DETAILED ACTION
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 .
Status of Claims
Claims 1-22 are pending.
This communication is in response to the communication filed October 11, 2022.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-22 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claims 1, 10, and 17 recite the limitation “a duration of the experiment and a number of patients to be enrolled in the experiment.” There is insufficient antecedent basis for “the experiment” limitation in the claim. The claim recites “the physical experiment” multiple times, but it is unclear if “the experiment” references “a physical experiment.”
Claims 1, 10, and 17 recite the limitation “the effectiveness describing a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state,” for which there is insufficient antecedent basis for “the effect” limitation in the claims.
Claims 1, 10, and 17 recite the limitation “the sensitivity of a patient representing a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient,” for which there is insufficient antecedent basis for “the sensitivity” and “the one or more intervention parameter” limitations in the claims.
Claims 1, 10, and 17 recite the limitation “the one or more intervention parameters will affect the metabolic state of the patient,” for which there is insufficient antecedent basis for “the metabolic state of the patient” limitation in the claims.
Claims 1, 10, and 17 recite the limitation “adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features,” for which there is insufficient antecedent basis for “the trial parameters” limitation in the claims. The claims reference “one or more trial parameters” and “the one or more trial parameters.”
Claims 3, 11, and 18 recite the limitation “the effectiveness determined by predicting the effect of each candidate treatment recommendation,” for which there is insufficient antecedent basis for “the effect” limitation in the claims.
Claim 10 recites the limitation “when executed by a processor, cause the one or more processor to”. There is insufficient antecedent basis for “the one or more processor” limitation in the claim.
All of dependent claims are rejected for being dependent on independent claims.
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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite inventions directed to determining trial parameters for a physical experiment for generating instructions for the physical experiment, which are statutory categories of inventions.
Specifically, the independent claims, taking claim 1 as exemplary, recite determining one or more trial parameters for a physical experiment to validate a candidate treatment recommendation, the one or more trial parameters comprising: a duration of the experiment and a number of patients to be enrolled in the experiment; for each of one or more variations of the physical experiment, determining an effectiveness of the variation in validating the candidate treatment recommendation, the effectiveness describing a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state; for a selected variation of the physical experiment satisfying a threshold effectiveness, determining one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation, the sensitivity of a patient representing a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient; and generating instructions for a medical professional to perform the selected variation of the physical experiment by adjusting the trial parameters according to the selected variation and enrolling patients sharing at least one of the one or more metabolic features.
The claim limitations are interpreted as being grouped within the “certain methods of organizing human activity” grouping of abstract ideas. The limitations are directed to making various determinations for clinical trial parameters to match patients to an experiment and to provide instructions the experiment, which is interpreted as managing human interactions. See MPEP 2106.04. The claims are interpreted to recite concepts relating to tracking or organizing clinical information. Accordingly, the claims recite an abstract idea.
The dependent claim limitations are directed towards describing trial parameters, identifying candidate treatment recommendations, generating shortlist of candidate treatment recommendations, generating variations of the physical experiment, identifying target outcomes, identifying a cohort of patients sensitive to intervention parameters, and using various metabolic features. The dependent claims recite the abstract ideas of the independent claims. The claims are interpreted to recite concepts relating to tracking or organizing clinical information. The claims recite additional elements that are not interpreted as part of the abstract idea, and are addressed below.
The additional elements of the claims include non-transitory computer-readable medium and processors. This judicial exception is not integrated into a practical application. Integration into a practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
The claims merely use the additional elements as tools to perform abstract ideas and generally link the use of a judicial exception to a particular technological environment. The use of the additional elements as tools to implement the abstract idea and generally to link the use of the abstract idea to a particular technological environment does not render the claim patent eligible, because it requires no more than a computer performing functions that correspond to acts required to carry out the abstract idea. Specifically, the non-transitory computer-readable medium and the processors may be part of a computer and perform the functions of storing and processing data (specification p. 13-14, 97-98). Moreover, any of the limitations described may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.
