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 response filed 29 September 2025, on an application filed 30 November 2023, which claims priority to a provisional application filed 30 November 2022.
Claims 1-12 and 14-20 have been amended.
Claims 1-20 are currently pending 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 1-20 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.
Step 1
Claims 1-20 are within the four statutory categories. Claims 1-3 and 18-20 are drawn to a monitor or dialysis device, which is within the four statutory categories (i.e. machine). Claims 4-17 are drawn to a method, which is within the four statutory categories (i.e. process).
Prong 1 of Step 2A
Claim 1 recites: A kidney health monitoring system, comprising:
a monitor device including a sensor configured to detect a physiological parameter of a patient;
a prediction system including a predictive model, the predictive model comprising at least one trained machine learning model;
the prediction system configured to:
receive information from the sensor indicative of the physiological parameter, the physiological parameter including a modifiable factor,
determine a kidney health score of the patient by providing the physiological parameter as input to the predictive model,
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, the prediction system further configured to:
determine that the modifiable factor is significant by determining that a predetermined change in the modifiable factor would cause the kidney health score to be inside of the predetermined range,
identify a management treatment predicted to achieve the predetermined change in the modifiable factor, and output a report indicating the management treatment; and
a treatment device configured to administer the management treatment to the patient.
Claim 4 recites: A method, comprising:
identifying one or more physiological parameters of a patient using a monitoring device;
passing the one or more physiological parameters to a prediction system;
at the prediction system, processing the one or more physiological parameters of the patient using at least one trained machine learning model, and determining a metric indicative of kidney health of the patient;
determining by the prediction system that the metric is outside of a predetermined range; and
in response to determining that the metric is outside of the predetermined range, outputting by the prediction system to a treatment device a recommendation based on the metric; and
administering by the treatment device a treatment to the patient based on the metric.
Claim 18 recites: A dialysis device system, comprising:
a treatment device configured to administer a dialysis treatment to a patient;
a monitor device including a sensor configured to detect a physiological parameter of a patient;
a prediction system having at least one processor and memory storing a predictive model;
the predictive model comprising at least one trained machine learning model configured to determine, based on the physiological parameter, a metric indicative of a health of a kidney of the patient; and
the prediction system configured to adjust an operation of the treatment device based on the metric indicative of a health of a kidney of the patient.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a certain method of organizing human activity because they recite a process that comprises managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case the determining a patient’s kidney health score is outside a range based on a parameter and determining a treatment to adjust that parameter), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea(s) are deemed “additional elements,” and will be discussed in further detail below.
Dependent claims 2, 3, 5-17, 19 and 20 include other limitations, for example claims 2, 3, 5-7, 9, 10-12 recite various parameters, metrics and modifiable factors, claims 13-16 recite details on the recommendation and claim 17 recites details on training the machine learning model, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent claims 2, 3, 5-17, 19 and 20 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent claims 2, 3, 5-17, 19 and 20 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 4 and 17.
Prong 2 of Step 2A
Claims 1, 4 and 17 are not integrated into a practical application because the additional elements (i.e. any limitations that are not identified as part of the abstract idea) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the machine learning model and the structural components of the computer, the dialysis device and the monitor, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see paragraphs 79-85 of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language limiting the data to physiological parameters, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering or output, selecting a particular data source or type of data to be manipulated, and/or insignificant application (e.g. see MPEP 2106.05(g)).
Additionally, dependent claims 2, 3, 5-17, 19 and 20 include other limitations, but these limitations also amount to no more than amount to mere instructions to apply the exception (e.g. claims 3, 8-10, 12 recite a sensor or wearable device), generally linking the abstract idea to a particular technological environment or field of use (e.g. claims 19 and 20 recite various treatments or physical treatment parameters and the types of data disclosed in dependent claims 2, 3, 5-7, 9, 10-12), and/or do not include any additional elements beyond those already recited in independent claims 1, 4 and 17, and hence also do not integrate the aforementioned abstract idea into a practical application.
