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. Election/Restriction Restriction to one of the following inventions is required under 35 U.S.C. 121: I. Claims 1-13 drawn to a system for cognitive disease prediction , classified in A61B 5/4088 . II. Claim 14 - 20 , drawn to a system for disease prediction model training , classified in G16H 50/30. I I I. Claim 21 , drawn to a system for predicting conversion of mild cognitive impairment to Alzheimer's disease in a patient , classified in G16H 50/20 . The inventions are independent or distinct, each from the other because: Inventions I and II are directed to related distinct products. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed have a materially different design, mode of operation, function, or effect. For example, Invention I (in claim 3) recites , “ wherein the clinical data includes at least one selected from a group of: a cognitive assessment score, an omega-3 score, or demographic statistics ,” while Invention II does not. Additionally, Invention II recites “ obtain a plurality of ground truth conversion time indications corresponding to the plurality of patients; and train a machine learning model based on the plurality of subsets of training data, and the plurality of ground truth conversion time indications corresponding to the plurality of patients ” (in claim 1 4 ), whereas Invention I does not. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants. Inventions I and III are directed to related distinct products. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed have a materially different design, mode of operation, function, or effect. For example, Invention I (in claim 3) recites, “ wherein the clinical data includes at least one selected from a group of: a cognitive assessment score, an omega-3 score, or demographic statistics ,” while Invention III does not. Additionally, Invention III recites “ track the result based on the trained machine learning model for a predetermined period of time ” (in claim 21 ) and “ transform the clinical data, genetic data, biospecimen data, and medical image data to runtime data; transform the runtime data to a plurality of risk factor indications, to follow a normal distribution ” (in claim 21), whereas Invention I does not. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants. Inventions II and III are directed to related distinct products. The related inventions are distinct if: (1) the inventions as claimed are either not capable of use together or can have a materially different design, mode of operation, function, or effect; (2) the inventions do not overlap in scope, i.e., are mutually exclusive; and (3) the inventions as claimed are not obvious variants. See MPEP § 806.05(j). In the instant case, the inventions as claimed have a materially different design, mode of operation, function, or effect. For example, Invention II recites “ obtain a plurality of ground truth conversion time indications corresponding to the plurality of patients; and train a machine learning model based on the plurality of subsets of training data, and the plurality of ground truth conversion time indications corresponding to the plurality of patients ” (in claim 14), whereas Invention III does not. Additionally, Invention III recites “ track the result based on the trained machine learning model for a predetermined period of time ” (in claim 21) and “ transform the clinical data, genetic data, biospecimen data, and medical image data to runtime data; transform the runtime data to a plurality of risk factor indications, to follow a normal distribution ” (in claim 21), whereas Invention II does not. Furthermore, the inventions as claimed do not encompass overlapping subject matter and there is nothing of record to show them to be obvious variants. Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply: Invention I would require searching in at least A61B 5/4088 along with a unique text search focusing on the specific features of “ wherein the clinical data includes at least one selected from a group of: a cognitive assessment score, an omega-3 score, or demographic statistics ”. Invention II would require searching in G16H 50/30 as well as a unique text search focusing on the specific features of “ obtain a plurality of ground truth conversion time indications corresponding to the plurality of patients ”. Invention III would require searching in G16H 50/20 and a unique text search focusing on the specific features of “ track the result based on the trained machine learning model for a predetermined period of time ”. Moreover, it must be noted that the examination burden is not limited exclusively to a prior art search but also includes the effort required to apply the art by making and discussing all appropriate grounds of rejection. Multiple inventions, such as those in the present application, require additional reference material and further discussion for each additional feature addressed. Concurrent examination of the multiple inventions claimed would thus involve a significant burden even if all searches were coextensive, which they are not. See MPEP 808.02. Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention . The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention. Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention. During a telephone conversation with Jonathan Young on 11 / 18 /202 5 a provisional election was made without traverse to prosecute the invention of Group I, claims 1 -13 . Affirmation of this election must be made by applicant in replying to this Office action. Claims 14-21 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention. Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i). Claim Rejections - 35 USC § 112 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. The following is a quotation of 35 U.S.C. 112 (pre-AIA), second paragraph: The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention. Claims 10-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. In re claim 10 , the limitation, “ the result ” lacks antecedent basis. In re claim 12 , see in re claim 10 above. Appropriate correction is required . 