CTNF 18/919,730 CTNF 87414 DETAILED ACTION Claims 1-17 are currently pending and have been examined. Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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 1-17 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more. Subject Matter Eligibility Criteria - Step 1: Claims 1 & 13-17 are directed to a method (i.e., a process); and Claims 2-12 are directed to a CRM (i.e., a manufacture). Accordingly, claims 1-17 are all within at least one of the four statutory categories. Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One: Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a). Representative independent claim 2 includes limitations that recite at least one abstract idea. Specifically, independent claim 2 recites: 2. A computer program product comprising instructions which, when executed by a computer and/or a computer network, cause the computer and/or the computer network to carry out a method of sleep-related event detection of a subject, wherein the method comprises: providing a trained sleep-related event detection model trained in accordance with a method comprising: providing, as input, a set of training data samples, wherein each training data sample comprises: one or more cardiac signals representative of a cardiac parameter of a subject; one or more audio recording signals representative of an environmental sound of the subject; and at least a first and second sleep-related event of the subject associated with the one or more cardiac and audio recording signals of the subject, wherein the first sleep-related event comprises a sleep-wake result, and wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event; and training the sleep-related event detection model to output sleep-wake detection and SDB event detection using the input; providing, as input to the sleep-related event detection model, one or more cardiac signals representative of a cardiac parameter of a subject and one or more audio recording signals representative of an environmental sound of the subject; detecting, by the sleep-related event detection model, a first sleep-related event and a second sleep-related event of the subject based at least in part on the input to generate a detection result, wherein the first sleep-related event comprises a sleep-wake result, wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event, wherein the detection result comprises the sleep-wake result and the SDB event; and providing the detection result. The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because detecting sleep-related events using a sleep-related event detection model and providing the detection result are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Accordingly, independent claim 2 and analogous independent claims 1 & 13 recite at least one abstract idea. Furthermore, dependent claims 3-12 & 14-17 further narrow the abstract idea described in the independent claims. Claims 3-4, 7-8, 14-15 recite the parts of the sleep detection model and using common features to detect results, Claims 6 & 17 recites generating environmental features and using those features to detect results, Claims 9 & 11 recites the type of cardiac and audio signals. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 2 and analogous independent claims 1 & 13, even when considered individually and as an ordered combination. Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two: Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A). In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”): 2. A computer program product comprising instructions which, when executed by a computer and/or a computer network, cause the computer and/or the computer network to carry out a method of sleep-related event detection of a subject, wherein the method comprises: providing a trained sleep-related event detection model trained in accordance with a method comprising: providing, as input, a set of training data samples, wherein each training data sample comprises: one or more cardiac signals representative of a cardiac parameter of a subject; one or more audio recording signals representative of an environmental sound of the subject; and at least a first and second sleep-related event of the subject associated with the one or more cardiac and audio recording signals of the subject, wherein the first sleep-related event comprises a sleep-wake result, and wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event; and training the sleep-related event detection model to output sleep-wake detection and SDB event detection using the input; providing, as input to the sleep-related event detection model, one or more cardiac signals representative of a cardiac parameter of a subject and one or more audio recording signals representative of an environmental sound of the subject; detecting, by the sleep-related event detection model, a first sleep-related event and a second sleep-related event of the subject based at least in part on the input to generate a detection result, wherein the first sleep-related event comprises a sleep-wake result, wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event, wherein the detection result comprises the sleep-wake result and the SDB event; and providing the detection result. For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application. Regarding the additional limitations of the computer, computer network the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation of using a sleep-related event detection model that is trained on training data samples including cardiac, audio, and event data, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1). Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application. Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2). For these reasons, representative independent claim 2 and analogous independent claims 1 & 13 do not recite additional elements that integrate the judicial exception into a practical application. Accordingly, the claims recites at least one abstract idea. The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below: Claims 5 & 16: These claims recite using a convolutional neural network on audio data to obtained additional environmental data which amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1). Claim 12: This claim recites sampling rates for obtaining the cardiac and audio signals and thus do no more than generally link use of the abstract idea to a particular technological environment or field of use without altering or affecting how the at least one abstract idea is performed (see MPEP § 2106.05(h)). Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea. Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B: Regarding Step 2B of the Alice/Mayo test, representative independent claim 2 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above, regarding the additional limitations of the computer, computer network the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)). Regarding the additional limitation of using a sleep-related event detection model that is trained on training data samples including cardiac, audio, and event data, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1). The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application. Therefore, claims 1-17 are ineligible under 35 USC §101. Claim Rejections - 35 USC § 103 07-20-aia AIA 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. 07-23-aia AIA 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. 07-21-aia AIA Claim s 1-4, 6-11, 13-15 & 17 are rejected under 35 U.S.C. 103 as being unpatentable over rejected over Zhang (WO2021208656A1) in view of Fox (US20230099622) . As per claim 1, Zhang teaches a computer-implemented method of training a sleep-related event detection model, wherein the method comprises: providing, as input, a set of training data samples (pg. 3: training of model), wherein each training data sample comprises: one or more cardiac signals representative of a cardiac parameter of a subject ( pg. 7: cardiac data); one or more audio recording signals representative of an environmental sound of the subject (pg. 7: sound data); and at least a first and second sleep-related event of the subject associated with the one or more cardiac and audio recording signals of the subject, wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event (pg. 11: system obtains training data where events are detected with different results including apnea, hypopnea, and normal); and training the sleep-related event detection model to output sleep-wake detection and SDB event detection using the input (pg. 11: trained model used to detect sleep disorders and apnea events). Zhang does not expressly teach wherein the first sleep-related event comprises a sleep-wake result. Fox, however, teaches to detecting one or more parameters such as audio and cardiac data of a user during a sleep session of the user and using those parameters to train a model (para. 18-19, 127). The method further includes processing the one or more parameters to determine a sleep status of the user where the sleep status is at least one of awake, asleep, or a sleep stage and wherein a classifier is trained with known sleep states (para. 18, 127). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Fox with Zhang based on the motivation of develop improved devices, systems, and methods for inferring states and stages of a respiratory therapy user's sleep in order to more accurately assess the user's condition and the efficacy of the applied therapy, which can improve sleep architecture by treating SDB (Fox – para. 13). As per claim 2, Zhang teaches a computer program product comprising instructions which, when executed by a computer and/or a computer network, cause the computer and/or the computer network to carry out a method of sleep-related event detection of a subject, wherein the method comprises: providing a trained sleep-related event detection model trained in accordance with a method (pg. 3: training of model) comprising: providing, as input, a set of training data samples (pg. 3: training of model), wherein each training data sample comprises: one or more cardiac signals representative of a cardiac parameter of a subject (pg. 7: cardiac data); one or more audio recording signals representative of an environmental sound of the subject (pg. 7: sound data); and at least a first and second sleep-related event of the subject associated with the one or more cardiac and audio recording signals of the subject, and wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event; and training the sleep-related event