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 .
Specification
The use of the terms MATLAB and Julia, which is a trade name or a mark used in commerce, has been noted in this application. The term should be accompanied by the generic terminology; furthermore the term should be capitalized wherever it appears or, where appropriate, include a proper symbol indicating use in commerce such as ™, SM , or ® following the term.
Although the use of trade names and marks used in commerce (i.e., trademarks, service marks, certification marks, and collective marks) are permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as commercial marks.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 14, 15, 18, and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 14 and 15, the preambles of the claims recite “The system of claim 9”. However, claim 9 introduces “A method”. Claim 9 does recite “a system” in line 4 but the system described in this portion of the claim refers to received objects that model behavior of such system. The system of claim 9 and does not describe a system in a context that makes sense for Claims 14 and 15 to build from, as claimed. This language discrepancy creates ambiguity as to what is being claimed. For purposes of this examination, claims 14 and 15 are understood to contain typographical errors in the preamble which should recite “The method of claim 9” instead of “The system of claim 9”.
Claim 18 recites the limitation "the different trial types" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation "each calibrated set of parameters" in line 1. There is insufficient antecedent basis for this limitation in the claim.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The following section follows the 2019 Patent Eligibility Guidance (PEG) for analyzing subject matter eligibility:
Step 1 - Statutory Category:
Step 1 of the PEG analysis entails considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101 (process, machine, manufacture, or composition of matter).
Step 2A Prong One - Judicial exception:
In Step 2A Prong 1, examiners evaluate whether the claim recites a judicial exception (an abstract idea, law of nature, or a natural phenomenon).
Step 2A Prong Two - Integration into a practical application:
If claims recite a judicial exception, the claim requires further analysis in Step 2A Prong 2. In Step 2A Prong 2, examiners evaluate whether the claim as a whole integrates the exception into a practical application. This evaluation considers any additional elements in the claim beyond any recited judicial exceptions.
Step 2B - Significantly More:
If the additional elements identified in Step 2A Prong 2 do not integrate the exception into a practical application, then the claim is directed to the recited judicial exception and requires further analysis under Step 2B- Significantly More. This evaluation is to evaluate if the additional elements of the claim provide an inventive concept.
As noted in MPEP 2106.05(II): The identification of the additional element(s) in the claim from Step 2A Prong 2, as well as the conclusions from Step 2A Prong 2 on the considerations discussed in MPEP 2106.05(a) -(c), (e), (f), and (h) are to be carried over. Claim limitations identified as Insignificant Extra-Solution Activities are re-evaluated to determine if the elements are beyond what is well -understood, routine, and conventional (WURC) activity, as dictated by MPEP 2106.05(II).
The additional elements are evaluated to determine if any additional element or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP § 2106.05(d).
Independent Claims:
Claim 1:
Step 1: Claim 1 and its dependent claims 2-8 are directed to a system which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 1 recites a judicial exception, noted in bold:
pairing the received data objects into a collection, wherein the collection includes dependencies between the received data objects; The claim limitation can be reasonably read to entail evaluating the received information and making a judgement as to how to organize such information. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a virtual population based on the received data objects, wherein the virtual population comprises multiple virtual items, with each virtual item comprising a combination of model parameters that describe data in the received data objects; The claim limitation can be reasonably read to entail evaluating the received information so as to produce representations of a population with corresponding model parameter data. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by drawing representative entities with the corresponding data as described by the claim. The necessitation that the population be “virtual” is merely the description of performing the process in a computer environment. The courts do not distinguish between mental processes performed entirely in the human mind or those using assistive aids, such as pen and paper or those that are performed using a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a prediction for the virtual population using a global sensitivity analysis across the virtual population, wherein the prediction includes a determined cost and an optimal configuration for the virtual population; and the claim limitation can be reasonably read to entail performing a global sensitivity analysis including cost and optimal configurations to evaluate a predictive value. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The claim limitation can be reasonably read to entail evaluating the prediction and making a judgment as to a visual representation to depict such prediction. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by using a pen and paper to draw out a visualized representation of the prediction obtained as part of the mental process. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering and also as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for the recitation of generic computing components as tools to perform existing processes.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application because the claimed invention does not provide an improvement to the functioning of a computer or an improvement to another technology, does not implement the judicial exception with a particular and distinct machine that is integral to the claim, or does not otherwise apply the judicial exception in some other meaningful way. The judicial exception appears to be implemented through use of generic computing components, and any conceivable improvement appears to be rooted in the mental process itself- and not provided by the additional element, alone or in combination with the exception.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system; – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, receiving data objects as computational models encompasses receiving and transmitting data over a network. This computer function, when recited generically such as in this claim, is considered to be well understood, routine, and conventional computer functionality. (See MPEP 2106.05(d)- 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))
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)), as stated previously. The courts have found that merely using a computer as a tool to perform existing processes and generic computing components added to the recited exception does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 9:
Step 1: Claim 9 and its dependent claims 10-15 are directed to a method which falls within one of the four statutory categories of a process. (Note claims 14 and 15 recite “the system of claim 9” which appears to be a typographical mistake, as described in the rejections of the claims under 35 U.S.C. § 112b in this action but are being interpreted to recite “the method of claim 9” for purposes of this examination since they depend from claim 9 which establishes a method.)
Step 2A Prong 1: Claim 9 recites a judicial exception, noted in bold:
pairing the received data objects into a collection, wherein the collection includes dependencies between the received data objects; The claim limitation can be reasonably read to entail evaluating the received information and making a judgement as to how to organize such information. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a virtual population based on the received data objects, wherein the virtual population comprises multiple virtual items, with each virtual item comprising a combination of model parameters that describe data in the received data objects; The claim limitation can be reasonably read to entail evaluating the received information so as to produce representations of a population with corresponding model parameter data. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by drawing representative entities with the corresponding data as described by the claim. The necessitation that the population be “virtual” is merely the description of performing the process in a computer environment. The courts do not distinguish between mental processes performed entirely in the human mind or those using assistive aids, such as pen and paper or those that are performed using a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a prediction for the virtual population using a global sensitivity analysis across the virtual population, wherein the prediction includes a determined cost and an optimal configuration for the virtual population; and The claim limitation can be reasonably read to entail performing a global sensitivity analysis including cost and optimal configurations to evaluate a predictive value. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The claim limitation can be reasonably read to entail evaluating the prediction and making a judgment as to a visual representation to depict such prediction. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by using a pen and paper to draw out a visualized representation of the prediction obtained as part of the mental process. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering and also as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for the recitation of generic computing components as tools to perform existing processes.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); and adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application because the claimed invention does not provide an improvement to the functioning of a computer or an improvement to another technology, does not implement the judicial exception with a particular and distinct machine that is integral to the claim, or does not otherwise apply the judicial exception in some other meaningful way. The judicial exception appears to be implemented through use of generic computing components, and any conceivable improvement appears to be rooted in the mental process itself- and not provided by the additional element, alone or in combination with the exception.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, receiving data objects as computational models encompasses receiving and transmitting data over a network. This computer function, when recited generically such as in this claim, is considered to be well understood, routine, and conventional computer functionality. (See MPEP 2106.05(d)- 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))
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)), as stated previously. The courts have found that merely using a computer as a tool to perform existing processes and generic computing components added to the recited exception does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Claim 16:
Step 1: Claim 16 and its dependent claims 17-20 are directed to a system which falls within one of the four statutory categories of a machine.
Step 2A Prong 1: Claim 16 recites a judicial exception, noted in bold:
pairing the received data objects into a collection that includes dependencies between the received data objects; The claim limitation can be reasonably read to entail evaluating the received information and making a judgement as to how to organize such information. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating a virtual population based on the received data objects, wherein the virtual population comprises multiple virtual patients, with each virtual patient comprising a combination of model parameters that describe data in the received data objects, and The claim limitation can be reasonably read to entail evaluating the received information so as to produce representations of a population with corresponding model parameter data. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by drawing representative entities with the corresponding data as described by the claim. The necessitation that the population be “virtual” is merely the description of performing the process in a computer environment. The courts do not distinguish between mental processes performed entirely in the human mind or those using assistive aids, such as pen and paper or those that are performed using a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
wherein the generation of the virtual population includes a user-determined or default cost function and an algorithm for finding an optimal configuration for the virtual population with respect to said cost; The claim limitation can be reasonably read to entail evaluating a cost function and an algorithm to make a judgement as to the optimal configuration to make a judgement of the virtual population characterization. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Because this claim also recites the use of a cost function, which is understood to me a mathematical equation, the claim further recites the abstract idea of mathematical concepts.
generating a prediction for the virtual population by simulating with each of the virtual patients; and The claim limitation can be reasonably read to entail performing a global sensitivity analysis including cost and optimal configurations to evaluate a predictive value. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The claim limitation can be reasonably read to entail evaluating the prediction and making a judgment as to a visual representation to depict such prediction. This task can be performed within the human mind or using a pen and paper as an assistive physical aid, for example by using a pen and paper to draw out a visualized representation of the prediction obtained as part of the mental process. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Therefore, the claim recites a judicial exception.
Step 2A Prong 2: Additional elements were identified and are noted in italics.
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple patients in a clinical trial;- This limitation has been identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) of mere data gathering, as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for the recitation of generic computing components as tools to perform existing processes, and as Field of Use and Technological Environment (MPEP 2106.05(h)) for generally limiting the models to the technological environment and field of use of medical patients in clinical trials.
The courts have found that merely including instructions to implement an abstract idea on a computer or merely using a computer as a tool to perform an abstract idea (Mere Instructions to Apply an Exception (MPEP 2106.05(f))); adding insignificant extra- solution activity to the judicial exception (Insignificant Extra Solution Activity (MPEP 2106.05(g))); and generally linking the use of a judicial exception to a particular technological environment or field of use (Field of Use and Technological Environment (MPEP 2106.05(h))) does not integrate the judicial exception into a practical application.
