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
Status of the Application
Claims 1-20 are currently pending in this case and have been examined and addressed below. This communication is a Non-Final Rejection in response to the Amendment to the Claims and Remarks filed on 02/09/2026.
Claims 1, 9, and 17 are currently amended.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1-8 fall within the statutory category of a process. Claims 9-16 fall within the statutory category of an apparatus or system. Claims 17-20 fall within the statutory category of an apparatus or system.
Step 2A, Prong One
As per Claims 1, 9, and 17, the limitations of generating a standardized training dataset by applying one or more data transformations to each clinical trial participant record in a corpus of data gathered about clinical trial participants of one or more historical clinical trials; determining one or more key features of the clinical trial participants, wherein one or more key features correspond to individual characteristics of the clinical trial participants; training a machine learning computer model using the standardized training dataset and the one or more key features of the clinical trial participants to generate a patient burden score, a site burden score, and a cost burden quantifying a patient’s burden and experience participant in at least one clinical trial which comprises determining a structure of the data and at least one protocol-specific dimension associated with patient burden or convenience factors based on clinical study data and patient data in the standardized training dataset, determining associations between the patient burden score and protocol performance outcomes including at least one of cycle times, number of amendments, enrollment rate, patient retention, and screen failure rates, based on historical operational parameters and the one or more key features, and updating the machine learning computer model based on testing and validating the association using de-identified protocols and previous studies assessing protocol design practices until one or more accuracy thresholds are satisfied; mapping the set of operational parameters and the set of endpoints to one or more procedures of the clinical trial; and calculating a patient burden score, a site burden score, and cost burden quantifying a patient’s burden and experience participating in the clinical trial based on the set of operational parameters, the set of endpoints, and the one or more key features, under its broadest reasonable interpretation, covers performance of the limitation in the mind. Generating a standardized training dataset by applying one or more data transformations to each clinical trial participant record does not recite a particular mathematical calculation used for the data transformations and therefore can be performed in any manner. Therefore, this can be performed using human mental observation, evaluation, judgement and opinion. Determining key features of the clinical trial participants can also be performed using human mental observation, evaluation, judgement and opinion. The type of training utilized by the claimed invention is only described as determining a structure of the data and a protocol-specific dimension associated with patient burden or convenience factors, determining associations between the patient burden score and protocol performance outcomes, and updating the machine learning model based on testing and validating the associations using de-identified protocols and previous studies until accuracy thresholds are reached. Because this does not recite or describe a particular mathematical calculation to perform the training, Examiner then analyzes the training based on the steps recited in the claims which can be performed using human mental evaluation, observation, judgment, and opinion. The updating of the model additionally is not described beyond testing and validating using de-identified protocols and determining accuracy thresholds are satisfied, which are activities that can be performed using human mental observation, evaluation, judgement and opinion. Therefore, the training a machine learning computing model is considered to be part of the abstract idea because the steps of training fall under data manipulations that humans perform and are thus a part of the mental process. Mapping the operational parameters and the endpoints to procedures of the clinical trial and calculating burden scores quantifying a patient’s burden and experience are concepts performed including observation, evaluation, judgement and opinion in the human mind. If a claim limitation, under its broadest reasonable interpretation, covers the performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application because the additional elements and combination of additional elements do not impose meaningful limits on the judicial exception. In particular, the claims recite the additional element – a server comprising a processor and non-transitory computer-readable medium. The server in these steps is recited at a high-level of generality, such that it amounts to no more than mere instructions to apply the exception using a generic computer component. 