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
This action is in reference to the communication filed on 24 DEC 2025.
Amendment to claim 9, entered and considered.
Claims 9, 13-18 are present and have been examined.
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 9, 13-18 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 9, 13-18 the independent claim(s) 9 recite(s) a system, which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 9, 13-18, the independent claim(s) (claims 9) is/are directed, in part, to
A fasting status assessing system, comprising:
wherein the fasting-status assessing classifier comprises a calculating module, the fasting blood
each of the plurality of HbA1c-derived averaged glucose level data is obtained by calculating an HbAic concentration data corresponding thereto by the calculating module with the following formula:
AG (mg/dL) = 28.7 x A1C - 46.7, wherein AG is the HbA1c-derived averaged glucose level data, and A1C is the HbAic concentration data; and
wherein when one of the plurality of ontological fasting glucose level data is not an ontological fasting glucose level data of a diabetic patient and is classified as the fasting blood glucose data, the one of the plurality of ontological fasting glucose level data satisfies the following conditions:
AContological < 100 mg/dL and A1C < 5.5%; or
AContological < AG - 1 standard deviation of AContological;
wherein AContological iS the ontological fasting glucose level data, A1iC is the HbAtc concentration data, and AG is the HbA1c-derived averaged glucose level data;
wherein the fasting status assessing classifier is obtained by training the plurality of fasting blood glucose data and the plurality of non-fating blood glucose data to achieve a convergence by a machine learning model, the machine learning model is a gradient descent algorithm, the gradient descent algorithm is an XGBoost machine learning model, a CatBoost matching learning model, or an HTO AutoML ensemble machine learning model.
These claim elements are considered to be abstract ideas because they are directed to mental processes, i.e. concepts that are performed in the human such as observation, evaluation, judgment and opinion. Storing data and classifying the results/status of a subject based on that data involve aspects as identified above – observation in the ontological data, and evaluation/judgement/opinion as far as the classifying.
The claims are further directed to mathematical concepts – i.e. mathematical relationships, formulas, equations, and/or calculations. The use of a “fasting status assessing classifier” to obtain a result, as well as the means through which the classifier is obtained using a trained machine learning model chosen from the models as claimed is a mathematical calculation or at the very least indicative of a mathematical relationship. Examiner further notes that the application of the selected claimed machine learning models are themselves, mathematical concepts/processes.
If a claim limitation, under its broadest reasonable interpretation, covers observation, evaluation, judgement, and/or opinion, then it falls within the “mental processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, formulas, equations, and/or calculations, then it/they falls/ fall into the “mathematical processes” category.
Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, the claim(s) recite(s) additional element(s) – “a non-transitory machine readable medium” and “a processor signally connected to the non-transitory machine readable medium,” as well as a “database” to store information, to perform the claim steps. The non-transitory machine readable medium, the processor signally connected therein, and the database is/are recited at a high level of generality and as such amount to no more than adding the words “apply it” to the judicial exception, or mere instructions to implement the abstract idea on a computer, or merely uses the computer as a tool to perform the abstract idea (see MPEP 2106.05f), or generally links the use of the judicial exception to a particular technological field of use/computing environment (see MPEP 2106.05h). In the interest of compact prosecution, Examiner notes that were the specific algorithms in exemplary claim 9 to be considered at this step, they too would be found to merely be “applied” or used as a tool in terms of the abstract idea(s) identified above. Examiner finds no improvement to the functioning of the computer or any other technology or technical field in above identified elements as claimed (see MPEP 2106.05a), nor any other application or use of the judicial exception in some meaningful way beyond a general like between the use of the judicial exception to a particular technological environment (see MPEP 2106.05e). Examiner further notes that a database storing information is generally found to be adding insignificant extra solution activity to the judicial exception(s) identified (see MPEP 2106.05g). Accordingly, this/these additional element(s) do(es) 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.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as “a non-transitory machine readable medium” and “a processor signally connected to the non-transitory machine readable medium”; as well as the use of a database to store glucose information. When considered individually, the above identified claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in
[0035] The non-transitory machine readable medium 210 is for storing an ontological data of a subject, and the ontological data includes a blood glucose concentration data. In detail, the non-transitory machine readable medium 210 can be further for storing a fasting blood glucose database, the fasting blood glucose database includes a plurality of fasting blood glucose data and a plurality of non-fasting blood glucose data, and the plurality of fasting blood glucose data and the plurality of non-fasting blood glucose data are used to establish a fasting-status assessing classifier. Further, the establishing method of the fasting-status assessing classifier is described in Step 120 of the method 100 for assessing fasting status of the present disclosure, so that details thereof will not be described herein again.
