DETAILED ACTION
This Office Action is sent in response to Applicant’s Communication received 11/12/2025 for application number 17/481,429.
Claims 1-20 are pending.
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 .
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 an abstract idea without significantly more. Independent claims 1 and 13 recite:
Claim 1 recites:
A system for estimating a patient's chronic kidney disease ("CKD") progression, the system comprising: a memory device storing patient characteristic data for a patient undergoing analysis, the patient characteristic data including demographic/physiological data, a CKD entry stage, a diagnosed cause of CKD, and a health history; an ensemble machine learning algorithm configured to predict a progression to a next stage of CKD and a timeframe of the progression of the next stage of CKD, the ensemble machine learning algorithm containing prediction decile classifiers that each includes (i) percentages of known patients that progressed from one moderate CKD stage to a next moderate or severe CKD stage for discrete timeframes and (ii) known patient characteristic data that corresponds to the known patients, the known patient characteristic data including demographic/physiological data for the known patients, a diagnosed cause of CKD, a health history, and a classification ofa positive or a negative result related to CKD stage progression wherein each prediction decile for the different CKD stages is assigned a probability of a positive outcome for CKD progression for each ofthe discrete timeframes using (i); and an analytics processor communicatively coupled to the memory device, the analytics processor in conjunction with the ensemble machine learning algorithm configured to: classify the patient undergoing analysis into a closest matching prediction decile for the CKD entry stage of the patient by comparing the patient characteristic data of the patient under analysis to classifications of patient characteristic data provided in the ensemble machine learning algorithm, determine a probability that the patient undergoing analysis will progress to a next moderate or severe CKD stage for each of the discrete timeframes based on the closest matching prediction decile, and display, via a user interface, the probability that the patient undergoing analysis will progress to the next moderate or severe CKD stage for the discrete timeframes.
Claim 13 recites:
A system for estimating a likelihood a patient with chronic kidney disease ("CKD") will need urgent start dialysis, the system comprising: a memory device storing patient characteristic data for a patient undergoing analysis, the patient characteristic data including demographic/physiological data, a CKD entry stage, a diagnosed cause of CKD, and a health history; a machine learning algorithm configured to predict a likelihood the patient undergoing analysis will need an urgent start of dialysis, the machine learning algorithm containing prediction decile classifiers that each includes (i) percentages of known patients that needed an urgent start of dialysis for discrete timeframes, and (ii) known patient characteristic data that corresponds to the known patients, the known patient characteristic data including demographic/physiological data for the known patients, a diagnosed cause of CKD, a health history, and a classification of a positive or a negative result related to an urgent start of dialysis, wherein each prediction decile for the different CKD stages is assigned a probability of a positive outcome for an urgent start of dialysis for each of the discrete timeframes using (i); and an analytics processor communicatively coupled to the memory device, the analytics processor in conjunction with the ensemble machine learning algorithm configured to: classify the patient undergoing analysis into a closest matching prediction group for the CKD entry stage of the patient by comparing the patient characteristic data of the patient under analysis to classifications of patient characteristic data provided in the machine learning algorithm, determine probabilities that the patient undergoing analysis will need an urgent start of dialysis for the discrete timeframes based on the closest matching prediction decile, and display, via a user interface, the probabilities that the patient undergoing analysis will need the urgent start of dialysis for the discrete timeframes.
(2A, prong 1) The underlined portions of the claims recite a mental process. A human can mentally judge a risk decile for predicting that a patient will progress to a next CKD stage (for claim 13, requiring start of dialysis is essentially predicting the patient is progressing to stage 5 CKD) for different timeframes based on patient data like CKD stage, health history, etc., the deciles being associated with known patient data (Applicant’s specification at para. 0005, as filed, describes the prior art stating that, “… healthcare providers estimate the patient's potential CKD progression timeline to determine possible treatments.”)
(2A, prong 2) This judicial exception is not integrated into a practical application. The additional elements of a memory storing patient characteristic data and displaying percentage likelihoods, are insignificant extra-solution activity because these elements merely amount to necessary data gathering and outputting). The processor and ensemble machine learning algorithm are mere instructions to apply the exception: the processor adds a generic computer after the fact to the abstract idea, and the ensemble machine learning algorithm merely recites the idea of a solution (that the patient is classified into a decile) and not the details of how the solution is accomplished (i.e. how the ensemble ML algorithm operates to classify patients). Even in combination with the mental process, the additional elements do not integrate the abstract idea into a practical application because they only add insignificant extra-solution activity and mere instructions to apply the exception to the mental process.
