DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 09/11/2025
Claims 1-13, 15-21 are presented for examination.
Information Disclosure Statement
The IDS dated 09/11/2025 has been reviewed. See attached.
Finality
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Response to Arguments - 101
Applicant's arguments filed 09/11/2025 have been fully considered but they are not persuasive.
Applicant argues that the claims do not recite an abstract idea.
Examiner responds by explaining that several abstract ideas are recited by the claims. Particularly:
Claim 1:
generating, via at least one processor, a plurality of base datasets using a random number generator, wherein the base datasets correspond to one or more clinical trial designs wherein the plurality of base datasets includes a survival time dataset, a treatment ID dataset, and a dropout time dataset;
A person could generate a set of random numbers in their by coming up with a series of arbitrarily chosen values. For example, a person could flip a coin a hundred times and tabulate the results using a pen and paper.
Doing so “via at least one processor” and “using a random number generator” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, scenario specifications corresponding to the one or more clinical trial designs;
Determining specifications for a clinical trials is practical to perform in the human mind, and consists of deciding arbitrary trial conditions, such as the length of the trial, how results should be analyzed, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
identifying, via the at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of the plurality of base datasets based on the scenario specifications;
A human mind could reasonably come up with a distinct function for a number of datasets to transform the data therein. For example, a person could write down several numeric datasets with a pencil and paper, then decide that the transformation function for the first set is to add 1 to every number, a second function to add 2 to every number in a second set, and so on.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
generating, via the at least one processor, scenario parameters based on the transformed datasets and corresponding to the one or more clinical trial designs;
Generating arbitrary parameters based on observed data is a mental process equivalent to observing that data and judging what kind of parameters best suit that data. For example if the datasets suggest high dropout rates, parameters that work better for small sample size trials may be chosen.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
transforming, via the at least one processor, the each of the plurality of base datasets using the corresponding one of the plurality of distinct transformation functions, wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset;
Transforming data through the use of a mathematic “transformation function” is an explicit recitation of a math operation in the claim, and is thus a mathematic concept. In other words, the claim element does not merely “involve” math; it explicitly discloses it in the claim itself.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Determining the statistical distribution of the simulation output is merely the act of determining the distribution of a set of numeric data. This can be done both mentally and purely mathematically; to perform this process mentally, data can be drawn on a plot and the shape of the resulting plot can give insight into the distribution of the data, for example data that is normally distributed will approximate a bell curve.
To determine such distribution purely numerically other tests can be used, for example the Shapiro–Wilk test for checking for normal distribution.
Claim 9:
determining, via at least one processor, information fraction sets for a set of a plurality of designs, wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions
A person could reasonably mentally design a set of theoretical clinical trials and write down target sample sizes for each, as with a pen and paper. That person could then observe the actual number of patients that show up to the trial, marking the number. Dividing the number of observed patients by the target sample size is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
This element is also a mathematic process, as analyzed below.
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that these elements are not mental processes, they are also examples of mere data gathering.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Dividing the number of available data points by the expected data point count is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
This element is also a mathematic process, as analyzed below.
simulating, via the at least one processor, a design to determine test statistics for the design;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
generating, via the at least one processor, a decision to stop or proceed with sample size re-estimation for the simulated design based on the test statistics and the precomputed boundary condition;
Deciding whether or not to change the sample size is a mental process that involves observing the results of the simulation, and judging if the sample population was the right size to get meaningful results. For instance, if a trial to determine what percentage of all people are over the age of 80 has a sample size of two and returns a result that indicates that 100% of the global population is over the age of 80, a researcher would reasonably conclude that the sample size was not large enough to be representative of the general population. Additionally, a person could look at statistics indicating a performance rating of 2 out of 10 and associated boundary conditions and decide to change the sample size to get better results.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via at least one processor, information fraction sets for a set of a plurality of designs, wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions
Determining fractions by calculating “a number of patients observed divided by a target patient sample size” is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Determining a ratio between two numbers is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Wherein the method further comprises precomputing, via at least one processor and for each information fraction set, a boundary condition for a test statistic;
Precomputing (i.e. calculating) a numeric boundary condition is a mathematic calculation and thus a mathematic concept.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Determining the statistical distribution of the simulation output is merely the act of determining the distribution of a set of numeric data. This can be done both mentally and purely mathematically; to perform this process mentally, data can be drawn on a plot and the shape of the resulting plot can give insight into the distribution of the data, for example data that is normally distributed will approximate a bell curve.
To determine such distribution purely numerically other tests can be used, for example the Shapiro–Wilk test for checking for normal distribution.
Claim 21:
generating, via at least one processor, a set of scenario parameters from a plurality of base datasets, wherein the plurality of base datasets includes a survival time dataset, a treatment ID dataset, and a dropout time dataset,
Generating arbitrary parameters based on observed data is a mental process equivalent to observing that data and judging what kind of parameters best suit that data. For example if the base data suggests high dropout rates, parameters that work better for small sample size trials may be chosen.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
wherein the generating includes identifying, via the at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of the plurality of base datasets based on the scenario parameters,
A human mind could reasonably come up with a distinct function for a number of datasets to transform the data therein. For example, a person could write down several numeric datasets with a pencil and paper, then decide that the transformation function for the first set is to add 1 to every number, a second function to add 2 to every number in a second set, and so on.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
identifying, via the at least one processor, look positions for a plurality of designs;
The specification describes look positions as “In embodiments, analysis times may be referred to as looks or look positions.” Determining the relevant time of a piece of data is a mental process that involves observing that data and finding the relevant time index, as with observing data on a time-indexed graph. This limitation is therefore mental process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, information fraction sets for the look positions for the plurality of designs, wherein determining information fraction sets comprises: … wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions.
This element is mental math process. The specification describes the information fraction as ([Par 873] “In one example, an information fraction may be defined as the number of patients observed divided by the target patient sample size at each look position. In another example, an information fraction may be defined as the number of observed deaths at a look position versus the total number of expected deaths.”) In light of this, determining the information faction sets is equivalent to observing a number of patients and dividing that number by the target patient sample size. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that these elements are not mental processes, they are also examples of mere data gathering.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Dividing the number of available data points by the expected data point count is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% result on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
transforming, via the at least one processor, the each of the plurality of base datasets using the corresponding one of the plurality of distinct transformation functions, wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset;
Transforming data through the use of a mathematic “transformation function” is an explicit recitation of a math operation in the claim, and is thus a mathematic concept. In other words, the claim element does not merely “involve” math; it explicitly discloses it in the claim itself.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, information fraction sets for the look positions for the plurality of designs, wherein determining information fraction sets comprises: … wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions.
This element is mental math process. The specification describes the information fraction as ([Par 873] “In one example, an information fraction may be defined as the number of patients observed divided by the target patient sample size at each look position. In another example, an information fraction may be defined as the number of observed deaths at a look position versus the total number of expected deaths.”) In light of this, determining the information faction sets is equivalent to observing a number of patients and dividing that number by the target patient sample size. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that these elements are not mental processes, they are also examples of mere data gathering.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Dividing the number of available data points by the expected data point count is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% result on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
determining, via the at least one processor, information fraction sets for the look positions for the plurality of designs, wherein determining information fraction sets comprises: … wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions.
Determining the value of a fraction is the act of mathematically calculating that fraction.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Determining a ratio between two numbers is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
wherein the method further comprises: computing, via the at least one processor and for each information fraction set, a boundary for a test statistic;
Computing (i.e. calculating) a numeric boundary condition is a mathematic calculation and thus a mathematic concept.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
In addition to the above explained elements, the applicant argues that: a person could not simulate a clinical trial design because it is too complex, and that the use of a clinical trial design with a sample size of one would be unreasonable.
Examiner responds by explaining that the term “clinical trial design” does not imply a numeric minimum sample size. While using a sample size of one may not be what the applicants envision, it nonetheless reads on the plain language of the claim. Even if a larger sample size was used, for example 15-20, this is still possible to perform in the human mind. The use of such a small sample size was merely used in previous arguments to highlight the simplicity. The arguments hold whether the sample size is 1, 5, 20, etc.
Once again, the examiner notes the claim does not require the consideration of numerous variables, parameters, tradeoffs, and the like resulting in a very large number of possible variations or that the system would “require 50,000 hours or more to complete” as argued by the applicants. Further the only “complexity” required by the claims is that there are a plurality of datasets including a survival time dataset, a treatment ID dataset, a dropout time dataset, scenario parameters and scenario specifications. A minimum size for any of these sets is not specified, and thus under BRI considering the data used for the simulations could involve the use of as little as 5 data points. Given the survival time dataset, a treatment ID dataset, a dropout time dataset, scenario parameters and scenario specifications {[5 min], [Peanut Butter],[Never], [Sample size: 6 people, half of which are extremely allergic to peanuts], [Performance criteria: Patient stays alive]} a person could “simulate” the trial in their mind and assume that the trial of giving a group of people comprised 50% of those extremely allergic to peanut butter will result in about half the patients having an allergic reaction.
Secondly, even if the complexity requirements were claimed, the element of simulating the trial is extremely high-level, generic, and results-oriented; the improvement in speed and capability to handle more data is merely a side effect of applying the abstract idea on a generic computer. See MPEP 2106.05(f): "The court thus held the claims ineligible, because the additional limitations provided only a result-oriented solution and lacked details as to how the computer performed the modifications, which was equivalent to the words "apply it". 850 F.3d at 1341-42; 121 USPQ2d at 1947-48 (citing Electric Power Group., 830 F.3d at 1356, 1356, USPQ2d at 1743-44 (cautioning against claims "so result focused, so functional, as to effectively cover any solution to an identified problem"))" and "TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept." As well as MPEP 2106.05(a): "a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);"
Applicant argues that transforming data is not a mathematic process.
Examiner responds by explaining that while the claims themselves may not explicitly recite an equation in numeric form (i.e. claiming “y=mx+b”), it is clear that this transformation is a textual placeholder for a mathematic equation. Transforming data through the use of a mathematic “transformation function” is an explicit recitation of a math operation in the claim, and is thus a mathematic concept. In other words, the claim element does not merely “involve” math; it explicitly discloses it in the claim itself. For further evidence see the specification at ([Par 738] “In some cases, the score components may be normalized or transformed before the score component is used in the computation of a score. Score components may be normalized according to the type of data (i.e. Boolean, integer, float, string, etc.), number of possible values (i.e. a set of possible values, continuous values), range of values (i.e. difference between maximum and minimum values in the simulation data), and the like. … For example, a score component x may be normalized to a score component x' according to x '= (x - x min ) / (xmax- x min ).”)
Applicant argues that the limitations of simulating, via the at least one processor, a design to determine test statistics for the design; … generating, via the at least one processor, a decision to stop or proceed with sample size re-estimation for the simulated design based on the test statistics and the precomputed boundary condition; and transmitting, via the at least one processor, the decision. are not mathematic processes.
Examiner responds by explaining that, none of these steps were alleged to have been math concepts, and therefore this argument is moot.
