Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.

Abstract: A system for utilizing digital forensics, artificial intelligence, and machine learning models to prevent suicidal behavior is disclosed. In particular, the system extracts data from devices associated with individuals that have died by suicide. The system applies intelligence techniques to collect additional data from third-party data aggregators and to collect online identities associated with the deceased individuals. The system may process the data from the devices, the additional data, and the online identities into datasets to be combined into a data lake. By utilizing artificial intelligence and/or machine learning algorithms on the data lake, the system generates a model associated with predictive behavior and patterns correlated with suicide. The system identifies individuals having characteristics and/or behaviors correlated with the model, and initiates services to reduce the risk for suicide for such individuals. The effectiveness of such services may also be determined by the system.

Abstract: A disclosed system for transporting items to destination locations, for example automatically placing stacked products at selected locations within a large retail facility, includes a conveyor assembly, an item identifier, a robotic arm with a plurality of end effectors, and a sorter component operable to select one of the plurality of end effectors and remove items selected for diversion from the conveyor assembly with the robotic arm. The sorter component stacks the removed items, and the stacked items are transported to a selected location within the retail facility, for example using an autonomous ground vehicle (AGV).

Abstract: A disclosed system for transport asset monitoring, for example monitoring truck trailer unloading progress at large retail locations, includes an artificial intelligence (AI) solution for managing operations at a facility. The AI solution analyzes current load percentage and other data to predict availability for moving the transport asset and ability to accept a new incoming transport asset. Predictions of availability can reduce response times, resulting in higher utilization rates for assets, thereby improving efficiency. An exemplary system includes a sensor configured to sense operation progress parameter data for a transport asset; and logic to receive the operation progress parameter data from the sensor; determine, using the AI solution and based at least on the operation progress parameter data, a predicted milestone parameter; and report the predicted milestone parameter to a remote node.