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. Claim Status Claims 1-20 are pending and under examination herein. Claims 1-20 are rejected. Priority The instant application claims priority to US Provisional Application 63 / 186101, filed 05/08/2021. As such, the effective filing date assigned to each of claims 1- 20 is 05/08/2021. Information Disclosure Statement No information disclosure statements have been filed here. Drawings The drawings filed 05/06/2022 are accepted by the examiner. Specification The disclosure is objected to because of the following informalities: When there are drawings, there shall be a brief description of the several views of the drawings . The current description is insufficient as it does not provide a brief description for the individual figures, but rather lists “FIGS. 2-25”. Applicant is required to provide a brief description for each figure, including figures with multiple parts (such as Gig. 3A-3C ). 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea/law of nature/natural phenomenon without significantly more. In accordance with MPEP § 2106, claims found to recite statutory subject matter ( Step 1: YES ) are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A , Prong 1). For example, i n the instant application, the claims recite the following limitations that equate to an abstract idea: Claim 1 recites extracting features from the patient data, the features including one or more polypharmacy features; based on the features extracted from the patient data, generating a prediction of the post-acute care patient suffering a fall within the future using one or more machine learning models; and initiating an action based on the prediction of the post-acute care patient suffering the fall. Claim 12 recites extracting features values from patient data of the post-acute care patient from an electronic medical record ( EMR ) of the post-acute care patient; based on the features values extracted from the patient data, generating the prediction of the post-acute care patient suffering the fall within the future using the one or more machine learning models trained using the training data; determining the prediction of the post-acute care patient suffering the fall is above a threshold; and initiating an action based on the prediction being above the threshold. Claim 17 recites identifying features comprising medication features and diagnosis features from the patient data received; selecting a subset of the features by: separating continuous values and binary values of the features and using a tree-based model for the continuous values and the binary values separately; identifying permutated features, based on using the tree-based model, that are above a permuted feature total gain threshold; and applying a logistic regression model to the permutated features above the permuted feature total gain threshold; and based on selecting the subset of the features, generating the fall risk prediction model. These recitations equate to steps of collecting information, analyzing data and making observations, evaluations and judgements that can be carried out in the human mind. Specifically, identifying and extracting features from data, generating a prediction using machine learning models (which could be as simple as linear regression, as disclosed by para 0033 in the instant specification), determining a prediction is above a threshold, initiating an action , selecting a subset of features by separating values of the features, identifying permutated features above a threshold and generating a fall risk can be performed mentally and are similar to the concepts of collecting and comparing known information in Classen Immunotherapies, Inc. v. Biogen IDEC , 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011) and collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) that the courts have identified as concepts that can be practically performed in the human mind. Therefore, each of the above recited limitations fall under the “Mental Processes” grouping of abstract ideas. Furthermore, the steps of generating a prediction using machine learning models and applying a logistic regression model equate to organizing information and manipulating information through mathematical correlations and reciting a mathematical equation, similar to the concepts of taking existing information, manipulating the data using mathematical functions, and organizing this information into a new form in Digitech Image Techs., LLC v. Electronics for Imaging, Inc. , 758 F.3d 1344, 1350, 111 USPQ2d 1717, 1721 (Fed. Cir. 2014). Therefore, these limitations fall under the “mathematical concepts” grouping of abstract ideas. Claims 2 - 4, 7 -9 , 11, 13-16 and 18-20 recite further judicial exceptions or further qualify the judicial exceptions. As such, claims 1-20 recite an abstract idea ( Step 2A , Prong 1: YES ). Claims found to recite a judicial exception under Step 2A , Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A , Prong 2). This judicial exception is not integrated into a practical application because the claims do not recite an additional element that reflects an improvement to technology, applies or uses the recited judicial exception to affect a particular treatment for a condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Rather, the instant claims recite additional elements that amount to mere data gathering and mere instructions to implement the abstract idea in a generic computing environment. Specifically, the claims recite the following additional elements : Claim 1 recites a non-transitory computer storage media ; receiving patient data for the post-acute care patient . Claims 4 recites using one or more natural language processing techniques . Claims 5 and 6 recite limitations for types of data included in the patient data. Claims 8-9 and 16 recite using an XGBoost model . Claim 12 recites storing training data associated with a plurality of patients for training one or more machine learning models that include one or more models for generating a prediction of the post-acute care patient suffering a fall within the future . Claim 17 recites receiving patient data for a plurality of patients, wherein at least a subset of the patient data is associated with one