When viewed independently and within the claim as a whole, the additional element does not appear to integrate the judicial exception into a practical application because the claimed invention does not provide an improvement to the functioning of a computer or an improvement to another technology, does not implement the judicial exception with a particular and distinct machine that is integral to the claim, or does not otherwise apply the judicial exception in some other meaningful way. The judicial exception appears to be implemented through use of generic computing components and is limited to the specified field of use, and any conceivable improvement appears to be rooted in the mental process itself- and not provided by the additional element, alone or in combination with the exception.
Step 2B: As discussed in Step 2A Prong 2, additional elements were identified as Insignificant Extra Solution Activity (MPEP 2106.05(g)) which must be further evaluated to determine if they are beyond WURC activities. Additional elements identified otherwise and conclusions from Step 2A Prong 2 are carried over for evaluating if the claim, as a whole, amounts to an inventive concept that is significantly more than the judicial exception:
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple patients in a clinical trial; - – This limitation has been identified as the insignificant extra solution activity of mere data gathering, as stated previously. Under broadest reasonable interpretation and when read in light of the specification, receiving data objects as computational models encompasses receiving and transmitting data over a network. This computer function, when recited generically such as in this claim, is considered to be well understood, routine, and conventional computer functionality. (See MPEP 2106.05(d)- 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))
The courts have found that simply appending insignificant extra solution activities that are well-understood, routine, and conventional activities to the judicial exception does not qualify the limitations as “significantly more” than the recited judicial exception. The remaining additional elements were identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) and Field of Use and Technological Environment (MPEP 2106.05(h)), as stated previously. The courts have found that merely using a computer as a tool to perform a mental process and generally linking the use of a judicial exception to a particular technological environment does not qualify the limitations as “significantly more” than the recited judicial exception.
With the additional elements viewed independently and as part of the ordered combination, the claim as a whole does not appear to amount to significantly more than the recited judicial exception because the claim is using generic computing components recited at a high level of generality and functioning in their normal capacity in conjunction with well-understood, routine, and conventional activity to enable the performance of a task that can practically be performed within the human mind or using pen and paper as an assistive physical aid. Therefore, the claim does not include additional elements, alone or in combination that are sufficient to amount to significantly more than the recited judicial exception.
Conclusion: Based on this rationale, the claim has been deemed to be ineligible subject matter under 35 U.S.C. 101.
Dependent Claims:
Examiner notes limitations identified as judicial exceptions are indicated in italicized bold and limitations identified as additional elements are indicated using italics.
Claim 2
Step 1: Regarding dependent claim 2, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 2 additionally recites the limitation wherein the computational models include a differential equation., which can reasonably be read to entail using a differential equation to characterize the model, wherein the differential equation is a mathematical equation being applied per the mental process as given previously. The requirement that the model is a computational model is the inclusion of using a computer as an assistive aid in such mental process. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept in addition to further describing the mental process of the preceding claim.
Step 2A Prong 2 & Step 2B: Claim 2 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 3
Step 1: Regarding dependent claim 3, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 3 additionally recites the limitation wherein the computational models include a stochastic model., which can reasonably be read to entail further describing the mental process of the preceding claim by characterization of the model. The requirement that the model is a computational model is the inclusion of using a computer as an assistive aid in such mental process. Under broadest reasonable interpretation and when read in light of the specification, a stochastic model is a mathematical model describing the mathematical relationship between inputs and outputs (see instant specification ¶62). Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept in addition to further describing the mental process of the preceding claim
Step 2A Prong 2 & Step 2B: Claim 3 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 4
Step 1: Regarding dependent claim 4, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 4 additionally recites the limitation wherein the parameters for the virtual population are optimized, which can reasonably be read to entail performing an evaluation and judgment to determine optimal parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 4 additionally recites the limitation simultaneously. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer as a tool by which to apply the judicial exception. The courts have ruled that invoking the use of generic computers to apply the judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to mere instructions to implement the abstract idea using a computer as a tool are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 5
Step 1: Regarding dependent claim 5, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 5 additionally recites the limitation wherein the parameters for the virtual population are optimized sequentially. which can reasonably be read to entail performing a sequential evaluation and judgement to derive optimal parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 5 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 6
Step 1: Regarding dependent claim 6, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 6 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 6 additionally recites the limitation wherein the received data objects include clinical trial data, and wherein the virtual items include virtual patients in a clinical trial. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the judicial exception to a particular technological environment and field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Limiting the modeled data objects to include clinical trial data and the patients associated with it merely limits to the claim(s) to the applicability of the method for the medical and clinical trial field. The concept(s) of the judicial exceptions as given in the claim(s) do not have altered process steps or calculations because of the limitation and therefore this linkage does not meaningfully limit the claim. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 7
Step 1: Regarding dependent claim 7, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 7 additionally recites the limitation wherein the computational models are defined using a common interexchange format., which can reasonably be read to entail defining the model according to a common format type. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. The limitation that the models are computational merely requires that the abstract idea occur using a computer as an assistive aid, whereby the courts do not distinguish between mental processes performed entirely in the human mind and those using assistive aids such as pen and paper or a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 7 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 8
Step 1: Regarding dependent claim 8, the judicial exception of independent claim 1 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 8 additionally recites the limitation wherein the computational models are defined using a symbolic domain-specific language representation. which can reasonably be read to entail defining the model according to a domain specific language representation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. The limitation that the models are computational merely requires that the abstract idea occur using a computer as an assistive aid, whereby the courts do not distinguish between mental processes performed entirely in the human mind and those using assistive aids such as pen and paper or a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 8 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 10
Step 1: Regarding dependent claim 10, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 10 additionally recites the limitation wherein the computational models include a differential equation., which can reasonably be read to entail using a differential equation to characterize the model, wherein the differential equation is a mathematical equation being applied per the mental process as given previously. The requirement that the model is a computational model is the inclusion of using a computer as an assistive aid in such mental process. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept in addition to further describing the mental process of the preceding claim.
Step 2A Prong 2 & Step 2B: Claim 10 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 11
Step 1: Regarding dependent claim 11, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 11 additionally recites the limitation wherein the computational models include a stochastic model., which can reasonably be read to entail further describing the mental process of the preceding claim by characterization of the model. The requirement that the model is a computational model is the inclusion of using a computer as an assistive aid in such mental process. Under broadest reasonable interpretation and when read in light of the specification, a stochastic model is a mathematical model describing the mathematical relationship between inputs and outputs (see instant specification ¶62). Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas as a mathematical concept in addition to further describing the mental process of the preceding claim.
Step 2A Prong 2 & Step 2B: Claim 11 does not recite any additional elements that would integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception(s).
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 12
Step 1: Regarding dependent claim 12, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 12 additionally recites the limitation wherein the parameters for the virtual population are optimized., which can reasonably be read to entail performing an evaluation and judgment to determine optimal parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2: Claim 4 additionally recites the limitation simultaneously. This limitation has been identified as Mere Instructions to Apply an Exception (MPEP 2106.05(f)) for invoking the use of a computer as a tool by which to apply the judicial exception. The courts have ruled that invoking the use of generic computers to apply the judicial exception does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to mere instructions to implement the abstract idea using a computer as a tool are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 13
Step 1: Regarding dependent claim 13, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 13 additionally recites the limitation wherein the parameters for the virtual population are optimized sequentially, which can reasonably be read to entail performing a sequential evaluation and judgement so as to derive optimal parameters. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 13 does not recite any additional elements that would integrate the judicial exception(s) into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 14
Step 1: Regarding dependent claim 14, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously. Note, the interpretation of this category is given by the examiner in the notes of the independent claim 9, whereby it appears that a typographical mistake has been made. Though the claim recites “the system of claim 9”, it appears that the applicant intended for the claim to state “the method of claim 9” since claim 9 preamble recites a method and not a system.
Step 2A Prong 1: Claim 14 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 14 additionally recites the limitation wherein the received data objects include clinical trial data, and wherein the virtual items include virtual patients in a clinical trial. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the judicial exception to a particular technological environment and field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to generally linking the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Limiting the modeled data objects to include clinical trial data and the patients associated with it merely limits to the claim(s) to the applicability of the method for the medical and clinical trial field. The concept(s) of the judicial exceptions as given in the claim(s) do not have altered process steps or calculations because of the limitation and therefore this linkage does not meaningfully limit the claim. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 15
Step 1: Regarding dependent claim 15, the judicial exception of independent claim 9 is further incorporated. The claim falls within the corresponding statutory category as stated previously. Note, the interpretation of this category is given by the examiner in the notes of the independent claim 9, whereby it appears that a typographical mistake has been made. Though the claim recites “the system of claim 9”, it appears that the applicant intended for the claim to state “the method of claim 9” since claim 9 preamble recites a method and not a system.
Step 2A Prong 1: Claim 15 additionally recites the limitation wherein the computational models are defined using a common interexchange format or are defined using a symbolic domain-specific language representation., which can reasonably be read to entail defining the model according to a common format type or according to a domain specific language representation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. The limitation that the models are computational merely requires that the abstract idea occur using a computer as an assistive aid, whereby the courts do not distinguish between mental processes performed entirely in the human mind and those using assistive aids such as pen and paper or a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 15 does not recite any additional elements that would integrate the judicial exception(s) into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 17
Step 1: Regarding dependent claim 17, the judicial exception of independent claim 16 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 17 additionally recites the limitation wherein the computational models are defined using a symbolic domain-specific language representation., which can reasonably be read to entail defining the model according to a domain specific language representation. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. The limitation that the models are computational merely requires that the abstract idea occur using a computer as an assistive aid, whereby the courts do not distinguish between mental processes performed entirely in the human mind and those using assistive aids such as pen and paper or a computer. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process.