Applicant argues that the steps of wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design; determining, via the at least one processor, a ratio of the available information to the total expected information; do not recite a math concept.
Examiner responds by explaining that these elements do in fact describe a math concept in concert with a mental process, specifically:
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Determining a ratio between two numbers is an explicit mathematic calculation and thus a mathematic process.
In short the elements describe a mathematic calculation based on a series of mental observations and judgements.
Note that these arguments apply for the relevant limitations of both claims 9 and 21.
Applicant argues that the claims provide an improvement to technology by reducing the computation time associated with clinical trial simulation.
Examiner responds by explaining that this alleged improvement arises from generating and transforming the data. Both of these operations are abstract ideas analyzed under Step 2A Prong 1, and thus cannot provide an improvement to technology. Further, any alleged improvement to speed or data scale capability (i.e. that such a process “would require 50,000 hours or more to complete” if done by hand) is merely a side effect of using a generic computer to perform the process. (MPEP 2106.05(f)(2): … Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).)
Applicant argues that the computer components, in combination, perform functions that are not merely generic.
Examiner responds by explaining that this is not the case. Even if, for the sake of argument, elements such as the simulation that are primarily treated as part of the abstract idea are treated as additional elements, the claims still do not recite a combination of additional elements that is anything more than generic. For example, the additional elements of claim 1, under such an assumption, consist of a generic processor, a “simulation” recited at an extremely high level of generality with no specifics recited, and a step of transmitting data. These are all very generic features that are not sufficient to provide significantly more.
Claims 9 and 21 add the step of generically obtaining data to this process, also recited with very little specificity. A claim element that amounts to merely gathering data is not indicative of integration into a practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP 2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v.
Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
For the features not explicitly recognized by the courts as being WURC (as generic data transmission over a network is. See MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)) A preponderance of evidence is provided to the WURC nature of those elements. For example:
simulating, via the at least one processor, a design to determine test statistics for the design;
The courts have found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
Evidence that simulating a clinical trial is WURC can be found in:
Fundamentals of Clinical Trials ([Section 6 Page 108 Par 1])
The Role Of Modeling & Simulation In Clinical Trials ([Page 1 Par 1-2])
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment ([Page 221 Par 2 – Page 222 Par 1])
How Simulation Can Transform Regulatory Pathways ([Page 1 Par 1-3])
Response to Arguments - 103
Applicant's arguments filed 09/11/2025 have been fully considered but they are not persuasive.
Applicant argues that no prior art teaches wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset; nor determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Examiner responds by explaining that, firstly, Springer teaches wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset. Specifically, it teaches transforming datasets ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) and describes associating treatments with survival time. ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 12 Page 220 Table 1] Shows a dataset including the use of different treatments [Section 16 Page 301 Fig. 16.4] Shows a graph of a dataset with separately identified treatments and survival rates over time per treatment {dataset including different treatment IDs with associated survival information over time})
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Further, Abbas teaches determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations; and ([Section 2.3.2 Page 1057 Col 1 Par 6-7] “Assuming that the patient attends the next visit, then the value of the outcome variable (cholesterol level) will be updated according to the sum of the new mean level: the value generated from the first normal distribution Dist1 at the baseline visit and the new value generated by the second normal distribution Dist2. That is, Li = Meanv + (dist1v = dist1v=1) + (dist2v = N{0;sv}). This process is repeated until the patient either reaches the last visit of the trial or the point when they drop out.”) [Section 2.3.2 Page 1057 Col 1 Par 5] “After generating the cholesterol value, the model applies the probability of a patient dropping out through another uniform distribution generator between 0 and 1. If the generated probability is equal to or less than the dropout probability, then the patient will continue receiving the treatment in the next visit; if not, the patient will drop out from the trial. In order to account for centrespecific dropout probability, we converted the probability of the trial shown in Table 1, Pd(v), into the k(v) rate given by formula 5. Then, we used the rate found to calculate the intended probability depending on c centre, Pd(v, c), given by formula 6. This equation assumes that this dropout probability increases as the number of centres increases according to an exponential distribution…. where Pd(v,c) is the dropout probability at visit v (v = 0, 1, 2,...) and centre c (c = 1, 2, 3,...). [Examiner’s note: the distribution for dropout depends on both the ordered simulated visit number through time, i.e. is based on the time of the simulation, and the center location, i.e. the simulation location])
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 1-13, 15-21 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without significantly more.
Claim 1(Statutory Category – Process)
The claim recites a mental process, specifically:
generating, via at least one processor, a plurality of base datasets using a random number generator, wherein the base datasets correspond to one or more clinical trial designs wherein the plurality of base datasets includes a survival time dataset, a treatment ID dataset, and a dropout time dataset;
A person could generate a set of random numbers in their by coming up with a series of arbitrarily chosen values. For example, a person could flip a coin a hundred times and tabulate the results using a pen and paper.
Doing so “via at least one processor” and “using a random number generator” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, scenario specifications corresponding to the one or more clinical trial designs;
Determining specifications for a clinical trials is practical to perform in the human mind, and consists of deciding arbitrary trial conditions, such as the length of the trial, how results should be analyzed, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
identifying, via the at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of the plurality of base datasets based on the scenario specifications;
A human mind could reasonably come up with a distinct function for a number of datasets to transform the data therein. For example, a person could write down several numeric datasets with a pencil and paper, then decide that the transformation function for the first set is to add 1 to every number, a second function to add 2 to every number in a second set, and so on.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
generating, via the at least one processor, scenario parameters based on the transformed datasets and corresponding to the one or more clinical trial designs;
Generating arbitrary parameters based on observed data is a mental process equivalent to observing that data and judging what kind of parameters best suit that data. For example if the datasets suggest high dropout rates, parameters that work better for small sample size trials may be chosen.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Determining the statistical distribution of the simulation output is merely the act of determining the distribution of a set of numeric data. This can be done both mentally and purely mathematically; to perform this process mentally, data can be drawn on a plot and the shape of the resulting plot can give insight into the distribution of the data, for example data that is normally distributed will approximate a bell curve.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
See below for analysis as a math process.
The claim also recites a mathematical process, specifically:
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Further, the MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
transforming, via the at least one processor, the each of the plurality of base datasets using the at least one the corresponding one of the plurality of distinct transformation functions, wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset;
Transforming data through the use of a mathematic “transformation function” is an explicit recitation of a math operation in the claim, and is thus a mathematic concept. In other words, the claim element does not merely “involve” math; it explicitly discloses it in the claim itself.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
To determine such a distribution purely numerically several mathematic tests can be used, for example the Shapiro–Wilk test for checking for normal distribution.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
Step 2A – Prong 2: Integrated into a Practical Solution?
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the clinical trial is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
Should it be found that this element is not a mental process or mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
transmitting, via the at least one processor, the one or more clinical trial design simulation results.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Further, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor, using a random number generator” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere instructions to apply an exception on a general purpose computer and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the clinical trial is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
transmitting, via the at least one processor, the one or more clinical trial design simulation results.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Well-Understood, Routine, Conventional Activity (WURC) has found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
WURC:
simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results;
The courts have found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
Evidence that simulating a clinical trial is WURC can be found in:
Fundamentals of Clinical Trials ([Section 6 Page 108 Par 1])
The Role Of Modeling & Simulation In Clinical Trials ([Page 1 Par 1-2])
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment ([Page 221 Par 2 – Page 222 Par 1])
How Simulation Can Transform Regulatory Pathways ([Page 1 Par 1-3])
transmitting, via the at least one processor, the one or more clinical trial design simulation results.
Transmitting and receiving data over a network is explicitly identified by the courts as an example of Well-Understood, Routine, Conventional Activity (MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
Additionally, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor, using a random number generator” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 9 (Statutory Category – Process)
The claim recites a mental process, specifically:
determining, via at least one processor, information fraction sets for a set of a plurality of designs, wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions
A person could reasonably mentally design a set of theoretical clinical trials and write down target sample sizes for each, as with a pen and paper. That person could then observe the actual number of patients that show up to the trial, marking the number. Dividing the number of observed patients by the target sample size is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
This element is also a mathematic process, as analyzed below.
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that these elements are not mental processes, they are also examples of mere data gathering.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Dividing the number of available data points by the expected data point count is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
This element is also a mathematic process, as analyzed below.
simulating, via the at least one processor, a design to determine test statistics for the design;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Determining the statistical distribution of the simulation output is merely the act of determining the distribution of a set of numeric data. This can be done both mentally and purely mathematically; to perform this process mentally, data can be drawn on a plot and the shape of the resulting plot can give insight into the distribution of the data, for example data that is normally distributed will approximate a bell curve.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
See below for analysis as a math process.
generating, via the at least one processor, a decision to stop or proceed with sample size re-estimation for the simulated design based on the test statistics and the precomputed boundary condition;
Deciding whether or not to change the sample size is a mental process that involves observing the results of the simulation, and judging if the sample population was the right size to get meaningful results. For instance, if a trial to determine what percentage of all people are over the age of 80 has a sample size of two and returns a result that indicates that 100% of the global population is over the age of 80, a researcher would reasonably conclude that the sample size was not large enough to be representative of the general population. Additionally, a person could look at statistics indicating a performance rating of 2 out of 10 and associated boundary conditions and decide to change the sample size to get better results.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
The claim also recites a mathematical process, specifically:
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Further, the MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
determining, via at least one processor, information fraction sets for a set of a plurality of designs, wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions
Determining fractions by calculating “a number of patients observed divided by a target patient sample size” is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Determining a ratio between two numbers is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Wherein the method further comprises precomputing, via at least one processor and for each information fraction set, a boundary condition for a test statistic;
Precomputing (i.e. calculating) a numeric boundary condition is a mathematic calculation and thus a mathematic concept.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
To determine such a distribution purely numerically several mathematic tests can be used, for example the Shapiro–Wilk test for checking for normal distribution.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining this data is merely the act of gathering that data, i.e. obtaining time indexes for data, how much information is known, how much is unknown, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere instructions to Apply:
simulating, via the at least one processor, a design to determine test statistics for the design;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the clinical trial is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
Should it be found that this element is not a mental process or mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
transmitting, via the at least one processor, the decision.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Further, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere data gathering or mere instructions to apply an exception on a general purpose computer and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining this data is merely the act of gathering that data, i.e. obtaining time indexes for data, how much information is known, how much is unknown, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP
2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an
equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v.
Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere instructions to Apply:
simulating, via the at least one processor, a design to determine test statistics for the design;
Applying a computer to perform a generic simulation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that simulation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. that the clinical trial is “simulated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
The courts have found that such mere instructions to apply are not indicative of integration into a practical application nor recitation of significantly more than the judicial exception (MPEP 2106.05(f) “Another consideration when determining whether a claim integrates a judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer. As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983”)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
transmitting, via the at least one processor, the decision.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Well-Understood, Routine, Conventional Activity (WURC) has found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
WURC:
simulating, via the at least one processor, a design to determine test statistics for the design;
The courts have found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
Evidence that simulating a clinical trial is WURC can be found in:
Fundamentals of Clinical Trials ([Section 6 Page 108 Par 1])
The Role Of Modeling & Simulation In Clinical Trials ([Page 1 Par 1-2])
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment ([Page 221 Par 2 – Page 222 Par 1])
How Simulation Can Transform Regulatory Pathways ([Page 1 Par 1-3])
transmitting, via the at least one processor, the decision.
