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. Claims 1- 34 are pending in the application under prosecution and have been examined. The specification has not been checked to the extent necessary to determine the presence of all possible minor errors. The specification should be amended to reflect the status of all related application, whether patented or abandoned. Therefore, applications noted by their serial number and/or attorney docket number should be updated with correct serial number and patent number if patented. The first instance of all acronyms or abbreviation should be spelled out for clarity , whether or not considered well known in the art. In the response to this Office action, the Examiner respectfully requests that support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line numbers in the specification and/or drawing figure(s). This will assist the Examiner in prosecuting this application. Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. 37 C.F.R. § 1.83(a) requires the Drawings to illustrate or show all claimed features. Applicant must clearly point out the patentable novelty that they think the claims present, in view of the state of the art disclosed by the references cited or the objections made, and must also explain how the amendments avoid the references or objections. See 37 C.F.R. § 1.111(c). 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. Clai m s 1-1 0 , 20, 22-24, 27- 34 are rejected under 35 U.S.C. 103 as being unpatentable over “ KIM YONG SUK, ” Cardiac Disease Prediction System Using Machine Learning Model” ( KR 2020-0068161A ) in view of US 20210090734 A1 ( SINGH et al) . With respect to claim s 1 and 33 , KIM discloses a n apparatus comprising: an acquisition unit configured to acquire electrical biosignal data (sensor unit 2200 sensing PhonoCardioGram (PCG) signal to generate PCG information) ; and an encoder configured to calculate a first numerical vector by receiving the electrical biosignal data using a deep learning algorithm (heart condition determination unit or cardiac condition discrimination unit 1600 or cardiac state determining unit 1600 that measures the electrical activ ity using an artificial neural network to generate PCG information unit ( vector input value )) , wherein the first numerical vector is structured data associated with features extracted from the electrical biosignal data, including at least one of anatomical features and temporal features that are capable of being extracted from the electrical biosignal data ( wherein the cardiac condition discrimination unit 1600 for deriving a patient's heart disease prediction result based on the learned ECG feature information, PCG feature information, and mixed feature information , i.e., deriving feature information from the PCG unit information and the ECG unit information) . KIM fails to specifically teach converting electrical biosignal data into numerical vectors . However, SINGH teaches recording unit configured to record set of heart sounds and store the set of heart sounds in a database operatively coupled to the recording unit; and a control unit configured to: segment the set of heart sounds into a plurality of slices, each having one or more audio slices; convert the audio slices into corresponding spectrograms; obtain a feature vector corresponding to the spectrograms [ABSTRACT; Par. 002 7 -002 9 ] . Therefore, it would have been obvious to one having at least ordinary skill in the art before the effective filing of the instant application to combine the learning features as taught by KIM with that of SINGH in order to classify each of the spectrograms into any or a combination of a normal spectrogram and an abnormal spectrogram, based on the comparison of the obtained feature vector with the predetermined set of feature vectors SINGH [Par. 0020]. With respect to claims 2 and 34, KIM and SINGH , combined teach the apparatus , wherein the apparatus comprises a plurality of downstream processing units configured to simultaneously process a plurality of downstream tasks using the first numerical vector as an input numerical vector, and wherein error signals from each of downstream task network output terminals are back- propagated and gathered at an end terminal of the encoder to train the encoder to improve universality in the first numerical vector [the control unit comprising one unit monitors using deep learning to analy z e data from the vital signs such as ECG/EKG , recognize pattern as the neural network ; recogniz e user habits or patterns ; compares the input user information with stored patterns to cha racterized treatment and generate warnings ; the neural network including back-propagation neural network statistical analyser be trained with training data where certain signals are determined to be undesirable or in error ) [ SINGH’s Par. 00 83-0084] . With respect to claim 3, KIM and SINGH , combined