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 .
Status of the Claims
Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement submitted on July 9, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by 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 1-20 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.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-7 are drawn to a method for determining patient burden for a clinical study, which is within the four statutory categories (i.e. process). Claims 8-20 are drawn to systems for determining patient burden for a clinical study, which are within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A method for generating a training dataset used to train a model to predict a burden of a clinical study for a patient using a decreased feature size based on iterative elimination of variables, the method comprising:
in response to transmitting a clinical study questionnaire to a set of patients associated with a set of clinical studies, retrieving, by a processor via an application programming interface heterogenous patient input corresponding to different categories of patient data and operational parameter data from a set of electronic data sources, input received via the set of patients, the input corresponding to demographic data and a quantified burden associated with each clinical study;
generating, by the processor, a training dataset comprising:
each patient's demographic data,
for at least some patients of the set of patients, an indication of a therapeutic area that a disease associated with the patient belongs to;
a patient burden score for each patient generated in accordance with an algorithm evaluating each patient's input with regards to participation logistics, lifestyle factors, caregiver involvement, and procedural burden associated with each clinical study, and
a set of operational parameters associated with the set of clinical studies;
segmenting, by the processor, in accordance with the operational parameters, the training dataset into one or more procedure subgroups representing procedure types of the clinical study associated with each patient according to a data splitting protocol based on the set of operational parameters associated with the set of clinical studies;
training, by the processor, a computer model including a predictive model associated with each procedure group using the segmented training dataset, such that the computer model is configured to ingest data associated with a new clinical study, generate an individual burden prediction for each procedure subgroup included in the new clinical study by executing one or more corresponding predictive models, and predict a new patient burden score based on the individual burden predictions of the one or more corresponding predictive models, wherein training the computer model comprises, with each iteration for each segment of the training dataset:
eliminating a least significant independent variable of the demographic data in each iteration of an iterative multivariate elimination regression modeling protocol for each procedure subgroup, wherein eliminating the least significant independent variable in each iteration reduces a feature set for the procedure subgroup until remaining variables in each procedure subgroup are significant predictors of procedure subgroup burden scores, thereby decreasing data dimensionality trained in subsequent iterations and allowing faster convergence and reduced computation time for training each predictive model; and
generating a coefficient associated with each procedure subgroup based on the iterative multivariate elimination regression modeling protocol; and
transmitting, by the processor, the new patient burden score to a graphical user interface of a clinical trial platform for display at the graphical user interface.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of cover the abstract idea of mathematical concepts, a mental process, and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the steps generating the training dataset, segmenting the training dataset into procedure subgroups according to a splitting protocol, training a model using a multivariate elimination regression modeling protocol, generating an individual burden prediction for each procedure group, and predicting a new patient burden score based on the individual burden predictions recite at least mathematical relationships and/or calculations), a process that could be practically performed in the human mind (i.e. observations, evaluations, judgments, and/or opinions – in this case, the steps of retrieving patient input, generating the training dataset, segmenting the training dataset into procedure subgroups according to a splitting protocol, training a model using a multivariate elimination regression modeling protocol, generating an individual burden prediction for each procedure group, and predicting a new patient burden score based on the individual burden predictions, and displaying the new burden score recite at least collecting information, analyzing the information, and displaying certain results of the analysis) or using a pen and paper, but for the recitation of generic computer components (i.e. a server and processor), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of transmitting the study questionnaire to patients, retrieving patient data, and displaying the new burden score recite following rules or instructions for organizing clinical trial participation for patients), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 8 and 15 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 8, and 15 is that Claim 1 recites a method, whereas Claim 8 recites a system including a server and a non-transitory computer-readable medium containing instructions that are executed by a processor, and Claim 15 recites a system including a server.
Dependent Claims 2-7, 9-14, and 16-20 include other limitations, for example Claim 2, 9, and 16 recite that the burden score is based on a new patient attribute, Claims 3, 10, and 17 recite using the new patient burden score to populate an interface, Claims 4, 11, and 18 recite various parameters in the training dataset, Claims 5, 12, and 19 recite identifying the strength of features in the training dataset, Claims 6, 13, and 20 recite determining which inputs have a statistically significant relationship to the output patient burden score, and Claims 7 and 14 recite utilizing a supervised training method, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-7, 9-14, and 16-20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent Claims 2-7, 9-14, and 16-20 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1, 8, and 15.
Hence Claims 1-20 recite the aforementioned abstract idea.
Prong 2 of Step 2A
Claims 1, 8, and 15 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the processor, the computer of the computer model, the application programming interface, and the step of transmitting the clinical study questionnaire) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of a server, a processor, and the API, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs [0004], [0012], [0015], and [0054] of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language of the types of data of the patient inputs, which amounts to limiting the abstract idea to the field of clinical trials, and the claim language of the training data and the training of the model, which amounts to limiting the abstract idea to machine learning, see MPEP 2106.05(h).