Transmitting and receiving data over a network is explicitly identified by the courts as an example of Well-Understood, Routine, Conventional Activity (MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
Further, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 21(Statutory Category – Process)
The claim recites a mental process, specifically:
generating, via at least one processor, a set of scenario parameters from a plurality of base datasets, wherein the plurality of base datasets includes a survival time dataset, a treatment ID dataset, and a dropout time dataset,
Generating arbitrary parameters based on observed data is a mental process equivalent to observing that data and judging what kind of parameters best suit that data. For example if the base data suggests high dropout rates, parameters that work better for small sample size trials may be chosen.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
wherein the generating includes identifying, via the at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of the plurality of base datasets based on the scenario parameters,
A human mind could reasonably come up with a distinct function for a number of datasets to transform the data therein. For example, a person could write down several numeric datasets with a pencil and paper, then decide that the transformation function for the first set is to add 1 to every number, a second function to add 2 to every number in a second set, and so on.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
identifying, via the at least one processor, look positions for a plurality of designs;
The specification describes look positions as “In embodiments, analysis times may be referred to as looks or look positions.” Determining the relevant time of a piece of data is a mental process that involves observing that data and finding the relevant time index, as with observing data on a time-indexed graph. This limitation is therefore mental process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, information fraction sets for the look positions for the plurality of designs, wherein determining information fraction sets comprises: … wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions.
This element is mental math process. The specification describes the information fraction as ([Par 873] “In one example, an information fraction may be defined as the number of patients observed divided by the target patient sample size at each look position. In another example, an information fraction may be defined as the number of observed deaths at a look position versus the total number of expected deaths.”) In light of this, determining the information faction sets is equivalent to observing a number of patients and dividing that number by the target patient sample size. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining the “look position” is a mental process equivalent to observing the time index of a set of data, for example if a graph plots distance on one axis and time on the other, determining the look position for a certain data point is equivalent to observing its location on the time axis.
Determining how much information is available is a mental process equivalent to observing a set of data and judging how much data there is; for example a person could observe the data set {1, 2, 3} and judge that there are three data elements available.
Determining how much data is expected is a mental process equivalent to making an arbitrary decision about the volume of data a researcher would like to have; for example, a researcher may decide they’d like to have at least 1000 data points.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that these elements are not mental processes, they are also examples of mere data gathering.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Dividing the number of available data points by the expected data point count is a case of simple mental math. This kind of simple division, done with or without a paper and pencil, is something taught to school children at a young age.
This element is also a mathematic process, as analyzed below.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
This is a mental process; a person could reasonably imagine a theoretical trial design in their mind, writing aspects of it such as desired target sample size on a piece of paper. A person could then reasonably imagine the outcome of said imagined trial, and write down statistics. For example, a person could imagine a trial to determine how many people with peanut allergies have allergic reactions, deciding to the use the parameters of a sample size of 100 and the use of a particular brand of peanut. They could imagine gathering the patients, testing each for the allergy, and reasonably estimate that near 100% of the patients would have an adverse reaction, noting the 100% result on the paper.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Should it be found that this element is not a mental process, it is also both mere instructions to apply an exception and Well-Understood, Routine, Conventional Activity.
The claim also recites a mathematical process, specifically:
MPEP 2106.4(a)(2)(I): “The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations.”
Further, the MPEP recites: “For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.”
transforming, via the at least one processor, the each of the plurality of base datasets using the corresponding one of the plurality of distinct transformation functions, wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset;
Transforming data through the use of a mathematic “transformation function” is an explicit recitation of a math operation in the claim, and is thus a mathematic concept. In other words, the claim element does not merely “involve” math; it explicitly discloses it in the claim itself.
Doing so “via at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, information fraction sets for the look positions for the plurality of designs, wherein determining information fraction sets comprises: … wherein at least one of the information fraction sets corresponds to a number of patients observed divided by a target patient sample size at each of a plurality of look positions.
Determining the value of a fraction is the act of mathematically calculating that fraction.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
determining, via the at least one processor, a ratio of the available information to the total expected information;
Determining a ratio between two numbers is an explicit mathematic calculation and thus a mathematic process.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
wherein the method further comprises: computing, via the at least one processor and for each information fraction set, a boundary for a test statistic;
Computing (i.e. calculating) a numeric boundary condition is a mathematic calculation and thus a mathematic concept.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Step 2A – Prong 2: Integrated into a Practical Solution?
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining this data is merely the act of gathering that data, i.e. obtaining time indexes for data, how much information is known, how much is unknown, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere instructions to Apply:
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
Applying a computer to perform a generic evaluation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that evaluation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. merely that the clinical trial is “evaluated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
Should it be found that this element is not a mental process or mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
transmitting, via the at least one processor, the one or more evaluation results.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Further, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
Step 2B: Claim provides an Inventive Concept?
No, as discussed with respect to Step 2A, the additional limitations are mere data gathering or mere instructions to apply an exception on a general purpose computer and do not impose any meaningful limits on practicing the abstract idea and therefore the claim does not provide an inventive concept in Step 2B.
Insignificant Extra-Solution Activity (MPEP 2106.05(g)) has found mere data gathering and
post solution activity to be insignificant extra-solution activity.
Data gathering:
wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions are for each of the plurality of designs; determining, via the at least one processor, available information at each of the look positions; determining, via the at least one processor, total expected information for a trial design;
Determining this data is merely the act of gathering that data, i.e. obtaining time indexes for data, how much information is known, how much is unknown, etc.
Doing so “via the at least one processor” amounts to mere instructions to apply this exception using a computer.
A claim element that amounts to merely gathering data is not indicative of integration into a
practical solution nor evidence that the claim provides an inventive concept, as exemplified by ((MPEP
2106.05)(g)(Mere Data Gathering) i. Performing clinical tests on individuals to obtain input for an
equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989); iv. Obtaining
information about transactions using the Internet to verify credit card transactions, CyberSource v.
Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011);
Mere Instructions to Apply (MPEP 2106.05(f)) has found that merely applying a judicial exception such as an abstract idea, as by performing it on a computer, does not integrate the claim into a practical solution.
Mere instructions to Apply:
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
Applying a computer to perform a generic evaluation at a high level of generality is simply the act of instructing a computer to perform generic functions to perform that evaluation, which is merely an instruction to apply a computer to the judicial exception. The claim only recites the idea of a solution or outcome, i.e. merely that the clinical trial is “evaluated” without reciting how this simulation is actually accomplished. Further, the computer elements claimed are cited as merely generic tools to perform the operations; for additional clarity on the generic nature of the application of a general purpose computer, see ([Par 210] “A user may interact with the platform 104 through one or more user devices 102 (e.g., computer, laptop computer, mobile computing device, and the like). The platform 104 may be implemented and/or leverage one or more computing resources 150 such as a cloud computing service 152,servers 154, software as a service (SaaS), infrastructure as a service (IaaS), platform as a service (PaaS), desktop as a Service (DaaS), managed software as a service (MSaaS), mobile backend as a service (MBaaS), information technology management as a service (ITMaaS), and the like.”)
Should it be found that this element is not a mental process or mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
transmitting, via the at least one processor, the one or more evaluation results.
Transmitting and receiving data over a network has been explicitly identified by the courts as an example of Mere Instructions to Apply(MPEP 2106.05(f)(1) i. Remotely accessing user-specific information through a mobile interface and pointers to retrieve the information without any description of how the mobile interface and pointers accomplish the result of retrieving previously inaccessible information, Intellectual Ventures v. Erie Indem. Co., 850 F.3d 1315, 1331, 121 USPQ2d 1928, 1939 (Fed. Cir. 2017); iii. Wireless delivery of out-of-region broadcasting content to a cellular telephone via a network without any details of how the delivery is accomplished, Affinity Labs of Texas v. DirecTV, LLC, 838 F.3d 1253, 1262-63, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016).)
Further, should it be found that this element is not an example of mere instructions to apply an exception, it is also an example of Well-Understood, Routine, Conventional Activity.
Well-Understood, Routine, Conventional Activity (WURC) has found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
WURC:
evaluating, via the at least one processor, designs at the look positions using the computed boundaries to generate one or more evaluation results;
The courts have found that claim elements that are understood to be Well-Understood, Routine, Conventional Activity are not indicative of Integration into a Practical Solution nor evidence of an Inventive Concept or Significantly More (MPEP 2106.05(d))
Evidence that simulating/evaluating a clinical trial on a computer is WURC can be found in:
Fundamentals of Clinical Trials ([Section 6 Page 108 Par 1])
The Role Of Modeling & Simulation In Clinical Trials ([Page 1 Par 1-2])
Clinical trial optimization: Monte Carlo simulation Markov model for planning clinical trials recruitment ([Page 221 Par 2 – Page 222 Par 1])
How Simulation Can Transform Regulatory Pathways ([Page 1 Par 1-3])
transmitting, via the at least one processor, the one or more evaluation results.
Transmitting and receiving data over a network is explicitly identified by the courts as an example of Well-Understood, Routine, Conventional Activity (MPEP 2106.05(d)(II) i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network))
Further, Mere Instructions To Apply An Exception (MPEP 2106.05(f)) has found that simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. In light of this, the additional generic computer component elements of “via at least one processor” are not sufficient to integrate a judicial exception into a practical application nor provide evidence of an inventive concept.
The additional elements have been considered both individually and as an ordered combination in the consideration of whether they constitute significantly more, and have been determined not to constitute such.
The claim is ineligible.
Claim 2 recites “wherein the plurality of base datasets further includes an enrollment time dataset”
This is merely a clarification of the kind of data that is contained in the randomly generated datasets, and is therefore merely an extension of the mental process.
Claim 3 recites “wherein the plurality of base datasets is generated according to a predefined value distribution and range.”
Generating the datasets according to a predefined value distribution and range merely clarifies the function of the random number generator, and is therefore merely an extension of the mental process and mere instructions to apply that mental process.
Claim 4 recites “wherein at least one of the plurality of distinct transformation functions changes a value distribution of a dataset.”
Applying a transformation function to a set of data is the process of applying a mathematic function to that data, which is a mathematic concept. Specifying that it changes the distribution of the dataset merely describes the effects of this transformation, and is therefore merely an extension of the mathematic concept.
Claim 5 recites “wherein at least one of the plurality of distinct transformation functions changes the range of the values of a dataset.”
Applying a transformation function to a set of data is the process of applying a mathematic function to that data, which is a mathematic concept. Specifying that it changes the range of the dataset merely describes the effects of this transformation, and is therefore merely an extension of the mathematic concept.
