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 .
DETAILED ACTION
In the preliminary amendments filed on 16 October 2024, the following has occurred: claims 1-15 have been canceled; claims 16-24 are newly added.
Now claims 16-24 are pending.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 16793565, filed on 18 February 2019.
Information Disclosure Statement
The Information Disclosure Statement(s) filed on 17 October 2024, has been considered by the Examiner.
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 16-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 16 and 23-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite:
Claim 16, which is representative of claims 23 and 24
[… maintaining …], a plurality of sets of data of clinical trials of a plurality of drugs, wherein data for each of the clinical trials in the plurality of sets of data comprises an outcome of a clinical trial for a drug and at least one of regulatory data associated with the clinical trial, drug molecule characteristics of the drug, and design information of the clinical trial; executing at least one […] model of a plurality of […] models to receive as output from execution of the at least one […] model a likelihood that a particular clinical trial associated with a particular drug will result in the particular drug being approved, wherein: the plurality of […] models were obtained by [… creating …] a plurality of different types of […] models using training data of clinical trials of the plurality of drugs from the plurality of sets of data to generate the plurality of […] models; [… outputting …], to an entity, a measure of the likelihood; [… outputting …], a set of parameters for the particular clinical trial, wherein: the set of parameters were used to execute the at least one […] model for the particular clinical trial; [… obtaining …], from a user, a modification of one or more parameters of the set of parameters from one or more input controls; responsive to [… obtaining …] the one or more modified parameters, performing at least one […] scenario by executing the at least one […] model for the particular clinical trial of interest with the modified one or more parameters and [… obtaining …] as output from the […] model a modified likelihood that the particular clinical trial associated with the particular drug will result in the particular drug being approved under the at least one […] scenario; and [… outputting …], to the entity, a measure of the modified likelihood.
, as drafted, is a system that under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions). That is, via a human user interacting with at least one processor with a memory, and an interface, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, by a human user interacting with at least one processor with a memory, and an interface, the claim encompasses a human user selecting a model and parameters to organize data, and based on the user’s selection to provide an output for the human user to use. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of at least one processor with a memory, and an interface. The at least one processor with a memory, and an interface is recited at a high level of generality (i.e., general purpose computers with processors and memory, performing/ implementing generic computer functions; see applicant’s specification pages 26-30) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claims recite the additional elements of “storing, in a database…”, “executing at least one trained machine learning model… training a plurality of different types of machine learning models using training data…”, “displaying, via an interface”, “receiving…” and “performing at least one simulation scenario…”. The “storing, in a database…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “executing at least one trained machine learning model… training a plurality of different types of machine learning models using training data…” is recited at a high-level of generality (i.e., training and using an off-the-shelf machine learning algorithm) and amounts to generally linking the abstract idea to a particular technological environment. The “displaying, via an interface” is recited at a high-level of generality (i.e., as a generic display interface for presentation of information to a user) and amounts to generally linking the abstract idea to a particular technological environment. “receiving…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “performing at least one simulation scenario…” is recited at a high-level of generality (i.e., as generic means of predicting results using a model and parameters) and amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of at least one processor with a memory, and an interface, to perform the noted steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (“significantly more”).
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “storing, in a database…”, “executing at least one trained machine learning model… training a plurality of different types of machine learning models using training data…”, “displaying, via an interface”, “receiving…” and “performing at least one simulation scenario…” were considered post/extra-solution activity and/or generally linking to a particular technological environment. The “storing, in a database…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “executing at least one trained machine learning model… training a plurality of different types of machine learning models using training data…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in 2019/0252036 (Elemento): see below but at least paragraph [0158]; 20180046780 (Gravier): paragraph [0314]; 20190362838 (Srivistava): paragraph [0046]; training and use of a machine learning model is well-understood, routine and conventional. The “displaying, via an interface” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in, 2019/0252036 (Elemento): see below but at least paragraph [0080]-[0082]; 20180046780 (Gravier): Figures 15-19, paragraph [0346]; 2021/0378747 (Emili): paragraphs [047]; displaying data on a generic user interface is well-understood, routine and conventional. The “receiving…” steps have been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(i) “Receiving or transmitting data over a network” is well-understood, routine, and conventional. The “performing at least one simulation scenario…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in 20100324874 (Bangs): paragraphs [0005]-[0007]; 2021/0378747 (Emili): paragraphs [0002]-[0005]; simulation of a scenario is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 17-22 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claim 17 describes optimization, however does not recite any additional elements sufficient to provide a practical application/significantly more.
