Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The following Office action in response to communications received April 13, 2026. Claims 1-20 have been canceled. Claims 21-40 have been added. Therefore, claims 21-40 are pending and addressed below.
Applicant’s amendments to the claims are not sufficient to overcome the rejections set forth in the previous office action dated April 16, 2009.
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 21-40 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception without significantly more.
Claims 21-36 and 38-40 are directed to a process, and claim 37 is directed to a machine, and therefore the claims fall within a statutory category.
Independent claims 21, 32, and 37 are directed to an abstract idea consisting of collecting clinical-trial data, building and updating statistical models from selected prior trials and input variables, running predictive analyses or simulations, comparing predicted results, and presenting the results to a user to support trial-planning decisions.
Independent claim 21 recites, in substance, selecting candidate clinical trials, storing a composite statistical model derived from those trials, receiving modified selections and variable values through a GUI, updating or reusing the model, running simulations, and updating a graphical artifact showing predicted trial outcomes.
Independent claim 32 recites, in substance, receiving intervention-arm and comparator-arm inputs and selected candidate trials through a GUI, generating respective statistical models, running simulations for the two arms, generating a graphical survival artifact and numerical comparison metric, and determining whether the intervention arm satisfies a predefined threshold to indicate success or failure of a target clinical trial.
Independent claim 37 recites, in substance, allocating processor subsets to generate trial models, compute a composite model and synthetic model, execute simulations, store models and parameters, receive two types of user inputs, selectively recompute or reuse a stored model, execute simulations, and update a graphical artifact in the GUI.
The limitations of claims 21-40, as drafted, under their broadest reasonable interpretation, cover the performance of:
Mental processes, including evaluation and selection of candidate clinical trials, analysis of variable values, comparison of predicted outcomes, and judgment as to whether a target clinical trial meets a threshold for success.
Mathematical concepts, including statistical modeling, generation of probability distributions, use of coefficients and weights, hazard-based and survival-based calculations, simulation of outcomes, and numerical comparison metrics such as hazard ratio, median survival, and progression-free survival.
Certain methods of organizing human activity, including planning and evaluating clinical-trial arms, comparing treatment strategies, and making a decision based on predicted outcomes.
But for the recitation of generic computer components, the claim steps are simply using prior clinical-trial information and user-provided variables to perform mathematical prediction and comparison for clinical-trial planning and then displaying the resulting predictions.
The claims recite additional elements such as one or more processors, memory, a clinical intelligence service, a virtual simulation computing system, a graphical user interface, processor subsets, distributed processors, and graphical artifacts.
These elements are recited at a high level of generality and merely use generic computer components to perform generic computer functions such as storing data, receiving inputs, executing software instructions, running models, generating outputs, and displaying information.
The additional elements do not integrate the abstract idea into a practical application. The claims do not improve the functioning of a computer, processor, memory, network, or display in a technological sense; rather, they use conventional computing resources as tools to perform statistical analysis and simulation for the particular field of clinical-trial planning.
Although the claims refer to selective retrieval of stored models, partial updating of parameters, distributed processors, and updating only portions of a graphical artifact, those features merely improve the efficiency of carrying out the underlying abstract modeling and presentation activity on generic computer hardware. Such efficiency improvements to the abstract analysis itself do not amount to a technological improvement to the computers or networks performing the calculations. The claimed graphical survival artifacts, comparison metrics, and success/failure outputs are also merely ways of presenting the results of the abstract mathematical analysis. Displaying the results of a model or simulation to a user is insignificant extra-solution activity and does not meaningfully limit the judicial exception. Likewise, limiting the analysis to the field of clinical trials, intervention arms, comparator arms, or target clinical trials merely links the abstract idea to a particular field of use. The claims remain directed to mathematical modeling and decision support performed with generic computer technology.
The ordered combination of claim elements adds nothing significantly more than the abstract idea itself. The claims use generic processors, memory, interfaces, storage, and displays to receive trial-related data, run models and simulations, compare results, and present outputs, which are conventional computer functions. Any storing, retrieving, displaying, transmitting, or updating of information is conventional and routine.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 22-31 and 33-40). Particularly, each of the dependent claims also fails to amount to “significantly more’ than the abstract idea since each dependent claim is directed to a further abstract idea, and/or a further conventional computer element/function utilized to facilitate the abstract idea. Accordingly, none of the current claims implements an element—or a combination of elements—directed to an inventive concept (e.g., none of the current claims is reciting an element—or a combination of elements—that provides a technological improvement over the existing/conventional technology). These information characteristics do not change the fundamental analogy to the abstract idea grouping of “Certain Methods of Organizing Human Activity,” and, when viewed individually or as a whole, they do not add anything substantial beyond the abstract idea. Furthermore, the combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology. Therefore, the claims when taken as a whole are ineligible for the same reasons as the independent claims.