Claim 6 recites “wherein generating scenario parameters comprises combining, via the at least one processor, a plurality of transformed datasets.”
Combining datasets is the mental process of that involves creating a new dataset by adding the elements of multiple original datasets into a single dataset. For example, a person could observe the data sets [1, 2, 3] and [4, 5, 6] and place the elements into a single list, [1, 2, 3, 4, 5, 6], as by writing it down with a pencil and paper
Claim 7 recites “wherein the plurality of base datasets is generated for all scenarios of a trial design analysis.”
This merely specifies the purpose of the datasets, and for which situations they should be generated and is therefore merely an extension of the mental process.
Claim 8 recites “evaluating, via the at least one processor, historical trial design selections to identify one or more parameters of a set of trial designs based at least in part on a first set of user interactions;”
Evaluating historical trial designs and learning from them is a mental process that involves observing the historical trial setup and its results, taking note of important parameters as by writing them down with a pencil and paper
Obtaining, via the at least one processor, the one or more clinical trial design simulation results based at least in part on a quick search data structure and the one or more parameters;
Obtaining the results of the simulation is merely the act of gathering data that represents those results.
Generating, via the at least one processor, a substitute for at least some of the one or more clinical trial design simulation results based at least in part on a relationship between the trial design simulation results and supplemental data;
Generating a substitute that represents other data is a mental process that involves observing a first set of data and generating a second set, as by writing it down with a pencil and paper, that summarizes the first data. For example given a list of participants {Craig, Sean, Mike, Sally}, a person could generate a substitute summary of this data of the form {Number of participants: 4}.
Generating, via the at least one processor, a performance surface based at least in part on a set of trial designs corresponding to the one or more clinical trial design simulation results;
Generating a performance surface is a mental process that involves creating a three-dimensional graph of performance metric data, as by observing the data and drawing the graph with pencil and paper.
Evaluating, via the at least one processor, one or more trial designs based at least in part on the performance surface; and
Evaluating this performance surface is a mental process that involves observing the drawn performance surface and making certain judgments about performance based on the observed graph.
Calculating, via the at least one processor, a score based on normalized score component values corresponding to the one or more clinical trial design simulation results.
Calculating a score based on a set of values representing simulation results is a mathematic calculation and therefore a mathematic concept.
Claim 10 recites “further comprising determining, via the at least one processor, spending function parameters for each information fraction set”
Determining parameters is mental process that involves coming up with an arbitrary set of numbers.
Claim 11 recites “further comprising precomputing, via the at least one processor, one or more boundary conditions.
Precomputing a numeric boundary is a mathematic process that involves calculating that boundary.
Claim 12 recites “further comprising determining, via the at least one processor, duplicate information fractions and filtering duplicate information fractions.”
Removing duplicate information from a dataset is a mental process that involves observing a set of data, evaluating which pieces of data are repeated, and removing them from the dataset.
Claim 13 recites “wherein precomputing, via the at least one processor, comprises, precomputing only for unique information fraction sets.”
Ensuring that the precomputing only uses unique data is a mental/math process that first involves mentally observing a set of data, evaluating which pieces of data are repeated, and removing them from the dataset, then mathematically precomputing the boundary conditions for the remaining, non-duplicate data.
Claim 15 recites “wherein the ratio is indicative of a number of patients.”
This merely clarifies the form of the mathematic basis for the information fraction as well as how it should be interpreted, and is therefore merely an extension of the mental process and mathematic concept.
Claim 16 recites “wherein the ratio is indicative of a number of deaths.”
This merely clarifies the form of the mathematic basis for the information fraction as well as how it should be interpreted, and is therefore merely an extension of the mental process and mathematic concept.
Claim 17 recites “wherein the plurality of designs is all the designs in a trial design study.”
This merely clarifies the scope of the trial designs, and is therefore merely an extension of the mental process.
Claim 18 recites “determining, via the at least one processor, a subset of the designs based on the distribution of simulations.
Creating a subset of a set of data based on certain criteria is a mental process that involves observing each piece of data in the larger set, judging if it meets those criteria or not, and based on that judgement deciding whether or not to move it to a new dataset.
Claim 19 recites “wherein determining information fraction sets comprises determining, via the at least one processor, information fraction sets for the subset of the designs.”
This merely clarifies the scope of the information fraction calculation, and is therefore merely an extension of the mental process and mathematic concept.
Claim 20 recites “wherein the distribution of simulations is further based on a location of the simulations.”
This merely clarifies the basis of the determining the distribution, i.e. what data should be considered in the determination, and is therefore merely an extension of the mental process and mathematic concept.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-11, 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Fundamentals of Clinical Trials (hereinafter Springer)in view of Optimal design of clinical trials with computer simulation based on results of earlier trials, illustrated with a lipodystrophy trial in HIV patients (hereinafter Abbas)
Claim 1. Springer makes obvious ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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wherein the plurality of base datasets includes a survival time dataset, ([Section 15 Page 277 Fig 15.6] shows a graphic depicting data from a dataset of survival times) a treatment ID dataset, ([Section 12 Page 220 Table 1] Shows a dataset including the use of different treatments [Section 16 Page 301 Fig. 16.4] Shows a graph of a dataset with separately identified treatments {dataset including different treatment IDs})
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and a dropout ([Section 14 Page 264 Par 1] “Electronic monitoring of adherence has been used [28]… The obvious advantage of electronic monitoring is that the dose-timing can be assessed to see if it is punctual and regular… Drug holidays, defined as omissions of all doses during three or more days, were recorded in 43% of the participants. An interesting observation was that participants with dosing problems were more likely later to become permanent drop-outs.” {Tracking dropout data}) determining, via at least one processor, scenario specifications corresponding to the one or more clinical trial designs; ([Section 1 Page 10 Par 3] “ …consider the context in which the trial is being conducted. The nature of the disease or condition being studied and the population and setting in which it is being done will influence the outcomes that are assessed, the kind of control, the size, the duration, and many other factors.” [Section 8 Page 153 Par 8] “All of the above methods assume that the hazard rate remains constant during the course of the trial. This may not be the case. The Beta-Blocker Heart Attack Trial [48] compared 3-year survival in two groups of participants with intervention starting 1–3 weeks after an acute myocardial infarction. The risk of death was high initially, decreased steadily, and then became relatively constant.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” [Examiner’s note: whether the hazard rate was constant or nonconstant is explicitly listed as an example of a scenario specification in the disclosure. See [Par 863] of the specification]) identifying, via at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of base datasets ([Section 8 Page 147 Par 2] “Continuous” variables such as length of hospitalization, blood pressure, spirometric measures, neuropsychological scores, and level of a serum component may be evaluated. Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) based on the scenario specifications; ([Section 1 Page 10 Par 3] “ …consider the context in which the trial is being conducted. The nature of the disease or condition being studied and the population and setting in which it is being done will influence the outcomes that are assessed, the kind of control, the size, the duration, and many other factors.”) transforming, via at least one processor, each of the plurality of base datasets using the corresponding one of the plurality of distinct transformation functions; ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” )
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wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset; ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 12 Page 220 Table 1] Shows a dataset including the use of different treatments [Section 16 Page 301 Fig. 16.4] Shows a graph of a dataset with separately identified treatments {dataset including different treatment IDs} [Examiner’s note: if the use of a different treatment effects mortality rate, as pictured in the graph, it is effecting survival time])
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generating, via at least one processor, scenario parameters ([Section 3 Page 42 Par 3] “When the question is conceived, investigators, at the very least have in mind a class or type of intervention. More commonly, they know the precise drug, procedure, or lifestyle modification they wish to study. In reaching such a decision, they need to consider several aspects. First, the potential benefit of the intervention must be maximized while possible toxicity is kept to a minimum. Thus, dose of drug or intensity of rehabilitation and frequency and route of administration are key factors that need to be determined. Can the intervention be standardized, and remain reasonably stable over the duration of the trial? Investigators must also decide whether to use a single drug, biologic, or device, fixed or adjustable doses of drugs, sequential drugs, or drug or device combinations” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) based on the transformed datasets. ([Section 8 Page 147 Par 2] “Continuous” variables such as length of hospitalization, blood pressure, spirometric measures, neuropsychological scores, and level of a serum component may be evaluated. Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.”) and corresponding to the one or more clinical trial designs; ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”) simulating, via the at least one processor, the one or more clinical trial designs using the scenario parameters to generate one or more clinical trial design simulation results; ([Section 3 Page 42 Par 3] “When the question is conceived, investigators, at the very least have in mind a class or type of intervention. More commonly, they know the precise drug, procedure, or lifestyle modification they wish to study. In reaching such a decision, they need to consider several aspects. First, the potential benefit of the intervention must be maximized while possible toxicity is kept to a minimum. Thus, dose of drug or intensity of rehabilitation and frequency and route of administration are key factors that need to be determined. Can the intervention be standardized, and remain reasonably stable over the duration of the trial? Investigators must also decide whether to use a single drug, biologic, or device, fixed or adjustable doses of drugs, sequential drugs, or drug or device combinations” [Section 16 Page 300 Par 1] “…performed computer simulations of such a clinical trial in which both the control group and intervention group event rates were assumed to be 30% at the end of the study. He performed 2,000 replications of this simulated experiment. He found that if 20 tests of significance are done within a trial, the chance of crossing the 5% significance level boundaries (i.e., Z = ±1.96) is, on the average, 35%. Thus, whether one calculates a test statistic for comparing proportions or for comparing time to event data, repeated testing of accumulating data without taking into account the number of tests increases the overall probability of incorrectly rejecting H0. If the repeated testing continues indefinitely, the null hypothesis is certain to be rejected eventually.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) the one or more clinical trial design simulation results. ([Section 18 Page 404 Par 1] “The extent of this additional documentation of important data depends on the design of each trial. Various models have been used for this purpose. A simple model requires each investigator to send a duplicate of all death or major event forms on an ongoing basis to an individual member of the independent monitoring committee. In one multicenter study, the investigators were asked at the end of the follow-up to send a list of all the deceased participants along with the date of death to an office independent of the data coordinating center... An extreme example employed in one large multicenter trial was the establishment of a second data coordinating center. Duplicates of key study forms were submitted to this center, which generated separate data reports.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
