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 May 18, 2026. Claims 11 and 21 have been amended. Claim 15 has been canceled. Therefore, claims 11-12, 14, 16-18, 21 and 27-28 are pending and addressed below.
Applicant’s amendments to the claims are sufficient to overcome the Claim Objections, rejections set forth in the previous office action dated December 17, 2025.
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 11-12, 14, 16-18, 21 and 27-28 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. Based upon consideration of all of the relevant factors with respect to the claims as a whole, the claims are directed to non-statutory subject matter which do not include additional elements that are sufficient to amount to significantly more than the judicial exception because of the following analysis:
Independent Claim(s) 11 and 21 are directed to the abstract idea of "collecting clinical data, analyzing correlations within that data using a mathematical model, and outputting a medical risk assessment." The present disclosure also describes a method of generating a model for predicting venous thromboembolism.
Independent Claim 11 recites “receiving, from a second subject, a second value of at least one clinical parameter of a plurality of clinical parameters; executing a model for predicting venous thromboembolism, performing operations comprising: generating a training database storing first values of a plurality of clinical parameters and venous thromboembolism associated with a plurality of first subjects; corresponding the plurality of clinical parameters to a plurality of model parameters; removing one or more model parameters of the plurality of model parameters; retaining a removed model parameter, wherein the removed model parameter causes a plurality of performance metrics to decrease in accordance with the prediction of venous thromboembolism; inputting clinical parameters corresponding to retained model parameters for predicting venous thromboembolism; generating, for predicting venous thromboembolism, output data indicating a prediction for venous thromboembolism; and outputting the predicted venous thromboembolism of the second subject.”
Independent Claim 21 recites “correspond the plurality of clinical parameters to a plurality of model parameters; remove one or more model parameters of the plurality of model parameters; retain a removed model parameter, wherein the removed model parameter causes a plurality of performance metrics associated with the machine learning model to decrease in accordance with the prediction of venous thromboembolism; input clinical parameters corresponding to retained model parameters for predicting venous thromboembolism; generate, for predicting venous thromboembolism, output data indicating a prediction for venous thromboembolism; receive, from a second subject, a second value of at least one clinical parameter of a plurality of clinical parameters; execute the model for predicting venous thromboembolism; and output data indicating a prediction for venous thromboembolism.”
The limitations of Claims 11 and 21, as drafted, under its broadest reasonable interpretation, covers the performance of a Mental Process concepts performed in the human mind (including an observation, evaluation, judgment, opinion) and/or Method of Organizing Human Activity which are concepts performed by managing personal behavior, relationships or interactions between people (including fundamental economic principles, commercial or legal interactions, social activities, teaching, and following rules or instructions such as medical diagnosis), but for the recitation of generic computer components. That is, other than reciting, “one or more processors; a memory; communication platform; training database; machine learning engine; machine learning model; display device; pre-trained model” nothing in the claim element precludes the step from practically being performed in the Mental Process and/or by Method of Organizing Human Activity. For example, but for the “machine learning engine” language, “storing” in the context of this claim encompasses the user manually store first values of a plurality of clinical parameters and venous thromboembolism outcomes associated with a plurality of first subjects. Similarly, the selecting a subset of model parameters from the plurality of clinical parameters, covers performance of the limitation in the Mental Process and/or by Method of Organizing Human Activity, but for the recitation of generic computer components. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the Mental Process and/or by Method of Organizing Human Activity, but for the recitation of generic computer components, then it falls within the “Mental Processes and/or 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 claims recite the additional elements of using a “one or more processors; a memory; communication platform; training database; machine learning engine; machine learning model; display device; pre-trained model” to perform all of the “obtaining, transforming, parsing, determining, transforming, selecting and storing” steps. The “one or more processors; a memory; communication platform; training database; machine learning engine; machine learning model; display device; pre-trained model” is/are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of executing computer-executable instructions for implementing the specified logical function(s) such that it amounts 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 claim is directed to an abstract idea.
Claim 11 has the following additional elements (i.e., training database; machine learning model; pre-trained model). Claim 21 has the following additional elements (i.e., one or more processors; a memory; communication platform; training database; machine learning engine; machine learning model; display device). Looking to the specification, these components are described at a high level of generality (¶ 148; In embodiments, the computer device, computer readable media, network, and remote device may be arranged in the architecture depicted in FIG. 4. The computing device400 houses at least, but is not limited to a processor(s)401, communication platform(s)402, input/output device(s)404, memory406, a machine learning engine418, and a prediction engine424. The memory includes at least, but is not limited to an application programming interface408, a client- facing application410, machine learned models412, training application414, and a training database416, a machine learning engine518 that comprises feature selection algorithms420, trained prediction models422, and a display device426. The memory also includes a prediction engine424). The use of a general-purpose computer, taken alone, does not impose any meaningful limitation on the computer implementation of the abstract idea, so it does not amount to significantly more than the abstract idea. Also, although the claims add “[storage]” steps, it is only considered as insignificant extrasolution activity. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. The combination of elements does not indicate a significant improvement to the functioning of a computer or any other technology and their collective functions merely provide a conventional computer implementation of the abstract idea. Furthermore, the additional elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generally linking the abstract idea to a particular technological environment or field of use, as the courts have found in Parker v. Flook. Therefore, there are no limitations in the claims that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception.
It is worth noting that the above analysis already encompasses each of the current dependent claims (i.e., claims 12, 14, 16-18 and 27-28). 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 “Mental Processes and/or Method 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.
