Abstract: A computer device may receive multiple images of instances of a label and timestamps or identifiers of the images, where the instances of the label are associated with a printer. Then, the computer may divide the images into subgroups based at least in part on the timestamps or the identifiers and/or differences between the images, and may train a predictive model using the subgroups and information specifying the printer. For a given subgroup, the predictive model may be associated with a predictive signature. Moreover, the predictive model may have a given image of a given instance of the label as an input, and may provide an identity or an identifier of the given subgroup associated with the given image and the printer as an output. Note that the predictive model may be used to activate and/or authenticate another instance of the label.

Abstract: A computer that generates a product tag for a product is described. During operation, the computer may obtain information specifying multiple document locations associated with the product based at least in part on different environmental conditions of the product. Then, the computer may generate the product tag (or additional information specifying the product tag), where the product tag includes location information specifying the document locations. Moreover, given location information is associated with a given functional ink or is associated with a given state of a circuit in the product tag that is responsive to a given environmental condition. Furthermore, the environmental conditions for different functional inks or different states of the circuit are different, such that, at a given time, the product tag presents location information for a given one of the document locations. Next, the computer may provide the additional information specifying the product tag to the electronic device.

Abstract: A computer system is described. This computer system may include: a network interface that communicates with an electronic device (which may be remotely located from the computer system); a processor; and memory that stores program instructions. During operation, the computer system may generate, in a decentralized manner, a globally unique product identifier for a product based at least in part on a smart contract. For example, the smart contract may be based at least in part on a blockchain. Moreover, the globally unique product identifier may be globally authenticated or may be capable of global authentication. Then, the computer system may associate the globally unique product identifier, one-to-one or many-to-one, with a non-fungible token (NFT) based at least in part on the smart contract. Next, the computer system may provide the globally unique product identifier intended for an electronic device associated with a recipient or a client.