Springer does not explicitly teach generating a plurality of datasets using a random number generator; a dropout time dataset; determining, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations;
Abbas makes obvious generating a plurality of datasets using a random number generator([Section 2.3.3 Page 1057 Col 2 Par 1] “This single simulation is repeated 100 times under different sequences of random numbers starting at 12,345 random seed and up to the 12,445, producing 100 independent data sets, each of the same sample size.”) a dropout time dataset; ([Section 2.3 Page 1054 Col 2 Par 9] “We developed a simulation model to describe the process of enrolling patients and their follow-up visits in clinical trials that study continuous variables at discrete points of time, including dropout. We divided input parameters into two categories. The first includes the design parameters: endpoints, number of follow-up visits, number of centres and sample size. The second includes external parameters: selection rate, recruitment and dropout rate, cost of recruitment, cost of a visit, centre cost and opportunity costs of trial duration”) determining, a distribution of simulations for the clinical trial designs, wherein the distribution of simulations is based on a time of the simulations; ([Section 2.3.2 Page 1057 Col 1 Par 6-7] “Assuming that the patient attends the next visit, then the value of the outcome variable (cholesterol level) will be updated according to the sum of the new mean level: the value generated from the first normal distribution Dist1 at the baseline visit and the new value generated by the second normal distribution Dist2. That is, Li = Meanv + (dist1v = dist1v=1) + (dist2v = N{0;sv}). This process is repeated until the patient either reaches the last visit of the trial or the point when they drop out.”) [Section 2.3.2 Page 1057 Col 1 Par 5] “After generating the cholesterol value, the model applies the probability of a patient dropping out through another uniform distribution generator between 0 and 1. If the generated probability is equal to or less than the dropout probability, then the patient will continue receiving the treatment in the next visit; if not, the patient will drop out from the trial. In order to account for centrespecific dropout probability, we converted the probability of the trial shown in Table 1, Pd(v), into the k(v) rate given by formula 5. Then, we used the rate found to calculate the intended probability depending on c centre, Pd(v, c), given by formula 6. This equation assumes that this dropout probability increases as the number of centres increases according to an exponential distribution…. where Pd(v,c) is the dropout probability at visit v (v = 0, 1, 2,...) and centre c (c = 1, 2, 3,...). [Examiner’s note: the distribution for dropout depends on both the ordered simulated visit number through time, i.e. is based on the time of the simulation, and the center location, i.e. the simulation location])
Abbas is analogous art because it is within the field of clinical trial design and simulation. It would have been obvious to combine it with Springer before the effective filing date. One of ordinary skill in the art would be motivated to make this combinate to automate clinical trial design based on the principles taught by Springer. As suggested by Abbas, designing and performing a proper randomized clinical trial can extremely costly, both in terms of time and money. ([Section 1 Page 1 Col 1 Par 1] “A randomised clinical trial can take a long time and be expensive”) Moreover, the traditional design process requires the designer to make certain basic assumptions to create trial that yields useful data; most of the time these assumptions, such as an expected dropout percentage amongst participants, are only able to verified once the trial has been completed, at which point the time and money put into the trial has already been used up, and a second trial to account for the corrected assumptions is no longer economically feasible. ([Section 1 Page 1 Col 1 Par 2 – Col 2 Par 2] “The design of a clinical trial depends, among other things, on prior knowledge. Trial designers must have an idea about the way that patients with specific characteristics will respond to different treatments, or the recruitment and dropout rates. These assumptions then form the basis of their designs for trial protocols… If the designer’s assumptions are accurate and s/he manages the trial strictly according to the protocol based on her/his assumptions, then the outcome of the trial will be reliable. If the assumptions are wrong, the trial may yield unsatisfactory results. If this is the case, it is always too late to start again; the sponsor’s investment of time, money, and effort may have been wasted and furthermore people may well have been subjected to unnecessary inconvenience, discomfort, and health risks.”) To solve this issue, Abbas presents a clinical trial simulation system that allows hypothetical trials to be simulated under a variety of assumption and general setup conditions, allowing for the optimal setup to be identified. ([Section 1 Page 1 Col 2 Par 4 - Page 2 Col 1 Par 1] “With simulation, our assumptions can form an explicitly defined basis for a model rooted in real-world practices. From here, we use powerful computing and statistical methods to generate data as if they had come from real patients. Simulation also allows us to test alternative models which highlight deviations from the designer’s original assumptions. In addition, we can test alternative protocols and discover which one will be the most robust when studying alternative models. Each model, together with a protocol, can be used to generate data that might have come from a real trial, and this data can then be used to test the proposed analysis procedures to ensure that the original assumptions can be tested. In this paper we attempt to verify the hypothesis that the total cost resulting from carrying out a clinical trial can be minimised by using simulation models as an alternative to conventional statistical approaches.”) It would have been obvious to one of ordinary skill in the art that combining Abbas with Springer would produce a clinical trial simulation system capable of simulating a wide range of trial scenarios allowing for the most accurate, cost effective setup to be identified and then practiced with real patients.
Claim 9. Springer makes obvious makes obvious determining, via at least one processor, information fraction sets ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 16 Page 320 Fig. 16.9] Shows spending function plotted against a plurality of determined information fractions [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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([Section 16 Page 319 Par 2 – Page 320 Par 1] “At any particular calendar time t in the study, a certain fraction t * of the total information is observed. That may be approximated by the fraction of participants randomized at that point, n, divided by the total number expected, N, or in survival studies, by the number of events observed already, d, divided by the total number expected D.”) wherein determining information fraction sets comprises: determining, via the at least one processor, the look positions, wherein the look positions ([Section 16 Page 300 Fig. 16.3] “Interim survival analyses comparing mortality in clofibrate- and placebo-treated participants in the Coronary Drug Project. A positive Z value favors placebo” [Examiner’s note: as can be plainly seen in the figure, this is an example of time-indexed analysis data. In other words, an analysis done at a variety of look positions (see claim interpretation section at top of action)] [Section 12 Page 22 Fig 12.1] shows other sets of time-indexed data, with a different treatment for each [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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information to the total expected information; ([Section 16 Page 319 Par 2 – Page 320 Par 1] “At any particular calendar time t in the study, a certain fraction t * of the total information is observed. That may be approximated by the fraction of participants randomized at that point, n, divided by the total number expected, N, or in survival studies, by the number of events observed already, d, divided by the total number expected D.” [Section 16 Page 321 Par 4] “For means and proportions, the information fraction can be approximated by the ratio of the number of participants observed to the total expected.” [Section 16 Page 300 Fig. 16.3] shows look positions with data at each[Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) wherein the method further comprises: precomputing, via at least one processor, and for each information fraction set, ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) a boundary condition for a test statistic; ([Section 16 Page 320 Par 1] “which is approximately 0.0006, corresponding to the boundary value 3.23 in Fig. 16.7. That is, the difference in a(t*) at two consecutive information fractions, t* and t** where t* is less than t**, a(t**) − a(t*), determines the boundary or critical value at t**” [Section 16 Page 317 Fig. 16.7] Shows boundary conditions for test statistics based on information fractions)
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simulating, via at least one processor, a design to determine test statistics for the design; ([Section 16 Page 300 Par 1] “…performed computer simulations of such a clinical trial in which both the control group and intervention group event rates were assumed to be 30% at the end of the study. He performed 2,000 replications of this simulated experiment. He found that if 20 tests of significance are done within a trial, the chance of crossing the 5% significance level boundaries (i.e., Z = ±1.96) is, on the average, 35%. Thus, whether one calculates a test statistic for comparing proportions or for comparing time to event data, repeated testing of accumulating data without taking into account the number of tests increases the overall probability of incorrectly rejecting H0. If the repeated testing continues indefinitely, the null hypothesis is certain to be rejected eventually.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) a decision to stop or proceed with sample size re-estimation ([Section 16 Page 310 Par 3 – Page 311 Par 3] “The issue about extending a trial beyond the original sample size or planned period of follow-up may arise… The issue of whether the control group event rate or the overall event rate should be used in this sample size reassessment must be considered …The use of the overall event rate would avoid this potential problem. Additionally, there are statistical arguments that under the null hypothesis, the overall rate is the more appropriate one to use because it is likely to be more stable, particularly if the sample size re-estimation is done early in the trial.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) for the simulated design ([Section 16 Page 300 Par 1] “…performed computer simulations of such a clinical trial in which both the control group and intervention group event rates were assumed to be 30% at the end of the study”) based on the test statistics ([Section 16 Page 300 Par 1] “He performed 2,000 replications of this simulated experiment. He found that if 20 tests of significance are done within a trial, the chance of crossing the 5% significance level boundaries (i.e., Z = ±1.96) is, on the average, 35%.”) and the precomputed boundary condition. ([Section 16 Page 320 Par 1] “which is approximately 0.0006, corresponding to the boundary value 3.23 in Fig. 16.7. That is, the difference in a(t*) at two consecutive information fractions, t* and t** where t* is less than t**, a(t**) − a(t*), determines the boundary or critical value at t**” [Section 16 Page 317 Fig. 16.7] Shows boundary conditions for test statistics based on information fractions)
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and transmitting, via the at least one processor, ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” [Section 18 Page 404 Par 1] “The extent of this additional documentation of important data depends on the design of each trial. Various models have been used for this purpose. A simple model requires each investigator to send a duplicate of all death or major event forms on an ongoing basis to an individual member of the independent monitoring committee. In one multicenter study, the investigators were asked at the end of the follow-up to send a list of all the deceased participants along with the date of death to an office independent of the data coordinating center... An extreme example employed in one large multicenter trial was the establishment of a second data coordinating center. Duplicates of key study forms were submitted to this center, which generated separate data reports.”) the decision. ([Section 16 Page 310 Par 3 – Page 311 Par 3] “The issue about extending a trial beyond the original sample size or planned period of follow-up may arise… The issue of whether the control group event rate or the overall event rate should be used in this sample size reassessment must be considered …The use of the overall event rate would avoid this potential problem. Additionally, there are statistical arguments that under the null hypothesis, the overall rate is the more appropriate one to use because it is likely to be more stable, particularly if the sample size re-estimation is done early in the trial.”)