Step 2B
Claims 1, 4 and 17 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the device and the structural components of the computer), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The Specification expressly disclosing that the additional elements are well-understood, routine, and conventional in nature:
paragraphs 79-85 of the Specification discloses that the additional elements (i.e. the structural components of the computer, the dialysis device and the monitor) comprise a plurality of different types of generic computing systems that are configured to perform generic computer functions (i.e. receive and process data) that are well-understood, routine, and conventional activities previously known to the pertinent industry (i.e. healthcare);
Relevant court decisions: The following are examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives usage data, processes the usage data, and transmits a command to the dispensing device based on the processed usage data to the dispensing device over a network, for example the Internet, e.g. see paragraph [0042] of the present Specification.
Dependent claims 2, 3, 5-17, 19 and 20 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because, as stated above, the aforementioned dependent claims do not recite any additional elements not already recited in independent claims 1, 4 and 17, and/or the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. claims 3, 8-10, 12 recite a sensor or wearable device), and/or generally link the abstract idea to a particular technological environment or field of use (e.g. t claims 19 and 20 recite various treatments or physical treatment parameters and the types of data disclosed in dependent claims 2, 3, 5-7, 9, 10-12), and hence do not amount to “significantly more” than the abstract idea.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, claims 1-20 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.);
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.), the prediction system further configured to:
determine that the modifiable factor is significant by determining 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, and output a report indicating the management treatment (Tangri, paragraphs 99, 108 and 112, and Fig. 4.); and
… administer the management treatment to the patient (Treatment recommendations include recommending and providing dialysis among other treatments, see paragraph 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 also fails to explicitly disclose:
a monitor device including a sensor; and
a treatment device configured to administer a dialysis treatment to a patient.
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.) 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 parameters 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. 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, Fig. 4.);
14. wherein the recommendation comprises: an instruction from the prediction system to the treatment device to administer a treatment to the patient; or 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, wherein the recommendation comprises an instruction from the prediction system to adjust the modifiable parameter (Tangri, paragraphs 99, 108 and 112, and Fig. 4.);
16. wherein the recommendation comprises 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 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 the health of the kidney 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 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 29 September 2025 concerning the rejection of all claims under 35 U.S.C. 112 have been fully considered and they are deemed persuasive. The Applicant argues that the amendments to the claims overcome the 35 U.S.C. 112 rejections and the Office agrees. Accordingly, these rejections have been withdrawn.
Applicant’s arguments filed 29 September 2025 concerning the rejection of all claims under 35 U.S.C. 101 and 103(a) have been fully considered but they are not persuasive.
With regard to the rejection of the claims under 35 USC 101, Applicant argues on pages 10-15 that:
A. (Step 2A, prong 1) The claims do not recite an abstract idea because they require a physiological parameter to be input into a prediction system, which could not be performed mentally by a human.
B. (Step 2A, prong 2) The system and device are comprised of a system and/or device that combine to create a practical application as they “are comprised of physical components that perform specific functions in monitoring” kidney health and dialysis.
C. “[T]he additional elements recited in the claims, as amended, provide technical improvements that demonstrate integration of the alleged abstract idea into a practical application and that Applicant's specification describes the technical improvements provided by the claims” because the claims require use and training of a predictive model and adjustment of the treatment device based thereon.
D. (Step 2B) The recitation of the predictive model that determines kidney health clearly adds significantly more than the judicial exception.
The Office respectfully disagrees. Please see the statutory rejection of the claims, issued above, wherein the claims are shown to be directed to a judicial exception without significantly more.
Regarding A., the parameter and prediction system are indicated as being either part of the judicial exception or do not amount to significantly more. Further, the Office submits that the abstract idea was not characterized as being directed to a mental process. The claimed invention is characterized as falling under Certain Methods of Organizing Human activity. As such, this argument cannot be persuasive.
Regarding B., the metes of the claim are so large as to capture the entire idea of monitoring a patient’s kidney health and changing a corresponding treatment, something done everytime a patient’s treatment is reviewed and updated; these are not specific functions as claimed.
Regarding C., the predictive model and training thereof and the provision of treatment is encompassed by a healthcare provider providing updated treatment based on their updated personal treatment experience, there is nothing that integrates the abstract idea into a practical application.
Regarding D., again, the predictive model is generically claimed and does not encompass anything more than what is used to provide a patient treatment, see response to argument C.
Accordingly, the rejection is upheld.
Conclusion
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 extension fee 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 date of this final action.
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
17 October 2025