teach the apparatus, wherein the first numerical vector is concatenated with itself or with other structured data information to be used as the input numerical vector of the downstream processing unit (heart sounds captured and extracted using several signal-processing tools) [SINGH ‘s Par. 0085] . With respect to claim 4, KIM and SINGH , combined teach the apparatus, wherein the encoder is two or more, and wherein a plurality of first numerical vectors output from each encoder are concatenated to provide a single input numerical vector (neural network designed with a variety of connectivity patterns having recurrent or feedback (top-down) connections) {SINGH’s Par. 0095] . With respect to claim 5 , KIM and SINGH, combined teach the apparatus , wherein N sequential electrical biosignal data is passed through a single encoder to provide N sequential first numerical vectors (connectivity pattern information passed in layers where the output is passed to a successive layer) [SINGH’s Par. 0095] . With respect to claim 6, KIM and SINGH , combined teach the apparatus, wherein the apparatus provides an analysis, diagnosis, or prediction of a particular disease based on result values for each time point obtained by dividing the electrical biosignal data into certain time intervals and passing information from each divided data section through the encoder or the encoder and a downstream processing unit, or a weighted average for each time point of the corresponding result values for each time point (resolutions and frequency ranges used as inputs into the neural network to identify heart sound to obtain favorable results ) [Par. 0085] . With respect to claim 7, KIM and SINGH , combined teach the apparatus, wherein the apparatus fixes a network weight of the encoder in training a network of the downstream task and then modifies a network weight of the downstream task through the training, and further modifies an entire weight of the network of the encoder and the network of the downstream task through additional training ( multi-layered architectures trained one layer at a time and fine-tuned using back propagation where classification scores can be improved with sharper spectrograms and trained data be applied for accurate classification of heart sounds with intermediate spectrograms , i.e., CNN training database being serve d as a growing training database for re-training the CNN model for improved accuracy) [Par. 0079-0080 ; Par. 0092 ] . With respect to claim 8, KIM and SINGH , combined teach the apparatus, wherein each of the plurality of downstream processing units is performed by a multi-layer perceptron (MLP) having two or more fully connected layers (connectivity pattern information passed in layers where the output is passed to a successive layer) [SINGH’s Par. 0079; Par. 0095] . With respect to claim 9, KIM and SINGH , combined teach the apparatus, wherein the MLP is trained through a multi- task learning jointly with an encoding network training of the encoder, or is trained separately after the encoder is trained first (connectivity pattern information passed in layers where the output is passed to a successive layer ; the layered neural networks architectures where the output of a first layer of neurons becomes an input to a second layer of neurons, the output of a second layer of neurons becomes an input to a third layer of neurons, and so on ) [SINGH’s Par. 0095 ; Par. 0079 ] . With respect to claim 10, KIM and SINGH , combined teach the apparatus , wherein the MLP receives additional structured data input information that is different from the first numerical vector, wherein the additional structured data input information comprises at least one of age, gender, biosignals, numerical test results, natural language based structured data information converted into structured data through natural language processing, and structured data information converted from a different kind of biosignal data than the electrical biosignal data acquired from an acquisition unit, wherein the biosignals comprise one or more biosignals selected from a group consisting of blood pressure, pulse rate, body temperature, respiratory rate, and oxygen saturation, and wherein the additional structured data entry information is either concatenated with the first numerical vector or input separately from the first numerical vector ( statistical analys is of data trained determined to be undesirable for the patient including aga, weight , and physical condi tions ; analy z e data from the vital signs such as ECG/EKG , recognize pattern as the neural network ; recogniz e user habits or patterns ; compares the input user information with stored patterns to cha racterized treatment and generate warnings ; the neural network including back-propagation neural network statistical analyser be trained with training data where certain signals are determined to be undesirable ) [ SINGH’s