Springer does not explicitly teach a plurality of designs; determining, a distribution of simulations for the design, wherein the distribution of simulations is based on a time of the simulations;
Abbas makes obvious data for a plurality of trial designs. ([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.”) a distribution of simulations for the design, wherein the distribution of simulations is based on a time of the simulations; ([Section 2.3.2 Page 1057 Col 1 Par 6-7] “Assuming that the patient attends the next visit, then the value of the outcome variable (cholesterol level) will be updated according to the sum of the new mean level: the value generated from the first normal distribution Dist1 at the baseline visit and the new value generated by the second normal distribution Dist2. That is, Li = Meanv + (dist1v = dist1v=1) + (dist2v = N{0;sv}). This process is repeated until the patient either reaches the last visit of the trial or the point when they drop out.”) [Section 2.3.2 Page 1057 Col 1 Par 5] “After generating the cholesterol value, the model applies the probability of a patient dropping out through another uniform distribution generator between 0 and 1. If the generated probability is equal to or less than the dropout probability, then the patient will continue receiving the treatment in the next visit; if not, the patient will drop out from the trial. In order to account for centrespecific dropout probability, we converted the probability of the trial shown in Table 1, Pd(v), into the k(v) rate given by formula 5. Then, we used the rate found to calculate the intended probability depending on c centre, Pd(v, c), given by formula 6. This equation assumes that this dropout probability increases as the number of centres increases according to an exponential distribution…. where Pd(v,c) is the dropout probability at visit v (v = 0, 1, 2,...) and centre c (c = 1, 2, 3,...). [Examiner’s note: the distribution for dropout depends on both the ordered simulated visit number through time, i.e. is based on the time of the simulation, and the center location, i.e. the simulation location])
Abbas is analogous art because it is within the field of clinical trial design and simulation. It would have been obvious to combine it with Springer before the effective filing date. One of ordinary skill in the art would be motivated to make this combinate to automate clinical trial design based on the principles taught by Springer. As suggested by Abbas, designing and performing a proper randomized clinical trial can extremely costly, both in terms of time and money. ([Section 1 Page 1 Col 1 Par 1] “A randomised clinical trial can take a long time and be expensive”) Moreover, the traditional design process requires the designer to make certain basic assumptions to create trial that yields useful data; most of the time these assumptions, such as an expected dropout percentage amongst participants, are only able to verified once the trial has been completed, at which point the time and money put into the trial has already been used up, and a second trial to account for the corrected assumptions is no longer economically feasible. ([Section 1 Page 1 Col 1 Par 2 – Col 2 Par 2] “The design of a clinical trial depends, among other things, on prior knowledge. Trial designers must have an idea about the way that patients with specific characteristics will respond to different treatments, or the recruitment and dropout rates. These assumptions then form the basis of their designs for trial protocols… If the designer’s assumptions are accurate and s/he manages the trial strictly according to the protocol based on her/his assumptions, then the outcome of the trial will be reliable. If the assumptions are wrong, the trial may yield unsatisfactory results. If this is the case, it is always too late to start again; the sponsor’s investment of time, money, and effort may have been wasted and furthermore people may well have been subjected to unnecessary inconvenience, discomfort, and health risks.”) To solve this issue, Abbas presents a clinical trial simulation system that allows hypothetical trials to be simulated under a variety of assumption and general setup conditions, allowing for the optimal setup to be identified. ([Section 1 Page 1 Col 2 Par 4 - Page 2 Col 1 Par 1] “With simulation, our assumptions can form an explicitly defined basis for a model rooted in real-world practices. From here, we use powerful computing and statistical methods to generate data as if they had come from real patients. Simulation also allows us to test alternative models which highlight deviations from the designer’s original assumptions. In addition, we can test alternative protocols and discover which one will be the most robust when studying alternative models. Each model, together with a protocol, can be used to generate data that might have come from a real trial, and this data can then be used to test the proposed analysis procedures to ensure that the original assumptions can be tested. In this paper we attempt to verify the hypothesis that the total cost resulting from carrying out a clinical trial can be minimised by using simulation models as an alternative to conventional statistical approaches.”) It would have been obvious to one of ordinary skill in the art that combining Abbas with Springer would produce a clinical trial simulation system capable of simulating a wide range of trial scenarios allowing for the most accurate, cost effective setup to be identified and then practiced with real patients.
Claim 21. Springer makes obvious generating, via at least one processor, a set of scenario parameters ([Section 3 Page 42 Par 3] “When the question is conceived, investigators, at the very least have in mind a class or type of intervention. More commonly, they know the precise drug, procedure, or lifestyle modification they wish to study. In reaching such a decision, they need to consider several aspects. First, the potential benefit of the intervention must be maximized while possible toxicity is kept to a minimum. Thus, dose of drug or intensity of rehabilitation and frequency and route of administration are key factors that need to be determined. Can the intervention be standardized, and remain reasonably stable over the duration of the trial? Investigators must also decide whether to use a single drug, biologic, or device, fixed or adjustable doses of drugs, sequential drugs, or drug or device combinations” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) from a plurality of base datasets wherein the plurality of base datasets includes a survival time dataset, ([Section 15 Page 277 Fig 15.6] shows a graphic depicting data from a dataset of survival times) a treatment ID dataset, ([Section 12 Page 220 Table 1] Shows a dataset including the use of different treatments [Section 16 Page 301 Fig. 16.4] Shows a graph of a dataset with separately identified treatments {dataset including different treatment IDs})
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and a dropout ([Section 14 Page 264 Par 1] “Electronic monitoring of adherence has been used [28]… The obvious advantage of electronic monitoring is that the dose-timing can be assessed to see if it is punctual and regular… Drug holidays, defined as omissions of all doses during three or more days, were recorded in 43% of the participants. An interesting observation was that participants with dosing problems were more likely later to become permanent drop-outs.” {Tracking dropout data}) wherein the generating includes identifying, via the at least one processor, a plurality of distinct transformation functions each corresponding to a distinct one of the plurality of base datasets ([Section 8 Page 147 Par 2] “Continuous” variables such as length of hospitalization, blood pressure, spirometric measures, neuropsychological scores, and level of a serum component may be evaluated. Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) based on the scenario parameters, ([Section 1 Page 10 Par 3] “ …consider the context in which the trial is being conducted. The nature of the disease or condition being studied and the population and setting in which it is being done will influence the outcomes that are assessed, the kind of control, the size, the duration, and many other factors.”)
transforming, via the at least one processor, the each of the plurality of base datasets using the corresponding one of the plurality of distinct transformation functions, ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” )
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wherein the treatment ID dataset influences at least one of the transformed survival time dataset or the transformed dropout time dataset ; ([Section 8 Page 147 Par 2] “… When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 12 Page 220 Table 1] Shows a dataset including the use of different treatments [Section 16 Page 301 Fig. 16.4] Shows a graph of a dataset with separately identified treatments {dataset including different treatment IDs} [Examiner’s note: if the use of a different treatment effects mortality rate, as pictured in the graph, it is effecting survival time])
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identifying, via at least one processor, look positions ([Section 16 Page 300 Fig. 16.3] “Interim survival analyses comparing mortality in clofibrate- and placebo-treated participants in the Coronary Drug Project. A positive Z value favors placebo” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” [Examiner’s note: as can be plainly seen in the figure, this is an example of time-indexed analysis data. In other words, an analysis done at a variety of look positions (see claim interpretation section at top of action)]) ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 16 Page 320 Fig. 16.9] Shows spending function plotted against a plurality of determined information fractions [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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for the look positions([Section 16 Page 300 Fig. 16.3] “Interim survival analyses comparing mortality in clofibrate- and placebo-treated participants in the Coronary Drug Project. A positive Z value favors placebo” [Section 12 Page 22 Fig 12.1] shows other sets of time-indexed data, with a different treatment for each [Examiner’s note: as can be plainly seen in the figure, this is an example of time-indexed analysis data. In other words, an analysis done at a variety of look positions (see claim interpretation section at top of action)])
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([Section 16 Page 300 Fig. 16.3] “Interim survival analyses comparing mortality in clofibrate- and placebo-treated participants in the Coronary Drug Project. A positive Z value favors placebo” [Examiner’s note: as can be plainly seen in the figure, this is an example of time-indexed analysis data. In other words, an analysis done at a variety of look positions (see claim interpretation section at top of action)] [Section 12 Page 22 Fig 12.1] shows other sets of time-indexed data, with a different treatment for each [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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information to the total expected information; ([Section 16 Page 319 Par 2 – Page 320 Par 1] “At any particular calendar time t in the study, a certain fraction t * of the total information is observed. That may be approximated by the fraction of participants randomized at that point, n, divided by the total number expected, N, or in survival studies, by the number of events observed already, d, divided by the total number expected D.” [Section 16 Page 321 Par 4] “For means and proportions, the information fraction can be approximated by the ratio of the number of participants observed to the total expected.” [Section 16 Page 300 Fig. 16.3] shows look positions with data at each[Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) and for each information fraction set, ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”) a boundary for a test statistic; ([Section 16 Page 320 Par 1] “which is approximately 0.0006, corresponding to the boundary value 3.23 in Fig. 16.7. That is, the difference in a(t*) at two consecutive information fractions, t* and t** where t* is less than t**, a(t**) − a(t*), determines the boundary or critical value at t**” [Section 16 Page 317 Fig. 16.7] Shows boundary conditions for test statistics based on information fractions)
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([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 16 Page 300 Fig. 16.3] “Interim survival analyses comparing mortality in clofibrate- and placebo-treated participants in the Coronary Drug Project. A positive Z value favors placebo” [Examiner’s note: as can be plainly seen in the figure, this is an example of time-indexed analysis data. In other words, an analysis done at a variety of look positions (see claim interpretation section at top of action)]) using the computed boundaries ([Section 16 Page 320 Par 1] “which is approximately 0.0006, corresponding to the boundary value 3.23 in Fig. 16.7. That is, the difference in a(t*) at two consecutive information fractions, t* and t** where t* is less than t**, a(t**) − a(t*), determines the boundary or critical value at t**” [Section 16 Page 317 Fig. 16.7] Shows boundary conditions for test statistics based on information fractions) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” [Section 18 Page 404 Par 1] “The extent of this additional documentation of important data depends on the design of each trial. Various models have been used for this purpose. A simple model requires each investigator to send a duplicate of all death or major event forms on an ongoing basis to an individual member of the independent monitoring committee. In one multicenter study, the investigators were asked at the end of the follow-up to send a list of all the deceased participants along with the date of death to an office independent of the data coordinating center... An extreme example employed in one large multicenter trial was the establishment of a second data coordinating center. Duplicates of key study forms were submitted to this center, which generated separate data reports.”)([Section 16 Page 319 Par 2 – Page 320 Par 1] “At any particular calendar time t in the study, a certain fraction t * of the total information is observed. That may be approximated by the fraction of participants randomized at that point, n, divided by the total number expected, N, or in survival studies, by the number of events observed already, d, divided by the total number expected D.”)