Step 2A Prong 2 & Step 2B: Claim 17 does not recite any additional elements that would integrate the judicial exception(s) into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 18
Step 1: Regarding dependent claim 18, the judicial exception of independent claim 16 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 18 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 18 additionally recites the limitation wherein the different trial types include a normal trial and a steady state trial. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the use of the judicial exception to a particular technological environment and field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to limiting the use of the judicial exception to a specified technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 19
Step 1: Regarding dependent claim 19, the judicial exception of independent claim 16 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 19 additionally recites the limitation wherein the prediction is based on a constant rate added to a differential equation for a fixed duration., which can reasonably be read to entail evaluating a constant rate added to a differential equation for a given time. This task can be performed within the human mind or using a pen and paper as an assistive physical aid. Therefore, this claim limitation includes the recitation of the judicial exception of abstract ideas of a mental process. Furthermore, the recitation of adding a value to a differential equation is a further recitation of a mathematical calculation and therefore the claim also recites the abstract idea of mathematical concepts.
Step 2A Prong 2 & Step 2B: Claim 19 does not recite any additional elements that would integrate the judicial exception(s) into a practical application nor amount to significantly more.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim 20
Step 1: Regarding dependent claim 20, the judicial exception of independent claim 16 is further incorporated. The claim falls within the corresponding statutory category as stated previously.
Step 2A Prong 1: Claim 20 does not recite any additional judicial exceptions.
Step 2A Prong 2: Claim 20 additionally recites the limitation wherein each calibrated set of parameters is a virtual patient, and a virtual population is a collection of virtual patients. This limitation has been identified as Field of Use and Technological Environment (MPEP 2106.05(h)). The courts have ruled generally linking the use of the judicial exception to a particular technological environment or field of use does not integrate the judicial exception into a practical application. With the additional element viewed in conjunction with the other limitations, the claim as a whole does not appear to integrate the judicial exception into a practical application.
Step 2B: The courts have found that limitations that amount to limiting the use of the judicial exception to a particular technological environment and field of use are not enough to qualify the claim as significantly more than the abstract idea. Therefore, the claim does not include additional elements, alone or in the ordered combination that are sufficient to amount to significantly more than the recited judicial exception.
This claim is not eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 4-6, 9, and 12-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bhattacharyya et al (US 2021/0319158 A1), hereinafter referred to as Bhattacharyya.
Regarding claim 1, Bhattacharyya teaches A system having a multiple-data fitting interface for design optimization, the system having at least one processor configured for: (Bhattacharyya, ¶249) "In embodiments, the platform may support global optimization of clinical trial design by connecting criteria space, design space, scenario space and performance space. The platform may provide users with visualizations for interactive exploration of the spaces. The platform may support global optimization by enabling design optimization and exploration across different styles of explorations."); ((Bhattacharyya, ¶209) "The platform 104 may provide for a system for providing users with facilities and methods for designing, evaluating, and/or comparing designs. The facilities described herein may be deployed in part or in whole through a machine that executes computer software, modules, program codes, and/or instructions on one or more processors, as described herein, which may be part of or external to the platform 104.")
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system; Models (data objects) include parameters and values combined (as pairs of data), wherein models are further described as numerical models describing behavior of elements of a clinical trial ((Bhattacharyya,¶217) " The simulation facility 110 may include models 126. As used herein, a model includes the combination of parameters and the values that describe a design and the scenario under which the design is evaluated."); ((Bhattacharyya,¶305) "In some embodiments, scenario data may include one or more mathematical or numerical models and methods that are related and/or describe one or more of human behavior, disease progress, drug behavior, and the like."). The models are described as being generated (received) (Bhattacharyya, ¶306) " In embodiments, simulation models may be generated by combining two or more categories of inputs, such as by combining design space and scenario space.").
pairing the received data objects into a collection, wherein the collection includes dependencies between the received data objects Each simulation model (received data object) is described as corresponding to a specific combination of trial design and scenario, which is understood to characterize distinct a clinical trial ((Bhattacharyya,¶309) " In embodiments, each simulation model (i.e., a specific combination of a trial design and scenario) may be evaluated over the course of numerous simulation runs, and the number of simulations may vary depending on the project stage."). A set of clinical trials (as a collection of data objects) may be evaluated ((Bhattacharyya, ¶518) " Referring now to FIG. 94, in addition to optimizing a design for a single clinical trial, embodiments of the platform 104 (FIG. 1) may provide for optimization of clinical trial designs across a plurality/set of clinical trials 9410 and/or aspects of the clinical trials. As will be appreciated, optimization over a set of related clinical trials may result in better overall performance for the set, as compared to optimizing each element, aspect, or clinical trial in the set individually and combining the results."). The multiple clinical trials of the sets are related (paired) according to associations (dependencies) (Bhattacharyya, ¶520) " As shown in FIG. 94, two or more of the clinical trials, e.g., clinical trial A 9412, clinical trial B 9414, and/or clinical trial C 9416 may be related to each other through one or more associations 9418. Non-limiting examples of associations 9418 include: trial sites 9420; an order of execution and/or dependencies 9422; shared resources 9424; clinical trial phases 9426; test subjects 9428, and/or other aspects of design space, scenario space and performance space.")
generating a virtual population based on the received data objects, wherein the virtual population comprises multiple virtual items, with each virtual item comprising a combination of model parameters that describe data in the received data objects; Population models/population data include characteristics of subjects in a clinical trial and are used to generate the virtual population, whereby the subjects include the plurality of characteristics ((Bhattacharyya, ¶313) " Population models may define characteristics of subjects in a clinical trial. "). ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."). Population data may be received from the data facility (Bhattacharyya, ¶224) "The platform 104 may include and/or provide access to one or more data facilities 138. Data in the data facilities may include design histories 140, simulation data 142, site data 144, resource data 146, population data 148, and the like."); (Bhattacharyya, ¶385) "Data flow through the platform 104 may be facilitated by data records that are stored and retrieved from one or more databases in data facility 138. ").
generating a prediction for the virtual population using a global sensitivity analysis across the virtual population, Subjects of the virtual population are used in simulations, whereby a simulation generates a response for the subject of the virtual population (Bhattacharyya, ¶317) "Subjects in the virtual population may be used for simulation of trials."). A Pareto analysis evaluates design simulations to determine and output identification of recommended globally optimum designs (prediction) upon which a sensitivity analysis is performed (Bhattacharyya, ¶414) " The optimality determining circuit 5110 may identify globally optimum designs 5114 based on Pareto analysis. “) ;( (Bhattacharyya, ¶416) " As shown in FIG. 53, a method for determining optimum designs using Pareto analysis may include obtaining trial design simulations 5302. The method may further include evaluating optimality for each design using Pareto analysis 5304. The method may include identifying optimal designs based on the Pareto analysis 5306. The optimum designs may be evaluated 5308. Evaluation may include feedback from user, statistical analysis, and the like. "); (Bhattacharyya, ¶482) " In embodiments, the recommendation engine 7114 may provide recommendations for designs 7210 (based on the Pareto 7118 and/or the convex hull 7120 engine outputs) and allow a user to compare and analyze the recommended designs 7210 (sensitivity analysis, weigh graphs, etc.). ") wherein the prediction includes a determined cost and an optimal configuration for the virtual population; and A list of recommended designs (as the generated prediction) includes cost and power indicating the number of required patients (optimal configuration of the virtual population) ((Bhattacharyya,¶479) " Referring to FIG. 74, in embodiments, the design recommendation engine 7114 may generate one or more outputs 7410, including a list or a set of the recommended designs 7210. The list of recommended designs 7210 may be provided with criterion values 7412, scenario parameters 7414, and/or trial design parameters 7416. A non-limiting example of a list of recommended designs is shown in FIG. 75. As shown, the list may include design ID, power, costs, and/or duration for each listed design. The term "power", as used herein with respect to a clinical trial design may represent a measure of one or more properties and/or statistics of the clinical trial, e.g., statistical power. For example, power may provide an indication of how many patients are required to avoid a type I (false positive) or type II (false negative) error. ")
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The identified recommended designs (predictions) can be visualized according to generated outputs (Bhattacharyya, ¶481) "In embodiments, the algorithm/engine 7114 may generate or output visualizations and/or interfaces (collectively shown as 7424) to compare two or more recommended designs 7210.")
Regarding claim 4, Bhattacharyy teaches The system of claim 1, as stated previously and further teaches wherein the parameters for the virtual population are optimized simultaneously. Data is received from simulated designs, wherein the simulated designs account for the characteristics of a subject of a virtual population, as described in the rejection of claim 1. Designs are characterized by parameters and optimal designs can be determined according to sequential or parallel (simultaneous) analysis ((Bhattacharyy, ¶242) "The optimality analysis component 302 may receive data from simulated designs 312 and determine one or more sets of optimal designs 322, 324. [[…]] In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality based on benchmark analysis 304. Benchmark analysis circuit 304 may determine optimality of designs based on a comparison of performance parameter values to one or more benchmark designs such as from historical data 314 and/or simulation data 312. In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality using sequential analysis 308 and/or parallel analysis 310. Sequential analysis circuit 308 and parallel analysis circuit 310 may use one or more different optimality functions 328 in parallel or sequentially to determine optimal designs"); ((Bhattacharyy, ¶229) "Optimality may be in relation to one or more performance parameters and the values of the performance parameters. An optimal design may be a design that achieves a most desirable value for one or more specific performance parameters."); ((Bhattacharyy, ¶212) " A trial design may define aspects of subjects that should be included in a trial. ")
Regarding Claim 5, Bhattacharyy teaches The system of claim 1, wherein the parameters for the virtual population are optimized sequentially. Data is received from simulated designs, wherein the simulated designs account for the characteristics of a subject of a virtual population, as described in the rejection of claim 1. Designs are characterized by parameters and optimal designs can be determined according to sequential or parallel analysis ((Bhattacharyy, ¶242) "The optimality analysis component 302 may receive data from simulated designs 312 and determine one or more sets of optimal designs 322, 324. [[…]] In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality based on benchmark analysis 304. Benchmark analysis circuit 304 may determine optimality of designs based on a comparison of performance parameter values to one or more benchmark designs such as from historical data 314 and/or simulation data 312. In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality using sequential analysis 308 and/or parallel analysis 310. Sequential analysis circuit 308 and parallel analysis circuit 310 may use one or more different optimality functions 328 in parallel or sequentially to determine optimal designs"); ((Bhattacharyy, ¶229) "Optimality may be in relation to one or more performance parameters and the values of the performance parameters. An optimal design may be a design that achieves a most desirable value for one or more specific performance parameters."); ((Bhattacharyy, ¶212) " A trial design may define aspects of subjects that should be included in a trial. ").