detection model to output sleep-wake detection and SDB event detection using the input (pg. 11: system obtains training data where events are detected with different results including apnea, hypopnea, and normal); providing, as input to the sleep-related event detection model, one or more cardiac signals representative of a cardiac parameter of a subject and one or more audio recording signals representative of an environmental sound of the subject (abstract; pg. 11-12: individual patient data input into model; physiological data obtained including heart and voice data); detecting, by the sleep-related event detection model, a first sleep-related event and a second sleep-related event of the subject based at least in part on the input to generate a detection result, wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event, wherein the detection result comprises the sleep-wake result and the SDB event (abstract; pg. 14-15: model can detect sleep-wake disorder and apnea type); and providing the detection result (pg. 14-15: model detects sleep disorder and apnea events for patient with results output). Zhang does not expressly teach wherein the first sleep-related event comprises a sleep-wake result. Fox, however, teaches to detecting one or more parameters such as audio and cardiac data of a user during a sleep session of the user and using those parameters to train a model (para. 18-19, 127). The method further includes processing the one or more parameters to determine a sleep status of the user where the sleep status is at least one of awake, asleep, or a sleep stage and wherein a classifier is trained with known sleep states (para. 18, 127). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. As per claim 3, Zhang and Fox teach the computer program product according to claim 2. Zhang does not expressly teach wherein the sleep-related event detection model comprises: a shared part configured to generate one or more common features of the at least first and second sleep-related event using only the one or more cardiac signals; and at least two sleep-related event specific parts comprising: a first sleep-related event specific part configured to generate a detection result for the first sleep-related event; and a second sleep-related event specific part configured to generate a detection result for the second sleep-related event. Fox, however, teaches to data from the one or more sensors can be analyzed to determine one or more sleep-related parameters, which can include a sleep state, a sleep stage, an apnea-hypopnea index (AHI), or any combination thereof (para. 111). The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, an intentional mask leak, an unintentional mask leak, a mouth leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof (para. 111). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. As per claim 4, Zhang and Fox teach the computer program product according to claim 3. Zhang does not expressly teach wherein each sleep-related event specific part is further configured to receive the one or more common features and to generate the detection result of the sleep-related event based at least in part on the one or more common features. Fox, however, teaches to data from the one or more sensors can be analyzed to determine one or more sleep-related parameters, which can include a sleep state, a sleep stage, an apnea-hypopnea index (AHI), or any combination thereof (para. 111). The one or more events can include snoring, apneas, central apneas, obstructive apneas, mixed apneas, hypopneas, an intentional mask leak, an unintentional mask leak, a mouth leak, a cough, a restless leg, a sleeping disorder, choking, an increased heart rate, labored breathing, an asthma attack, an epileptic episode, a seizure, increased blood pressure, or any combination thereof (para. 111). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. As per claim 6, Zhang and Fox teach the computer program product according to claim 3. Zhang does not expressly teach wherein the sleep-related event detection model comprises a further part configured to generate environmental features based on the one or more audio recordings; and wherein the second sleep-related event specific part is further configured to generate the detection result of the second sleep-related event based on the environmental features. Fox, however, teaches to obtaining audio parameters from the audio data based on features including passive sounds, sounds emitted and received back via an echo (para. 149-150. Fox also teaches to generating a detection result using the audio data (para. 154). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. As per claim 7, Zhang and Fox teach the computer program product according to claim 2. Zhang teaches wherein the sleep-related detection model comprises: a first part configured to generate a detection result for the first sleep-related event and a preliminary detection result for the second sleep-related event using the one or more cardiac signals (pg. 12-13: sleep apnea event and duration of event detected using physiological data including cardiac and other data); a second part configured to generate a preliminary detection result for the second sleep-related event using the one or more audio recording signals (pg. 13: system uses voice data and compares against various rules to determine type of sleep apnea); and a third part configured to generate a detection result for the second sleep-related event based on the preliminary detection results (pg. 13: specific type of sleep apnea syndrome determined if voice data indicates snoring during an apnea event). As per claim 8, Zhang and Fox teach the computer program product according to claim 7. Zhang does not expressly teach wherein the third part is further configured to generate a weighting factor; and wherein generating the detection result for the second sleep-related event is further based on the weighting factor. Fox, however, teaches to where a conflict between two or more conflicting parameters is resolved by disregarding a parameter derived on a basis of low quality data and/or increased weight is given to a parameter extracted from data of higher quality (para. 162). Sleep status (e.g., sleep state and sleep stage) can be determined from the modality and/or parameter with the higher weighting based on, for example, that modality and/or parameter being traditionally more accurate (para. 162). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. As per claim 9, Zhang and Fox teach the computer program product according to claim 2. Zhang teaches wherein the one or more cardiac signals include electrocardiography, ECG, signals, in particular ECG-derived respiration, EDR, signals and/or signals of respiratory effort; and/or wherein the one or more audio recording signals comprise Mel Frequency Cepstral Coefficients, MFCCs, extracted from the one or more audio recording signals and/or spectrograms extracted from the one or more audio recording signals (pg. 13, 18: ECG sensor obtains cardiac data and microphone obtaining mfcc data). . As per claim 10, Zhang and Fox teach the computer program product according to claim 2. Zhang teaches wherein the one or more cardiac signals are captured using a first sensor means, in particular wherein the first sensor means is in contact with the subject, in particular a photoplethysmography or a chest-worn accelerometer; and/or wherein the one or more audio recording signals are captured using a second sensor means such as a microphone, a smartphone and/or a smartwatch, in particular wherein the second sensor means is not in contact with the subject and/or is located near to the subject (pg. 9: physiological signals captures via sensors placed around patient; PPG sensor attached to patient’s finger and microphone placed near patient). As per claim 11, Zhang and Fox teach the computer program product according to claim 2. Zhang teaches wherein the one or more audio recording signals serve as a surrogate for measured respiratory signals of the subject (pg. 13: system uses voice data and compares against various rules to determine type of sleep apnea). As per claim 13, Zhang teaches a method of sleep-related event detection of a subject, wherein the method comprises: providing a trained sleep-related event detection model trained in accordance with a method (pg. 3: training of model) comprising: providing, as input, a set of training data samples (pg. 3: training of model), wherein each training data sample comprises: one or more cardiac signals representative of a cardiac parameter of a subject (pg. 7: cardiac data); one or more audio recording signals representative of an environmental sound of the subject (pg. 7: sound data); and at least a first and second sleep-related event of the subject associated with the one or more cardiac and audio recording signals of the subject, and wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event; and training the sleep-related event detection model to output sleep-wake detection and SDB event detection using the input (pg. 11: system obtains training data where events are detected with different results including apnea, hypopnea, and normal); providing, as input to the sleep-related event detection model, one or more cardiac signals representative of a cardiac parameter of a subject and one or more audio recording signals representative of an environmental sound of the subject (abstract; pg. 11-12: individual patient data input into model; physiological data obtained including heart and voice data); detecting, by the sleep-related event detection model, at least a first sleep-related event and/or a second sleep-related event of the subject based at least in part on the input to generate a detection result, wherein the second sleep-related event comprises a sleep disorder breathing, SDB, event, wherein the detection result comprises the SDB event; and providing the detection result (abstract; pg. 14-15: model can detect sleep-wake disorder and apnea type). Zhang does not expressly teach wherein the first sleep-related event comprises one or more sleep stages. Fox, however, teaches to detecting one or more parameters such as audio and cardiac data of a user during a sleep session of the user and using those parameters to train a model (para. 18-19, 127). The method further includes processing the one or more parameters to determine a sleep status of the user where the sleep status is at least one of awake, asleep, or a sleep stage and wherein a classifier is trained with known sleep states (para. 18, 127). The motivations to combine the above mentioned references are discussed in the rejection of claim 1, and incorporated herein. Claims 14-15 & 17 recite substantially similar limitations as those already addressed in claims 3-4 & 6, and, as such, are rejected for similar reasons as given above . 