Accordingly, claims 21-40 are directed to an abstract idea without significantly more and therefore are not patent eligible under 35 U.S.C. §101.
Subject Matter Free of Prior Art
Examiner notates below the reasons why the claims overcome the prior art. The limitations most likely to distinguish over the cited combination of Krishnan et al., Dunlavey, Lakatos and Freedman are:
retrieving the stored composite statistical model from the memory without recomputing the composite statistical model; and
updating the at least one graphical simulation artifact in the GUI in response to the execution of the plurality of virtual simulations, wherein selectively retrieving the stored composite statistical model and generating the updated synthetic statistical model without recomputing the composite statistical model enables re-execution of the plurality of virtual simulations based on a subset of previously computed model data.
generating a first synthetic statistical model for the intervention arm based on the selection of candidate clinical trials and the one or more first variable values; generating a second synthetic statistical model for the comparator arm based on the selection of candidate clinical trials and the one or more second variable values;
generating, based on the first plurality of virtual simulations and the second plurality of virtual simulations, (i) a graphical survival artifact representing simulated survival outcomes over time for the intervention arm and the comparator arm, and (ii) a numerical comparison metric including at least one of a median survival value, a hazard ratio, or a progression-free survival metric; and
determining, based on the numerical comparison metric, whether the intervention arm satisfies a predefined improvement threshold relative to the comparator arm and generating an output indicative of a success or failure of the target clinical trial based on the determination.
wherein the second processor subset is further configured to: in response to receiving the first type of user input, recompute the composite statistical model and generate a new synthetic statistical model; and in response to receiving the second type of user input, retrieve the composite statistical model from the memory and generate an updated synthetic statistical model without recomputing the composite statistical model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Krishnan et al. US 20200098451 A1: A facility for predicting patient outcomes on the basis of clinical trials is described. The facility obtains information describing one or more completed clinical trials, and extracts features from the obtained clinical trial information. The facility uses the extracted features to train both a time-series data model for predicting clinical outcomes and a non-time-series data model for predicting clinical outcomes. The facility applies these trained models to information describing a subject patient to predict a clinical outcome for the subject patient.
Dunlavey US 6,985,846 B1: A system and method for simulating clinical trial protocols with compiled state machines is presented. The system receives input data representing a clinical trial protocol. This information is sorted into a plurality of schedules, including dosage schedules, observation schedules, and various other types of schedules. The schedules are translated into a general-purpose high-level programming language and then compiled into and executable file. The executable file may comprise a plurality of discrete state machines, each state machine corresponding directly to a single schedule. During the clinical trial simulation, the executable file is run by the trial simulator to simulate the trial protocol.
Lakatos US 20100063741 A1: A method for calculating a sample size for a clinical trial of a first treatment can be provided. The method can include reading a survival curve from a clinical trial for a second treatment, wherein the clinical trial may be selected by a user interacting with a user interface. The method can further include selecting a plurality of points on the survival curve and storing coordinates for each of the plurality of points, wherein the plurality of points are selected so as to capture substantial features of the survival curve. Then, a hazard curve is generated based on the coordinates that were stored, wherein the hazard curve may be a step function. The method can further include calculating a sample size for the clinical trial of the first treatment using a Markov model based on the hazard curve.
Freedman US 20110238317 A1: A system for forecasting outcomes of clinical trials including a digital computer having a central processor, a programmable graphical or programmable analog coprocessor in communication with the central processor, interface software in communication with the central processor and coprocessor, modeling software having simulation capability substantially executed by the central processor and in communication with the interface software, integration software substantially executed by the central processor in communication with the modeling software, partial derivative software substantially executed by the coprocessor and in communication with the interface software, a historical clinical trial data component, a model data component specifying the time course of a clinical observation by a differential equation, and a protocol data component specifying the characteristics of a population intended to undergo a future clinical trial, all the said data components in communication with the modeling software.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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/E.B.W/Examiner, Art Unit 3683
/ROBERT W MORGAN/Supervisory Patent Examiner, Art Unit 3683
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