The additional elements do not show an improvement to the functioning of a computer or to any other technology, rather the additional elements perform general computing functions and do not indicate how the particular combination improves any technology or provides a technical solution to a technical problem. See Apple v. Ameranth, 842 F.3d 1229, 1240 (Fed. Cir. 2016). The additional elements do not use the exception to affect a particular treatment or prophylaxis for a disease, do not apply the exception using particular machines, and do not effect a transformation or reduction of a particular article to a different state or thing, rather the computer elements are generally stated as to their structure and function and are only used to generate instructions instead of directly performing a specific treatment or prophylaxis. Therefore, the additional elements do not impose any meaningful limits on practicing the abstract idea and the additional limitations are not indicative of materializing into a practical application. Accordingly, the claim is directed to an abstract idea.
Generic computer elements recited as performing generic computer functions that are well-understood, routine, or conventional activities amount to no more than implementing the abstract idea with a computerized system (Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network and performing repetitive calculations); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); See MPEP 2106.05(d) and July 2015 Update: Section IV). Here, the claim limitations are similar to using a computer to perform repetitive calculations for validations, determining effectiveness, satisfying a threshold, ranking metabolic features, determining a power calculation, determining metabolic features associated with binary values, and determining metabolic features associated with a range of values.
The claims 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 of the abstract idea into a practical application, the additional elements of using a non-transitory computer readable medium and processors to perform the steps of determining trial parameters, determining an effectiveness of a variation in the physical experiment, determining metabolic features among a cohort of patients, and generating instructions to perform a variation of the physical experiment amount to no more than using computer related devices to automate or implement the abstract idea for determining trial parameters for a physical experiment for generating instructions for the physical experiment.
The use of a computer or processor to merely automate or implement the abstract idea cannot provide significantly more than the abstract idea itself. (See MPEP 2106.05(f) where mere instructions to apply an exception does not render an abstract idea patent eligible). There is no indication that the additional limitations alone or in combination improves the functioning of a computer or any other technology, improves another technology or technical field, or effects a transformation or reduction of a particular article to a different state or thing. Therefore, the claims are not patent eligible.
In conclusion, the claims are directed to the abstract idea for determining trial parameters for a physical experiment for generating instructions for the physical experiment. The claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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, 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 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Abu El Ata et al. US20220076841 (hereinafter Abu) in view of “Reducing HbA1c in Type 2 Diabetes Using Digital Twin Technology-Enabled Precision Nutrition: A Retrospective Analysis” Shamanna et al. (IDS NPL Reference, (hereinafter Shamanna).
As per claim 1, Abu teaches
a method comprising: determining one or more trial parameters for a physical experiment to validate a candidate treatment recommendation, the one or more trial parameters comprising: (Abu par. 73, 149-150 247 teaches using computers with processors and computer readable mediums for recommending a treatment plan with patient model simulation using the same or the additional sets of parameters to determine updated health metrics, and testing and validating the efficacy of the remedy. Preventative actions against adverse outcomes may lead to recommended changes or modification of the patient model.)
a duration of the experiment and…patients to be enrolled in the experiment; (Abu par. 105, 220 teaches long-term simulated scenarios for a patient, where monitoring may be continuous, daily, or weekly. An example of a two week data collection is presented, which is interpreted as a duration for the experiment.)
for each of one or more variations of the physical experiment, determining an effectiveness of the variation in validating the candidate treatment recommendation, the effectiveness describing a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state; (Abu par. 182-184, 195 teaches instructions to perform physical in vivo experiments to validate performance of a model’s simulation of a set of scenarios. Key insights gained in Step 3 should guide the construction of in vivo experiments that are designed to confirm in silica findings and further enhance subject matter expertise. After submitting scenarios, interpreting results to expose the unknown influences that may increase the risk of cancer.)