Regarding claim 6, Bhattacharyya teaches The system of claim 1, as stated above and further teaches wherein the received data objects include clinical trial data, Models (data objects) include parameters and values combined (as pairs of data), wherein models are further described as numerical models describing behavior of elements of a clinical trial ((Bhattacharyya,¶217) " The simulation facility 110 may include models 126. As used herein, a model includes the combination of parameters and the values that describe a design and the scenario under which the design is evaluated.”); ((Bhattacharyya, ¶05) " Scenario space may include environmental and external factors that may affect trial design. In some embodiments, scenario data may include one or more mathematical or numerical models and methods that are related and/or describe one or more of human behavior, disease progress, drug behavior, and the like. Scenarios may include a combination of environmental variables that provide a specification or guidelines for generating virtual patient populations for a design study. Human behavior inputs may include trial execution characteristics, including how subjects adhere to regimen, dropout rates, and the like. Drug behavior may include models of drug behavior in a body and may include pharmacokinetic and pharmacodynamic models. The inputs may further include deviation models for one or more of the parameters of the models. Deviation models may be based on expected or previously measured distributions or variations in aspects such as human behavior, demographics, and the like. In embodiments, a plurality of different scenarios may be generated as potential inputs to the platform wherein each scenario may include different aspects of human behavior, disease progress, and drug behavior, and the like."). and wherein the virtual items include virtual patients in a clinical trial. ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."); ((Bhattacharyya, ¶316) "The virtual population 3002 may include data representing individual subjects (virtual patients) and characteristics of the subjects."); ((Bhattacharyya, ¶548) "In addition to the design of a clinical trial, the success of the clinical trial often depends on the ability to recruit a satisfactory number of patients, also referred to herein as "subjects", suitable to participate in the clinical trial").
Regarding claim 9, Bhattacharyya A method for design optimization using a multiple-data fitting interface, the method comprising: (Bhattacharyya, ¶249) "In embodiments, the platform may support global optimization of clinical trial design by connecting criteria space, design space, scenario space and performance space. The platform may provide users with visualizations for interactive exploration of the spaces. The platform may support global optimization by enabling design optimization and exploration across different styles of explorations."); ((Bhattacharyya, ¶209) "The platform 104 may provide for a system for providing users with facilities and methods for designing, evaluating, and/or comparing designs.")
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple items in a system; Models (data objects) include parameters and values combined (as pairs of data), wherein models are further described as numerical models describing behavior of elements of a clinical trial ((Bhattacharyya,¶217) " The simulation facility 110 may include models 126. As used herein, a model includes the combination of parameters and the values that describe a design and the scenario under which the design is evaluated."); ((Bhattacharyya,¶305) "In some embodiments, scenario data may include one or more mathematical or numerical models and methods that are related and/or describe one or more of human behavior, disease progress, drug behavior, and the like."). The models are described as being generated (received) (Bhattacharyya, ¶306) " In embodiments, simulation models may be generated by combining two or more categories of inputs, such as by combining design space and scenario space.").
pairing the received data objects into a collection, wherein the collection includes
dependencies between the received data objects; Each simulation model (received data object) is described as corresponding to a specific combination of trial design and scenario, which is understood to characterize distinct a clinical trial ((Bhattacharyya,¶309) " In embodiments, each simulation model (i.e., a specific combination of a trial design and scenario) may be evaluated over the course of numerous simulation runs, and the number of simulations may vary depending on the project stage."). A set of clinical trials (as a collection of data objects) may be evaluated ((Bhattacharyya,¶518) " Referring now to FIG. 94, in addition to optimizing a design for a single clinical trial, embodiments of the platform 104 (FIG. 1) may provide for optimization of clinical trial designs across a plurality/set of clinical trials 9410 and/or aspects of the clinical trials. As will be appreciated, optimization over a set of related clinical trials may result in better overall performance for the set, as compared to optimizing each element, aspect, or clinical trial in the set individually and combining the results."). The multiple clinical trials of the sets are related (paired) according to associations (dependencies) (Bhattacharyya, ¶520) " As shown in FIG. 94, two or more of the clinical trials, e.g., clinical trial A 9412, clinical trial B 9414, and/or clinical trial C 9416 may be related to each other through one or more associations 9418. Non-limiting examples of associations 9418 include: trial sites 9420; an order of execution and/or dependencies 9422; shared resources 9424; clinical trial phases 9426; test subjects 9428, and/or other aspects of design space, scenario space and performance space.")
generating a virtual population based on the received data objects, wherein the virtual population comprises multiple virtual items, with each virtual item comprising a combination of model parameters that describe data in the received data objects; Population models/population data include characteristics of subjects in a clinical trial and are used to generate the virtual population, whereby the subjects include the plurality of characteristics ((Bhattacharyya, ¶313) " Population models may define characteristics of subjects in a clinical trial. "); ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."). Population data may be received from the data facility (Bhattacharyya, ¶224) "The platform 104 may include and/or provide access to one or more data facilities 138. Data in the data facilities may include design histories 140, simulation data 142, site data 144, resource data 146, population data 148, and the like."); (Bhattacharyya, ¶385) "Data flow through the platform 104 may be facilitated by data records that are stored and retrieved from one or more databases in data facility 138. ").
generating a prediction for the virtual population using a global sensitivity analysis across the virtual population, Subjects of the virtual population are used in simulations, whereby a simulation generates a response for the subject of the virtual population (Bhattacharyya, ¶317) "Subjects in the virtual population may be used for simulation of trials."). A Pareto analysis evaluates design simulations to determine and output identification of recommended globally optimum designs (prediction) upon which a sensitivity analysis is performed (Bhattacharyya, ¶414) " The optimality determining circuit 5110 may identify globally optimum designs 5114 based on Pareto analysis. “) ;( (Bhattacharyya, ¶416) " As shown in FIG. 53, a method for determining optimum designs using Pareto analysis may include obtaining trial design simulations 5302. The method may further include evaluating optimality for each design using Pareto analysis 5304. The method may include identifying optimal designs based on the Pareto analysis 5306. The optimum designs may be evaluated 5308. Evaluation may include feedback from user, statistical analysis, and the like. "); (Bhattacharyya, ¶482) " In embodiments, the recommendation engine 7114 may provide recommendations for designs 7210 (based on the Pareto 7118 and/or the convex hull 7120 engine outputs) and allow a user to compare and analyze the recommended designs 7210 (sensitivity analysis, weigh graphs, etc.). ") wherein the prediction includes a determined cost and an optimal configuration for the virtual population; and A list of recommended designs (as the generated prediction) includes cost and power indicating the number of required patients (optimal configuration of the virtual population) ((Bhattacharyya,¶479) " Referring to FIG. 74, in embodiments, the design recommendation engine 7114 may generate one or more outputs 7410, including a list or a set of the recommended designs 7210. The list of recommended designs 7210 may be provided with criterion values 7412, scenario parameters 7414, and/or trial design parameters 7416. A non-limiting example of a list of recommended designs is shown in FIG. 75. As shown, the list may include design ID, power, costs, and/or duration for each listed design. The term "power", as used herein with respect to a clinical trial design may represent a measure of one or more properties and/or statistics of the clinical trial, e.g., statistical power. For example, power may provide an indication of how many patients are required to avoid a type I (false positive) or type II (false negative) error. ")
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The identified recommended designs (predictions) can be visualized according to generated outputs (Bhattacharyya, ¶481) "In embodiments, the algorithm/engine 7114 may generate or output visualizations and/or interfaces (collectively shown as 7424) to compare two or more recommended designs 7210.")