(2B) The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The memory and UI mentioned as insignificant extra-solution activity above and well-understood, routine, and conventional activities, analogous to storing and retrieving information in memory, see MPEP 2106.05(d) citing Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The processor and ensemble ML algorithm are mere instructions to apply the abstract idea on a generic computer, as explained above. Even when the claim is considered as a whole, the additional elements do not amount to significantly more than the judicial exception itself because they only add insignificant extra-solution activity that is well-understood, routine, and conventional and mere instructions to apply the exception to the mental process.
With respect to dependent claims 2-11 and 14-18, these claims add additional steps to the mental process. Claims 2-4, 8-10, 14-16, and 18 specify particular types of existing health data that a human would use in the mental process, and claims 5-7, 11, and 17 specify existing types of data are used in the percentages of known patients that progressed to a next CKD stage.
Dependent claim 12 adds the additional limitation that the UI is displayed on a clinician computer. This limitation does not integrate the abstract idea into a practical application because it is insignificant extra-solution activity of necessary data output, and the limitation does not add up to significantly more than the abstract idea itself because presenting data is well-understood, routine, and conventional (analogous to presenting offers and gathering statistics, see MPEP 2106.05(d) citing OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1362-63, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015)).
Dependent claims 19-20 recites receiving an indication to start dialysis, and cause a treatment to be prepared, and the system comprising a dialysis machine configured to perform dialysis. These additional limitations are intended use / field of use limitations, and do not integrate the judicial exception into a practical application under the treatment or prophylaxis consideration because it does not actually recite any action to effect a particular treatment. In other words, the claims do not actually require dialysis treatment, they merely require the availability of a dialysis machine. “For example, a step of "prescribing a topical steroid to a patient with eczema" is not a positive limitation because it does not require that the steroid actually be used by or on the patient, and a recitation that a claimed product is a "pharmaceutical composition" or that a "feed dispenser is operable to dispense a mineral supplement" are not affirmative limitations because they are merely indicating how the claimed invention might be used.” See MPEP 2106.04(d)(2).
Response to Arguments
Applicant’s arguments and amendments with respect to the 103 rejection have been fully considered and are persuasive. In particular, Dovgan and Tangri (see Final Rejection of 8/12/2025) do not teach:
prediction decile classifiers that each includes (i) percentages of known patients that needed an urgent start of dialysis for discrete timeframes, and (ii) known patient characteristic data that corresponds to the known patients, the known patient characteristic data including demographic/physiological data for the known patients, a diagnosed cause of CKD, a health history, and a classification of a positive or a negative result related to an urgent start of dialysis, wherein each prediction decile for the different CKD stages is assigned a probability of a positive outcome for an urgent start of dialysis for each of the discrete timeframes using (i)
The 103 rejection of claims 1-20 has been withdrawn.
Applicant's arguments with respect to the 101 rejection have been fully considered but they are not persuasive. The Examiner respectfully disagrees. Specifically, Applicant argues that the claims recite a technical improvement and therefore recite statutory subject matter, and the arguments point to claim 3 of example 47 of the Office’s SME Guidance. However, the example claim recites additional elements (detecting a source address, dropping packets, blocking traffic) that amount to an inventive concept and are not mere instructions to apply an exception, field of use limitations, etc
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018)). Thus, it is important for examiners to analyze the claim as a whole when determining whether the claim provides an improvement to the functioning of computers or an improvement to other technology or technical field.
See MPEP 2106.05(a)
In the claimed invention, the additional limitations only amount to mere instructions to apply the exception and insignificant extra-solution activity that is well-understood, routine, and conventional. Even when the claim is considered as a whole, the additional elements amount to storing data for the mental process in memory, performing the mental process with a processor and ensemble ML algorithm, then outputting the prediction. Unlike Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), the claimed invention does reflect any improvements in machine learning; the claims merely state the ensemble ML algorithm classifies patients into deciles without any details on how the ML algorithm operates or accomplishes the task. Therefore, the claims are not patent-eligible for being a technical improvement.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Andrew T. Chiusano whose telephone number is (571)272-5231. The examiner can normally be reached M-F, 10am-6pm.
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/ANDREW T CHIUSANO/Primary Examiner, Art Unit 2144