DETAILED ACTION
Status of Claims
The present application, filed on or after 16 March, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the request for continued examination (“RCE”) filed 21 April 2026, on an application filed 30 November 2023, which claims priority to a provisional application filed 30 November 2022.
Claims 1, 2, 4, 7, 9 and 13-18 have been amended.
Claims 1-20 are currently pending and have been examined.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 21 April 2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-7 and 10-20 are rejected under 35 U.S.C. 103 as being obvious over Tangri (U.S. PG-Pub 2023/0054069 A1), further in view of Chbat et al. (U.S. PG-Pub 2022/0409114 A1), hereinafter Chbat, further in view of Eibl et al. (U.S. PG-Pub 2019/0275247 A1), hereinafter Eibl.
As per claims 1, 4 and 18, Tangri discloses A kidney health monitoring system (See Tangri, Figs. 1-3D.), comprising:
… configured to detect a physiological parameter of a patient (Tangri discloses collection of physiological parameters of a patient, see paragraphs 59 and 68-70.); and
a prediction system including a predictive model, the predictive model comprising at least one trained machine learning model (Tangri, Fig. 3B #s314-316 and paragraphs 94-98 disclose application of various modifiable patient physiological parameters to a trained machine learning model in order to determine the patient’s risk of experiencing chronic kidney disease [“CKD”] within a time period.);
the prediction system configured to:
receive information from the sensor indicative of the physiological parameter, the physiological parameter including a modifiable factor (Tangri, Fig. 3B #s314-316 and paragraphs 94-98 disclose application of various modifiable patient physiological parameters to a trained machine learning model in order to determine the patient’s risk of experiencing chronic kidney disease [“CKD”] within a time period.),
determine a kidney health score of the patient by providing the physiological parameter as input to the predictive model (Tangri, Fig. 3B #s314-316 and paragraphs 94-98 disclose application of various modifiable patient physiological parameters to a trained machine learning model in order to determine the patient’s risk of experiencing chronic kidney disease [“CKD”] within a time period.),
determine that the kidney health score is outside of a predetermined range, in response to determining that the kidney health score is outside of the predetermined range (Tangri, Fig. 3B #316 and paragraph 98 disclose the determination that the patient’s CKD risk satisfies a risk threshold.), and
determine that a predetermined change in the modifiable factor would cause the kidney health score to be inside of the predetermined range (Tangri discloses recommendation of a particular treatment based on one or more of the laboratory parameters used in the calculation of the patient’s CKD risk satisfying the risk threshold, see paragraph 99: “The acts 318A, 318B, 318C, and/or 318D performed responsive to the prediction of CKD progression satisfying the one or more thresholds in accordance with act 316 may be selected based upon the particular time period associated with the prediction of CKD progression (e.g., 2 year or 5 year), the particular threshold(s) satisfied (e.g., whether the patient is classified as being at “intermediate” or “high” risk), and/or one or more other factors such as at least some of the set of laboratory for the new patient (e.g., used as part of the input dataset for generating the prediction of CKD progression for the new patient). See also paragraphs 108 and 112 and Fig. 4.)”;,
identify a management treatment predicted to achieve the predetermined change in the modifiable factor (Tangri, paragraphs 99, 108 and 112, and Fig. 4.); and
… administer the management treatment to the patient … corresponding to the predetermined range (Treatment recommendations include recommending and providing dialysis among other treatments, see paragraphs 99, 120 and claim 9.).
For the sake of expediting prosecution, the Office will utilize a secondary reference to disclose the determination of treatment recommendations based on modifiable parameter determination, see Chbat Fig. 6 and corresponding text.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for predicting kidney health decline of Tangri to include the determination of treatment recommendations based on modifiable parameter determination, as taught by Chbat, in order to provide a method for predicting kidney health decline that can manage patient treatment.
Tangri fails to explicitly disclose:
a monitor device including a sensor; and
a treatment device configured to administer a dialysis treatment to a patient.
cause a delivery component of a treatment device to administer the management treatment to the patient in accordance with a flow rate.
