Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: Systems and methods for predictively responding to real-time sensor data from an computer-augmented environment are provided. A system may receive sensor data from the computer-augmented environment. The system may obtain a feature model, a prediction model, and a filter criteria. The system may derive, based on the feature model and the sensor data, a first event. The system may determine the first event is associated with an authorized event identifier in the filter criteria. The system may forecast, based on the authorized event identifier and the prediction model that a second event occurs after the first event. The system may transmit an action message to the computer-augmented environment, the action message indicative of the second event. Updates to the feature model, the prediction model, and/or the filter criteria may occur in batch using machine learning and statistical analytics while the real-time platform predictively responds to the sensor data.

Abstract: A device obtains an image and generates, based on the image and using an image generation model, a set of candidate images for object recognition. The device determines, based on the set of candidate images and using an image evaluation model, that one or more candidate images, of the set of candidate images, are processed during image generation and using the image generation model to improve a resolution of the image. The device determines image embeddings for the one or more candidate images and based on the image, and determines an embeddings error for the one or more candidate images based on the image embeddings and the image. The device determines an identification of a feature of the image based on the image embeddings and the embeddings error, and performs an action relating to the identification of the feature based on determining the identification of the feature.