for a selected variation of the physical experiment satisfying a threshold effectiveness, determining one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation, (Abu par. 53, 61 teaches response by the human being to one or more diseases, as well as a relation between health metrics and the diseases. Examples are provided for interventions related to scenarios in which a patient makes positive changes to his/her health attributes, such as quitting smoking, changing a diet or exercise routine, engaging in physical therapy, or taking a medication. Abu fig. 27 provides sensitivity analysis) the sensitivity of a patient representing a likelihood that adjustments to the one or more intervention parameters will affect the metabolic state of the patient; and (Abu par. 182-184, 195 teaches once the model is fully constructed, sensitivity analysis and what-if analysis are performed to see how different values of an independent variable impact a dependent variable under a given set of assumptions)
generating instructions for a medical professional to perform the selected variation of the physical experiment by adjusting the trial parameters according to the selected variation and (Abu par. 182-184, 195 teaches instructions to perform physical in vivo experiments to validate performance of a model’s simulation of a set of scenarios.) .
Abu teaches validating experiments in vivo, but may not specifically teach the following limitations met by Shamanna, a number of patients (Shamanna p. 1 teaches results of the 89 patients who initially enrolled in the TPN Program, 64 patients remained in the program and adhered to it for at least 90 days; all analyses were performed on these 64 patients.); enrolling patients sharing at least one of the one or more metabolic features (Shamanna p. 1 teaches enrolling patients with the following features: type 2 diabetes, twin precision nutrition, and three months of follow-ups).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Abu to enroll patients sharing metabolic features as taught by Shamanna with the motivation to promote the importance of good glycemic control and the association of postprandial glycemic response (PPGR) with diabetes complications and a need for predicting the impact of specific foods on PPGR due to the high variability in different people’s response to the same food. (Shamanna p. 3). Applying the method of personalized treatment recommendations with patients of similar metabolic features would yield predictable results of similar responses to treatments.
As per claim 2, Abu and Shamanna teach all the limitations of claim 1 and further teach
wherein the one or more trial parameters further comprise: a number of intervention parameters adjusted in the candidate treatment recommendation; (Abu par. 61, 204 teaches various parameters that may be changed in patient treatment models)
adjustments to each intervention parameter; (Abu par. 61, 204 teaches changing various parameters)
a composition of patients to be enrolled in the experiment; and (Shamanna p. 2-3 teaches patient cohorts that are included or excluded from enrollment)
a magnitude of the adjustment to each intervention parameter (Shamanna p. 4 teaches a magnitude of adjustment as an insulin dosage based on daily average blood glucose levels).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Abu to use trial parameters of a composition of patients to be enrolled in the experiment and a magnitude of the adjustment to each intervention parameter as taught by Shamanna with the motivation to promote the importance of good glycemic control and the association of postprandial glycemic response (PPGR) with diabetes complications and a need for predicting the impact of specific foods on PPGR due to the high variability in different people’s response to the same food. (Shamanna p. 3). Applying the method of adjusting trial parameters for patients of similar metabolic features would yield predictable results of modifying expected responses to treatments.
As per claim 3, Abu and Shamanna teach all the limitations of claim 1 and further teach identifying the candidate treatment recommendation based on the effectiveness of the candidate treatment recommendation, the effectiveness determined by predicting the effect of each candidate treatment recommendation on a cohort of patients (Abu par. 70, 74 teaches identifying effective remedies and interventions by identifying a causal relation between those remedies and interventions and an improvement to the outcome of the patient model under the modeled scenarios. The remedies and interventions may be populated by actions or modifications to the patient model or external resources that are effective in preventing or avoiding adverse outcomes associated with a given risk.).