Regarding claim 12, Bhattacharyya teaches The method of claim 9, as stated previously and further teaches wherein the parameters for the virtual population are optimized simultaneously. Data is received from simulated designs, wherein the simulated designs account for the characteristics of a subject of a virtual population, as described in the rejection of claim 1. Designs are characterized by parameters and optimal designs can be determined according to sequential or parallel (simultaneous) analysis ((Bhattacharyy, ¶242) "The optimality analysis component 302 may receive data from simulated designs 312 and determine one or more sets of optimal designs 322, 324. [[…]] In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality based on benchmark analysis 304. Benchmark analysis circuit 304 may determine optimality of designs based on a comparison of performance parameter values to one or more benchmark designs such as from historical data 314 and/or simulation data 312. In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality using sequential analysis 308 and/or parallel analysis 310. Sequential analysis circuit 308 and parallel analysis circuit 310 may use one or more different optimality functions 328 in parallel or sequentially to determine optimal designs"); ((Bhattacharyy, ¶229) "Optimality may be in relation to one or more performance parameters and the values of the performance parameters. An optimal design may be a design that achieves a most desirable value for one or more specific performance parameters."); ((Bhattacharyy, ¶212) " A trial design may define aspects of subjects that should be included in a trial. ")
Regarding claim 13, Bhattacharyya teaches The method of claim 9, as stated previously and further teaches wherein the parameters for the virtual population are optimized sequentially. Data is received from simulated designs, wherein the simulated designs account for the characteristics of a subject of a virtual population, as described in the rejection of claim 1. Designs are characterized by parameters and optimal designs can be determined according to sequential or parallel analysis ((Bhattacharyy, ¶242) "The optimality analysis component 302 may receive data from simulated designs 312 and determine one or more sets of optimal designs 322, 324. [[…]] In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality based on benchmark analysis 304. Benchmark analysis circuit 304 may determine optimality of designs based on a comparison of performance parameter values to one or more benchmark designs such as from historical data 314 and/or simulation data 312. In embodiments, the optimality analysis circuit 302 may include circuits for determining optimality using sequential analysis 308 and/or parallel analysis 310. Sequential analysis circuit 308 and parallel analysis circuit 310 may use one or more different optimality functions 328 in parallel or sequentially to determine optimal designs"); ((Bhattacharyy, ¶229) "Optimality may be in relation to one or more performance parameters and the values of the performance parameters. An optimal design may be a design that achieves a most desirable value for one or more specific performance parameters."); ((Bhattacharyy, ¶212) " A trial design may define aspects of subjects that should be included in a trial. ").
Regarding claim 14, Bhattacharyya teaches The system of claim 9, as stated previously and further teaches wherein the received data objects include clinical trial data, Models (data objects) include parameters and values combined (as pairs of data), wherein models are further described as numerical models describing behavior of elements of a clinical trial ((Bhattacharyya,¶217) " The simulation facility 110 may include models 126. As used herein, a model includes the combination of parameters and the values that describe a design and the scenario under which the design is evaluated.”); ((Bhattacharyya, ¶05) " Scenario space may include environmental and external factors that may affect trial design. In some embodiments, scenario data may include one or more mathematical or numerical models and methods that are related and/or describe one or more of human behavior, disease progress, drug behavior, and the like. Scenarios may include a combination of environmental variables that provide a specification or guidelines for generating virtual patient populations for a design study. Human behavior inputs may include trial execution characteristics, including how subjects adhere to regimen, dropout rates, and the like. Drug behavior may include models of drug behavior in a body and may include pharmacokinetic and pharmacodynamic models. The inputs may further include deviation models for one or more of the parameters of the models. Deviation models may be based on expected or previously measured distributions or variations in aspects such as human behavior, demographics, and the like. In embodiments, a plurality of different scenarios may be generated as potential inputs to the platform wherein each scenario may include different aspects of human behavior, disease progress, and drug behavior, and the like."). and wherein the virtual items include virtual patients in a clinical trial. ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."); ((Bhattacharyya, ¶316) "The virtual population 3002 may include data representing individual subjects (virtual patients) and characteristics of the subjects."); ((Bhattacharyya, ¶548) "In addition to the design of a clinical trial, the success of the clinical trial often depends on the ability to recruit a satisfactory number of patients, also referred to herein as "subjects", suitable to participate in the clinical trial").
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 2-3 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharyya as applied to claims 1 and 9 above, and further in view of Irurzun-Arana et al (Irurzun-Arana, I., Rackauckas, C., McDonald, T., and Trocóniz, I., “Beyond Deterministic Models in Drug Discovery and Development”, October 5, 2020, Trends in Pharmacological Sciences, Volume 41, Issues 11, pp 882-895) hereinafter referred to as Irurzun-Arana.
Regarding claim 2 Bhattacharyya discloses The system of claim 1, as stated previously; however, fails to explicitly disclose wherein the computational models include a differential equation.
The deficiency of what Bhattacharyya fails to explicitly teach is cured by Irurzun-Arana which discloses wherein the computational models include a differential equation (Irurzun-Arana, Page 883, ¶4) "Quantitative models can be further divided into deterministic and stochastic systems. Deterministic models are often described by a system of ODEs"); ((((Irurzun-Arana, Page 882, ¶2) " Especially in pharmacometrics, quantifying the mean tendency of a population using ordinary differential equations (ODEs) is the most common procedure, but also finding ways of modeling the variance of the data, which may be the result of extrinsic random events and/or sampling errors. This is the case of nonlinear mixed-effects (NLME) models (more commonly known as population PK/PD models), extensively used by the pharmaceutical industry and regulatory agencies to analyze experimental and clinical data, allowing the optimization of trial designs and proposing dosing paradigms to maximize treatment efficacy. ")
Bhattacharyya is analogous to the claimed invention because it is directed to the same field of endeavor of efficiency optimizations for modeling and simulations of clinical trial designs. Irurzun-Arana is analogous to the claimed invention because it likewise is related to the same field of endeavor of modeling and simulation approaches for clinical trials. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have specified that the computational models used by the system include a differential equation because some teaching, suggestion, or motivation in the art would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the use of models in a clinical trial simulation application and provides example models for describing drug behavior in the bodies of patients in the trial using exemplary models such as PK and PD models (Bhattacharyya, ¶305) " Drug behavior may include models of drug behavior in a body and may include pharmacokinetic and pharmacodynamic models. "). Bhattacharyya does not particularly describe the PK or PD models or any other models used by the system in terms of being characterized by or including differential equations. However; Irurzun-Arana explicitly states that the use of ordinary differential equations is a common procedure for modeling in pharmacometrics, particularly in the case of PK/PD modeling ((Irurzun-Arana, Page 882, ¶2) "Especially in pharmacometrics, quantifying the mean tendency of a population using ordinary differential equations (ODEs) is the most common procedure, but also finding ways of modeling the variance of the data, which may be the result of extrinsic random events and/or sampling errors. This is the case of nonlinear mixed-effects (NLME) models (more commonly known as population PK/PD models), extensively used by the pharmaceutical industry and regulatory agencies to analyze experimental and clinical data, allowing the optimization of trial designs and proposing dosing paradigms to maximize treatment efficacy."). Accordingly, because Bhattacharyya suggests the use of PK and PD models and Irurzun-Arana describes PK and PD models as commonly being characterized by differential equations, it would have accordingly been obvious to make the combination. The predictable results of this combination would be to have a traditionally characterized model with a familiar and straightforward implementation approach.
Regarding claim 3, Bhattacharyya teaches The system of claim 1, as stated previously; however, fails to explicitly disclose wherein the computational models include a stochastic model.
The deficiency of what Bhattacharyya fails to explicitly teach is cured by Irurzun-Arana which discloses wherein the computational models include a stochastic model. Combining stochastic models with existing computer modeling techniques is discussed for drug-disease modeling and simulation applications (Irurzun-Arana, Page 882, ¶1) "One such case is the stochastic modeling approach, which can be important when modeling small populations because random events can have a huge impact on these systems. In this review, we aim to raise awareness of stochastic models and how to combine them with existing modeling techniques, with the ultimate goal of making future drug–disease models more versatile and realistic."); ((Irurzun-Arana, Page 892, ¶2) " This structural model could be replaced with a stochastic model to introduce stochasticity in the dynamics of the model itself. ")
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have combined the PD and PK models disclosed by Bhattacharyya with stochastic models, as described by Irurzun-Arana for use in clinical trial modeling because some teaching, suggestion, or motivation would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the use of models in a clinical trial simulation application and provides example models for describing drug behavior in the bodies of patients in the trial using exemplary models such as PK and PD models (Bhattacharyya, ¶305) " Drug behavior may include models of drug behavior in a body and may include pharmacokinetic and pharmacodynamic models. "). Bhattacharyya does not particularly describe the PK or PD models or any other models used by the system in terms of being characterized as stochastic models. Irurzun-Arana suggests that stochastic modeling provides enhancements to accuracy in biomedical phenomena models because integrating stochasticity more accurately reflects realistic behavior and makes models more versatile and useful ((Irurzun-Arana, Page 892, ¶4) " Stochastic models could help to decipher part of this unexplained intrinsic variability by allowing the simulation of processes that can happen at random in every individual and, hence, help in differentiating between real source of errors during measurement collection and errors due to model misspecifications."); ((Irurzun-Arana, Page 892, ¶5) " Genetically identical cells differ widely in their responsiveness to drugs even in a uniform environment due to stochasticity in gene expression levels or other biochemical phenomena [61]. Thus, applying single-cell assays and quantitative modeling to determine which of the observed phenomena arise from processes that are inevitably stochastic, and which ones are explained by the presence of special subpopulation of cells (characterized by covariate models) could be important for the evaluation of different treatments [62] and can possibly explain large amounts of what was previously unknown variability."); ((Irurzun-Arana, Page 893, ¶2) " Here, we have raised awareness of stochastic models and how to combine them with existing modeling techniques with the ultimate goal of making future models more versatile and useful. "). Accordingly, the combination would have been obvious to achieve such benefits.
Regarding claim 10, the limitations are substantially similar to those recited for claim 2 (wherein the computational models include a differential equation) except for dependence on The method of claim 9. Accordingly, the claim is rejected for the same rationale as given for claim 2.
Regarding claim 11, the limitations are substantially similar to those recited for claim 3, (wherein the computational models include a stochastic model) except for the dependence on The method of claim 9. Accordingly, the claim is rejected for the same rationale as given for claim 3.
Claims 7, 8, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharyya as applied to claims 1 and 9 above, and further in view of Keating et al (Keating S. et al, “SBML Level 3: an extensible format for the exchange and reuse of biological models.” Molecular Systems Biology, August 2020, Volume 16), hereinafter referred to as Keating.