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 claims recite the additional elements of using a strength of feature analysis and executing the machine learning computer model to carry out the abstract idea. This amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(2) because the use of a mathematical algorithm (a strength of feature analysis, and the machine learning computer model) applied on a general purpose computer amounts to mere instructions to apply the exception. The claim elements do not include any details of how the training is performed or how the learning a strength of relationship between the parameters of historical trials and key features is performed. Based on the claim language, any method of determining a strength of relationship could be used to learn a strength of relationship which is used to train the model. This is merely claiming the result or solution and not the details of how the solution is accomplished, which amounts to mere instructions to apply the exception, as per MPEP 2106.05(f)(1). Mere instructions to apply the exception does not integrate the abstract idea into a practical application or provide significantly more than the abstract idea. The claims also recite the additional elements of receiving a set of operational parameters associated with a clinical trial, a set of endpoints associated with the clinical trial, and at least one attribute associated with the clinical trial which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the step of receiving operational parameter attributes are mere data gathering in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). The claims also recite the additional element of populating a set of visual elements within a graphical user interface, which indicated the scores and burden classification for each procedure of the clinical trial based on a comparison between the scores and a range of scores from a set of historical clinical studies which amounts to insignificant extra-solution activity, as in MPEP 2106.05(g), because the steps of populating a set of elements in a graphical user interface are mere data outputting in conjunction with the abstract idea where the limitation amounts to necessary data gathering and outputting, (i.e., all uses of the recited judicial exception require such data gathering or data output). See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). The key features are described as corresponding to individual characteristics of the clinical trial participants. This is merely a description of the data type and not a functional limitation, therefore, this does not integrate the abstract idea into a practical application. Because the additional elements do not impose meaningful limitations on the judicial exception, the claim is directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered both individually and as an ordered combination do not amount to significantly more than the abstract idea. As discussed above with the respect to integration of the abstract idea into a practical application, the additional element of a server comprising a processor and non-transitory computer-readable medium to perform the method of the invention amounts to no more than mere instructions to apply the exception using a generic computing component. The server including the processor and non-transitory computer-readable medium are recited at a high level of generality and are recited as generic computer components by reciting any of various processors including circuitry that performs a function or operation embodied as a microprocessor, microcontroller, etc. (Specification, [0003-0004]), and a RAM, ROM, or other optical disk storage (Specification [00148]), which do not add meaningful limitations to the abstract idea beyond mere instructions to apply an exception. The claims also include the use of mathematical algorithms applied on the general purpose computer to carry out the abstract idea, including a strength of feature analysis and machine learning computer model. These amount to mere instructions to apply the exception, as per MPEP 2106.05(f)(2). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims also include the additional elements of receiving a set of operational parameter attributes associated of a clinical trial and populating a set of visual elements within a graphical user interface, which indicated the scores and burden classification based on a comparison between the scores and a range of scores from a set of historical clinical studies which are both elements that are well-understood, routine and conventional computer functions in the field of data management because they are claimed at a high level of generality and include receiving or transmitting data as well as presenting data, which have been found to be well-understood, routine and conventional computer functions by the Court (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); but see DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1258, 113 USPQ2d 1097, 1106 (Fed. Cir. 2014) ("Unlike the claims in Ultramercial, the claims at issue here specify how interactions with the Internet are manipulated to yield a desired result‐‐a result that overrides the routine and conventional sequence of events ordinarily triggered by the click of a hyperlink." (emphasis added) and (iv) Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of the computer or improves another technology. The claims do not amount to significantly more than the underlying abstract idea.
Dependent Claims 2-8, 10-16, and 18-20 add further limitations which are also directed to an abstract idea. For example, Claims 2-4, 10-12, and 18-20 recite the historical clinical studies share a common operational parameter attribute with the clinical trial, the operational parameter is one of timing, medications, lab tests, blood tests, etc., and the patient burden score corresponds to one of demographics, participating logistics, lifestyle factors, etc. which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 9, and 17. Claims 5 and 13 include extracting an attribute from an electronic document which is an additional element which amounts to insignificant extra-solution activity as data gathering that, similar to the independent claims, is well-understood, routine and conventional in the field of data management because it involves electronically scanning or extracting data from a document, as per MPEP 2106.05(d)(II). Claims 6-7 and 14-15 include a description of the visual elements which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 9. Claims 8 and 16 include a description of the operational parameter attributes which further specify or limit the elements of the independent claims, and hence are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 9. Because the additional elements do not impose meaningful limitations on the judicial exception and the additional elements are well-understood, routine and conventional functionalities in the art, the claims are directed to an abstract idea and are not patent eligible.
