Abstract: An apparatus for labeling time-series data using machine learning models, comprising a processor and a memory containing instructions configuring the processor to receive time-series data, identify a plurality of time-series segments from received time-series data, pre-train at least a classifier using labeled training data, annotate, at a labeling module, each time-series segment of the plurality of time-series segments with at least one segment identification, retrain the at least a classifier using the annotated plurality of time-series segments, generate, using the at least a classifier, one or more segment identifications at each time-series segment subsequently identified based on continuous time-series data, and display a visual representation of the continuous time-series data with the segment identifications on a user interface.

Abstract: An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Abstract: An apparatus and method for validating, using user input, labeled data generated by a prediction generator. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The processor receives at least a potential signal, generates, processed data using the at least a potential signal, trains a prediction generator on a plurality of labeled training data, wherein the plurality of labeled training data comprises the processed data associated with at least an annotation, generates, using the prediction generator, a plurality of labeled data as a function of dynamic data, displays the plurality of labeled data to a user interface of a graphical user interface, receives a user input associated with the plurality of labeled data, and retrains the prediction generator using the user input.

Abstract: A system for visualization of cardiac signals including at least a processor configured to receive electrocardiogram (ECG) signal data including at least a cardiac signal, label the ECG signal data as a function of an ECG machine learning model, wherein training the ECG machine learning model includes receiving a plurality of de-identified medical data from a medical database, generating ECG training data as a function of the plurality of de-identified medical data, wherein the ECG training data includes the plurality of de-identified medical data correlated to a plurality of signal labels, training the ECG machine learning model as a function of the ECG training data and labeling the ECG signal data as a function of the trained ECG machine learning model, and generate a visualization output as function of the labeled ECG signal data.

Abstract: An apparatus for labeling a plurality of time series data is disclosed. The apparatus includes at least processor and a memory communicatively connected to the processor. The memory instructs the processor to receive a plurality of time series data. The memory instructs the processor to generate a plurality of time series segments for each time series represented within the plurality of time series data. The memory instructs the processor to identify one or more segment attributes for each time series segment. The memory instructs the processor to classify each time series segment of the plurality of time series segments to at least one time series label as a function of the one or more segment attributes. The memory instructs the processor to generate at least one labeled time series segment for each time series segment of the plurality of time series segments as a function of the classification.

Abstract: An apparatus and method for determining a label dynamically using a potential signal. The apparatus includes a memory and at least a processor communicatively connected to the memory, wherein the memory contains instructions configuring the at least a processor to receive a user input comprising a labeled datum associated with the at least a potential signal, receive a plurality of stored data associated with the at least a potential signal, generate, using the at least a processor, a plurality of canonicalized data by processing the plurality of stored data, transmit, using a real time data simulator, the plurality of canonicalized data to a simulation module, generate, using the simulation module, a labeled prediction corresponding to a segmented datum.

Abstract: An apparatus and method for generating clinical decision support is disclosed. The apparatus includes at least a processor and a computer-readable storage medium communicatively connected to the at least a processor, wherein the computer-readable storage medium contains instructions configuring the at least processor to receive user data, generate a fused feature vector correlating the user data to a plurality of clinical outcomes by training a plurality of deep neural networks (DNNs) to output a first set of feature vectors, a second set of feature vectors and a third set of feature vectors, fusing the first, second, and third set of features vectors to form the fused feature vector, generate a procedural output using the fused feature vector, and display the procedural output through a user interface.

Abstract: Apparatus for correcting machine learning model predictions and methods used therein include a processor and a memory connected to the processor, wherein the memory contains instructions configuring the processor to receive a cardiac signal having a plurality of segments, generate, for at least a segment of the plurality of segments, a label representing at least a signal feature, generate at least an automated annotation as a function of the label using an annotation machine learning model, generate, using a correction module, at least a correction upon detecting an absence of annotations, receive, using a user interface, an input from a user, create a user annotation within the cardiac signal using the input, update the cardiac signal and the annotation machine learning model as a function of the correction and the user annotation, and display, using the user interface, the updated cardiac signal to the user.

Abstract: An apparatus for labeling time-series data using machine learning models, comprising a processor and a memory containing instructions configuring the processor to receive time-series data, identify a plurality of time-series segments from received time-series data, pre-train at least a classifier using labeled training data, annotate, at a labeling module, each time-series segment of the plurality of time-series segments with at least one segment identification, retrain the at least a classifier using the annotated plurality of time-series segments, generate, using the at least a classifier, one or more segment identifications at each time-series segment subsequently identified based on continuous time-series data, and display a visual representation of the continuous time-series data with the segment identifications on a user interface.