Regarding claim 7, Bhattacharyya teaches The system of claim 1, as stated previously; however, fails to explicitly disclose wherein the computational models are defined using a common interexchange format.
The deficiency of Bhattacharyya is cured by Keating which teaches wherein the computational models are defined using a common interexchange format. ((Keating, Page 2, Col 2, ¶2) "SBML's success in serving as an interchange format for basic types of models led communities of modelers to ask whether it could be adapted or expanded to support more types. "); ((Keating, Page 8, Col 1, ¶2) "Using SBML‐encoded models has become the norm to assess the accuracy of modeling software: initially it is done manually using models from BioModels Database (Bergmann & Sauro, 2008), and now, it is more commonly done using the SBML Test Suite (Box 2). SBML's semantics are defined precisely enough that many simulation systems can produce equivalent results for over 1200 test cases, lending confidence that SBML‐based simulations can be reproducible in different software environments."); (Keating (Page 8, Col 2, ¶2) "Others, such as Biophysical Journal (Nickerson & Hunter, 2017), recommend authors deposit models in repositories such as BioModels Database, which encourages the use of common standard formats such as SBML."); ((Keating, Page 2, Col 1, ¶2) " This drove efforts to create tool‐independent ways of representing models that could avoid the potential for human translation errors, be stored in databases, and provide a common starting point for simulations and analyses regardless of the software used (Goddard et al, 2001; Hucka et al, 2001; Lloyd et al, 2004). One such effort was SBML, the Systems Biology Markup Language. "). See also Figure 1 depicting exemplary applications of SBML to include systems pharmacology applications:
PNG
media_image1.png
622
716
media_image1.png
Greyscale
Keating is analogous to the claimed invention because it is reasonably pertinent to the problem faced by the inventor- that is, the present application pertains to modeling and simulating using computational models in a clinical trial application and Keating provides a data format for computational systems biology as a mechanism by which to characterize computational models of biological processes. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to incorporate the teachings of Keating into the disclosed system of Bhattacharyya because some teaching, suggestion, or motivation in the prior art would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the use of computational models in the system for modeling biological systems processes for clinical trial design, such as through PD and PK models, but does not particularly describe how they are defined. Keating provides a model description language that is largely used in the community for defining computational models for modeling and simulation applications in biological systems. Keating describes the Systems Biology Markup Language (SBML) as a successful interchange format that enables reproducibility and platform-agnostic data exchanges and further describes the SBML as being largely recognized and adopted in the community (Keating, Page 1, Col 1, ¶Abstract) "To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose "); ((Keating, Page 6, Col 2, ¶2) "SBML’s success can be attributed largely to its community-based development and its consensus-oriented approach. "). Because Bhattacharyya discloses the use of computational models without describing how they are particularly defined and because Keating provides a standardized mechanism by which to define such models, the combination would have accordingly been obvious. Particularly, because of the language’s high support and interoperability, one would have been compelled to use SMBL for ease of use, re-use, and exchange of models (Keating, Page 12, Col 1, ¶5) "SBML and associated software libraries and tools have been instrumental in the growth of systems biology. As modeling and simulation grew in popularity, SBML allowed researchers to exchange and (re)use new models in an open, well-supported, interoperable format. SBML has made possible much of the research pursued by the authors of this article and also helped us to structure our thoughts about our models and the biology they represent. Today, scientists can build, manipulate, annotate, store, reuse, publish, and connect models to each other and to basic data sources. In effect, SBML has turned models into a kind of data and transformed modeling in biology from an art to an exercise in engineering.")
Regarding claim 8, Bhattacharyya teaches The system of claim 1, as stated previously; however, fails to explicitly disclose wherein the computational models are defined using a symbolic domain-specific language representation.
The deficiency of Bhattacharyya is cured by Keating which teaches wherein the computational models are defined using a symbolic domain-specific language representation Suggestions for incorporating packages with symbolic representations (text-based, GUI-based, etc.) are given as well as suggestions for incorporating domain-specific languages integrated with programming language are given as avenues for enhancing SBML capabilities, wherein SBML is a language used by the community to define computational models in the biological technology space (see above rejection claim 7) ((Keating, Page 9, Table ¶Layout and Rendering) "The “layout” (Gauges et al, 2015) and “render” (Bergmann et al, 2018) packages extend SBML to allow graphical representations of networks or pathways to be stored within SBML files. "); (Keating, Page 11, Table ¶SBML throughout the model life cycle) "A signaling pathway can be designed graphically using CellDesigner (Funahashi et al, 2003). The resulting model can then be semi‐automatically annotated using the online tool semanticSBML (Krause et al, 2010). “; (Keating, Page 10, Col 1, ¶1) " A related challenge concerns human usability of SBML and similar XML‐based formats. Though SBML is intended for software, not humans, to use directly, desire for a text‐based or spreadsheet‐based equivalent is often voiced (e.g., Kirouac et al, 2019). Various answers have been developed in the form of text‐based notations (e.g., Gillespie et al, 2006; Smith et al, 2009) and spreadsheet conventions (e.g., Lubitz et al, 2016), with bidirectional translators for SBML. These formats have undeniable appeal for many users and use cases, despite that they do not capture the entirety of SBML (often having limited or missing facilities to express units, annotations, or SBML packages). Their chief drawback is that they become error‐prone to use as model size increases. Graphical user interfaces (GUIs; e.g., Funahashi et al, 2003; Hoops et al, 2006; Moraru et al, 2008) can overcome this; software with GUIs can help with the cognitive burden of tracking large numbers of model elements. On the other hand, GUIs can be tedious to use when entering large models, performance of some software does not scale well with increasing model sizes, and some cannot be controlled programmatically for automation purposes. A middle ground may be domain‐specific modeling languages layered on top of programming languages such as Python (e.g., Lopez et al, 2013; Olivier et al, 2005. However, these tend to appeal only to users who are comfortable with (or willing to take time to learn) the programming language used as a substrate. Overall, further innovation in this area would be welcome, both to help support SBML Level 3 packages and to help users cope with ever‐increasing model sizes. ")
It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to having incorporated the teachings of Keating into the system disclosed by Bhattacharyya because some teaching, suggestion, or motivation in the art would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the use of computational models in the system for modeling biological systems processes for clinical trial design, such as through PD and PK models, but does not particularly describe how they are defined. Keating provides a model description language that is largely used in the community for defining computational models for modeling and simulation applications in biological systems. Keating describes the Systems Biology Markup Language (SBML) as a successful interchange format that enables reproducibility and platform-agnostic data exchanges and further describes the SBML as being largely recognized and adopted in the community (Keating, Page 1, Col 1, ¶Abstract) "To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose "); ((Keating, Page 6, Col 2, ¶2) "SBML’s success can be attributed largely to its community-based development and its consensus-oriented approach. "). Because Bhattacharyya discloses the use of computational models without describing how they are particularly defined and because Keating provides a standardized mechanism by which to define such models and suggestions for improvements of the SBML by expressing the need for packages which capture symbolic domain-specific representations, the combination would have accordingly been obvious. Particularly, because of the language’s high support and interoperability, one would have been compelled to use SMBL and the suggested package modifications for ease of use, re-use, and exchange of models (Keating, Page 12, Col 1, ¶5) "SBML and associated software libraries and tools have been instrumental in the growth of systems biology. As modeling and simulation grew in popularity, SBML allowed researchers to exchange and (re)use new models in an open, well-supported, interoperable format. SBML has made possible much of the research pursued by the authors of this article and also helped us to structure our thoughts about our models and the biology they represent. Today, scientists can build, manipulate, annotate, store, reuse, publish, and connect models to each other and to basic data sources. In effect, SBML has turned models into a kind of data and transformed modeling in biology from an art to an exercise in engineering.")
Regarding claim 15, Bhattacharyya teaches The system of claim 9, as stated previously. The remaining limitations (wherein the computational models are defined using a common interexchange format or are defined using a symbolic domain-specific language representation) are an optional combination (or) of those features claimed in claims 7 and 8. Such features are both taught by Bhattacharyya in view of Keating, as given above in the rejections of claims 7 and 8 whereby the same rationale is applicable to this claim but not restated for brevity.
Claims 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharyya (US 2021/0319158 A1), in view of Allen et al (Allen, R., Rieger, T., and Musante, C., “Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models”, March, 2016, CPT Pharmacometrics Syst. Pharmacol, Volume 5, pp 140-146), hereinafter referred to as Allen.