[0036] The processor 220 is signally connected to the non-transitory machine readable medium 210 and includes a fasting-status assessing classifier 230, wherein the ontological data of the subject is analyzed by the fasting-status assessing classifier 230 to obtain an assessing result of fasting status of the subject. The assessing result of fasting status of the subject is for analyzing whether the subject is under the fasting condition or not so as to facilitate the diagnosis of the diabetes mellitus and to rapidly and accurately formulate an appropriate treatment strategy.
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. Examiner finds no further discussion beyond these identified passages, which at best describe these elements in functional terms only – i.e. any storage device would be appropriate. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense.
In the interest of compact prosecution, Examiner also makes reference to the gradient descent algorithms listed – XGBoost, CatBoost, or H2O AutoML. It is similarly clear no improvement is made to the functioning of any of these algorithms, nor is the selection itself intended to be limiting. Examiner turns to the specification:
[0028] “Preferably, the gradient descent algorithm can be an XGBoost machine-learning model, a CatBoost machine-learning model or an H2O AutoML ensemble machine-learning model so as to better handle the categorical variables and then output the features for assessing the fasting status of subjects, and the present disclosure is not limited thereto.”
P0052] …wherein the machine-learning model can be a gradient descent algorithm, and the a gradient descent algorithm can be a XGBoost machine-learning model, a CatBoost machine-learning model or an H2O AutoML ensemble machine-learning model so as to better handle the categorical variables and explore the predictive performance using multiple machine-learning models. Further, a multiple logistic regression model is used to analyze the training dataset and the testing dataset as a comparative example so as to further illustrate the assessing efficacy of the fasting-status assessing classifier of the present disclosure.
[0073] As shown in FIG. 8 and FIG. 9, the AUROC and the calibration performance of the XGBoost machine-learning model, the CatBoost machine-learning model and the H2O AutoML ensemble machine-learning model are generally better than those of the multiple logistic regression model (AUROC 0.887 vs. 0.868, p<0.001).
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement, and instead, relies upon the existing algorithms and their widely accepted improved predictive performance(s).
The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 13-18:
Dependent claims 13-18 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above. Claims 17, 19 disclose types of data stored and/or the types of ontological data considered in the mental process/mathematical relationships. Claims 13-16 provide context and information about the types of mathematical relationships/algorithms used. While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Non-Obvious Subject Matter
Applicant’s amendments as filed on 7 AUG 2025 have incorporated claim limitations previously indicated as non-obvious in the Non-Final Rejection mailed on 8 MAY 2025. As such, claims 9, 13-18 are found to be non-obvious in view of the prior art.
The closest prior art of record is believed to be:
Frank et al (US 20210401330 A1, hereinafter Frank)
Heaton (US 20110053121 A1)
Dunn et al (US 20160239622 A1, hereinafter Dunn)
Goldner et al (US 20200077931 A1, hereinafter Goldner)
Masciotti et al (US 20190142314 A1, hereinafter Masciotti)
Frank teaches storing ontological data for a subject including blood glucose concentration data and other historical information about a user, and using the glucose information/status to obtain the result of a blood sugar result/status of the user. Frank teaches training the classifying model based on prior glucose readings of a plurality of subjects, and storing the data for future training/use. Masciotti teaches a variability measurement in A1C monitoring using a trained model for a similar outcome. Heaten teaches using ontological data to obtain a fasting status/state of the user based on the gluclose level of the user as received. Dunn as cited teaches calculating ontological data using HbAic averaged gluclose levels, and deriving averaged gluclose values from the calculation using a modeling classifier. Goldner as cited teaches the use of a gradient descent XGBoost algorithm for evaluating and training the model.