Springer does not explicitly teach a dropout time dataset; a plurality of designs; evaluating a design to generate one or more evaluation results
Abbas makes obvious a dropout time dataset; ([Section 2.3 Page 1054 Col 2 Par 9] “We developed a simulation model to describe the process of enrolling patients and their follow-up visits in clinical trials that study continuous variables at discrete points of time, including dropout. We divided input parameters into two categories. The first includes the design parameters: endpoints, number of follow-up visits, number of centres and sample size. The second includes external parameters: selection rate, recruitment and dropout rate, cost of recruitment, cost of a visit, centre cost and opportunity costs of trial duration”) a plurality of designs; ([Section 2.3.5 Page 1057 Col 2 Par 5] “… For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters…”) evaluating a design to generate one or more evaluation results ([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.” [Table 4 and Figs. 3-7] Show examples of design evaluation results)
Abbas is analogous art because it is within the field of clinical trial design and simulation. It would have been obvious to combine it with Springer before the effective filing date. One of ordinary skill in the art would be motivated to make this combinate to automate clinical trial design based on the principles taught by Springer. As suggested by Abbas, designing and performing a proper randomized clinical trial can extremely costly, both in terms of time and money. ([Section 1 Page 1 Col 1 Par 1] “A randomised clinical trial can take a long time and be expensive”) Moreover, the traditional design process requires the designer to make certain basic assumptions to create trial that yields useful data; most of the time these assumptions, such as an expected dropout percentage amongst participants, are only able to verified once the trial has been completed, at which point the time and money put into the trial has already been used up, and a second trial to account for the corrected assumptions is no longer economically feasible. ([Section 1 Page 1 Col 1 Par 2 – Col 2 Par 2] “The design of a clinical trial depends, among other things, on prior knowledge. Trial designers must have an idea about the way that patients with specific characteristics will respond to different treatments, or the recruitment and dropout rates. These assumptions then form the basis of their designs for trial protocols… If the designer’s assumptions are accurate and s/he manages the trial strictly according to the protocol based on her/his assumptions, then the outcome of the trial will be reliable. If the assumptions are wrong, the trial may yield unsatisfactory results. If this is the case, it is always too late to start again; the sponsor’s investment of time, money, and effort may have been wasted and furthermore people may well have been subjected to unnecessary inconvenience, discomfort, and health risks.”) To solve this issue, Abbas presents a clinical trial simulation system that allows hypothetical trials to be simulated under a variety of assumption and general setup conditions, allowing for the optimal setup to be identified. ([Section 1 Page 1 Col 2 Par 4 - Page 2 Col 1 Par 1] “With simulation, our assumptions can form an explicitly defined basis for a model rooted in real-world practices. From here, we use powerful computing and statistical methods to generate data as if they had come from real patients. Simulation also allows us to test alternative models which highlight deviations from the designer’s original assumptions. In addition, we can test alternative protocols and discover which one will be the most robust when studying alternative models. Each model, together with a protocol, can be used to generate data that might have come from a real trial, and this data can then be used to test the proposed analysis procedures to ensure that the original assumptions can be tested. In this paper we attempt to verify the hypothesis that the total cost resulting from carrying out a clinical trial can be minimised by using simulation models as an alternative to conventional statistical approaches.”) It would have been obvious to one of ordinary skill in the art that combining Abbas with Springer would produce a clinical trial simulation system capable of simulating a wide range of trial scenarios allowing for the most accurate, cost effective setup to be identified and then practiced with real patients.
Claim 2. Springer makes obvious wherein the plurality of base datasets ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets)
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further includes an enrollment time dataset, ([Section 10 Page 193 Fig. 10.3] Shows a plotted enrollment time dataset)
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Claim 3. Springer makes obvious wherein the plurality of base datasets([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets)
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Abbas makes obvious that a plurality of datasets is generated according to a predefined value distribution and range. ([Section 2.3 Page 1055 Col 1 Par 1 – Col 2 Par 1] “The flow of patients varies according
to three probability distributions: (1) The probability of inclusion, which determines if the patient is included or excluded. (2) The probability of treatment allocation, which determines if the patient is allocated to A treatment or to B treatment. (3) The probability of dropout after treatment allocation at each follow-up visit.[Section 2.3.1 Page 1055 Col 2 Par 2] “We assume that the monthly number of arrivals is a random variable with a Poisson distribution and that the time between arrivals is a random variable with an exponential distribution given by formula 4.” [Section 2.3.3 Page 1057 Col 2 Par 1] “This single simulation is repeated 100 times under different sequences of random numbers starting at 12,345 random seed and up to the 12,445, producing 100 independent data sets, each of the same sample size”)
Claim 4. Springer makes obvious wherein at least one of the plurality of distinct transformation functions changes a value distribution of a dataset. ([Section 8 Page 147 Par 2] “Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.”)
Claim 5. Springer makes obvious wherein at least one of the plurality of distinct transformation functions changes the range of the values of a dataset. ([Section 8 Page 147 Par 2] “Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.” [Section 13 Page 246 Par 4] “In utility approaches, one or more scaling methods are used to assign a numerical value from 0.0 (death) to 1.0 (full health) to indicate an individual’s quality of life.” [Examiner’s note: taking the logarithm of a dataset changes the range of the dataset, reducing every value])
Claim 6. Springer makes obvious wherein generating scenario parameters ([Section 1 Page 10 Par 3] “ …consider the context in which the trial is being conducted. The nature of the disease or condition being studied and the population and setting in which it is being done will influence the outcomes that are assessed, the kind of control, the size, the duration, and many other factors.” comprises combining ([Section 3 Page 42 Par 3] “Investigators need to be satisfied that newer versions that appear during the course of the trial function sufficiently similarly in important ways to the older versions so that combining data from the versions is appropriate.”) via at least one processor, ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) a plurality of transformed datasets ([Section 8 Page 147 Par 2] “…Distributions of such measurements frequently can be approximated by a normal distribution. When this is not the case, a transformation of values, such as taking their logarithm, can still make the normality assumption approximately correct.”) [Section 5 Page 75 Fig. 5.1] shows a plurality of datasets)
Claim 7. Springer makes obvious wherein the plurality of base datasets([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets)
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Abbas makes obvious that a plurality of datasets is generated for all scenarios of a trial design analysis. ([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.”)
Claim 10. Springer makes obvious further comprising determining, via at least one processor, spending function parameters for each information fraction set. ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 16 Page 320 Fig. 16.9] Shows spending functions with different alpha value parameters plotted against a plurality of determined information fractions. [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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Claim 11. Springer makes obvious further comprising precomputing, via at least one processor, one or more boundary conditions ([Section 16 Page 320 Par 1] “which is approximately 0.0006, corresponding to the boundary value 3.23 in Fig. 16.7. That is, the difference in a(t*) at two consecutive information fractions, t* and t** where t* is less than t**, a(t**) − a(t*), determines the boundary or critical value at t**” [Section 16 Page 317 Fig. 16.7] Shows boundary conditions for test statistics based on information fractions [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
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Claim 15. Springer makes obvious wherein the ratio is indicative of a number of patients. ([Section 16 Page 321 Par 4] “For means and proportions, the information fraction can be approximated by the ratio of the number of participants observed to the total expected.” [Section 16 Page 300 Fig. 16.3] shows look positions with data at each)
Claim 16. Springer makes obvious wherein the ratio ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.”) is indicative of a number of deaths. ([Section 16 Page 319 Par 1] “As indicated in the BHAT example, the numbers of deaths between analyses were not equal…”)
Claim 17. Abbas makes obvious wherein the plurality of designs is all the designs in a trial design study. ([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.”)
Claim 18. Abbas makes obvious determining, via at least one processor, a subset of the designs ([Section 2.3.5 Page 1057 Col 2 Par 2 – Page 1058 Col 1 Par 2] “For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters… (1) Scenario 1. There are no dropouts from the trial … (2) Scenario 2. This scenario is similar to scenario 1, but differs in that it considers dropout rates before and after patient allocation to one or other treatment … (3) Scenario 3. This scenario replicates scenario 2, but uses the intention to treat method (ITT) for data analysis” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” [Examiner’s note: each scenario and its associated set of designs and simulations is a subset of the total set of designs and simulations across all scenarios and models] based on the distribution of simulations. ([Section 2.3.2 Page 1057 Col 1 Par 6-7] “Assuming that the patient attends the next visit, then the value of the outcome variable (cholesterol level) will be updated according to the sum of the new mean level: the value generated from the first normal distribution Dist1 at the baseline visit and the new value generated by the second normal distribution Dist2. That is, Li = Meanv + (dist1v = dist1v=1) + (dist2v = N{0;sv}). This process is repeated until the patient either reaches the last visit of the trial or the point when they drop out.”)
Claim 19. Springer makes obvious wherein determining, via at least one processor, information fraction sets comprises determining information fraction sets for ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” )
Abbas makes obvious the subset of the designs ([Section 2.3.5 Page 1057 Col 2 Par 2 – Page 1058 Col 1 Par 2] “For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters… (1) Scenario 1. There are no dropouts from the trial … (2) Scenario 2. This scenario is similar to scenario 1, but differs in that it considers dropout rates before and after patient allocation to one or other treatment … (3) Scenario 3. This scenario replicates scenario 2, but uses the intention to treat method (ITT) for data analysis”)
Claim 20. Abbas makes obvious wherein the distribution of simulations is further based on a location of the simulations. ([Section 2.3.2 Page 1057 Col 1 Par 6-7] “Assuming that the patient attends the next visit, then the value of the outcome variable (cholesterol level) will be updated according to the sum of the new mean level: the value generated from the first normal distribution Dist1 at the baseline visit and the new value generated by the second normal distribution Dist2. That is, Li = Meanv + (dist1v = dist1v=1) + (dist2v = N{0;sv}). This process is repeated until the patient either reaches the last visit of the trial or the point when they drop out.”) [Section 2.3.2 Page 1057 Col 1 Par 5] “After generating the cholesterol value, the model applies the probability of a patient dropping out through another uniform distribution generator between 0 and 1. If the generated probability is equal to or less than the dropout probability, then the patient will continue receiving the treatment in the next visit; if not, the patient will drop out from the trial. In order to account for centrespecific dropout probability, we converted the probability of the trial shown in Table 1, Pd(v), into the k(v) rate given by formula 5. Then, we used the rate found to calculate the intended probability depending on c centre, Pd(v, c), given by formula 6. This equation assumes that this dropout probability increases as the number of centres increases according to an exponential distribution…. where Pd(v,c) is the dropout probability at visit v (v = 0, 1, 2,...) and centre c (c = 1, 2, 3,...). [Examiner’s note: the distribution for dropout depends on both the ordered simulated visit number through time, i.e. is based on the time of the simulation, and the center location, i.e. the simulation location])
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Fundamentals of Clinical Trials (hereinafter Springer)in view of Optimal design of clinical trials with computer simulation based on results of earlier trials, illustrated with a lipodystrophy trial in HIV patients (hereinafter Abbas) in further view of In Defence of 3D charts (Hereinafter Charts) as well as How to Alphabetize in Excel: A Guide to Organized and Efficient Lists (Hereinafter Udemy)
Claim 8. Springer makes obvious ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) of ([Section 3 Page 42 Par 3] “When the question is conceived, investigators, at the very least have in mind a class or type of intervention. More commonly, they know the precise drug, procedure, or lifestyle modification they wish to study. In reaching such a decision, they need to consider several aspects. First, the potential benefit of the intervention must be maximized while possible toxicity is kept to a minimum. Thus, dose of drug or intensity of rehabilitation and frequency and route of administration are key factors that need to be determined. Can the intervention be standardized, and remain reasonably stable over the duration of the trial? Investigators must also decide whether to use a single drug, biologic, or device, fixed or adjustable doses of drugs, sequential drugs, or drug or device combinations”) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) the one or more clinical trial design([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”) ([Section 3 Page 42 Par 3] “When the question is conceived, investigators, at the very least have in mind a class or type of intervention. More commonly, they know the precise drug, procedure, or lifestyle modification they wish to study. In reaching such a decision, they need to consider several aspects. First, the potential benefit of the intervention must be maximized while possible toxicity is kept to a minimum. Thus, dose of drug or intensity of rehabilitation and frequency and route of administration are key factors that need to be determined. Can the intervention be standardized, and remain reasonably stable over the duration of the trial? Investigators must also decide whether to use a single drug, biologic, or device, fixed or adjustable doses of drugs, sequential drugs, or drug or device combinations”) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”) ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 5 Page 75 Fig. 5.1] Shows data taken from a plurality of datasets [Section 5 Page 74 Par 4 – Page 75 Par 1] “The first trial, the Prospective Randomized Amlodipine Survival Evaluation, referred to as PRAISE-I [55], randomized participants to amlodipine or placebo … A second trial, PRAISE-2 [56], was conducted in only those with nonischemic causes of heart failure.”)