Eibl teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide a monitor device including a sensor; and a treatment device configured to administer a dialysis treatment to a patient (Eibl discloses using sensors to monitor patient parameters while a patient is undergoing dialysis treatment in order to adjust a patient treatment, see paragraphs 12-20, 33, 34 and 37.) and cause a delivery component of a treatment device to administer the management treatment to the patient in accordance with a flow rate (Eibl monitors patient parameters in order to provide delivery or withdrawal of fluids to a patient, see Abstract and paragraphs 5, 14, 18, 19 and 25.) in order to dynamically control a patient’s treatment (Eibl, Abstract.).
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for predicting kidney health decline of Tangri/Chbat to include a monitor device including a sensor; and a treatment device configured to administer a dialysis treatment to a patient, as taught by Eibl, in order to provide a method for predicting kidney health decline that can dynamically control a patient’s treatment (Eibl, Abstract.).
Tangri, Chbat and Eibl are all directed to the electronic processing of patient healthcare data and specifically to the determination of patient kidney function. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
As per claims 2, 3, 5-7, 10-17 and 19, Tangri/Chbat/Eibl discloses claims 1, 4 and 18, detailed above. Tangri/Chbat/Eibl also discloses:
2. the physiological parameter including at least one of an albumin level of the subject, a creatinine level of the patient, an albumin/creatinine ratio of the patient, a calcium level of the patient, a phosphorus level of the patient, a potassium chloride level of the patient, or a bicarbonate level of the patient; and the modifiable factor including a blood glucose of the patient (Tangri, paragraph 94.);
3. wherein the modifiable factor includes at least one a blood glucose of the patient, a medication consumed by the patient, a diet of the patient, a water consumption of the patient, a blood pressure of the patient, a heart rate of the patient, a weight of the patient, or a body mass index (BMI) of the patient (Tangri, paragraphs 83 and 94.);
5. wherein the one or more physiological parameters comprise at least one of blood pressure, respiratory rate, heart rate, pulse rate, urine output, estimated glomerular filtration rate (GFR), measured GFR, sepsis risk score, body mass index (BMI), weight, a medication dosage, age, body temperature, or a concentration of one or more markers in a fluid of the patient (Tangri, paragraph 94.);
6. wherein the one or more physiological parameters comprise: at least one first physiological parameter indicative of kidney function (Tangri, paragraph 94, note paragraph 22 of the present published specification specifically states that “creatinine is indicative of the patient's 102 kidney function.”); and at least one second physiological parameter indicative of kidney stress ((Tangri, paragraph 94, note paragraph 22 of the present published specification specifically states that album can degrade kidney function, which is also an indication of kidney stress.);
7. wherein the one or more physiological parameters comprise at least one of a water consumption, a diet, or medication consumption (Tangri, paragraph 108 discloses medication parameters.);
10. wherein identifying the one or more physiological parameters of the patient comprises: detecting, by at least one sensor, at least one of the one or more physiological parameters in a blood sample or a urea sample obtained from the patient (Tangri, paragraphs 83 and 94. It is well known that laboratory measurement data is collected using sensors.);
11. wherein identifying the one or more physiological parameters of the patient comprises obtaining a plurality of samples of a particular physiological parameter among the one or more physiological parameters in a sampling period, and wherein determining the metric is based on the plurality of samples (Tangri, paragraph 83 discloses collecting plural samples of the same physiological parameter over a time period.);
12. wherein identifying the one or more physiological parameters of the patient comprises receiving, from a sensor, data indicating a measurement of at least one of the one or more physiological parameters, and wherein determining the metric is in response to receiving the data (Tangri, paragraphs 83 and 94. It is well known that laboratory measurement data is collected using sensors. Metric/risk score is determined using the collected laboratory data so it is inherently determined in response to receiving the data.);