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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, specifically an abstract idea without significantly more. Step 1: Independent claim 1 is directed to a system for cognitive disease prediction . Thus, it is directed to statutory categories of invention. Step 2A, Prong 1: Claim 1 recites the following claim limitations: “receive a plurality of risk factor indications for a given patient and determine a plurality of interaction indications of the patient, each interaction indication of the plurality of interaction indications being an indication of interaction between at least two risk factor indications of the plurality of risk factor indications”, which are directed to mental processes, since a person could receive a plurality of risk factor indications and determine a plurality of interaction indications, which may between at least two risk factor indications. These limitations, under their broadest reasonable interpretation, cover concepts that can be practically performed in the human mind, i.e., using pen and paper (i.e. mental processes) . Step 2A, Prong 2: Claim 1 recites the following additional elements: a memory; and a processor communicatively coupled to the memory; wherein the memory stores a set of instructions which, when executed by the processor, cause the processor to: obtain a trained machine learning model; apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model; and output a prediction of conversion to the cognitive disease for the patient based on the trained machine learning model . The following limitations: “ obtain a trained machine learning model ” and “ apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model ” are interpreted as insignificant extra solution activities. Specifically, the above recited limitations are directed towards pre-solution activity (see MPEP §2106.05(g)) since they’re used to obtain information about the user to provide an output regarding a prediction of conversion to the cognitive disease for the patient (i.e. mere data gathering). Additionally, t here is nothing in the claims which show how outputting the above recited limitations integrates the judicial exception into a practical application. Regarding the limitation , “ output a prediction of conversion to the cognitive disease for the patient based on the trained machine learning model ” , Examiner asserts that these limitations are directed to additional elements, specifically insignificant post solution activity (see MPEP 2106.05(g)). 32. The above recited limitations merely process information and then output the results of the above identified abstract ideas. Additionally, the recited outputting is neither particular enough to meaningfully limit the recited exception nor does it have more than a nominal relationship to the exception. In other words, the breadth of the recited “ output ” is such that it substantially encompasses all applications of the recited exception (such as moving information around to output a prediction of conversion to the cognitive disease for the patient ). 33. There is nothing in the claims which show how outputting the above recited limitations integrates the judicial exception into a practical application. 34. Further, there is no evidence of record that would support the assertion that this step is an improvement to a computer or a technological solution to a technological problem. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Further, the limitation s: a memory; and a processor communicatively coupled to the memory; wherein the memory stores a set of instructions… are directed toward generically recited computer element s which do not improve the functioning of a computer, or any other technology or technical field. Accordingly, the combination of these additional elements is no more than insignificant extra solution activity. Thus, the abstract ideas are not integrated into a practical application. Step 2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, regarding the detecting device, Applicant’s specification discloses: “ process 1100 can obtain a trained machine learning model. In some examples, the trained machine learning model can include a multivariate linear regression machine learning model. However, it should be appreciated that the machine learning model is not limited to the multivariate linear regression machine learning model. As one example, a machine learning model can be configured as a feedforward network, in which the connections between nodes do not form any loops in the network. As another example, a machine learning algorithm can be configured as a recurrent neural network ("RNN"), in which connections between nodes are configured to allow for previous outputs to be used as inputs while having one or more hidden states, which in some instances may be referred to as a memory of the RNN... ” [0038] . Regarding the limitations directed to the trained machine learning mode, the memory, and the processor , see Wang et al. ( US 2021/0110926 ) , which discloses predicting outcomes using prediction models [0002] comprising of a trained machine learning mode [0032] which may be a linear regression model having multiple variables [0032, 0043] , a memory [0061] , and a processor [0061]. Wang further teaches that traditionally, statistical correlations that generate predictions based on multiple variables use techniques such as linear regression [0004], and that a prediction model can be a linear regression model [0032]. Thus, the limitations directed to the trained machine learning mode , the memory , and the processor are well-understood, routine, and conventional, as evidenced by the reference above. As discussed with respect to Step 2A, Prong 2 above, the additional elements in the claim amount to no more than insignificant extra solution activity and applying the exception in a general way, as well as establishing an environment for which data is gathered. Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer. Thus, none of the claims 1-13 amount to significantly more than the abstract idea itself. Accordingly, claims 1-13 are not patent eligible and rejected under 35 U.S.C. 101 as being directed to abstract ideas in view of the Supreme Court Decision in Alice Corporation Pty. Ltd. v. CLS Bank International, et al., MPEP §2106.04(a)(2), MPEP §2106.04(d)(2),and MPEP §2106.05(g). Claim Rejections - 35 USC § 102 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claim s 1-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Reitermann et al. (US 2024/0003918). In re claim 1 , Reitermann discloses a system for cognitive disease prediction [0007] , comprising: a memory [0009] ; and a processor [0009] communicatively coupled to the memory [0009] ; wherein the memory stores a set of instructions [0009] which, when executed by the processor [0009] , cause the processor to: receive a plurality of risk factor ([0070-0072]: factors such as genetic markers, age, gender, education, etc.) indications for a given patient and determine a plurality of interaction indications of the patient ( [0070]: plurality of interaction indications are correlations between protein markers and other factors ) , each interaction indication of the plurality of interaction indications being an indication of interaction between at least two risk factor indications of the plurality of risk factor indications ([0070]: risk score algorithm may determine correlation between protein markers and other factors on subjects’ likelihood to develop AD and may identity top components for the risk score algorithm; [0072, 0177-0178]) ; obtain a trained machine learning model ([0177-0178]: machine learning model is trained using patient records that correlates protein markers to AD surrogate variables; [0163]: AI-based algorithm) ; apply the plurality of risk factor indications and the plurality of interaction indications to the trained machine learning model [0177-0178] ; and output a prediction of conversion to the cognitive disease for the patient ([0053]: AD risk score predicts brain neurodegeneration disease risk) based on the trained machine learning model ( [0163]: AI algorithm used to generate AD risk score ) . In re claim 2 , Reitermann discloses wherein the plurality of risk factor indications includes at least one selected from a group of: clinical data ([0173]: cognitive assessment, subject’s gender, and subject’s education may be used) , genetic data [0173] , biospecimen data ([0052]: at least 4 protein markers are selected; [0074, 0077]) , and medical image data [0188 , 0254 ] . In re claim 3 , Reitermann discloses wherein the clinical data includes at least one selected from a group of: a cognitive assessment score [0173]: subject’s performance on a cognitive assessment , an omega-3 score, or demographic statistics ([0188]: age and gender; [0173]) . In re claim 4 , Reitermann discloses wherein the genetic data includes: Apolipoprotein E (APOE) genotyping ([0214-0217]: APOE status may be associated with each subject) . In re claim 5 , Reitermann discloses wherein the biospecimen data includes at least one selected from a group of: a quantity of P-tau protein, tau protein [0074] , or Beta-amyloid [0077] . In re claim 6 , Reitermann discloses wherein the medical image data includes at least one selected from a group of: H ippocampus ([0243-0245]: variables can be categorized from images such as from TAU images; [0083]: Tau accumulation seen in the hippocampus) , ventricles, entorhinal ([0243-0245]: variables can be categorized from images such as from TAU images; [0083]: Tau accumulation seen in the entorhinal cortex) , intracranial volume (ICV), and fusiform medical image data. In re claim 7 , Reitermann discloses wherein the plurality of risk factor indications comprises at least one selected from a group of: an age indication [0178] , an education indication [0178] , a ventricles indication, a hippocampus indication, an entorhinal indication, a fusiform indication, an amyloid- beta indication, a tau indication, a pTau indication, and an Alzheimer Disease Assessment Scale (ADAS) indication for the patient. In re claim 8 , Reitermann discloses wherein the plurality of interaction indications comprises at least one selected from a group of: a first interaction indication between amyloid-beta ([0074]: amyloid peptide can be used, for instance amyloid beta [0077]) and tau ([0074]: tau peptide marker ; [0052]: AD risk score comprises at least 4 protein markers ) , a second interaction indication between hippocampus and pTau, a third interaction indication between ventricles and pTau, and a fourth interaction indication between hippocampus and education. Claim Rejections - 35 USC § 103 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. 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. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Reitermann et al. (US 2024/0003918) in view of NPL “ Alzheimer’s Disease: The Relative Importance Diagnostic ” (hereinafter referred to as “ Habadi ”) . In re claim 9 , Reitermann fails to disclose wherein the trained machine learning model comprises a multivariate linear regression machine learning model. Habadi teaches an analogous method for predicting a status of Alzheimer’s disease in a patient (pg. 80, 3. Statistical Method , lines 1-6 ) and wherein a trained machine learning model (abstract: statistical prediction model with multiple logistic regression is used to identify Alzheimer’s disease patients) comprises a multivariate linear regression machine learning model (pg. 81, 4. Implementation of the Multiple Logistic Model : equation 2 provides a logistic regression module with multiple variables ; pg. 80: 3. Statistical Method , lines 7-14: equation 1 is a linear form of the logistic regression model ). Habadi further teaches that the full logistic regression model predicts the probability of Alzheimer’s disease in a subject based on all predictors and all possible interactions (pg. 81, 4. Implementation of the Multiple Logistic Model : lines 1- 8 ) to determine which interactions are statistically signification ( pg. 80, 3. Statistical Method , lines 1-5; pg. 81, 4. Implementation of the Multiple Logistic Model : lines 1-8, lines 22-26 ) fo r predicting Alzheimer’s disease ( pg. 80, 3. Statistical Method , lines 1-5 ). Habadi also teaches wherein a first coefficient ( pg. 81, 4. Implementation of the Multiple Logistic Model : lines 1-8: B 1 ) and a second coefficient (pg. 81, 4. Implementation of the Multiple Logistic Model : lines 1-8: B 2 ) were determined during a training phase of the multivariate linear regression machine learning model ( pg. 81, 4. Implementation of the Multiple Logistic Model : lines 1-8: data set is divided into training and testing and the coefficients must be determined ). It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the system for cognitive disease prediction taught by Reitermann, to provide wherein the trained machine learning model comprises a multivariate linear regression machine learning model, which would include a first and second coefficient determined during a training phase of the multivariate linear regression machine learning model, as taught by Habadi, because the linear form of the logistic regression model predicts the probability of Alzheimer’s disease in a subject based on all predictors and all possible interactions to determine which interactions are statistically signification for predicting Alzheimer’s disease. Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Reitermann et al. (US 2024/0003918) in view of NPL “ Alzheimer’s Disease: The Relative Importance Diagnostic ” (hereinafter referred to as “Habadi”) in view of Huiku ( US 2010 / 0081942 ). In re claim 10 , the proposed combination fails to yield wherein the multivariate linear regression machine learning model is defined as: the result = β 0 + ∑ i a i x i + ∑ j Y j k j + £ i , wherein β 0 is an intercept of the multivariate linear regression machine learning model, a i is a first coefficient of i th individual risk factor indication x i of the plurality of risk factor indications, Y j is a second coefficient of j th interaction indication k j of the plurality of interaction indications, and £ i is a residual error of the multivariate linear regression machine learning model. Habadi teaches wherein the multivariate linear regression machine learning model is defined as: the result = β 0 + ∑ i a i x i + ∑ j Y j k j (pg. 81, equation 3) wherein β 0 is an intercept of the multivariate linear regression machine learning model (equation 3: inherent that β 0 is an intercept) , a i is a first coefficient of i th individual risk factor indication x i of the plurality of risk factor indications (pg. 81, 4. Implementation of the Multiple Logistic Model , lines 1-8: β 1 is a first coefficient of the risk factors X’s ) , Y j is a second coefficient of j th interaction indication k j of the plurality of interaction indications (pg. 81, 4. Implementation of the Multiple Logistic Model , lines 1-8: β 2 is a first coefficient of the interaction indications X’s which are also for the risk factors) . For substantially the same reasons as discussed in re claim 10 above, it would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the system for cognitive disease prediction yielded by the proposed combination, to provide wherein the multivariate linear regression machine learning model is defined as: the result = β 0 + ∑ i a i x i + ∑ j Y j k j , as taught by Habadi. Regarding the limitation, “ £ i is a residual error of the multivariate linear regression machine learning model ”, Huiku teaches estimating blood plasma volume using a linear regression model [0069], wherein the linear regression module includes adding a residual error [0069]. Huiku further teaches that the residual error should be minimized [0072], and the residual error is known for representing unexplained variation after a model has been made. It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the result yielded by the proposed combination, to provide wherein the result includes adding the residual error, as taught by Huiku , because the residual error should be minimized, and because the residual error is known for representing unexplained variation after a model has been made. In re claim 11 , regarding the limitation, “ wherein the first coefficient and the second coefficient were determined during a training phase of the multivariate linear regression machine learning model ”, see in re claim 10 above. Claims 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Reitermann et al. (US 2024/0003918) in view of Reiman ( US 2005 / 0283054 ). In re claim 12 , the proposed combination fails to yield wherein the result comprises a predicted conversion time of Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) in the patient. Reiman teaches a method of measuring activity in a human brain [0003] determine efficacy of treatments for brain-related disorders [0003], and teaches wherein a result comprises a predicted conversion time of Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) in a patient ([0050]: rates of conversion of MCI to AD; [0046-0048]). Reiman further teaches that a treatment administered to a patient may be used to predict decreased rates of conversion of MCI to AD [0050], and that prevention therapy provides extraordinary public health benefit [0008] such that delaying AD onset reduces a number of cases [0008]. It would have been obvious to someone of ordinary skill in the art at the time the instant invention was filed to modify the system for cognitive disease prediction yielded by the proposed combination , to provide wherein the result comprises a predicted conversion time of Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) in the patient , as taught by Reiman, because a treatment administered to a patient may be used to predict decreased rates of conversion of MCI to AD, which provides extraordinary public health benefit such as reducing a number of cases. In re claim 13 , regarding the limitation, “ wherein the result further comprises an effectiveness indication of a drug in increasing or decreasing the predicted conversion time in the patient ”, see the proposed combination yielded in re claim 12 above. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure: Slotman (US 2017/0276676) discloses monitoring patients (abstract) using an outcome prediction model [0075] which may include a multivariable regression model [0075]. Contact Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT RUMAISA R BAIG whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-0175 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Mon-Fri: 8am- 5pm . 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, FILLIN "SPE Name?" \* MERGEFORMAT David Hamaoui can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 270-5625 . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /RUMAISA RASHID BAIG/ Examiner, Art Unit 3796 /William J Levicky/ Primary Examiner, Art Unit 3796