Abstract: A method, computer system, and a computer program product for product safety is provided. The present invention may include mapping one or more products within a smart container by identifying each of the one or more products and determining a location for each of the one or more products. The present invention may include determining a safety level for each of the one or more products. The present invention may include determining a recommended location to store a new product within the smart container based on a safety confidence score, wherein the safety confidence score is determined based on at least the safety score of the one or more products within the smart container, the safety score of the new product, and a machine learning model. The present invention may include displaying the recommended location to a user.

Abstract: Provided are a computer program product, system, and method for using a predictive model to determine virtual usages of storage spaces in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical boundaries and usages of the storage spaces and a predictive model used to determine storage spaces for items to store in the storage spaces. The predictive model receives as inputs physical boundaries and usages of a selected storage space. The predictive model processes the inputs to output a virtual usage of the selected storage space within the physical boundaries of the selected storage space. The virtual usage is defined by coordinates in the physical boundaries of the selected storage space. Augmented reality representation overlaying the virtual usage at the coordinates within a view of the selected storage space are generated in a computer display.

Abstract: Provided are a computer program product, system, and method for using a predictive model to determine virtual usages of storage spaces in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical boundaries and usages of the storage spaces and a predictive model used to determine storage spaces for items to store in the storage spaces. The predictive model receives as inputs physical boundaries and usages of a selected storage space. The predictive model processes the inputs to output a virtual usage of the selected storage space within the physical boundaries of the selected storage space. The virtual usage is defined by coordinates in the physical boundaries of the selected storage space. Augmented reality representation overlaying the virtual usage at the coordinates within a view of the selected storage space are generated in a computer display.

Abstract: Provided are a computer program product, system, and method for using a predictive machine learning to determine storage spaces to store items in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical configurations of the storage spaces, usages of the storage spaces, items placed in the storage spaces, and a predictive model. The predictive model receives as inputs, attributes of a target item to add to the storage spaces, the physical configuration of the storage spaces, attributes of the storage spaces, and the usages of the storage spaces. The predictive model processes the inputs to output an available storage space to optimize storage of the target item. An augmented reality representation of information on storage of the target item with respect to the available storage space is generated on a computer display.

Abstract: A computer system provides patient monitoring and treatment using electronic textiles. Biometric data pertaining to a user is received that is obtained over time from an electronic textile device associated with a body of the user. The biometric data is analyzed to identify positions and movements of the body. The identified positions and movements are analyzed to generate a knowledge corpus for the user that maps one or more of: positions, movements, and portions of the body to a health condition of the user. A treatment plan is generated, based on the knowledge corpus, that indicates a relatedness between the health condition of the user and the one or more positions, movements, and portions of the body. Embodiments of the present invention further include a method and program product for providing patient monitoring and treatment in substantially the same manner described above.

Abstract: A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.

Abstract: A processor may establish a virtual collaboration between two or more users. The processor may determine whether there is an interference with the virtual collaboration. The processor may maintain the virtual collaboration. The processor may generate an interference report associated with the interference. The processor may provide the interference report to at least one of the two or more users.

Abstract: A method, computer system, and a computer program product for product safety is provided. The present invention may include mapping one or more products within a smart container by identifying each of the one or more products and determining a location for each of the one or more products. The present invention may include determining a safety level for each of the one or more products. The present invention may include determining a recommended location to store a new product within the smart container based on a safety confidence score, wherein the safety confidence score is determined based on at least the safety score of the one or more products within the smart container, the safety score of the new product, and a machine learning model. The present invention may include displaying the recommended location to a user.

Abstract: A method, computing system, and computer program product are provided. Items at a source location are detected and classified, with respect to fragility and perishability, based on characteristics of the each respective item and is performed by trained machine learning models. Item boundaries are predicted based on applying respective data regarding points on a surface of the each respective item to a trained second machine learning model to predict the item boundaries. The each respective item is classified into a respective group with respect to an available volume of the cargo space based on sensor data of the cargo space, the classified fragility and perishability, the predicted item boundaries, and a third machine learning model. An arrangement of the items in the cargo space is determined based on the group classifications and a corresponding destination location associated with the each respective item and is visualized relative to the cargo space.