As per claim 4, Abu and Shamanna teach all the limitations of claim 1 and further teach generating a shortlist of candidate treatment recommendations for validation by a physical experiment; and generating instructions for a medical professional to perform a physical experiment to validate each candidate treatment recommendation of the shortlist (Abu par. 182-184, 195 teaches instructions to perform physical in vivo experiments to validate performance of a model’s simulation of a set of scenarios. Key insights gained in Step 3 should guide the construction of in vivo experiments that are designed to confirm in silica findings and further enhance subject matter expertise. After submitting scenarios, interpreting results to expose the unknown influences that may increase the risk of cancer. Since results given may be narrowed from a larger subset, they are interpreted as a shortlist.)
As per claim 5, Abu and Shamanna teach all the limitations of claim 1 and further teach generating the one or more variations of the physical experiment by adjusting the one or more trial parameters, wherein each variation of the physical experiment represents a distinct combination of the one or more trial parameters (Abu par. 204 teaches predictively analyzing how changing patient parameters result in a health risk or support more optimal health outcome. Such capabilities would enable the rapid identification of a potential problem with immediate analysis of root causes and proposed corrective actions. The active monitoring of IAM process outcomes would thereby provide a fully vetted platform to support individualized and proactive patient risk avoidance and suggest personalized treatment protocols when necessary.).
As per claim 6, Abu and Shamanna teach all the limitations of claim 1 and further teach identifying a target outcome of the candidate treatment recommendation; (Abu par. 170-174 teaches an example target outcome of homeostasis) determining an acceptable risk of failure backed on past physical experiments designed to validate candidate treatment recommendations with the same target outcome; and determining a power calculation for the candidate treatment recommendation based on the acceptable risk of failure (Abu par. 67, 170-174 teaches an occurrence probability may be determined based on historical data about the patient model, epidemiological data (e.g., data derived from a given population that relates health attributes and incidences of various changes exhibited by the population), historical simulation data, data about comparable patient models, the patient's health attributes, and/or other sources. Based on the occurrence probability of each of the states, one or more risks (e.g., the probability of an outcome including one or more of the adverse outcomes) can be determined. The risks may be reported to a user, including details of the predicted adverse outcomes and the likelihood of each. The risks may also be further processed, for example, to generate a lookup table, an example of which is described below with reference to FIG. 5. Here, power calculation is interpreted as determining a sample size or determining a sample population as stated in Abu par. 3, 67, 220).
As per claim 7, Abu and Shamanna teach all the limitations of claim 1 and further teach
wherein determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation comprises: identifying a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; (Abu par. 184, 196, 233, fig. 27 teaches sensitivity analysis on covid-19 patients and precancerous patient scenarios. Sensitivity analysis and what-if analysis are performed to see how different values of an independent variable impact a dependent variable under a given set of assumptions. The goal in this case is to expose the unknown influences that increase the risk of cancer in order to derive algorithmic knowledge that can be tested on a specified patient population through controlled experimentation.)
identifying one or more metabolic features shared among all patients in the cohort of patients, wherein patients sharing the one or more metabolic features are predicted to experience improvements in metabolic health by adhering to the candidate treatment recommendation; and (Abu par. 54 teaches various metabolic features for a population as certain aspects of the blood lipid profile, such as C reactive protein. Health metrics may correspond to a single disease, or may give a more general estimation of the patient's health, fitness or capability of resisting a range of given diseases. The health metrics rules may govern how the patient's health attributes, a change in those health attributes, and potential affliction of diseases affect the health metrics. For example, the health metrics rules may include a set of rules that govern a resistance metric indicating a patient's resistance to arteriosclerosis. The set of rules may determine this resistance metric as a function of a blood lipid profile to indicate resistance to arteriosclerosis.)
generating instructions for a medical professional to enroll patients in the physical experiment with at least one of the one or more identified metabolic features (Abu par. 182-184, 195 teaches instructions to perform physical in vivo experiments to validate performance of a model’s simulation of a set of scenarios. Shamanna p. 1 teaches enrolling patients with the following features: type 2 diabetes, twin precision nutrition, and three months of follow-ups).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Abu to enroll patients sharing identified metabolic features as taught by Shamanna with the motivation to promote the importance of good glycemic control and the association of postprandial glycemic response (PPGR) with diabetes complications and a need for predicting the impact of specific foods on PPGR due to the high variability in different people’s response to the same food. (Shamanna p. 3). Applying the method of personalized treatment recommendations with patients of similar metabolic features would yield predictable results of similar responses to treatments.