Claims 11-12, 14, 16-18, 21 and 27-28 are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Response to Arguments
Applicant’s arguments filed May 18, 2026 have been fully considered but they are not persuasive. In the remarks applicant argues (1) Claims 11-12, 14, 16-18, 21 and 27-28 stand rejected under 35 U.S.C. § 101 as allegedly being directed to non-statutory subject matter.
Claims 11, 12, 14-18, 21, 27, and 28 stand rejected under 35 U.S.C. § 101 as allegedly being directed to non-statutory subject matter. As a preliminary matter, claim 15 has been canceled.
Applicant respectfully traverses this rejection. Nevertheless, for the sole purpose of expediting allowance and without commenting on the propriety of the Office's rejections, Applicant herein amends claims 11 and 21 as shown above. Applicant respectfully submits that these amendments render the § 101 rejection moot.
In response to argument (1), The Examiner has considered Applicant's arguments and amendments. Some arguments merely rehash issues addressed in the previous rejections mailed and are incorporated herein. The rejection under 35 U.S.C. 101 is MAINTAINED for at least the following reasons.
On “mental process” vs. “intractability”:
Applicant argues that cross-validation with many iterations is “intractable” as a mental process. While that may be practically true, USPTO eligibility analysis is not limited to what is practically convenient; it looks at whether the steps are, in principle, mathematical/mental in nature. Leave-one-out cross-validation, computing sensitivity/specificity/AUC, and deciding whether metric sums decreased by a threshold are quintessential mathematical/statistical operations and evaluations, which fall under the abstract-idea categories regardless of scale.
On asserted “improvement in computer functionality”:
Applicant analogizes to Finjan and claims an improvement in “computer functionality.” However, the claims here do not improve the operation of the computer itself; they use existing computing capability to perform a domain-specific prediction. Under the 2019 Guidance and 2024 AI update, an improvement to a mathematical model’s accuracy in a particular field, without more, is not automatically an improvement to computer technology.
On integration into a practical application (medical field):
Applicant points to venous thromboembolism prediction as an improvement to a technical field (medicine). The claims, however, stop at generating “output data indicating a prediction” and “outputting the predicted venous thromboembolism” to a display; they do not recite any concrete medical intervention or device control that applies the prediction. As the 2024 AI Guidance notes, medical AI claims tend to be eligible when they specify particular treatments or device adjustments based on model output; the present claims do not do so and therefore remain at the level of “an idea of a solution” (improved risk prediction) rather than a specific technological application.
On Step 2B “significantly more”:
Applicant argues the claims include significantly more because they embody a specific ML architecture and cross-validation approach. But all of these recited techniques (backwards elimination, leave-one-out cross-validation, use of sensitivity/specificity/AUC thresholds) are conventional ML/model-selection methods, and the claims implement them on a general-purpose computer. Under the guidance, using known algorithms and metrics on generic hardware is insufficient to supply an inventive concept.
For these reasons, the §101 rejection of claims 11, 12, 14–18, 21, 27, and 28 is maintained.
Subject Matter Free of Prior Art
Examiner notates below the reasons why the claims overcome the prior art.
-- “retaining a removed model parameter, wherein removed during an iteration of the plurality of iterations such that the removed model parameter causes the plurality of performance metrics associated with the machine learning model to decrease in accordance with the prediction of venous thromboembolism;”
-- “training, during each iteration of the plurality of iterations, the machine learning model by leave-one-out cross validation to determine a plurality of performance metrics associated with the machine learning model after removing a model parameter;”
-- “inputting clinical parameters corresponding to retained model parameters into the machine learning model for predicting venous thromboembolism;” and
-- “output a trained machine learning model for predicting venous thromboembolism.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10963795 B2; Methods and apparatus, including computer program products, implementing and using techniques for text analysis of medical study data to extract predictive data. Natural language processing is performed on a document in a collection of documents to determine whether the document contains medical model data. In response to determining that the document contains medical model data, content relating to the medical model data in the document is annotated. A first medical model is generated based on the annotations for the identified medical model data and a certainty threshold In response to the certainty threshold meeting a user setting, the first medical model is added to a predictive model for determining a risk score, based on the analyzed data.
US 11749404 B1; An improved decision support tool is provided for detecting and treating human patients at risk for having (or developing) venous thromboembolism VTE. The tool determines a quantitative probability of VTE by utilizing a smart sensor based on a particular machine-learning model for detecting specific biomarkers determined to be related to VTE. In particular, a quantitative probability of VTE may be determined via a model based on interrelationships between multiple components of the human body's complement cascade and their coupling to coagulation processes. In one aspect, a quasi-Dirichlet distribution “mixture” relationship between total hemolytic complement (CH50) activity and complement protein C3 levels is employed as part of a smart sensor and decision support tool to provide predictive, diagnostic, and prognostic applications and for guiding prevention and treatment of acute VTE. Where the smart sensor determines a risk for VTE, then the decision support tool may initiate an intervening action.
US 11769592 B1; Technologies are provided for an improved classifier apparatus and processes for improving the accuracy of classification technology including example applications of such classifiers. A process includes applying clustering to variables contributing to the classification task. The clusters may be represented in a 1-dimensional, 2-dimensional, or 3-dimensional matrix that is a spatial abstraction of the interrelationships. A convolutional transformation may be applied to the matrix so as to reduce the effective dimensionality of the classification problem and improve the signal-to-noise ration. A deep learning neural network method may be applied to the transformed network to generate an improved classification model, which may be utilized by a decision support tool.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683
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