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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/20/2026 has been entered.
Response to Amendment
In the amendment, filed 01/20/2026, the following has occurred: claims 1, 4-5, 8-9, 12-13, and 17-21 have been amended. Now, claims 1, 4-9, and 12-21 are pending.
The previous claim objections are withdrawn based on the amendments.
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, 4-9, and 12-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 2A Prong One
Claims 1 and 16 recite obtaining a set of features about the subject, wherein the set of features comprises: (i) chronic kidney disease status of the subject; (ii) age of the subject; (iii) weight of the subject; (iv) height of the subject; (v) hypertension status of the subject; (vi) at least one of nephritis status of the subject, nephrosis status of the subject, or renal sclerosis status of the subject; (vii) Charlson comorbidity index for the subject; (viii) weight increase from hospital admission to ICU admission; and (ix) history of calcineurin inhibitor intake for the subject; analyzing, using a trained baseline creatinine determination model, the obtained set of features to generate a baseline creatinine value for the subject; reporting, via a user interface, the generated baseline creatinine value for the subject.
Claim 9 recites obtain a set of features about the subject, wherein the set of features comprises: (i) chronic kidney disease status of the subject; (ii) age of the subject; (iii) weight of the subject; (iv) height of the subject; (v) hypertension status of the subject; (vi) at least one of nephritis status of the subject, nephrosis status of the subject, or renal sclerosis status of the subject; (vii) Charlson comorbidity index for the subject; (viii) weight increase of the subject from hospital admission to ICU admission; and (ix) history of calcineurin inhibitor intake for the subject; and analyze, using a trained baseline creatinine determination model, the obtained set of features to generate a baseline creatinine value for the subject; and report the generated baseline creatinine value for the subject.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass Mental Processes, including observation and mental evaluations. For example, obtaining a subject feature set and analyzing the feature set by mentally applying a trained baseline creatinine determination model could be carried out by a user observing subject feature data and mentally evaluating it. If needed, the user could perform the analysis with the aid of pen and paper, applying the model in the form of a mathematical formula. Additionally, obtaining subject information and reporting the baseline creatinine value as a result of an analysis could be carried out manually by one user providing the value to another user. Such an interaction between people encompasses Certain Methods of Organizing Human Activity.
Claims 4-8, 12-15, and 17-21 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, claims 5 and 13 further expand on the feature set. As explained above, the obtained feature set observations of information, which is a Mental Process. Claims 4, 6, 12, 14, 17, and 19 further encompass training the model using a subset of the feature set and further defining the model as a gradient boosting regression model. As described at paragraphs 0068-0071 of the specification, the broadest reasonable interpretation of the model and training encompasses Mathematical Concepts. Claims 7 and 15 further expand on the reporting. As explained above, the reporting could be carried out manually by one user providing the value to another user. Claim 8 is further directed to administering a treatment to the subject based on the reported value. There is no indication that this step provides an improved treatment. Additionally, the treatment encompasses “putting the subject on an acute kidney injury watchlist.” Such a step is merely providing data regarding the patient and does not provide and improved treatment. Therefore, this recitation is part of the abstract idea as described above. Claims 18 and 20-21 further expand on generating the baseline creatinine value that correlates to an actual baseline creatinine value of the subject. For the same reasons explained above, this could be carried out mentally with the aid of pen and paper.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas.
Claims 1, 4-7, and 21, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “a user interface.”
Claims 9, and 12-15, and 20, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “a processor,” and “a user interface.”
Claims 16-19, directly or indirectly, recite the following generic computer components configured to implement the abstract idea: “on-transitory computer-readable storage medium having stored a computer program comprising instructions, which, when executed by a processor, cause the processor,” “a user interface.”
The written description discloses that the recited computer components encompass generic components including “the system may communicate the information to a mobile phone, computer, laptop, wearable device, and/or any other device configured to allow display and/or other communication of the information” (see paragraph 0044). As set forth in the MPEP 2106.04(d) “merely including instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”).
Additionally, the aforementioned additional elements, considered in combination, do not provide an improvement to a technical field or provide a technical improvement to a technical problem. Furthermore, as explained above, administering a treatment as recited in claim 8 does not provide an improved treatment. Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
Distinguishing Subject Matter
The following is a statement of reasons for the indication of distinguishing subject matter: Claims 1, 4-9, and 12-21 distinguish over the prior art. The closest prior art (Dauvin and Chiofolo) discloses modeling a variety of subject features to determine a baseline creatinine value. However, the prior art does not describe applying the combination of 9 features, as recited in claims 1, 4-9, and 12-21, to the recited model to derive the baseline creatinine value.
Response to Arguments
In the remarks filed 01/20/2026, Applicant argues (1) reporting the baseline creatinine value, alone, is not sufficient to conclude that the claims, as a whole, are directed to an abstract idea; (2) the claimed invention integrates the abstract idea into a practical application by providing improved machine learning, citing Ex Parte Desjardins.
In response to argument (1), the examiner agrees that this single step, alone, and without consideration of the claim as a whole, does not define the claim as being directed to an abstract idea. However, this is not the analysis applied in the 101 rejections set forth above. As explained in the above rejections, obtaining subject data, analyzing the data, and reporting the results of the analysis define the abstract idea. Categories of both Certain Methods of Organizing Human Activity and Mental Processes define this abstract idea, when considering the limitations as a whole. Furthermore, such an analysis is consistent with that of Electric Power Group v. Alstom (see MPEP 2106.04(a)(2)IIIA). Furthermore, the examiner respectfully disagrees that a human cannot mentally perform the recited analysis. The claimed analysis is only defined in terms of using a trained model. However, there is no limitation on the scope of the trained model. A user could, with pen and paper, apply numerical values to the features, and a compute a simple math problem to derive a result. Therefore, these arguments are not found to be persuasive.
In response to argument (2), Ex Parte Desjardins highlights technical improvements in machine learning such as a “machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of ‘catastrophic forgetting’” which allows “the system to reduce use of storage capacity; and the enablement of reduced complexity in the system.” These improvements are identified as improvements to “how the machine learning model itself would function in operation.” Applicant identifies paragraphs 0002-0003, 0032, and 0081 as supporting description in the specification for how the claims result in improvements to machine learning technology. These paragraphs explain how prior art would calculate a patient’s baseline creatinine value using a MDRD equation and how calculating the patient’s baseline creatinine value with the six or nine feature model provides more accurate results. However, the identified improvement in accuracy simply relies on identifying the particular feature data set that is used in a model to derive the result. There is no description here of any improvements to the operation of a machine learning technology. This stands in stark contrast to what is identified in Ex Parte Desjardins. There is no similar “catastrophic forgetting” being addressed, nor is there any identified improvements to the operation of a machine learning model, such as “reduced use of storage capacity” or “reduced complexity in the system.” Therefore, these arguments are not found to be persuasive.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to C. Luke Gilligan whose telephone number is (571)272-6770. The examiner can normally be reached Monday through Friday 9:00 - 5:00.
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C. Luke Gilligan
Primary Examiner
Art Unit 3683
/CHRISTOPHER L GILLIGAN/ Primary Examiner, Art Unit 3683