Par. 00 83-0084] . With respect to claim 2 0, KIM and SINGH , combined teach the apparatus , wherein the electrical biosignal data is a signal of a single-channel or multi-channel, and wherein the electrical biosignal signals input to the encoder are in the form of a two- dimensional array of C x M (the number of each input lead (channel) X the number of measured values for each channel) [SINGH’s Par. 0069] . With respect to claim 22 , KIM and SINGH , combined teach the apparatus , further comprising: an analysis unit configured to analyze, predict, or provide diagnostic auxiliary information related to a disease or health using the first numerical vector ( c ollected data utilized for training the CNN trained model on the spectrogram dataset generated from heart sound training dataset for the control unit classify ing using a deep convolutional neural network (CNN) trained model spectrograms into any or a combination of the normal spectrogram and the abnormal spectrogram in detection of disease or disorders of the patient ) [SINGH’s Par. 0068; Par. 0078] . With respect to claim 23 , KIM and SINGH , combined teach the apparatus , wherein the analysis results include a disease prediction, wherein the electrical biosignal data is the electrical biosignal data of the single-channel, when the analysis unit predicts disease, and wherein the disease includes shock, respiratory failure, cardiac arrest, acute coronary syndrome, myocardial infarction, and hyperkalemia, when the electrical biosignal data is electrocardiogram (ECG) data (control unit classifying using a deep convolutional neural network (CNN) trained model spectrograms into any or a combination of the normal spectrogram and the abnormal spectrogram in detection of disease or disorders of the patient) [SINGH’s Par. 0078] ; ( KIM teaches cardiac condition discrimination unit for deriving a patient's heart disease prediction result based on the learned ECG feature information) . With respect to claim 24 , KIM and SINGH , combined teach the apparatus , wherein the analysis results comprises disease diagnostic auxiliary information to determine whether a disease has improved or worsened using the first numerical vector, wherein the electrical biosignal data is a plurality of electrical biosignal data measured at regular intervals when the analysis unit provides the disease diagnostic auxiliary information, and wherein each of the plurality of electrical biosignal data passes through a pooling layer of the encoder to provide the diagnostic auxiliary information (multi-layered architectures trained one layer at a time and fine-tuned using back propagation where classification scores can be improved with sharper spectrograms and trained data be applied for accurate classification of heart sounds with intermediate spectrograms , i.e., CNN training database being serve d as a growing training database for re-training the CNN model for improved accuracy) [ SINGH’s Par. 0079-0080 ; Par. 0092 ] . With respect to claim 27 , KIM and SINGH , combined teach the apparatus , wherein the network of the encoder is trained through a self- supervised learning on the basis of clinically defined morphological characteristics among characteristics of the electrical biosignal data ( obtain ing f eature d vector corresponding to the spectrograms using a deep convolutional neural network for compar ing the obtained feature vector with a predetermined set of feature vectors ) [SINGH’s Par. 0028-0029] . With respect to claim 2 8 , KIM and SINGH , combined teach the apparatus , wherein the network of encoder is trained by a self-supervised learning using electrical biosignal data transformed in a particular way as training data ( analy z e data from vital signs such as ECG/EKG , recognize pattern as the neural network ; recogniz e user habits or patterns ; compares the input user information with stored patterns to cha racterized treatment and generate warnings ; the neural network including back-propagation neural network statistical analyser be trained with training data where certain signals are determined to be undesirable ) [ SINGH’s Par. 00 83-0084] . With respect to claim 2 9 , KIM and SINGH , combined teach the apparatus , wherein the network of the encoder is trained by an unsupervised learning, using the augmented electrical biosignal data as training data, and wherein the encoder network comprises a process of inputting each of the augmented electrical biosignal data having the original electrical biosignal data in common to the encoder, and calibrating each of the calculated first numerical vectors to be identical or have a high degree of similarity ( propagation where classification scores can be improved with sharper spectrograms and trained data be applied for accurate classification of heart sounds with intermediate spectrograms , i.e., CNN training database being serve d as a growing training database for