Regarding claim 16, Bhattacharyya teaches except the limitations surrounded by brackets ([[…]]) A system having a multiple-data fitting interface for design optimization, the system having at least one processor configured for: ((Bhattacharyya, ¶249) "In embodiments, the platform may support global optimization of clinical trial design by connecting criteria space, design space, scenario space and performance space. The platform may provide users with visualizations for interactive exploration of the spaces. The platform may support global optimization by enabling design optimization and exploration across different styles of explorations."); ((Bhattacharyya, ¶209) "The platform 104 may provide for a system for providing users with facilities and methods for designing, evaluating, and/or comparing designs. The facilities described herein may be deployed in part or in whole through a machine that executes computer software, modules, program codes, and/or instructions on one or more processors, as described herein, which may be part of or external to the platform 104.")
receiving data objects that include pairs of data with information in the form of one or more computational models that model the behavior of multiple patients in a clinical trial; Models (data objects) include parameters and values combined (as pairs of data), wherein models are further described as numerical models describing behavior of subjects of a clinical trial ((Bhattacharyya,¶217) " The simulation facility 110 may include models 126. As used herein, a model includes the combination of parameters and the values that describe a design and the scenario under which the design is evaluated."); ((Bhattacharyya, ¶305) " Scenario space may include environmental and external factors that may affect trial design. In some embodiments, scenario data may include one or more mathematical or numerical models and methods that are related and/or describe one or more of human behavior, disease progress, drug behavior, and the like. Scenarios may include a combination of environmental variables that provide a specification or guidelines for generating virtual patient populations for a design study. Human behavior inputs may include trial execution characteristics, including how subjects adhere to regimen, dropout rates, and the like. Drug behavior may include models of drug behavior in a body and may include pharmacokinetic and pharmacodynamic models. The inputs may further include deviation models for one or more of the parameters of the models. Deviation models may be based on expected or previously measured distributions or variations in aspects such as human behavior, demographics, and the like. In embodiments, a plurality of different scenarios may be generated as potential inputs to the platform wherein each scenario may include different aspects of human behavior, disease progress, and drug behavior, and the like."). The models are described as being generated (received) (Bhattacharyya, ¶306) " In embodiments, simulation models may be generated by combining two or more categories of inputs, such as by combining design space and scenario space.").
pairing the received data objects into a collection that includes dependencies between the received data objects; Each simulation model (received data object) is described as corresponding to a specific combination of trial design and scenario, which is understood to characterize distinct a clinical trial ((Bhattacharyya,¶309) " In embodiments, each simulation model (i.e., a specific combination of a trial design and scenario) may be evaluated over the course of numerous simulation runs, and the number of simulations may vary depending on the project stage."). A set of clinical trials (as a collection of data objects) may be evaluated ((Bhattacharyya,¶518) " Referring now to FIG. 94, in addition to optimizing a design for a single clinical trial, embodiments of the platform 104 (FIG. 1) may provide for optimization of clinical trial designs across a plurality/set of clinical trials 9410 and/or aspects of the clinical trials. As will be appreciated, optimization over a set of related clinical trials may result in better overall performance for the set, as compared to optimizing each element, aspect, or clinical trial in the set individually and combining the results."). The multiple clinical trials of the sets are related (paired) according to associations (dependencies) (Bhattacharyya, ¶520) " As shown in FIG. 94, two or more of the clinical trials, e.g., clinical trial A 9412, clinical trial B 9414, and/or clinical trial C 9416 may be related to each other through one or more associations 9418. Non-limiting examples of associations 9418 include: trial sites 9420; an order of execution and/or dependencies 9422; shared resources 9424; clinical trial phases 9426; test subjects 9428, and/or other aspects of design space, scenario space and performance space.")
generating a virtual population based on the received data objects, Population models/population data include characteristics of subjects in a clinical trial and are used to generate the virtual population (Bhattacharyya, ¶313) " Population models may define characteristics of subjects in a clinical trial. "). ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."). Population data may be received from the data facility (Bhattacharyya, ¶224) "The platform 104 may include and/or provide access to one or more data facilities 138. Data in the data facilities may include design histories 140, simulation data 142, site data 144, resource data 146, population data 148, and the like."); (Bhattacharyya, ¶385) "Data flow through the platform 104 may be facilitated by data records that are stored and retrieved from one or more databases in data facility 138. ").
wherein the virtual population comprises multiple virtual patients, with each virtual patient comprising a combination of model parameters that describe data in the received data objects, and ((Bhattacharyya, ¶315) "The virtual population may be generated according to a population model and/or real-world population data. The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects. Each subject in the virtual population may be associated with characteristics such as demography data, survival models, dropout rate, expected responses, and the like for each subject."); ((Bhattacharyya, ¶316) "The virtual population 3002 may include data representing individual subjects (virtual patients) and characteristics of the subjects.")
[[wherein the generation of the virtual population includes a user-determined or default cost function and an algorithm for finding an optimal configuration
for the virtual population with respect to said cost;]]
generating a prediction for the virtual population by simulating with each of the virtual patients; and Subjects of the virtual population are used in simulations, whereby a simulation generates a response for the subject of the virtual population (Bhattacharyya, ¶317) "Subjects in the virtual population may be used for simulation of trials."). A Pareto analysis evaluates design simulations to determine and output identification of recommended globally optimum designs (prediction) upon which a sensitivity analysis is performed (Bhattacharyya, ¶414) " The optimality determining circuit 5110 may identify globally optimum designs 5114 based on Pareto analysis. “) ;((Bhattacharyya, ¶416) " As shown in FIG. 53, a method for determining optimum designs using Pareto analysis may include obtaining trial design simulations 5302. The method may further include evaluating optimality for each design using Pareto analysis 5304. The method may include identifying optimal designs based on the Pareto analysis 5306. The optimum designs may be evaluated 5308. Evaluation may include feedback from user, statistical analysis, and the like. "); (Bhattacharyya, ¶482) " In embodiments, the recommendation engine 7114 may provide recommendations for designs 7210 (based on the Pareto 7118 and/or the convex hull 7120 engine outputs) and allow a user to compare and analyze the recommended designs 7210 (sensitivity analysis, weigh graphs, etc.). ")
generating an output, wherein the output includes a visualization of the prediction for the virtual population. The identified recommended designs (predictions) can be visualized according to generated outputs (Bhattacharyya, ¶481) "In embodiments, the algorithm/engine 7114 may generate or output visualizations and/or interfaces (collectively shown as 7424) to compare two or more recommended designs 7210.")
Bhattacharyya does not particularly disclose; however, Allen teaches wherein the generation of the virtual population includes a user-determined or default cost function and an algorithm for finding an optimal configuration for the virtual population with respect to said cost; A typical cost function (default) is modified (user-determined) to generate a plausible patient population ((Allen, Page 142, Col 2, ¶1-2) "An important prerequisite to this approach is the ability to generate a large number of plausible patients within the region of the empirical data. To accelerate this process we take an initial parameter guess (within the predefined bounds) and optimize this choice until the required outputs are within physiologically plausible ranges. Rather than optimize to specific points, it is more efficient to be agnostic as to where in the plausible ranges the optimization routine ends. To implement this we shift the typical cost function f(p) we would use optimizing a model to a new function, g(p), where we consider both as purely dependent on the parameter set p. If we constrain parameters using a number of model outputs Mi(p), with data di then f (in the simplest, unweighted case) would be:
PNG
media_image2.png
34
154
media_image2.png
Greyscale
To generate plausible patients, we modify this sum-of-squared errors expression to:
PNG
media_image3.png
52
304
media_image3.png
Greyscale
where ui and li are the predefined plausible upper and lower bounds, respectively, for Mi(p). This expression ensures that if Mi(p) is in the plausible range then the contribution of the corresponding term in the expression is zero. "). An algorithm is depicted and described in Figure 1 for the generation of an optimized best virtual population according to the patients in the plausible population (Allen, Page 141, ¶Figure 1 Description) "Overview of algorithm for efficient generation and prevalence-based selection of virtual patients. To generate virtual patients from a model, the prior information (green boxes) is used to define physiologically reasonable ranges for model outputs and parameter values. An initial parameter guess is optimized until model outputs are physiologically plausible. This is repeated multiple times to form a plausible population. A virtual population is constructed by selecting from this population with probability proportional to the prevalence in the real population relative to the prevalence in the plausible population. This selection is optimized to produce the best virtual population given the patients in the plausible population.") See corresponding Figure 1:
PNG
media_image4.png
1055
1000
media_image4.png
Greyscale
Allen is analogous to the claimed invention because it is related to the same field of endeavor of the present application of optimizations in biological systems modeling applications. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the disclosure of Bhattacharyya to include the teachings of Allen because some teaching, suggestion, or motivation would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the generation of virtual populations from population data and population models but does not particularly consider a cost function or describe a mechanism/algorithm by which the generation occurs (Bhattacharyya, ¶315) " In embodiments, a virtual population may be pre generated. The virtual population may be generated according to a population model and/or real-world population data."); ((Bhattacharyya, ¶317) " In embodiments, a virtual population 3002 may be pre-generated before simulation start or may be generated in real time during simulation. In some embodiments, subjects may be generated as they are needed and/or requested for simulation using population models and the subjects may be added to a virtual population each time it is generated. "). Allen provides a computationally efficient virtual population generation technique in the context of modeling for clinical trial simulations and analysis and notes that the approach leads to better confidence in predictions and better quantification of uncertainty ((Allen, Page 140, ¶Study Highlights) "Generation of realistic virtual populations, and a deeper exploration of parameter uncertainty, should lead to better confidence in the predictions and better quantification of uncertainty of systems pharmacology models, particularly in the context of clinical trial simulations and analysis "); ((Allen, Page 141, Col 1, ¶2-Col 2, ¶1) " Here we propose a new algorithm for generating biologically reasonable VPops. We will show how this algorithm complements previous approaches by being intuitive, computationally efficient, and avoiding the problem of over- weighting VPs. "). Accordingly, to achieve the touted benefits disclosed by Allen, it would have been obvious to use the efficient generation approach as the particular mechanism for virtual population generation for enhancing the disclosed methods by Bhattacharyya.
Regarding claim 18, the proposed combination of Bhattacharyya in view of Allen teaches The system of claim 16, as given above and wherein Bhattacharyya further teaches wherein the different trial types include a normal trial and a steady state trial. Under broadest reasonable interpretation and when read in light of the specification, a normal trial is interpreted to be a single dose trial, and a steady state trial is interpreted to be a repeated, multi-dose trial. The parameters of the clinical trial designs include the dose frequency of drug, including all permutations of the different frequencies which would be inclusive of a singular dose trial and a repeated multi-dose trial. ((Bhattacharyya, ¶213) " Space definitions 116 may include aspects of design space. As used herein, design space may include the set of parameters and values of the parameters that define different options and variations of designs. Parameters may include design type, dose of drug, frequency of drug, maximum duration, patient inclusion/exclusion criteria, randomization type, and the like. The design space may include all possible permutations of the parameters. For example, one design type may be configured with different doses of a drug and different frequency of the administration of the drug. The design space may include all possible permutations of the different doses of the drug for all the different frequencies of the administration of the drug. The design space may include all the permutations of all the parameters associated with design.")