or more patients who experienced a fall; wherein at least one of the features are identified using natural language processing from one or more free-text fields within at least one electronic medical record . Claim s 1 , 12 and 17 recite limitations of how data is obtained and claims 5-6 recites further limitations on that data. These limitations equate to mere data gathering activity to obtain the data necessary for the mental evaluations and judgements (see MPEP 2106.05(g)). Claims 4, 8-9 and 16-17 merely indicates a field of use or technological environment in which the judicial exception is performed and confines the use of the abstract idea to a particular technological environment (NLP and XGBoost models). See MPEP 2106.05(h). Under the broadest reasonable interpretation, the method of claims 12 and 12 and 17 are computer implemented, as they use machine-learning models, and c laims 1 , 12 and 17 merely recites using a generic computing systems and computer program products to carry out instructions to implement an abstract idea on a computer. The computer system and computer program product as claimed fails to recite details of how a solution to a problem is accomplished and only recites the idea of a solution or outcome. There are no limitations that indicate that the claimed steps require anything other than generic computing systems. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp. , 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984. Furthermore, the use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). There is no indication that any of these additional elements provide a practical application of the recited judicial exception outside of the judicial exception itself. As such, claims 1- 20 are directed to an abstract idea ( Step 2A , Prong 2: NO ). Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself ( Step 2B ). Further analyzing the additional elements under step 2B , the additional elements as described above do not rise to the level of significantly more than the judicial exception. As set forth in the MPEP, determinations of whether or not additional elements (or a combination of additional elements) may provide significantly more and/or an inventive concept rests in whether or not the additional elements (or combination of elements) represents well-understood, routine, conventional activity. Said assessment is made by a factual determination stemming from a conclusion that an element (or combination of elements) is widely prevalent or in common use in the relevant industry, which is determined by either a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s) ; a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s) . With respect to the instant claims under the 2B analysis, c laims 4, 8-9 and 16-17 merely are at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f).e claims significantly more than the judicial exception ( MPEP2106.05 (g)&(h)). Furthermore, the use of a computer or other machinery in its ordinary capacity for economic or other tasks ( e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea ( e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Therefore, the additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception, and the claims do not amount to significantly more than the judicial exception itself ( Step 2B : NO ). As such, claims 1-2 0 are not patent eligible under 35 U.S.C. 101. Claim Rejections - 35 USC § 102 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 ( i.e., changing from AIA to pre-AIA ) 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 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. Claim s 1, 3 , 5-13, and 15-16 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Ye et al. ( International journal of medical informatics 2020 , 137, p. 1 -7; hereafter referred to as Ye) . With respect to claims 1 and 12 , Ye discloses an EHR -based risk assessment tool , wherein a one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost , and tested on an independent validation cohort to identify elders at a higher risk of falls (highlights; abstract). Ye discloses obtaining electronic health records ( EHR ) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age) t hat visited Maine health care facilities, including 35 hospitals, 34 federally qualified health centers, from April 1, 2016 to March 30, 2018 , which is used to train the machine learning models (i.e. included post-acute care patients)(abstract; section 21 -2.2 ). Ye discloses candidate predictors were extracted from the EHR dataset , including medication prescriptions (section 2.2). Ye further discloses predicting risk of fall in the next year , and that t he stratified (low/intermediate/high) risk groups were assigned according to the relative risk, which was calculated by the calibrated risk score divided by population-based fall incidence rate , with the intermediate-risk group formed by a group of patients with averaged relative risk greater than 1 but less than 5, indicating a moderate risk of fall for the next year, while the high-risk category caught the individuals with averaged relative risk of fall equal to or greater than 5, indicating a much higher risk of fall for the next year comparing to the general population (i.e. above a threshold) (section 2.3; fig 1). Ye further discloses that with the advantages of diverse and readily accessible data source, the EHR -based fall prediction could be an ideal and beneficial tool as the first step to a feasible fall prevention strategy , and w hen those older adults with high risk of fall were identified (i.e. above a threshold) , their personal unique risk factors would be captured, and their fall preventing strategies could be designed and proposed accordingly , such as educations to improve the awareness of fall risk, minimization or withdrawal of specific psychoactive or cardiovascular medications, detailed exercise therapies for patients with balance and gait issues (section 4.4). Ye discloses using software, indicating the method is computer implemented (section 2..3, para 1). With respect to claim 3, Ye discloses the candidate features extracted from the EHR data included a disease diagnosis from ICD-10 codes (section 2.2, para 2). With respect to claims 5-7, Ye discloses the extracted