Abbas makes obvious evaluating historical trial design selections; ([Section 1 Page 1054 Col 1 Par 2] “Data and analysis results from a previous trial can provide a strong basis for the prior knowledge necessary to simulate a new trial and design an improved protocol, in turn, leading to stronger assumptions.”) a set of trial designs based at least in part on a first set of user interactions; ([Section 1 Page 1054 Col 1 Par 3] “We used the Sigma for Windows general modelling and simulation package, which allows investigators to develop simulation models according to their own needs and specifications”) obtaining trial design simulation results; ([Section 2.3.5 Page 1058 Table 4] Shows simulation results under different trial designs. ([Section 3.3 Page 1060 Col 1 Par 3] “The conventional approach represented by the inverted formula (2) (invF2) estimates that the trial would achieve a power of 0.75 at a cost of €619,000 if M is used to measure the efficacy of the trial. However, the modelling approach estimates that a power of 0.85 would be achieved at a lower cost (€606,000).”) [Examiner’s note: invF2 represents an alternative approach and is not based on any of the simulation or modeling practices established by Abbas. In other words, its results are substitute results to those produced by the trial design simulations.])
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based at least in part on a relationship between the trial design simulation results and supplemental data; ([Section 3.2 Page 1059 Fig. 4] Shows simulation results for expected trial duration plotted against cost data for a variety of simulation design configurations. [Examiner’s note: the specification explicitly lists cost as well as duration as forms of supplemental data. ([Par 298] “examples of supplemental data include: costs of a clinical trial; time to completion of a clinical trial; NPV of a clinical trial; actual personnel costs of a clinical trial; or actual facility costs of a clinical trial.”)
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([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.”) corresponding to the trial design simulation results; ([Section 2.3.5 Page 1058 Table 4] Shows simulation results under different trial designs.) evaluating one or more trial designs ([Section 2.3.5 Page 1057 Col 2 Par 5] “The simulation model analysed the impact of each model and dropout assumptions on power, total duration and cost of a new trial. For each model, we created three scenarios, and for each scenario we ran different simulations using different parameters. Each simulation involved generating 100 independent simulated data sets for a given parameter combination.”) ([Section 3.2 Page 1058 Col 2 Par 4] “The relative errors of simulated and observed parameter standard deviations are below 10%. The standardised differences between observed and simulated means are also below 10%. We also quantified the coverage assessment of the model using the 95% confidence interval. We estimate the proportion of times that the 100
simulated confidence intervals contain the parameter of interest, which is the difference between the two treatments. We found that 98% and 99% of simulated intervals contained this parameter for
the D and M endpoints, respectively. The two standard deviations, s1 and s2 were estimated at 38 and 34 for treatment A, and 36 and 27 for treatment B.”)
The combination of Springer and Abbas does not explicitly teach a quick search data structure or generating a performance surface.
Charts makes obvious generating a performance surface. ([Page 2 Par 7-8] “I decided to focus on two factors – the number of rows in the underlying base table, and the selectivity of the query window (measured as a percentage of the bounding box). I ran a test query repeatedly, changing these two independent variables and measuring the execution time of the query (my dependent variable). At the end of my tests, I had gathered over 15,000 individual test results, and I decided to plot them using a 3d surface chart as follows:” [Examiner’s note: under the BRI the term “performance surface” is interpreted as a three-dimensional graph plotting performance metrics.])
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Charts is analogous art because it is within the field of statistical data analysis. It would have been obvious to one of ordinary skill in the art to combine it with Springer and Abbas before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination to give the clinical trial simulation system of Springer and Abbas an easy to interpret analysis interface that allows the trial designer to quickly comprehend the relationship between different setup variables on the output of the simulation, and identify which have the most significant impact on the results. During the simulation process, a large variety of trial setup parameters are varied. While a simple two-dimensional chart allows easy interpretation of the relationship between two variables, it requires multiple graphs to represent higher-dimensional relationships. Three-dimensional graphs on the other hand, allow the effects of a greater number of independent variables on a dependent variable to be understood at a glance. For example, Charts applies this three-dimensional graph functionality to measuring server performance ([Page 2 Par 8] “I was doing some performance testing of spatial indexes in SQL Server Denali. There are lots of factors that can influence the performance of SQL Server spatial queries, but I decided to focus on two factors – the number of rows in the underlying base table, and the selectivity of the query window (measured as a percentage of the bounding box). I ran a test query repeatedly, changing these two independent variables and measuring the execution time of the query (my dependent variable).”) As stated by Charts, and as is evident in the figure below, the effect of the two independent variables on the dependent is immediately understandable. This also allows for extremely easy identification of the significance of each variable on the results. ([Page 3 Par 1] “It’s clear to see, at a glance, the way in which, separately, the number of rows in the table and the size of the query window affects execution time, and also the way in which they affect it in combination. Since I’m using only a single set of axes for all my data, you can easily see which of the independent variables has a greater effect on the dependent variable.”) It would have been obvious to one of ordinary skill in the art to combine Charts with Springer and Abbas to create a system that produces extremely easily interpreted results, allowing even those designers with limited technical knowledge to operate the simulation system, understand the results at a glance, and apply the results to developing actual clinical trials.
The combination of Springer, Abbas, and Charts does not explicitly teach a quick search data structure
Udemy makes obvious a quick search data structure; ([Page 2 Fig. 1] Shows initial, unsorted data [Page 3 Fig. 1] Shows sorted data. Note that more alphabetically similar data is next to each other, i.e. “Adam” is next to “Alli”, “Jose” next to “June” and so on. [Examiner’s note: the specification describes a “quick search data structure” as a structure in which “geometries that localize designs resulting in similar criteria, i.e., different designs that result in the same outputs are located next to (or near) each other.” In other words, the “quick search data structure” is a structure in which similar data elements are placed next to each other, as with the sorted values in the excel sheet below. Note that if there had been any identical names in the data below they would have been next to each other])
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Udemy is analogous art because it is within the field of structured computer data processing. It would have been obvious to one of ordinary skill in the art to combine it with Springer, Abbas, and Charts before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to make the data easier to read and search. As noted, by Udemy, as dataset sizes increase, the ability to effectively find and retrieve data becomes more and more difficult. To this, end, Udemy presents a method of organizing data into easily searchable lists ([Page 1 Par 2] “In some cases, you may find yourself with a sprawling record of names, titles, and tasks that need to be placed in alphabetical order to really be useful. Luckily for you, this is a pretty basic function, though not without its quirks. In this guide, we’ll go over how to alphabetize in Excel, and what to be aware of when sorting long lists of data”) Overall, one of ordinary skill in the art would have recognized that combining the sorting ability of Udemy with Springer, Abbas, and Charts would result in a system with significantly more user-friendly and easily interpreted output.
Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Fundamentals of Clinical Trials (hereinafter Springer)in view of Optimal design of clinical trials with computer simulation based on results of earlier trials, illustrated with a lipodystrophy trial in HIV patients (hereinafter Abbas) in further view of Krishna (US 20200303075 A)
Claim 12. Springer makes obvious ([Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.” ) ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.”)([Section 16 Page 320 Par 1])
The combination of Springer and Abbas does not explicitly teach further comprising determining duplicate data and filtering duplicate data.
Krishna makes obvious further comprising determining duplicate data and filtering duplicate data. ([Par 34] “The data collected in the database of historical pathological reports of the individuals is processed to remove any duplicate data”)
Krishna is analogous art because it is within the field of bioinformatics data processing. It would have been obvious to combine it with Springer and Abba before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to create more accurate simulations. The introduction of erroneous or mistakenly duplicated data into a dataset can cause a simulation, model, or simple mathematic operation based on that dataset to vary drastically from the actual result it is intended to achieve. Take the dataset [1, 2, 100]; this dataset has a simple average of 34.3. If the number 100 is mistakenly entered into the dataset twice, the new false dataset [1, 2, 100, 100] has an average 50.75. This error can further compounds issues as the complexity of the model using the data increases, such as in the case of advanced simulations or training machine learning systems. To this end, Krishna presents a system that preens datasets, removing duplicate entries in the process to ensure accuracy ([Par 34] “The data collected in the database of historical pathological reports of the individuals is processed to remove any duplicate data. The removal of duplicate data ensures the accurate performance of the DNN model..”) It would have been obvious to one of ordinary skill in the art that combining Krishna with Springer and Abba would produce a simulation system with increased accuracy that is truer to the results of the simulated clinical trial if it had been actually carried out in real life.
Claim 13. Springer makes obvious wherein precomputing comprises, precomputing, via at least one processor, ([Section 16 Page 320 Par 1] “The information fraction is more generally defined in terms of ratio of the inverse of the variance of the test statistic at the particular interim analysis and the final analysis. The alpha spending function, a(t*), determines how the pre-specified a is allocated at each interim analyses as a function of the information fraction.” [Section 6 Page 99 Par 3] “For large studies, a more convenient method … is to use a … algorithm, available on most computer systems.”)
The combination of Springer and Abbas does not explicitly teach processing only unique data.
Krishna makes obvious processing only unique data ([Par 34] “The data collected in the database of historical pathological reports of the individuals is processed to remove any duplicate data. The removal of duplicate data ensures the accurate performance of the DNN model.”)
Krishna is analogous art because it is within the field of bioinformatics data processing. It would have been obvious to combine it with Springer and Abba before the effective filing date. One of ordinary skill in the art would have been motivated to make this combination in order to create more accurate simulations. The introduction of erroneous or mistakenly duplicated data into a dataset can cause a simulation, model, or simple mathematic operation based on that dataset to vary drastically from the actual result it is intended to achieve. Take the dataset [1, 2, 100]; this dataset has a simple average of 34.3. If the number 100 is mistakenly entered into the dataset twice, the new false dataset [1, 2, 100, 100] has an average 50.75. This error can further compounds issues as the complexity of the model using the data increases, such as in the case of advanced simulations or training machine learning systems. To this end, Krishna presents a system that preens datasets, removing duplicate entries in the process to ensure accuracy ([Par 34] “The data collected in the database of historical pathological reports of the individuals is processed to remove any duplicate data. The removal of duplicate data ensures the accurate performance of the DNN model..”) It would have been obvious to one of ordinary skill in the art that combining Krishna with Springer and Abba would produce a simulation system with increased accuracy that is truer to the results of the simulated clinical trial if it had been actually carried out in real life.
Conclusion
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/M.P.M./ Examiner, Art Unit 2187
/ANDRE PIERRE LOUIS/ Primary Patent Examiner, Art Unit 2187 November 26, 2025
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