07-21-aia AIA Claim s 5 & 16 are rejected under 35 U.S.C. 103 as being unpatentable over rejected over Zhang (WO2021208656A1) in view of Fox (US20230099622) as applied to claims 2 & 13, and in further view of Tiron (US20230190140) . As per claim 5, Zhang and Fox teach the computer program product according to claim 2, but do not expressly teach wherein method comprises: applying, before providing the input, a convolutional neural network, CNN, on the one or more audio recording signals to obtain additional environmental information; and providing, as part of the input, the additional environmental information. Tiron, however, teaches to detection and monitoring of health parameters for detecting of a sleep disorder breathing state where a CNN is used to analyze audio data and output a result (para. 604). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Tiron with Zhang and Fox based on the motivation of provide improvements in the technological field of automated management, screening, monitoring and/or treatment of sleep related conditions and/or respiratory conditions, including, for example, sleep disordered breathing (Tiron – para. 63). Claim 16 recites substantially similar limitations as those already addressed in claim 5, and, as such, is rejected for similar reasons as given above . 07-21-aia AIA Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over rejected over Zhang (WO2021208656A1) in view of Fox (US20230099622) as applied to claim 2, and in further view of Bradley (US20080319277) . As per claim 12, Zhang and Fox teach the computer program product according to claim 2, but do not expressly teach wherein a sampling rate of the one or more cardiac signals is different from a sampling rate of the one or more audio recording signals. Bradley, however, teaches to a sleep disorder monitoring and diagnostic system where audio data is obtained at a frequency of 1000 or higher and ECG data is obtained at 100 hz or higher (para. 38, 42). It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Bradley with Zhang and Fox based on the motivation of collect auditory signals for snoring, bruxism or breathing sounds at high enough sampling rates to allow for a proper analysis of the subject's condition to be carried out (Bradley – para. 8) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shouldice (US20200367810) teaches to detecting bio-motion associated with living subjects. More particularly, the present technology relates to using acoustic, radio frequency and/or infrared sensing to detect physiological characteristics such as from physiological movement such as breathing movement, cardiac movement and/or other less cyclical body movement of a living subject. Gilbert (US20250302380) teaches to detect and classify sleep apnea using wearable technology. In an example, a wearable monitoring device is configured to capture electrical potential measurements of a heart of a user, e.g., electrocardiogram (ECG) data, over an extended observation period, e.g., multiple days. These measurements may then be analyzed using one or more sleep apnea models or algorithms to predict a sleep apnea classification for the user. In one or more implementations, the one or more sleep apnea models or algorithms include a machine learning model that is trained for a sleep apnea classification task using historical electrical potential measurements and historical outcome data from a user population as training data. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM. 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, Anita Coupe can be reached at 571-270-7949. 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. /Jonathan Ng/ Primary Examiner, Art Unit 3619 Application/Control Number: 18/919,730 Page 2 Art Unit: 3619 Application/Control Number: 18/919,730 Page 3 Art Unit: 3619 Application/Control Number: 18/919,730 Page 4 Art Unit: 3619 Application/Control Number: 18/919,730 Page 5 Art Unit: 3619 Application/Control Number: 18/919,730 Page 6 Art Unit: 3619 Application/Control Number: 18/919,730 Page 7 Art Unit: 3619 Application/Control Number: 18/919,730 Page 8 Art Unit: 3619 Application/Control Number: 18/919,730 Page 9 Art Unit: 3619 Application/Control Number: 18/919,730 Page 10 Art Unit: 3619 Application/Control Number: 18/919,730 Page 11 Art Unit: 3619 Application/Control Number: 18/919,730 Page 12 Art Unit: 3619 Application/Control Number: 18/919,730 Page 13 Art Unit: 3619 Application/Control Number: 18/919,730 Page 14 Art Unit: 3619 Application/Control Number: 18/919,730 Page 15 Art Unit: 3619 Application/Control Number: 18/919,730 Page 16 Art Unit: 3619 Application/Control Number: 18/919,730 Page 17 Art Unit: 3619 Application/Control Number: 18/919,730 Page 18 Art Unit: 3619 Application/Control Number: 18/919,730 Page 19 Art Unit: 3619 Application/Control Number: 18/919,730 Page 20 Art Unit: 3619 Application/Control Number: 18/919,730 Page 21 Art Unit: 3619 Application/Control Number: 18/919,730 Page 22 Art Unit: 3619 Application/Control Number: 18/919,730 Page 23 Art Unit: 3619
