Optum Technology, Inc.

Optum, Inc.

V Kishore Ayyadevara

Vineet Shukla

Sree Harsha Ankem

Gregory J Boss

Ninad D Sathaye

Kartik Chaudhary

Raghav Bali

Santosh Kumar Jami

Snigdha Sree Borra

Shyam Charan Mallena

Aditya Madhuranthakam

Yeshwanth Reddy

Yerraguntla Yeshwanth Reddy

Rohan Khilnani

Nilav Baran Ghosh

Ankit Varshney

Swaroop S Shekar

Fritz T Wilhelm

Vinod Burugupalli

Joseph C Cremaldi

Rajat Gupta

Ravi Kumar Raju Gottumukkala

Saikumar Chintareddy

Ranraj Rana Singh

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing intervention recommendation operations. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform intervention recommendations by using at least one of reinforcement learning machine learning models and event scoring machine learning models.

Reinforcement learning machine learning models for intervention recommendation

There is a need for more effective and efficient anomalous text detection. This need can be addressed by, for example, solutions for anomalous text detection that include the steps of performing a group of exploration-exploitation keyword extraction iterations based at least in part on one or more training corpus data entries until a per-iteration keyword list for an ultimate exploration-exploitation keyword extraction iteration satisfies a keyword list threshold condition; and subsequent to performing the exploration-exploitation keyword extraction iterations: processing one or more input corpus data entries using the language-model-based binary classification model to generate one or more inferred anomaly probabilities, processing the one or more input corpus data entries using the keyword model to generate explanatory metadata for the one or more inferred anomaly probabilities, and performing one or more prediction-based actions based at least in part on the one or more inferred anomaly probabilities and the explanatory metadata.

Anomalous text detection and entity identification using exploration-exploitation and pre-trained language models

There is a need for more effective and efficient predictive document conversion. This need can be addressed by, for example, solutions for performing document conversion using a trained convolutional neural document conversion machine learning. In one example, the trained convolutional neural document conversion machine learning model is associated with a preprocessing block having a plurality of preprocessing subblocks, one or more main processing blocks each having a plurality of main processing subblocks, and a plurality of postprocessing subblocks each having one or more postprocessing subblocks, and the trained convolutional neural document conversion machine learning model is further associated with a preprocessing subblock repetition count hyper-parameter that defines a preprocessing subblock count of the plurality of preprocessing subblocks.

Predictive document conversion

Apparatuses, systems, and methods for more accurate remote monitoring of a user's body to stabilize the user during fall events and to thereby prevent the user from falling. In some embodiments, a wearable device comprising a power source, one or more sensors configured to monitor a user's COG (COG), at least one plurality of electrodes, a communications interface and a control device is provided. The wearable device is configured to apply electrical pulses according to defined electrical pulse stimulation protocols via the electrodes to target muscle groups of the user's body, causing those target muscle groups to contract and thereby stabilize the user's body during a fall event.

Systems, apparatuses and methods for stabilizing a user's body during detected fall events

There is a need for more effective and efficient prediction data analysis. This need can be addressed by, for example, solutions for performing first-occurrence multi-disease prediction. In one example, a method includes determining a per-event-type loss value for each event type of a group of event types; determining a cross-event-type loss value based at least in part on each per-event-type loss value; training a multi-event-type prediction model based at least in part on the cross-event type loss value; generating a first-occurrence prediction based at least in part on the multi-event-type prediction model, wherein the first occurrence-prediction comprises a first-occurrence prediction item for each event type of the group of event types; and performing one or more prediction-based actions based at least in part on the first-occurrence prediction.

Weighted adaptive filtering based loss function to predict the first occurrence of multiple events in a single shot

Solutions for more efficient and effective optical character recognition with respect to an input text segment are disclosed. In one example, a method includes processing an input text image using a deep character overlap detection machine learning model in order to generate a character map for the input text image, an overlap map for the input text image, and an affinity map for the input text image; generating an overlap-aware word boundary recognition output based at least in part on the character map, the overlap map, and the affinity map, wherein the overlap-aware word boundary recognition output describes one or more inferred word regions of the input text image; and performing one or more prediction-based actions based at least in part on the overlap-aware word boundary recognition output.

Overlap-aware optical character recognition

Systems and methods are configured to extract text from images (e.g., document images) utilizing a combination of optical character recognition processes and neural network-based analysis of various images to train a machine-learning object recognition model that is configured to identify text within images based on object-comparisons between known and unknown text within images. In certain embodiments, identified text within images may be utilized to identify corresponding regions-of-interest for extraction of image data encompassing a portion of an image that may be indexed based at least in part on text identified as corresponding to the particular region-of-interest.

Automated systems and methods for identifying fields and regions of interest within a document image

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Hyderabad Telangana, India

V Kishore Ayyadevara