predictors included demographic characteristics, clinical utilization features, disease diagnosis from ICD-10 codes, medication prescriptions from National Drug Code ( NDC ), and laboratory test results from Logical Observation Identifiers Names and Codes ( LOINC ) , indicating the EHR data included these data types (section 2.2). With respect to claim s 8 and 15 , Ye discloses the obtaining medication information, considering concurrent medication therapies , and counts of unique medications (section 2.2, para 2; section 2.3, para 3; fig 4; section 3.3, para 3; fig 5). With respect to claims 9-10, Ye discloses XGBoost , to automatically integrate useful clinical information of disease diagnoses, medication consumption, clinical utilization, lab-test results and predicted an older individual’s risk of fall in the future one year (section 2.3; section 4.1). With respect to claim 11, Ye discloses using logistic regression as part of the pipeline for generating fall risk (section 2.3). With respect to claim 1 5 , Ye discloses p atients who suffered multiple falls during the targeted time frame were chart-reviewed by internal physician curators such that only the first fall records were utilized in our analysis , and further extracting predictors from standard nomenclature and clinical ontology codes , including binarizing medication information, indicating standardization of data (section 2.2 ; section 4.4, para 2 ) . With respect to claim 16, Ye discloses identifying and separating continuous values and binary values for features (section 2.2; section 4.4, para 2). Ye further discloses using a member of tree-based modeling algorithm, XGBoost , for the prediction models using the extracted features, and determining a relative risk of diseases (continuous values) and a relative risk of medications (binary values) against confirmed falls for the validation, and that b ased on a pool of 10,198 predictor candidates, the XGBoost algorithm eventually captured 157 impactful features to form the final predictive model and t hese identified predictors were mainly demographic features (age and gender), chronic disease diagnoses, medication prescriptions and clinical utilization indicators (section 3.2-3.3.; section 4.4, para 2; fig 4). 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 ( i.e., changing from AIA to pre-AIA ) 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, 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 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 . 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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Ye et al. ( International journal of medical informatics 2020 , 137, p. 1 -7; hereafter referred to as Ye), as applied to claim 1 above. With respect to claim 2, Ye discloses the limitations of claim 1, as applied above . Ye further discloses this constructed fall risk assessment tool could be immediately deployed to provide early warnings to older adults with increased fall risk and identifying their personalized risk factors to facilitate customized fall interventions (Section 5). Therefore, it would have been prima facie obvious to one of ordinary skill in the art to have applied this model within 36 hours of the post- acute care patient being admitted into a post-acute care facility to predict the fall risk of older adults, since it can be immediately deployed to provide early warnings. Therefore, the invention is prima facie obvious. Claim 14 rejected under 35 U.S.C. 103 as being unpatentable over Ye et al. ( International journal of medical informatics 2020 , 137, p. 1 -7; hereafter referred to as Ye) as applied to claim s 12-13 above, and further in view of Wang et al. ( NPJ digital medicine, 2(1), 127, p 1-7; hereafter referred to as Wang) . With respect to claim 1 4 , Ye discloses the steps of claims 12-13, as applied above. Ye discloses record of fall in EHR -based data essentially requires injury and subsequent visit to the doctor/hospital (section 3.1; section 4.3) . However, Ye does not disclose labeling the training data based on the severity of injury. However, the prior art to Wang , in the same field of endeavor, discloses develop a general predictive model for severity of falls among patient populations and predict ing the severity of all kinds of injuries of inpatient falls , using an advanced machine learning method multi-view ensemble leaning to efficiently exploit the multidimensional patient data , and s tudying the data in further detail, statistical tests indicated some differences between minor and severe injury levels with respect to certain variables , indicating the severity of the injury cause by a fall was labeled in the training data (abstract ; p 1, col 2, para 3-p 2 col 1, para 1; table 1 ). Wang further discloses p atient falls lead to increased medical care costs, lengthened hospital stays, more litigation, and even death , and e xisting methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries , and t he severe fall index is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients (abstract) . Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the steps of Ye for determining fall risk with the method for predicting the severity of injury associated with a fall, by including labeled severity of injury training data as disclosed by Wang, because p atient falls lead to increased medical care costs, lengthened hospital stays, more litigation, and even death , and e xisting methods and technology to address this problem mostly focus on stratifying inpatients at risk, without predicting fall severity or injuries , and t he severe fall index is calculated to identify inpatients who are at risk of having severe injuries if they fall, thus triggering additional steps of intervention to prevent a harmful fall, beyond the standard-of-care procedure for all high-risk fall patients. There would be a reasonable expectation of success because extracting and using severity injury data that is available in the EHR as a predictor would not impede the methods of Ye . Therefore, the invention is prima facie obvious. Claim s 4 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ye et al. ( International journal of medical informatics 2020 , 137, p. 1 -7; hereafter referred to as Ye) as applied to