re-training the CNN model for improved accuracy) [ SINGH’s Par. 0079-0080 ; Par. 0092 ] . With respect to claim 30 , KIM and SINGH , combined teach the apparatus , wherein the process of calibrating each of the calculated first numerical vectors to be identical or have a high degree of similarity is to minimize a distance of each of the calculated first numerical vectors (resolutions and frequency ranges used as inputs into the neural network to identify heart sound to obtain favorable results) [Par. 0085] . With respect to claim 31 , KIM and SINGH , combined teach the apparatus , wherein the apparatus is concatenated with a smart watch, a medical device or exercise equipment equipped with an electrical biosignal measurement device or a device with a smartphone app or an electronic health record system ( train ed database being serve d as a growing training database for re-training the CNN model for improved accuracy and shared with patient terminal or with health provider ) [ KIM ] . With respect to claim 32 , KIM and SINGH , combined teach the apparatus , wherein the electrical biosignal is an electrocardiogram (ECG) ( analy z e data from vital signs such as ECG/EKG) [ SINGH’s Par. 00 83-0084] ; KIM teaches cardiac condition discrimination unit for deriving a patient's heart disease prediction result based on the learned ECG feature information) . Allowable Subject Matter Claims 11-19, 21, and 25-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. Conclusion The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20220101527 (WANG et al) teaching visualization method for evaluating brain addiction traits, an apparatus, and a computer-readable storage medium are provided to capture the change of blood oxygen neuronal activity. combining the biosignal with deep learning technology can be obtained from original data extracting the complex features . US 20210345934 A1 (LANDGRAF et al) teaching methods, devices, and systems for determining a state or condition of a subject. A method for determining a state or condition of a heart of a subject may include using a monitoring device comprising an electrocardiogram (ECG) sensor and an audio sensor to measure ECG data and audio data from an organ of the subject, and transmitting the ECG data and the audio data wirelessly to a computing device. A trained algorithm may be used to process the ECG data and the audio data to determine the state or condition of the organ of the subject. More specifically, the trained algorithm can be customized for a specific indication or condition. An output indicative of the state or condition of the heart of the subject may be provided on the computing device. US 20240065568 A1 (ANDREOU et al) teaching appliance for monitoring blood flow is provided. The appliance includes a plurality of spatially separated acousteomic sensors for auscultation detection of a patient; a hardware processor and a non-transitory computer-readable medium that stores a trained computer model for modeling a function of a healthy heart for analyzing the acousteomic signals; and a transmitter that transmits the acousteomic signals from the plurality of acousteomic sensors. US 20210000356 A1 (KIMBAHUNE et al) teaching Embodiments herein provide a system and method for screening and monitoring of cardiac diseases by analyzing acquired physiological signals , t he system synchronously captur ing physiological signals such as photo plethysmograph (PPG), phonocardiogram (PCG) and electrocardiogram (ECG) from subject(s) and builds an analytical model in the cloud for analyzing heart conditions from the captured physiological signals. Q. He, A. Maag and A. Elchouemi, "Heart disease monitoring and predicting by using machine learning based on IoT technology," 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia, 2020, pp. 1-10 . M. Gjoreski, M. Simjanoska, A. Gradišek, A. Peterlin, M. Gams and G. Poglajen, "Chronic Heart Failure Detection from Heart Sounds Using a Stack of Machine-Learning Classifiers," 2017 International Conference on Intelligent Environments (IE), Seoul, Korea (South), 2017, pp. 14-19 . R. Hettiarachchi et al., "A Novel Transfer Learning-Based Approach for Screening Pre-Existing Heart Diseases Using Synchronized ECG Signals and Heart Sounds," 2021 IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea, 2021, pp. 1-5 . J. Dastagir, F. A. Khan, M. S. Khan and K. N. Khan, "Computer-aided Phonocardiogram Classification using Multidomain Time and Frequency Features," 2021 International Conference on Artificial Intelligence (ICAI), Islamabad, Pakistan, 2021, pp. 50-55 . Con tact Informat ion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT PIERRE MICHEL BATAILLE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)272-4178 . 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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. /PIERRE MICHEL BATAILLE/ Primary Examiner, Art Unit 2138