Claims 18 and 19 describe adjusting parameters of the trial design, however do not recite any additional elements sufficient to provide a practical application/significantly more.
Claim 20 recites “retrieving…” however, communication of data was already considered above and is incorporated herein.
Claims 21 and 22 recite the additional elements of a “control”, however these various controls are recited at a high level of generality (i.e., simply outputting generic buttons for user interaction) and amounts to simply output of data or user interaction, which amounts to generally linking the abstract idea to a particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of various “controls” were considered generally linking to a particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in 2019/0252036 (Elemento): see below but at least paragraph [0080]; 20180046780 (Gravier): paragraph [0154], [0360]; 2021/0378747 (Emili): see below but at least paragraph [0047], claim 27; presentation of controls for a user to interact with is well-understood, routine and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 16-18 and 20-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2019/0252036 (hereafter “Elemento”), in view of U.S. Patent Pub. No. 2021/0378747 (hereafter “Emili”).
Regarding (New) claim 16, Elemento teaches a system (Elemento: Figs. 1-2, paragraphs [0003]-[0004], “a system to improve the accuracy of drug toxicity predictions can include a data processing system”), comprising:
at least one processor in communication with a memory storing instructions configured to cause the at least one processor (Elemento: Figs. 1-2, paragraphs [0003]-[0004], “The data processing system can include one or more processors that are coupled to a memory”) to perform:
storing, in a database, a plurality of sets of data of clinical trials of a plurality of drugs, wherein data for each of the clinical trials in the plurality of sets of data comprises an outcome of a clinical trial for a drug and at least one of regulatory data associated with the clinical trial, drug molecule characteristics of the drug, and design information of the clinical trial (Elemento: Figs. 2, 11, 18-19, paragraphs [0005]-[0008], “a first plurality of reference chemicals and a second plurality of reference chemicals… Each of the reference chemicals can have a respective structural vector that can include values corresponding to one or more features derived from the chemical structure of the respective reference chemical. The references chemicals can each have a respective target vector that can include values corresponding to one or more features derived from one or more gene targets and the respective reference chemical… structural vector can be based on at least one chemical property feature and at least one drug-likeness feature…. the target vector can be based on at least one of a gene expression feature or a target feature”, paragraph [0013], “The first plurality of reference chemicals can include a plurality of drugs that passed clinical trials and the second plurality of reference chemicals includes a plurality of drugs that failed clinical trials”, paragraph [0103], “a first group of drugs that have failed clinical trials and a second group of drugs that have passed clinical trials (e.g., drugs that received Food and Drug Administration (FDA) approval) to determine similarities between the test chemical and the first and second groups. The system can retrieve data”, paragraphs [0108]-[0109], “The chemical databases 218 can be remote databases that the drug toxicity predictor 120 can access via a network”, paragraph [0218], “the chemical database 218 can be the SIDER database that contains information on marketed medicines and their recorded adverse drug reactions”);
executing at least one trained machine learning model of a plurality of trained machine learning models to receive as output from execution of the at least one trained machine learning model a likelihood that a particular clinical trial associated with a particular drug will result in the particular drug being approved (Elemento: Figs. 1-2, paragraphs [0003]-[0004], “predict the likelihood of toxicity in clinical trials and whether the drug would pass the toxicity requirements of a clinical trial… identify drugs likely to possess manageable toxicity in clinical trials and can help drive the design of therapeutic agents with less toxicity… generate, using a machine learning classifier, a toxicity predictor score”, paragraph [0157], “The drug toxicity predictor 120 illustrated in FIG. 11 can include a classifier 206 that includes a plurality of tissue specific classifiers 250(1)-250(n). For example, the classifier 206 can include a tissue specific classifier 250 for liver tissue, kidney tissue, blood, heart tissue, lung tissue, and pancreas tissue. The tissue specific classifiers 250 can generate toxicity prediction scores 220 that indicate the likelihood that a specific drug is toxic to individual tissues”), wherein:
