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 August 29, 2025. Claims 11, 16-17, 21 and 27 have been amended. Claims 13 and 23 have been canceled. Therefore, claims 11-12, 14-18,21 and 27-28 are pending and addressed below.
Applicant’s amendments to the claims are not sufficient to overcome the 35 USC § 101, rejections set forth in the previous office action dated April 29, 2025.
Claim Objections
Claims 11-12, 14-18,21 and 27-28 are objected to because of the following informalities: As amended, Examiner does not see “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; and ; input clinical parameters corresponding to retained model parameters into the machine learning model for predicting venous thromboembolism” as teaching in the original claims or specification. Appropriate correction is required.
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, 14-18,21 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 12-18 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, 14-18,21 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 August 29, 2025 have been fully considered but they are not persuasive. In the remarks applicant argues (1) Claims 11-18, 21, 23, 27, and 28 stand rejected under 35 U.S.C. § 101 as allegedly being directed to non-statutory subject matter. As a preliminary matter, claims 13 and 23 are 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. The rejection under 35 U.S.C. 101 is MAINTAINED for the following reasons.
Applicant's claims 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." This core concept—evaluating patient information to predict a medical outcome—can fall under a fundamental mental process, and a method of organizing human activity (medical diagnosis) that falls within the judicial exceptions. The recitation of a "pre-trained machine learning model" and generic steps for its creation and use, such as generating a training database, performing feature selection by removing parameters, and inputting data to generate a prediction, merely automate this abstract intellectual process using conventional computer technology. The claims are functionally directed to the automation of a doctor's analytical judgment.
Regarding Step 2A Prong Two, the additional elements, including the specific steps of the machine learning workflow, do not integrate the abstract idea into a practical application that amounts to significantly more. The recited steps of corresponding parameters, removing and retaining model parameters based on performance metrics, and executing the model describe well-understood, routine, and conventional activities in the field of applied machine learning. The claims apply these generic techniques to the particular field of venous thromboembolism prediction, which is no more than limiting the use of the abstract idea to a particular technological environment, as found in *Parker v. Flook*. There is no recitation of an improvement to the functioning of the computer itself or to another technology, nor a specific, non-conventional technological arrangement that would transform the nature of the claim. The claims are therefore directed to non-statutory subject matter.
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. One embodiment comprises a decision support tool for detecting risk of venous thrombosis and venous thromboembolism (VTE) in a patient, based
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD B WINSTON III whose telephone number is (571)270-7780. The examiner can normally be reached M-F 1030 to 1830.
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/E.B.W/ Examiner, Art Unit 3683
/ROBERT W MORGAN/ Supervisory Patent Examiner, Art Unit 3683