13. in response to determining that the metric is outside of the predetermined range, outputting a recommendation based on the metric, wherein the recommendation comprises at least one of a numerical indicator of the metric or a graphical display of a trend in the metric over time (Tangri, paragraphs 99, 108 and 112 and Fig. 4.);
14. in response to determining that the metric is outside of the predetermined range, outputting a recommendation based on the metric, outputting an instruction from the prediction system for the patient to engage in a lifestyle change (Tangri, paragraphs 99, 108 and 112, and Fig. 4.);
15. identifying by the prediction system a modifiable parameter among the physiological parameters with greater than a threshold contribution to the metric, outputting a recommendation comprising an instruction from the prediction system to adjust the modifiable parameter (Tangri, paragraphs 99, 108 and 112, and Fig. 4.);
16. outputting a recommendation comprising an instruction from the prediction system to obtain an updated measurement of at least one of the one or more physiological parameters at a predetermined frequency (Tangri, paragraphs 115-118.);
17. the one or more physiological parameters being a first of the one or more physiological parameters of the patient detected at a first time, the metric being a first metric (Tangri, paragraphs 99, 108 and 112, and Fig. 4.), the method further comprising:
training the at least one trained machine learning model by: identifying training data, the training data comprising (Tangri, paragraphs 66-68.):
a second of the one or more physiological parameters of the patient, the second of the one or more physiological parameters being detected at a second time (Tangri discloses supervised learning, which uses confirmed data points from historic patient data, such as previous determined metrics from identified patient parameters, see paragraph 78.);
a second metric indicative of kidney health of the patient at the second time (Tangri discloses supervised learning, which uses confirmed data points from historic patient data, such as previous determined metrics from identified patient parameters, see paragraph 78.);
a third of the one or more physiological parameters of a population of subjects omitting the patient (Tangri, paragraphs 66-68.); and
kidney health outcomes of the population of subjects (Tangri, paragraph 66.); and
optimizing one or more model parameters of the at least one trained machine learning model using the training data (Tangri, paragraphs 66-68.); and
19. wherein the dialysis treatment device comprises a hemodialysis treatment device or a peritoneal dialysis treatment (Tangri, paragraphs 120 and 177. Eibl discloses various dialysis devices, as shown above.).
As per claim 20, Tangri/Chbat/Eibl discloses claim 18, detailed above. Tangri/Eibl discloses wherein operation of the treatment device is adjusted based on the metric indicative of a health of a kidney of the patient the treatment parameter comprises and includes adjusting a concentration of at least one solute in a dialysate administered by the treatment device (Eibl discloses operating a treatment device dynamically, as shown above.).
Tangri fails to explicitly disclose adjusting a concentration of at least one solute in a dialysate.
Chbat teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to recommend adjusting a concentration of at least one solute in a dialysate (Chbat, paragraph 86.) in order to better assist in dialysis treatment parameter determinations.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for predicting kidney health decline of Tangri/Chbat/Eibl to include adjusting a concentration of at least one solute in a dialysate, as taught by Chbat, in order to provide a method for predicting kidney health decline that can better assist in dialysis treatment parameter determinations. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Claims 8 and 9 are rejected under 35 U.S.C. 103 as being obvious over Tangri/Chbat/Eibl further in view of Haddad et al. (U.S. PG-Pub 2020/0353250 A1), hereinafter Haddad.
As per claims 8 and 9, Tangri/Chbat/Eibl discloses claims 1, 4 and 18, detailed above. Tangri also discloses:
8. wherein identifying the one or more physiological parameters of the patient comprises: detecting … at least one of the one or more physiological parameters of the patient (Tangri, paragraph 94.).
9. wherein the detecting at least one of the one or more physiological parameters comprises detecting … a blood glucose level of the patient, a heart rate of the patient, a body temperature of the patient, or a blood oxygenation of the patient (Tangri, paragraph 94 discloses collection of blood glucose.)
Tangri/Chbat/Eibl fail to explicitly disclose use of a wearable device.