the plurality of trained machine learning models were obtained by training a plurality of different types of machine learning models using training data of clinical trials of the plurality of drugs from the plurality of sets of data to generate the plurality of trained machine learning models (Elemento: Figs. 1-2, paragraphs [0005]-[0007], “The machine learning classifier can be trained using a first plurality of reference chemicals and a second plurality of reference chemicals”, paragraphs [0157]-[0158], “The classifier 206, and each of the tissue specific classifiers 250, can be trained using reference chemicals that cause known toxicity to specific tissues”);
displaying, via an interface, to an entity, a measure of the likelihood (Elemento: Figs. 1-2, paragraphs [0003]-[0004], “predict the likelihood of toxicity in clinical trials and whether the drug would pass the toxicity requirements of a clinical trial”, paragraph [0090], “the computing device 100 may include or connect to multiple display devices 124a-124n”, paragraph [0129], “output a toxicity predictor score”); […].
Elemento may not explicitly teach (Underlined below for clarity):
displaying, via the interface, a set of parameters for the particular clinical trial, wherein: the set of parameters were used to execute the at least one trained machine learning model for the particular clinical trial;
receiving, from a user, a modification of one or more parameters of the set of parameters from one or more input controls;
responsive to receiving the one or more modified parameters, performing at least one simulation scenario by executing the at least one trained machine learning model for the particular clinical trial of interest with the modified one or more parameters and receiving as output from the trained machine learning model a modified likelihood that the particular clinical trial associated with the particular drug will result in the particular drug being approved under the at least one simulation scenario; and
displaying, to the entity, a measure of the modified likelihood.
Emili teaches displaying, via the interface, a set of parameters for the particular clinical trial, wherein: the set of parameters were used to execute the at least one trained machine learning model for the particular clinical trial (Emili: claim 27, “providing a user interface comprises providing a plurality of user-selectable templates, associated with respective types of simulation, and wherein each template comprises: a plurality of input parameters which can be selected for the simulation, each parameter being associated with a respective range of permitted values, within which a parameter value can be set; a plurality of selectable output parameters, comprising the desired quantities as an output result; a plurality of displaying and reporting options, which can be selected by the user to choose the format of the results and/or the methods to analyze the results”);
receiving, from a user, a modification of one or more parameters of the set of parameters from one or more input controls (Emili: paragraph [0027], “receiving selection and/or setting information I which can be entered by the user through the user interface 4. Such selection and/or setting information comprises: information (I1) on the selection and/or definition and/or setting of a medical device model; information (I2) on the selection and/or definition and/or setting of an anatomical and/or physiological model of patient based on said stored anatomical and/or physiological modeling digital data; information (I3) on the selection and/or setting of a simulation type, and/or information on the selection and setting of one or more input simulation parameters (I4) and one or more output simulation parameters (I5)”);
responsive to receiving the one or more modified parameters, performing at least one simulation scenario by executing the at least one trained machine learning model for the particular clinical trial of interest with the modified one or more parameters and receiving as output from the trained machine learning model a modified likelihood that the particular clinical trial associated with the particular drug will result in the particular drug being approved under the at least one simulation scenario (Emili: paragraph [0025], “A step of executing the computational simulation is then provided, by the one or more computational simulation software programs, on the basis of said input setting data Din, of the aforementioned medical device model M1 and of the aforementioned anatomical and/or physiological model M2, to obtain output data Dout of the computational simulation”, paragraph [0032], “selecting from a plurality of digital models”); and
displaying, to the entity, a measure of the modified likelihood (Emili: paragraph [0026], “express simulation results R, representative of a functional and/or structural behavior of the medical device and/or patient (i.e., the results desired by the user), in a format selected by the user; and of providing said simulation results through the user interface”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using adjust parameters and simulating the adjusted parameters to update results as taught by Emili within the measurements of trial approval likelihood as taught by Elemento with the motivation of “complement, complete or partially replace the so-called “in vivo clinical trials”” (Emili: paragraph [0004]).