However, the prior art when considered individually or as a whole does not specifically disclose the elements of claims 10-12 as amended into independent claim 9. In particular Examiner finds that the specific valuations used in, as well as the actual calculating/derivation of the HbA1c modeling as claimed in amended claim 9 is not fairly taught by the prior art.
The Examiner hereby asserts that the totality of the evidence neither anticipates nor renders obvious the particular combination of elements as claimed. That is, the Examiner emphasizes the claims as a whole and hereby asserts that the totality of the evidence fails to set forth, either explicitly or implicitly, an appropriate rationale for combining or otherwise modifying the available prior art to arrive at the claimed invention. The combination of features as claimed would not be obvious to one of ordinary skill in the art because any combination of the evidence at hand to reach the combination of features as claimed would require a substantial reconstruction of Applicant’s claimed invention relying on improper hindsight bias.
Response to Arguments
Applicant’s remarks as filed on 24 DEC are fully considered.
Applicant’s remarks regarding the 101 rejection begin on page 9.
Examiner finds Applicant’s remarks are largely directed to the newly amended limitations. As noted above Examiner finds these limitations to be classified in the mathematical category of abstract ideas. Machine learning models, as referenced by Applicant and described in the specification, are by definition, mathematical. Similarly, the specific examples of machine learning as claimed are, by definition, existing mathematical relationships. I.e. even if the XGBoost, CatBoost, or H2O ML were to be considered at subsequent steps in the analysis, i.e. additional elements, as claimed they would be at best applying one of these models, rather than any improvement to them therein.
Applicant’s remarks on page 10 reference table 3 of the specification, however Examiner respectfully disagrees with Applicant’s conclusions, and notes that there is no requirement for the “period of time” for the mental process itself to require. Table 3 essentially provides a group of factors to be considered in the modeling – Examiner finds that consideration of the factors in table 3 is analogous to perhaps a medical history questionnaire or intake process common to almost all medical attention.
Applicant’s remarks on page 11 regarding the AUROC/calibration of XGBoost, CatBoost, and H20 AutoML are noted. Examiner respectfully submits Applicant has mischaracterized the “improvement” for purposes of the analysis. In order to constitute eligible subject matter, the improvement to an additional element needs to be to the “functioning of a computer, or to any other technology or technical field.” These models have been identified as part of the abstract idea(s) as noted above, and as such are ineligible for consideration as additional elements. However, the act of screening for diabetes mellitus is not in and of itself considered a technical field for purposes of the analysis, and so an “improvement” therein is not considered a technical improvement for eligibility considerations. Further, Applicant does not purport to have “invented” the use of an AUROC, nor of XGBoost, CatBoost, and H20 AutoML, so even these elements were eligible for consideration as an additional element, again, this is analogous to adding the words “apply it” with the judicial exception, or a general link of the judicial exception to a particular environment. For at least those reasons, Examiner finds Applicant’s remarks unpersuasive.
Applicant’s references to the specification on page 12 are noted, however, Examiner reaches a similar conclusion as above. Any improvement is not technical in nature nor to a technical field, and instead any discussion about the modeling in amended claim 9 is more appropriately classified as part of the mathematical relationships/abstract idea identification. Results 1-3 and the discussion thereof on pages 12-15 are clearly examples of calculations and results thereof.
Applicant’s remaining remarks on page 15/16 are unpersuasive at least in view of the discussion above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687