Haddad teaches that it was old and well known in the art of healthcare communications before the effective filing date of the claimed invention to provide use of a wearable device (Haddad, paragraphs 25, 32, 48 and 51.) in order to provide portable monitoring and treatment of a patient.
Therefore, it would have been obvious to one of ordinary skill in the art of healthcare communications before the effective filing date of the claimed invention to modify the method for predicting kidney health decline of Tangri/Chbat/Eibl to include use of a wearable device, as taught by Haddad, in order to provide portable monitoring and treatment of a patient.
Both Tangri and Haddad are directed to the electronic processing of patient healthcare data. Moreover, merely adding a well-known element into a well-known system, to produce a predictable result to one of ordinary skill in the art, does not render the invention patentably distinct over such combination (see MPEP 2141).
Response to Arguments
Applicant’s arguments filed 21 April 2026 concerning the rejection of all claims under 35 U.S.C. 101 have been fully considered and they are deemed persuasive in view of the amendments to the claims. Accordingly, this rejection has been withdrawn.
Applicant’s claimed invention is patent-eligible because of comparison to elements of Examples 37, 39 and 42 from the 2019 PEG. A process for cause a treatment device to administer treatment with a flow rate that will provide a determined change to a patient’s kidney health score would be a practical application of an abstract idea that otherwise would be defined as an abstract idea overall. Alternatively, the limitations amount to “significantly more’ than the abstract idea. For at least these reasons, the claims are patent eligible under 35 U.S.C. §101.
Applicant’s arguments filed 21 April 2026 concerning the rejection of all claims under 35 U.S.C. 103(a) have been fully considered but they are not persuasive.
Applicant argues on pages 13-14 that the cited references fail to disclose the limitation directed to causing a treatment device to administer treatment.
The Office respectfully disagrees. As shown above, the limitation is disclosed by a combination of Tangri and Eibl.
Merely repeating the claim language, and the teachings relied upon by the Office in the rejection are not tantamount to a responsive argument. Such a response to the Office’s findings is insufficient to persuade us of Examiner error, as mere attorney arguments and conclusory statements that are unsupported by factual evidence are entitled to little probative value. In re Geisler, 116 F.3d 1465, 1470 (Fed. Cir. 1997); see also In re De Blauwe, 736 F.2d 699, 705 (Fed. Cir. 1984); Ex parte Belinne, No. 2009-004693, slip op. at 7-8 (BPAI Aug. 10, 2009) (informative); see also In re Lovin, 652 F.3d 1349, 1357 (Fed. Cir. 2011) (“[W]e hold that the Board reasonably interpreted Rule 41.37 to require more substantive arguments in an appeal brief than a mere recitation of the claim elements and a naked assertion that the corresponding elements were not found in the prior art.”); cf. In re Baxter Travenol Labs., 952 F.2d 388, 391 (Fed. Cir. 1991) (“It is not the function of this court to examine the claims in greater detail than argued by an appellant, looking for [patentable] distinctions over the prior art.”). Our rules require that an Appeal Brief include “arguments” that “shall explain why the examiner erred.” 37 C.F.R. § 41.37(c)(l)(iv). “[M]ere statements of disagreement... do not amount to a developed argument.” SmithKline Beecham Corp. v. Apotex Corp., 439 F.3d 1312, 1320 (Fed. Cir. 2006).
Accordingly, the rejection is upheld.
Conclusion
Unused but cited relevant prior art includes:
Chiofolo et al. (U.S. PG-Pub 2020/0221990 A1) discloses a system and method for assessing and evaluating renal health diagnosis, staging and therapy recommendation that use a model configured to predict a kidney condition and calculate a kidney health score.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Mark Holcomb, whose telephone number is 571.270.1382. The Examiner can normally be reached on Monday-Friday (8-5). If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Kambiz Abdi, can be reached at 571.272.6702.
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/MARK HOLCOMB/
Primary Examiner, Art Unit 3685
8 July 2026
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