Regarding (New) claim 17, Elemento and Emili teach the limitations of claim 16, and further teach wherein the instructions are further configured to cause the at least one processor to perform optimizing the at least one trained machine learning model to maximize the likelihood (Elemento: paragraphs [0003]-[0004], “predict the likelihood of toxicity in clinical trials and whether the drug would pass the toxicity requirements of a clinical trial”, paragraph [0130], “the drug toxicity predictor 120 was tested by performing a 10-fold cross validation on a set of 784 FDA approved drugs with known targets and a second set of 100 FTT drugs that had at least one annotated target and known chemical structure”; Emili: paragraph [0050], “a PIDO (Process Integration and Design Optimization) software program S6, configured to manage the workflow of the software programs comprised in the computer platform and to optimize the computational simulations”, paragraph [0056], “paragraphs [0003]-[0004], “predict the likelihood of toxicity in clinical trials and whether the drug would pass the toxicity requirements of a clinical trial””).
The motivation to combine is the same as in claim 16, incorporated herein.
Regarding (New) claim 18, Elemento and Emili teach the limitations of claim 17, and further teach wherein the instructions are further configured to cause the at least one processor to perform selectively changing one or more parameters of the clinical trial design to maximize the likelihood (Elemento: paragraphs [0003]-[0004], “predict the likelihood of toxicity in clinical trials and whether the drug would pass the toxicity requirements of a clinical trial”; Emili: paragraph [0048], “The one or more processing components 3 are further configured to carry out, by means of one or more software programs or applications (S1-S6), the following further actions: processing the aforementioned information l1 on the selection and/or definition and/or setting of a medical device model… furthermore, processing the aforementioned information l2 on the selection and/or definition and/or setting of a patient model l2… furthermore, processing the aforementioned selection and/or setting information I entered by the user to prepare input setting data Din (in particular dependent on l3, l4) for one or more computational simulation software programs S4 included in the computer platform 1; then, carrying out the computational simulation, by the one or more computational simulation software programs S4, based on the input setting data Din, on the medical device model M1 and on the anatomical and/or physiological model M2, to obtain output data Dout of the computational simulation”).
The motivation to combine is the same as in claim 16, incorporated herein.
Regarding (New) claim 20, Elemento and Emili teach the limitations of claim 16, and further teach wherein the instructions are further configured to cause the at least one processor to perform retrieving data associated with the plurality of drugs and corresponding clinical trials from one or more data sources (Elemento: Figures 1-2, 11, paragraph [0103], “The system can retrieve data, such as from The Database for Aggregate Analysis from ClinicalTrials.gov (AACT), and extract the names of the drugs associated with clinical trials of any phase that were annotated as having failed for toxicity reasons”, paragraph [0110], “the drug toxicity predictor 120 can retrieve data from the chemical databases 218”).
The motivation to combine is the same as in claim 16, incorporated herein.
Regarding (New) claim 21, Elemento and Emili teach the limitations of claim 16, and further teach wherein the instructions are further configured to cause the at least one processor to perform permitting a user to select one or more input controls that permit a user to adjust the simulation scenario (Emili: claim 27, “providing a user interface comprises providing a plurality of user-selectable templates, associated with respective types of simulation, and wherein each template comprises: a plurality of input parameters which can be selected for the simulation, each parameter being associated with a respective range of permitted values, within which a parameter value can be set; a plurality of selectable output parameters, comprising the desired quantities as an output result; a plurality of displaying and reporting options, which can be selected by the user to choose the format of the results and/or the methods to analyze the results”).
The motivation to combine is the same as in claim 16, incorporated herein.
