Abstract: Software for securely storing digital content using a distributed ledger. The software performs the following operations: (i) receiving identification information of an owner of a digital asset, metadata of the digital asset, a digital hash of the digital asset, and the digital asset; (ii) verifying authenticity of the owner based, at least in part, the received identification information; (iii) in response to verifying the authenticity of the owner, creating a watermarked version of the digital asset based on the identification information of the owner, the metadata of the digital asset, and the digital hash; and (iv) storing the digital asset and the watermarked version of the digital asset in a first distributed ledger.

Abstract: Software for preventing fraud in digital content licensing and distribution using a distributed ledger technology. The software performs the following operations: (i) receiving a request for a license of a digital asset, wherein a record of the digital asset is stored in a first distributed ledger; (ii) verifying a consensus for the request of the license of the digital asset; and (iii) responsive to verifying the consensus for the request of the license of the digital asset: storing a transaction settlement record in a second distributed ledger, creating a sharded copy of the digital asset including a plurality of shards of the digital asset, and storing at least one shard of the sharded copy of the digital asset in the second distributed ledger with sharding instructions for reconstructing the digital asset from the sharded copy.

Abstract: Machine logic for: receiving data set (for example, a website) that includes a plurality of links, with the links respectively identifying logical locations of data stored on a set of server computer(s); and making a deep copy of the servable data set on the set of server computer(s), with the deep copying including: copying each link of the plurality of links of the servable data set from a respective source logical location to a respective target location, and adding each copied link of the plurality of links as a record in a mapping table. The mapping table can help to automatically update/replace links wherever they are found in related system artifacts. If the resolution can't be automatically determined then the system suggests possible target links for manual inspection/updates based on the likelihood of correctness, artifact types, and user roles.

Abstract: Software for preventing fraud in digital content licensing and distribution using a distributed ledger technology. The software performs the following operations: (i) receiving a request for a license of a digital asset, wherein a record of the digital asset is stored in a first distributed ledger; (ii) verifying a consensus for the request of the license of the digital asset; and (iii) responsive to verifying the consensus for the request of the license of the digital asset: storing a transaction settlement record in a second distributed ledger, creating a sharded copy of the digital asset including a plurality of shards of the digital asset, and storing at least one shard of the sharded copy of the digital asset in the second distributed ledger with sharding instructions for reconstructing the digital asset from the sharded copy.

Abstract: Software for securely storing digital content using a distributed ledger. The software performs the following operations: (i) receiving identification information of an owner of a digital asset, metadata of the digital asset, a digital hash of the digital asset, and the digital asset; (ii) verifying authenticity of the owner based, at least in part, the received identification information; (iii) in response to verifying the authenticity of the owner, creating a watermarked version of the digital asset based on the identification information of the owner, the metadata of the digital asset, and the digital hash; and (iv) storing the digital asset and the watermarked version of the digital asset in a first distributed ledger.

Abstract: An energy-usage management system continuously monitors energy usage of energy-consuming Internet of Things devices by streaming time-stamped usage data from sensors embedded into the devices. The system infers from the streamed data time-dependent usage patterns for each device. Further meaning is ascribed to these patterns by interpreting them in light of contextual information retrieved from external data sources. The system then predicts each device's future energy usage as a function of the previous-usage patterns and further refines those predictions to account for environmental changes that will occur if the energy consumer moves to a new location. The system computes total energy costs for the devices for each candidate energy supplier or service plan at the consumer's final location, selects an optimal candidate that offers the lowest cost, and automatically switches the devices' energy source to the optimal candidate.

Abstract: Machine logic for: receiving data set (for example, a website) that includes a plurality of links, with the links respectively identifying logical locations of data stored on a set of server computer(s); and making a deep copy of the servable data set on the set of server computer(s), with the deep copying including: copying each link of the plurality of links of the servable data set from a respective source logical location to a respective target location, and adding each copied link of the plurality of links as a record in a mapping table. The mapping table can help to automatically update/replace links wherever they are found in related system artifacts. If the resolution can't be automatically determined then the system suggests possible target links for manual inspection/updates based on the likelihood of correctness, artifact types, and user roles.