Anumana, Inc.

Unisense Tech, Inc.

Anand Ramani

Rohit Jain

Karthik K Bharadwaj

Sanjeev Shrinivas Nadapurohit

Shiva Verma

Yogisha H J

Animesh Agarwal

Leon Ptaszek

Shashi Kant

Kappagantula Gopalakrishna Murty

Bharat Vijay

Yogisha Heggadahalli Jayendra

Karpagam Gobalakrishna

K N Amarnath

Amar K Ray

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.

Apparatus and method for dynamically determining a label for a cardiac potential signal via simulation of neural network trained data

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.

Apparatus and method for validating labeled data generated by a prediction generator

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.

Apparatus and method for generating clinical decision support

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.

Apparatus and methods for labeling time-series data using machine learning models

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.

Method and apparatus for predicting pulsed field ablation durability

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.

Growing community of inventors

Pleasanton, CA, United States of America

Anand Ramani