are not obvious with respect to element (9), as JAMEI’s reference to AI processing solutions does not imply or suggest developing simulation models based on parameters input by a user/customizing model algorithms using AI based on parameters entered by a user (remarks pg 22). However, JAMEI teaches developing simulation models based on parameters input by a user as explained above, and also teaches that the main structure of the simulator is based in Microsoft foundation classes (pg 212 col 2 last par, Fig 3) and the interface developed using Microsoft ASP.NET pg 214 par 1); as Microsoft is known as one of many software provider with processing solutions based on artificial intelligence (evidenced on pg 12 of the specification), one of ordinary skill in the art would be able to integrate Microsoft’s own AI into these processes, as there is an obvious expectation of success. Further, broadly claiming that artificial intelligence is used to accomplish the same result does not distinguish over the prior art.
Therefore, the claims are obvious under the cited references.
Conclusion
No claim is allowable.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.C.L./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687