Regarding (New) claim 22, Elemento and Emili teach the limitations of claim 16, and further teach wherein the instructions are further configured to cause the at least one processor to perform permitting a user to select at least one user interface control that when selected, causes the interface to modify one or more parameters of the particular clinical trial associated with the particular drug (Emili: paragraph [0025], “A step of executing the computational simulation is then provided, by the one or more computational simulation software programs, on the basis of said input setting data Din, of the aforementioned medical device model M1 and of the aforementioned anatomical and/or physiological model M2, to obtain output data Dout of the computational simulation”, paragraph [0032], “selecting from a plurality of digital models”).
The motivation to combine is the same as in claim 16, incorporated herein.
REGARDING CLAIM(S) 23 AND 24
Claim(s) 23 and 24 is/are analogous to Claim(s) 16, thus Claim(s) 23 and 24 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 16.
Claim(s) 19 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 2019/0252036 (hereafter “Elemento”) and U.S. Patent Pub. No. 2021/0378747 (hereafter “Emili”) as applied to claim 18 above, and further in view of U.S. Patent Pub. No. 2017/0147794 (hereafter “Harder”).
Regarding (New) claim 19, Elemento and Emili teach the limitations of claim 18, and further teach wherein the instructions are further configured to cause the at least one processor to perform modifying at least one or one or more parameters of the particular clinical trial design […] (Emili: paragraph [0027], “receiving selection and/or setting information I which can be entered by the user through the user interface 4. Such selection and/or setting information comprises: information (I1) on the selection and/or definition and/or setting of a medical device model; information (I2) on the selection and/or definition and/or setting of an anatomical and/or physiological model of patient based on said stored anatomical and/or physiological modeling digital data; information (I3) on the selection and/or setting of a simulation type, and/or information on the selection and setting of one or more input simulation parameters (I4) and one or more output simulation parameters (I5)”).
Elemento and Emili may not explicitly teach (underlined below for clarity):
wherein the instructions are further configured to cause the at least one processor to perform modifying at least one or one or more parameters of the particular clinical trial design including, without limitation, a number of patients in the particular clinical trial, a number of arms of the particular clinical trial, a number of comparators, a number of sites, and/or a number and type of endpoints in the particular clinical trial.
Harder teaches wherein the instructions are further configured to cause the at least one processor to perform modifying at least one or one or more parameters of the particular clinical trial design including, without limitation, a number of patients in the particular clinical trial, a number of arms of the particular clinical trial, a number of comparators, a number of sites, and/or a number and type of endpoints in the particular clinical trial (Harder: Figures 3, 13, 23-25, paragraph [0010], “a trial comparator”, paragraph [0023], “an investigator site editor”, paragraph [0031], “a patients editor”, paragraph [0078], “comparing multiple trial candidates and identifying which elements of the candidates differ. This allows a user to quickly identify the differences and can help in determining which trial to pursue or whether to make changes to a candidate. In some examples, a graphical representation comprising a trial comparator that graphically overlaps selected trial candidates is provided”, paragraph [0095]-[0098], “A design and controls editor comprises a means by which one or more users may input data including one or more of: design, patient numbers, randomization, and extension data. In some examples, a design and controls editor comprises multiple pages or panes where patient numbers and design inputs may be inputted separately… a treatment arm represents the treatment(s) to be provided to the patient(s) participating in the trial”, paragraph [0107], “an investigator site editor”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include clinical trial parameters with the clinical trial simulation that is taught by Elemento and Emili with the motivation of “help in determining which trial to pursue or whether to make changes to a candidate” (Harder: paragraph [0078]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Patent Pub. No. 20100324874 (hereafter “Bangs”) teaches predicting an individual’s response to a therapy, using simulation of data.
U.S. Patent Pub. No. 20180046780 (hereafter “Graiver”) teaches clinical trial protocol suitability using eligibility criteria.
U.S. Patent Pub. No. 20190362838 (hereafter “Srivastava”) teaches clinical trial management and feasibility given trial parameters.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew E Lee whose telephone number is (571)272-8323. The examiner can normally be reached M-Th 9-5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached on 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/A.E.L./ Examiner, Art Unit 3684
/Shahid Merchant/ Supervisory Patent Examiner, Art Unit 3684