Response to Arguments
Applicant’s arguments, see Pages 10-11, “Rejections Under 35 U.S.C. §112”, filed 02/09/2026 with respect to claims 1-20 have been fully considered and they are persuasive. The limitations at issue have been removed and therefore, the rejections have been withdrawn.
Applicant’s arguments, see Pages 11-19, “Rejections Under 35 U.S.C. §101”, filed 02/09/2026 with respect to claims 1-20 have been fully considered but they are not persuasive.
Applicant argues that the proposed amendments are not directed to an abstract idea because the claims are not directed to a mental process or a mathematical concept. Examiner respectfully disagrees. The steps are recited at a level of generality, that under BRI they can be performed mentally and therefore, the rejection would not be overcome. Applicant argues that the claims merely involve, but do not recite, the abstract idea because the steps of the claims are a specific computer-implemented process for producing a calibrated machine learning model that can reliably predict and compare patient, site, and cost burden for new trial designs, which allows for improvement in the design of future clinical trials. Examiner asserts that the predicting and comparing patient, site, and cost burden for trial designs is the abstract idea itself because it can be performed using human mental observation, evaluation, judgment, and opinion. The improvement which is realized from the claims, which Applicant asserts is design of future clinical trials, is an improvement to the abstract idea itself because improving clinical trial design is not a technical improvement to a technical problem but rather is an improvement to a business problem of decision making. The use of a computer to implement the steps of the claims amounts to mere instructions to apply the exception because the computer is recited at a high-level of generality as a processor which is a general purpose computer that applies the abstract idea, as per MPEP 2106.05(f). The claim also recites the training and updating of a machine learning computer model. However, the type of training utilized by the claimed invention is described as determining a structure of the data and a protocol-specific dimension associated with patient burden or convenience factors, determining associations between the patient burden score and protocol performance outcomes, and updating the machine learning model based on testing and validating the associations using de-identified protocols and previous studies until accuracy thresholds are reached. Because this does not recite or describe a particular mathematical calculation to perform the training, Examiner then analyzes the training based on the steps recited in the claims which can be performed using human mental evaluation, observation, judgment, and opinion. The updating of the model additionally is not described beyond testing and validating using de-identified protocols and determining accuracy thresholds are satisfied, which are activities that can be performed using human mental observation, evaluation, judgement and opinion. Therefore, the training a machine learning computing model is considered to be part of the abstract idea because the steps of training fall under data manipulations that humans perform and are thus a part of the mental process. Therefore, the claims are directed to a mental process as recited by the steps of the claims.
Applicant argues that the training process of the claims is analogous to claim 39 (Examiner interprets Applicant to intend this to mean Example 39) because in the example training the neural network does not specify a mathematical algorithm to perform the training and therefore is not directed to a mathematical concept. Examiner notes that in Example 49, the claims do not provide any manner for which the training is performed and the claim does not recite any other limitations which are directed to any abstract idea. This is not analogous to the present claims in which the training is described as being performed by determining a structure of the data and protocol-specific dimension associated with the patient burden or convenience factors, determining associations between the patient burden score and protocol performance outcomes, and updating the machine learning computer model (which is performed by testing and validating the associations). The data manipulations that are performed to accomplish the training step, including the data manipulations to update the model, are recited such that under BRI of the claim language, they could be performed using human mental observation, evaluation, judgment, and opinion. Additionally, the present claims recites other elements which fall into the abstract grouping of a mental process and therefore is not analogous to Example 39. Applicant asserts that the training process in the present claims may merely involve an abstract idea, but are unlike Claim 2 of Example 47. Examiner agrees that the present claims do not recite a particular mathematical algorithm used to train the machine learning computer model similar to Claim 2 of Example 47 and the rejection reflects this, as no element is identified as falling into the abstract grouping of mathematical concepts. However, similar to Claim 2, Example 47, the claims do recite elements which are directed to a mental process and therefore, the claims are directed to an abstract idea.