Regarding claim 20, the proposed combination of Bhattacharyya in view of Allen teaches The system of claim 16, as stated previously, and Bhattacharyya further teaches wherein each calibrated set of parameters is a virtual patient ((Bhattacharyya, ¶314) " In embodiments, for a simulation, virtual subjects may be selected from population models. A population model may include subject models that include various subject characteristics such as demography data, survival models (control and treatment), dropout rate (control and treatment), expected responses, and the like. Characteristics of subjects in a population model may be associated with different distributions "); ((Bhattacharyya, ¶316) " The virtual population 3002 may include data representing individual subjects (virtual patients) and characteristics of the subjects."), and a virtual population is a collection of virtual patients. ((Bhattacharyya, ¶315) " The virtual population may be a list or other data structure that includes thousands or even millions of different virtual subjects.")
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over the proposed combination of Bhattacharyya in view of Allen as applied to claim 16 above, and further in view of Keating (Keating S. et al, “SBML Level 3: an extensible format for the exchange and reuse of biological models.” Molecular Systems Biology, August 2020, Volume 16).
Regarding claim 17, the proposed combination above teaches The system of claim 16, as stated previously; however, fails to explicitly disclose wherein the computational models are defined using a symbolic domain-specific language representation.
What the proposed combination fails to explicitly disclose; however, is taught by the proposed combination in view of Keating, as described in the rejection of claim 8, which includes a substantially similar limitation. For brevity, the rationale for the rejection of this claim is not restated and the motivation to further combine references follows that as provided in the rejection of claim 8.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharyya in view of Allen as applied to claim 16 above, and further in view of Moore (Moore, H., “How to mathematically optimize drug regimens using optimal control”, February 2018, Journal of Pharmacokinetics and Pharmacodynamics, Volume 45, pp 127-137), hereinafter referred to as Moore.
Regarding claim 19, the proposed combination teaches The system of claim 16, as stated previously; however, fails to disclose wherein the prediction is based on a constant rate added to a differential equation for a fixed duration. A model is described for capturing the behavior of a dynamical system for use in drug development, whereby the model is characterized by differential equations that account for time and whereby the differential equations include non-negative constants. ((Moore, Page 32, Col 1, ¶5) " The dynamical systems of interest in drug development are those that represent states related to diseases. For example, in the case of a cancer of the blood, the concentration of cancerous cells in a patient’s peripheral blood could be a state we are interested in. We can incorporate anti-cancer treatments as controls in the system. In the dynamics of cancer and therapy, there are host immune system cells that play important roles, and they would be included as states as well. The idea of a “minimal model” that captures the key characteristics of the state and control dynamics leads us to “semi-mechanistic models” [49, p. 38]. The model in [53] is semi-mechanistic and includes cancer cells, C(t), and two types of immune system cells: naive T cells, T_n(t), and effector T cells, T_e(t) . Each of the cell types is dependent on time t, and time-dependent drug levels (controls) are denoted by u_1(t) and u_2(t) . The relationships between the cell concentrations and the controls are represented in the differential equations shown here:
PNG
media_image5.png
144
330
media_image5.png
Greyscale
where ,T_n(0), T_e(0) , and C(0) are known. The parameters s_n, d_n, k_n, a_n, a_e, d_e, y_e, r_c, C-max, d_c, y_c , , are all assumed to be non-negative constants. More information about the system and the parameters is given in Moore and Li [52] and Nanda et al. [53]."). The models are described as being used to predict optimal regimens (Moore, Page 136, Col 1, ¶3) " In planning therapeutic regimens for preclinical or clinical use, we can apply optimal control to these models, to predict optimal regimens based on quantitative therapeutic goals.")
Moore is analogous to the claimed invention because it is related to the same field of endeavor of computational modeling for disease treatment, particularly with respect to clinical trial design. It would have been obvious to one of ordinary skill to which said subject matter pertains at the time the invention was filed to have modified the disclosure of Bhattacharyya to include the teachings of Moore because some teaching, suggestion, or motivation would have led one having skill in the art to do so in order to arrive at the claimed invention. Bhattacharyya discloses the use of computational models in clinical trial design applications whereby models are simulated to identify/predict optimal designs. The simulation models of Bhattacharyya are not described in any explicit manner to include being characterized by differential equations, though Bhattacharyya does disclose the utilization of PK and PD models which are understood to be traditionally characterized by sets of differential equations. Moore discloses the use of a model that captures the dynamics of disease and the effects of therapies on the dynamics whereby the treatment can be quantified and an optimal regimen can be predicted from such model ((Moore, Page 128, Col 1,¶2) " The first step is to create an appropriate model of the dynamics of the disease and the effects of therapies on the dynamics. The model should be detailed enough to incorporate effects of the particular therapies of interest. Next, the goal of the treatment needs to be quantified. Usually we want to maximize the benefits of the therapies and minimize their side effects. When we combine terms representing these effects, using appropriate signs and weights, we obtain a mathematical expression to be optimized. Once we have determined parameter values to use for the system (see the “Discussion” section for more on this), we can compute the optimal control solution. We should then evaluate the method by comparing outcomes for a predicted optimal regimen with outcomes for standard regimens."). Moore further suggests the integration of PK models into the model of the disease and therapy dynamics ((Moore, Page 135, Col 1, ¶3) " There are situations in which pharmacokinetics (PK, the drug concentrations over time) should be incorporated into the mathematical model of the disease and therapy dynamics. In Example 1, this was not done, as the dosing of the drugs was daily and the time period considered was most of a year. For reference, Shudo et al. [57, 2.5–2.6] considered the effect of once-weekly dosing over a period of a few weeks. Models without PK were able to describe the drug effect, with only slight differences from models with PK. For similar reasons, the other examples in this paper also do not include PK. Martin and Teo [47] specifically address the incorporation of PK in optimal control models when it is needed, such as when optimizing the timing of doses."). Accordingly, by integrating the particular model implementations of Moore as a simulation model as used in Bhattacharyya such that Moore’s model is used to determine the optimal trial design, one would arrive at the claimed invention, whereby one would be compelled to do so because Moore explicitly provides a motivation by stating “The techniques and examples in this article are intended to support mathematical modelers in the biopharma industry in using optimal control to optimize drug regimens.” (Moore, Page 135, Col 2, ¶6).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Reiger et al (Reiger, T. et al, “Improving the generation and selection of virtual populations in quantitative systems pharmacology models”, 2018, Progress in Biophysics and Molecular Biology, Volume 139, pp 15-22) discloses a guide for the selection and implementation of various algorithms to facilitate the generation of robust virtual populations in mathematical models, particularly in the realm of clinical trial design.
Liu et al (Liu, Q. et al, “A Stochastic Analysis of the One Compartment Pharmacokinetic Model Considering Optimal Controls”, 2020, IEEE Access, Volume 8, pp 181825-181834) suggests the extension of deterministic PK/PD models to include a stochastic component, whereby the PK/PD models are characterized by a differential equation model. The model used predicts optimal dosing and timing schedules for clinical trial applications.
Bois (Bois, F., “Physiologically Based Modelling and Prediction of Drug Interactions”, 2010, Basic & Clinical Pharmacology & Toxicology, Volume 106, pp 154-161) teaches the use of the SBML syntax for representing biochemical pathway models, wherein the model representation is described as symbolic and not directly referring to a set of differential equations or mathematical representations. The models described therein are used as a global model that is used to predict substance interaction and to study the behavior of complex mixtures, representative human population exposure reality.
Zhang et al (Zhang, T. et al, “Two heads are better than one: current landscape of integrating QSP and machine learning”, February 1, 2022, Journal of Pharmacokinetics and Pharmacodynamics, Volume 49, pp 5-18) teaches the idea of integration of machine learning with quantitative systems pharmacology models for drug development applications. The reference describes using stochastic approaches for the generation of virtual populations and explicitly notes that increased confidence in QSP model predictions requires model calibration, parameter estimation, sensitivity analysis, and proper virtual patient population generation and describes the need for leveraging ML for reduced computational requirements.
Zhang (Zhang, T., and Tyson, J., “Understanding virtual patients efficiently and rigorously by combining machine learning with dynamical modeling”, January 5, 2022, Journal of Pharmacokinetics and Pharmacodynamics, Volume 49, pp 117-131) teaches the combination of machine learning with dynamical modeling for QSP. The reference describes the generation of virtual patients and virtual populations and further provides a computational workflow for the efficient and robust analysis of virtual populations by combining ML and dynamical analysis.
Rackauckas et al (Rackauckas, C. et al, “Accelerated Predictive Healthcare Analytics with Pumas, a High Performance Pharmaceutical Modeling and Simulation Platform”, November 30, 2020, bioRxiv) presents a NLME framework which Pumas utilizes for personalized precision dosing. The framework is described as demonstrating acceleration over previous software in the standard ODE NLME cases while demonstrating automated parallelism. The reference describes the approach that enables predictions which were previously unattainable due to prohibitive computational costs. A code snippet is provided for creating a virtual population of a plurality of virtual subjects whereby the model is simulated, fitted, and again simulated according to fitted parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY GORMAN LEATHERS whose telephone number is (571)272-1880. The examiner can normally be reached Monday-Friday, 9:00 am-5:00 pm ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, EMERSON PUENTE can be reached at (571) 272-3652. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/E.G.L./Examiner, Art Unit 2187
/IFTEKHAR A KHAN/Primary Examiner, Art Unit 2187
Prosecution Insights