Additionally, dependent Claims 2-7, 9-14, and 16-20 include other limitations, but these limitations also amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 4, 11, and 18), and/or adding insignificant extra-solution activity to the abstract idea (e.g. populating a graphical user interface recited in Claims 3, 10, and 17), and/or do not include any additional elements beyond those already recited in independent Claims 1, 8, and 15, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-20 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1, 8, and 15 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the server, the processor, the computer of the computer model), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
paragraphs [0004], [0015], and [0054] of the Specification disclose that the additional elements (i.e. the server, the processor, and the computer of the computer model) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention transmits a clinical study questionnaire to a set of patients and in response receives input data over a network, for example the Internet, e.g. see [0044] of the present Specification;
Performing repetitive calculations, e.g. see Parker v. Flook, and/or Bancorp Services v. Sun Life – similarly, the current invention performs basic calculations (i.e. calculating an individual burden prediction for each procedure group, calculating a patient burden score based on the individual burden prediction, performing multiple iterations of training and removing the least significant independent variable with each iteration) and does not impose meaningful limits on the scope of the claims;
Dependent Claims 2-7, 9-14, and 16-20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly generally link the abstract idea to a particular technological environment or field of use (e.g. the types of data recited in dependent Claims 4, 11, and 18), represent no more than generic structural elements performing generic functions (e.g. populating a graphical user interface recited in Claims 3, 10, and 17), and/or do not recite any additional elements not already recited in independent Claims 1, 8, and 15, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-20 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1-20 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 112 and 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Walpole (US 2019/0206521) teaches transmitting surveys to patients to obtain various patient data, generating a training dataset for a machine learning system, training the machine learning system, and calculating a patient burden index. However, Walpole does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Walpole does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Neumann (US 2021/0004715) teaches training datasets including an area of expertise for an advisor. However, Neumann does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Neumann does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Clark (US 2020/0258599) teaches labeling training data with various labels, and utilizing at least a subset of the labeled training data to train separate machine learning models. However, Clark does not teach that the training dataset includes a therapeutic area for the patient disease, generating an individual burden score for each procedure subgroup, and performing the training of the machine learning system by eliminating a least significant independent variable with each training iteration. Additionally, Clark does not teach modifying a visit schedule or procedure assignment for patients based on the patient burden score.
Lash (US 2001/0020229) teaches calculating a likelihood of a patient becoming a high user of healthcare resources based on existing coefficients for claims. Additionally, Lash teaches utilizing iterative multivariate logistic regression that eliminates the least predictive variable with each iteration until all remaining variables are determined to be significant. However, Lash does not teach that the training dataset includes a therapeutic area for the patient disease, segmenting the training dataset into procedure subgroups, and generating an individual burden score for each procedure subgroup.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claim 1-20 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed May 26, 2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 112(a) have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 1-20 under 35 U.S.C. 112(a) have been withdrawn.
Applicant’s arguments, see Remarks, filed May 26, 2026, with respect to the rejections of Claims 1-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant alleges that the present invention is patent eligible because it is not directed towards an abstract idea, specifically because it recites a specific computerized method for training a segmented machine learning model, e.g. see pgs. 10-13 of Remarks – Examiner disagrees.
Regarding the generating of the training dataset, the segmenting of the training dataset, and the training of the predictive model, Examiner notes that, as shown above, the aforementioned steps recite an abstract idea because they recite mathematical relationships and/or calculations (i.e. mathematical concepts), collecting data, analyzing the data, and displaying certain results of the analysis (i.e. a mental process), and/or following rules or instructions for organizing clinical trial participation for patients (i.e. a certain method of organizing human activities).
Examiner also notes that, given the broadest reasonable interpretation, “a computer model including a predictive model” does not require a machine learning model and/or artificial intelligence. For example, “training a predictive model” may be interpreted as defining an equation and/or any model for calculating the burden score, with or without any type of machine learning. Moreover, [0070] of the as-filed Specification discloses that “the computer model may include any algorithm (whether utilizing AI/ML techniques or not),” and hence, the training dataset, the model itself, and the training of the model are reasonably interpreted as mathematical concepts, mental processes, and/or rules or instructions defining a certain method of organizing human activities because the claim language and the Specification discloses that the model need not even be a machine learning and/or artificial intelligence model.
Similarly, the claimed invention is not properly analogized to the invention of Example 39 of the USPTO-issued examples at least because the invention of Example 39 recited the training of a neural network, whereas the present invention recites a predictive model that could include models not requiring any type of machine learning and/or artificial intelligence. Additionally, the Background of Example 39 specifically states that the invention results in “a robust face detection model that can detect faces in distorted images while limiting the number of false positives.” In contrast, [0003] of the as-filed Specification discloses that conventional systems are “tedious, time-consuming, and expensive,” “unreliable because the results depend directly on the human reviewer’s subjective skills and understanding.” That is, the Background of Example 39 specifically states that the invention of Example 39 solves a problem in digital facial detection, and in contrast, the present claimed invention addresses the problems of tediousness of calculations, the amount of time required for the calculations, and the expense associated with the calculations, all of which represent problems which have existed since long before the advent of any type of computer technology. Hence, the claimed invention does not represent a technological solution rooted in computer technology addressing a problem specifically arising in the realm of computer networks.
Additionally, regarding the specificity of the claimed limitations, the Claims being narrowly claimed is not dispositive in determining the eligibility of the Claims. The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow, e.g. see MPEP 2106.04. That is, a claim reciting a narrow abstract idea nonetheless recites an abstract idea, and must be evaluated under the remainder of the requirements under 35 U.S.C. 101.
For the aforementioned reasons, Claims 1-20 are nonetheless rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Horne (US 2013/0031038) – teaches a system for analyzing neural response data including EEG data, wherein the system includes a training module that eliminates insignificant features in the training data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H CHOI can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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