claim 1 above, and further in view of Ford et al. ( Journal of the American Medical Informatics Association 2016 , 23(5), p 1-9; hereafter referred to as Ford) . With respect to claim 4, Ye discloses the limitations of claim 1, as applied above. Ye further discloses the candidate features extracted from the EHR data included a disease diagnosis from ICD-10 codes (section 2.2, para 2). With respect to claim 17, Ye discloses generating an EHR -based risk assessment tool , wherein a one-year fall prediction model was developed using the machine-learning-based algorithm, XGBoost , and tested on an independent validation cohort to identify elders at a higher risk of falls (highlights; abstract). Ye discloses obtaining electronic health records ( EHR ) of Maine from 2016 to 2018, comprising 265,225 older patients (≥65 years of age) t hat visited Maine health care facilities, including 35 hospitals, 34 federally qualified health centers, from April 1, 2016 to March 30, 2018 , which is used to train the machine learning models (i.e. included post-acute care patients)(abstract; section 21-2.2). Ye discloses candidate predictors were extracted from the EHR dataset , including medication prescriptions (section 2.2). Ye further discloses predicting risk of fall in the next year, and that t he stratified (low/intermediate/high) risk groups were assigned according to the relative risk, which was calculated by the calibrated risk score divided by population-based fall incidence rate , with the intermediate-risk group formed by a group of patients with averaged relative risk greater than 1 but less than 5, indicating a moderate risk of fall for the next year, while the high-risk category caught the individuals with averaged relative risk of fall equal to or greater than 5, indicating a much higher risk of fall for the next year comparing to the general population (i.e. above a threshold) (section 2.3; fig 1). Ye further discloses that with the advantages of diverse and readily accessible data source, the EHR -based fall prediction could be an ideal and beneficial tool as the first step to a feasible fall prevention strategy , and w hen those older adults with high risk of fall were identified (i.e. above a threshold) , their personal unique risk factors would be captured, and their fall preventing strategies could be designed and proposed accordingly , such as educations to improve the awareness of fall risk, minimization or withdrawal of specific psychoactive or cardiovascular medications, detailed exercise therapies for patients with balance and gait issues (section 4.4). Ye discloses using software, indicating the method is computer implemented (section 2..3, para 1). Ye discloses identifying and separating continuous values and binary values for features (section 2.2 ; section 4.4, para 2 ) Ye further discloses an initial univariate logistic regression was introduced in the derivation cohort to perform the feature pre-screening routine , using a member of tree-based modeling algorithm, XGBoost , for the prediction model s, and determining a relative risk of diseases (continuous values) and a relative risk of medications (binary values) against confirmed falls for the validation, and further that m ost existing EHR -based fall risk models were developed using traditional statistical approaches, such as logistic regressions (section 2.2; section 3.2- 3.3; section 4.4, para 2; fig 4 ; summary points ). Ye further discloses they wonder whether the accuracy of such tools could be further improved if advanced machine learning algorithms were introduced (introduction, para 3). Therefore, it would have been prima facie obvious to have applied logistic regression model to the permutated features above the permuted feature total gain threshold to the methods of Ye, as m ost existing EHR -based fall risk models were developed using traditional statistical approaches, such as logistic regressions and therefore this technique is known in the art , and Ye teaches improving the accuracy of known tools using advanced machine learning algorithms. With respect to claim s 18 and 20 , Ye discloses the extracted predictors included demographic characteristics, clinical utilization features, disease diagnosis from ICD-10 codes, medication prescriptions from National Drug Code ( NDC ), and laboratory test results from Logical Observation Identifiers Names and Codes ( LOINC ) , (section 2.2). With respect to claim 19, Ye discloses p atients who suffered multiple falls during the targeted time frame were chart-reviewed by internal physician curators such that only the first fall records were utilized in our analysis , and further extracting predictors from standard nomenclature and clinical ontology codes, including binarizing medication information, indicating transforming and standardizing of data (section 2.2; section 4.4, para 2). However, with respect to claims 4 and 17, Ye does not disclose using a natural language processing techniques to extract the features. However, the prior art to Ford, in the same field of endeavor, reviews e xtracting information from the free-form text of electronic medical records ( EMRs ) to improve case detection and discloses t ext in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with code , and further discloses numerous established NLP algorithms used for information extraction from EMRs (title; abstract; p 4, col 1, para 2-4). Therefore, it would have been prima facie obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention to have used an NPL model to extract the features from free-form text in the EHR as this was a known technique at the effective filing date of the claimed invention. Therefore, the invention is prima facie obvious. Conclusion No claims allowed. Inquiries Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT NIDHI DHARITHREESAN whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-5486 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT Monday - Friday 9:00 - 5:00 . 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 Larry D Riggs II can be reached at FILLIN "SPE Phone?" \* MERGEFORMAT (571) 270-3062 . 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. /N.D./ Examiner, Art Unit 1686 /Karlheinz R. Skowronek/ Supervisory Patent Examiner, Art Unit 1687