Abstract: Described herein is an apparatus and method for predicting Pulsed Field Ablation (PFA) durability. An apparatus may include at least a processor; and a memory communicatively connected to the at least processor, wherein the memory contains instructions configuring the at least processor to receive a training dataset comprising a plurality of example PFA device parameters correlated to a plurality of example PFA outcomes; train a PFA durability machine learning model using the training dataset; receive a PFA device parameter; and generate a PFA durability datum as a function of the PFA device parameter using a trained PFA durability machine learning model.

Abstract: Described herein is a method by which more than one person is enabled to actively participate in the process of finalizing a real estate property either for purchase or rent. Each deciding party is enabled to create a custom style profile capturing their individual preferences at an attribute level by providing both visual and verbal feedback. A Collective Preferences Profile (CPP) is created by integrating the multiple style profiles of the chosen deciding parties. The generated CPP is then utilized to curate the different houses available and to surface those houses that are most likely to fit the aesthetic and requirements of the combined audience. The CPP evolves on an ongoing basis by active solicitation of feedback on properties viewed or waitlisted to accommodate changing preferences and provide the most suited recommendation at any time.

Abstract: The present invention relates to 3D brain organoids, uses thereof, methods and culture medium for generating such organoids. An aspect of the invention provides brain organoids and methods of generating such organoids with bilaterally symmetric optic vesicles, containing both neuronal and non-neuronal cell types, and exhibiting functional circuitry. These organoids can be generated within short time intervals (e.g., 50 days) and therefore are useful for medical modelling and applications.

Abstract: Described herein is a method and system for collective shopping wherein one or more than one user is involved in the decision making process of an online purchase. The style profile is built for each individual contributor by exposing them to a set of product images and analysing their inputs about their preferences. The analysis is performed at an attribute level to understand each style aesthetic in a deep and meaningful manner. A backend algorithm runs on the attribute level feedback given by each contributor to calculate their combined style performance at an attribute and product level. This input is taken and run across the annotated online catalogue to surface products that fit the style aesthetic of the combined audience.

Abstract: Described herein is a method by which more than one person is enabled to actively participate in the process of finalizing a real estate property either for purchase or rent. Each deciding party is enabled to create a custom style profile capturing their individual preferences at an attribute level by providing both visual and verbal feedback. A Collective Preferences Profile (CPP) is created by integrating the multiple style profiles of the chosen deciding parties. The generated CPP is then utilized to curate the different houses available and to surface those houses that are most likely to fit the aesthetic and requirements of the combined audience. The CPP evolves on an ongoing basis by active solicitation of feedback on properties viewed or waitlisted to accommodate changing preferences and provide the most suited recommendation at any time.

Abstract: Described herein is a method for evaluating the performance gap of a proposed personalization solution versus a default solution without the need for integration. The method comprises utilizing the historical data, the data including a sample of engagement and transactions of a specific audience, and catalog feed; simulating the user actions in two environments, the environments being the proposed solution and the default solution; comparing the product exposure data between the two environments, and generating a report analyzing the number of sessions with transactions and/or engagement in each environment.

Abstract: A method for generating recommendations for a user from a product database is provided. The method comprises determining a taxonomy for the product database, said taxonomy comprising a plurality of attributes assigned to each product in the product database; performing an exploratory procedure in which the user is systematically exposed to products from the product database in order to test the user's preference for products in the product database, wherein the user preference is tested based on a user action; and generating a style profile for the user based on the user's indicated preferences.

Abstract: A control system for operating a beam pumping unit includes a strain gauge and a beam pumping unit controller. The strain gauge is coupled to a Samson post of the beam pumping unit, and is configured to measure a Samson post strain. The beam pumping unit controller is coupled to the strain gauge and is configured to operate the beam pumping unit to induce a variable load on a rod of the beam pumping unit. The beam pumping unit controller is further configured to receive the Samson post strain from the strain gauge and compute the variable load based on the Samson post strain.

Abstract: A method for generating recommendations for a user from a product database is provided. The method comprises determining a taxonomy for the product database, said taxonomy comprising a plurality of attributes assigned to each product in the product database; performing an exploratory procedure in which the user is systematically exposed to products from the product database in order to test the user's preference for products in the product database, wherein the user preference is tested based on a user action; and generating a style profile for the user based on the user's indicated preferences.

Abstract: A method for generating recommendations for a user from a product database is provided. The method comprises determining a taxonomy for the product database, said taxonomy comprising a plurality of attributes assigned to each product in the product database; performing an exploratory procedure in which the user is systematically exposed to products from the product database in order to test the user's preference for products in the product database; and generating a style profile for the user based on the user's indicated preferences.

Abstract: Techniques are provided to allow users to access interested information without performing engaging searches. A content-providing system may track collective interests of a large set of users and/or individual interests of a user. User-selectable items may be presented to the user directly without the user performing searches. Some of the user-selectable items may be associated with slideshows that are, for example, pre-deposited or curated to provide interesting information relating to these user-selectable items. The user may easily access a slideshow and the content therein using easily comprehensible controls presented on a display page.