As per claim 8, Abu and Shamanna teach all the limitations of claim 1 and further teach wherein the one or more metabolic features comprise: metabolic features associated with binary values; and metabolic features associated with a range of values (Abu par. 54 teaches binary values and range of values for various metabolic features as certain aspects of the blood lipid profile, such as C reactive protein. The resistance metric may increase or decrease depending on various values such as absolute value, relative value, or position above or below a threshold.).
As per claim 9, Abu and Shamanna teach all the limitations of claim 1 and further teach
wherein determining the one or more metabolic features shared among the cohort of patients sensitive to the candidate treatment recommendation comprises: identifying a cohort of patients sensitive to intervention parameters adjusted by the candidate treatment recommendation; (Abu par. 184, 196, 233, fig. 27 teaches sensitivity analysis on covid-19 patients and precancerous patient scenarios. Sensitivity analysis and what-if analysis are performed to see how different values of an independent variable impact a dependent variable under a given set of assumptions. The goal in this case is to expose the unknown influences that increase the risk of cancer in order to derive algorithmic knowledge that can be tested on a specified patient population through controlled experimentation.)
determining a significance that each metabolic feature has on patients in the cohort of patients; (Abu par. 170-174 teaches important metabolic features)
ranking each of the one or more metabolic features based on the determined significance; and (Abu par. 190 teaches metrics or metabolic features exposing a risk of developing cancer, where the predicted risk of cancer is based on the specified immune cells in order of importance)
generating instructions for a medical professional to prioritize enrollment of patients sharing higher ranked metabolic features (Abu par. 182-184, 195 teaches instructions to perform physical in vivo experiments to validate performance of a model’s simulation of a set of scenarios. Abu par. 238-246, fig. 30 teaches prioritization of ranked features as following a sequence where one may select or deselect patient parameters in real-time from a list of choices that cover metabolic features, compute the case, and deliver color coded results to the attending doctor so that they may review the factors limiting the health of the patient and disease criticality. With this information the doctor may enroll patients that fit the criteria. Shamanna p. 1 teaches enrolling patients with the following features: type 2 diabetes, twin precision nutrition, and three months of follow-ups).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the systems and methods as taught by Abu to prioritize enrolling patients based on ranked metabolic features as taught by Shamanna with the motivation to promote the importance of good glycemic control and the association of postprandial glycemic response (PPGR) with diabetes complications and a need for predicting the impact of specific foods on PPGR due to the high variability in different people’s response to the same food. (Shamanna p. 3). Applying the method of personalized treatment recommendations with patients of metabolic features that a doctor deems most important would yield predictable results of similar responses to treatments.
As per claim 10, (see rejection for claim 1).
As per claim 11, (see rejection for claim 3).
As per claim 12, (see rejection for claim 4).
As per claim 13, (see rejection for claim 5).
As per claim 14, (see rejection for claim 6).
As per claim 15, (see rejection for claim 7).
As per claim 16, (see rejection for claim 9).
As per claim 17, (see rejection for claim 1).
As per claim 18, (see rejection for claim 3).
As per claim 19, (see rejection for claim 5).
As per claim 20, (see rejection for claim 6).
As per claim 21 (see rejection for claim 7).
As per claim 22 (see rejection for claim 9).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAY M. PATEL whose telephone number is (571)272-6793 and email is jay.patel2@uspto.gov. The examiner can normally be reached on Monday-Friday 8AM-4:30PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Peter H. Choi can be reached on (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAY M. PATEL/Primary Examiner, Art Unit 3686