Abstract: A method for tokenizing assets via a blockchain-to-blockchain bridge may include: receiving a first instruction from a client to mint first tokens for a client asset; encumbering the client asset on a custody ledger; creating a balance of the first tokens on a first distributed ledger; receiving a second instruction to lock and mint the first tokens to a second distributed ledger as second tokens; receiving verification from a custodian that the first tokens are locked on a lock ledger for the custodian; instructing a blockchain bridge computer program to mint second tokens to a client address on the second distributed ledger; receiving a third instruction to return the second tokens to the first distributed ledger; executing a public burn of the second tokens on the second distributed ledger; and instructing the custodian to unlock the first tokens and move the first tokens to the first distributed ledger.

Abstract: A system is disclosed that can have a first device and a second device. The first device can have a tube, a deflectable section, and a camera. The second device can have a first tube. A stabilizer can be advanceable from the first tube. The second device can have a second tube. A third tube can be advanceable from the second tube. The first device can be an endoscope. The first device can be attachable to the second device via a connector.

Abstract: User information may be used to create a training exercise representing simulated package delivery. The user information can include metrics corresponding to physical package delivery. The user information may be used as part of a predictive model to determine the content of the training exercise, including the type and number of tasks comprising the exercise. Once created, the training exercise can be presented to a user as a graphical simulation. The presentation can occur in response to one or more triggering conditions.

Abstract: Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives an image showing a drop-off of an item, the item associated with a delivery to a delivery location. The computer system inputs the image to a first artificial intelligence (AI) model. The computer system receives first data comprising an indication of whether the drop-off is correct from the first AI model. The computer system causes a presentation of the indication at a device associated with the delivery of the item to the delivery location.

Abstract: Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives image data showing a portion of a delivery location. The computer system determines an artificial intelligence (AI) model associated with the delivery location. The computer system inputs the image data to the AI model. The computer system receives an indication of whether the portion corresponds to a correct drop-off location and causes a presentation about the indication to be provided at a device.