Applicant argues that the present claims are directed to a particular application of the abstract idea because they provide a particular solution that improves computer-related technology for clinical trial simulation. Applicant asserts that the improvement solves the problem of very slow, expensive, inefficient, and inaccurate processes by using computer-implemented clinical trial simulation. Examiner respectfully disagrees. The use of a computer and machine learning computer model applied to designing clinical trials amounts to mere instructions to apply the exception, as described in the rejection above. As per MPEP 2106.05(f)(2), invoking computers as a tool to perform the process does not integrate the abstract idea into a practical application or provide significantly more, and claiming the improved speed or efficiency inherent with applying the abstract on a computer does not integrate the abstract idea into a practical application or provide significantly more. Applicant also argues that the proposed amendments differ from traditional machine learning computer models because the model is configured to identify relationships between clinical trial variables, etc. Examiner notes that this is how the particular model is novel over other previous models developed, but not an improvement to machine learning as a technology. Applicant additionally argues that the claims integrate the abstract idea into a practical application by improving the computer-implemented design and optimization of clinical trials. Examiner respectfully disagrees. The improvement of design and optimization of clinical trials is the abstract idea itself. No matter how much of an advance in the field the claims recite, the advance lies entirely in the realm of abstract ideas, with no plausibly alleged innovation in the nonabstract application realm. An advance of that nature is ineligible for patenting. The use of a computer to carry out this design and optimization of clinical trials is the use of a general purpose computer to execute the abstract idea and is therefore mere instructions to apply the exception, as per MPEP 2106.05(f)(2).
Applicant argues that the proposed amendments provide significantly more than the abstract idea because the steps including training a machine learning model have specificity of not only saying what is done, but specific steps that specify how it is achieved as in McRO. Examiner respectfully disagrees that the proposed amendments are similar to the claims of McRO. The distinction in McRO is that the method primarily existed in computer technology (i.e., computer-based animation) and the method was previously subjective depending on the human animator that created the animation. The McRO invention provided a technological improvement allowing computers to produce “accurate and realistic lip synchronization and facial expressions in animated characters” that previously could only be produced by human animations and that might have slight differences depending on the human animator's formulation. In other words, the claimed rules in McRO transformed a traditionally subjective process performed by human artists into a mathematically automated process executed on computers. In the instant application, the proposed formulas could theoretically be calculated by a human or by a computer to achieve the exact same results. The computer can do it more quickly, but the basic method is the same and the claimed invention, unlike McRO, is not a solution to a problem that primarily existed in an intrinsically computer-based technology ( i.e., animation), and it is not an improvement to any computer technology (e.g., hardware or database access) itself. The proposed amendments for the current application recite steps that when performed by a person or a computer would produce the same result when the claimed rules are followed. Applicant argues that the improvement provided by the present claims is improving a very slow, expensive, inefficient, and inaccurate process. This differs from McRO and the speed and efficiency which is achieved by the use of a computer and applying a machine learning model amount to mere instructions to apply the exception and do not integrate the abstract idea into a practical application or provide significantly more, as described above. Applicant further argues that the present claims replace reliance on human reviewers qualitative judgment by using a trained machine learning model. Examiner notes that the process which is used to train and update the machine learning model is recited in the claims and any improvement that is realized by following the steps of the claims are an improvement to the abstract idea itself and not an improvement to technology. Therefore, the rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Evangeline Barr whose telephone number is (571)272-0369. The examiner can normally be reached Monday to Friday 8:00 am to 4:00 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, Fonya Long can be reached at 571-270-5096. 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.
/EVANGELINE BARR/Primary Examiner, Art Unit 3682