Abstract: An innovative method is implemented to determine a robocall and blocks the incoming communication deemed to be a robocall. The method leverages blockchain's shared storage, memory, and ability to transact all information across a network and independently verified and stored on the immutable blockchain. The method takes advantage high-speed cellular network to process each communication with high-speed. Further, the method integrates blockchain encryption, swarm intelligence (SI), artificial intelligence (AI) and machine learning (ML) algorithms, a telecommunication expert knowledge graph (TEKG), and real-time parsing of records to block robocalls and reduce connection delays. All modules can evolve and update themselves with each use of the present invention through various SI, AI, and ML technologies.

Abstract: In some embodiments, a method receives a message that identifies an asset being viewed on a video service at a timestamp. The message is associated with a profile identifier and a plurality of messages are received for the profile identifier while using the video service. The method determines a channel identifier for a channel in which the asset is offered during the timestamp and determines whether a channel change occurred based on the channel identifier and a prior channel identifier determined from a prior message in the plurality of messages. When the channel change has occurred, the method stores the channel identifier and timestamp for the profile identifier in a data structure, wherein a list of channels is generated by querying the data structure to determine a set of channel identifiers and timestamps for the profile identifier.

Abstract: In one embodiment, a method receives a first bundle and a second bundle for a video based on metadata for the video. The method stores the second bundle with a dependency on the first bundle when the second bundle is associated with a first set of control data parameters found in the first bundle that control a first aspect of access to the video. The second bundle is stored with a second set of control data parameters to control a second aspect of access to the video. When the first bundle is selected, the method accesses the first set of control data parameters for the first bundle. When the second bundle is selected, the method accesses the first set of control data parameters in the first bundle via the dependency to the first bundle and the second set of control data parameters for the second bundle.

Abstract: In some embodiments, a method receives a message that identifies an asset being viewed on a video service at a timestamp. The message is associated with a profile identifier and a plurality of messages are received for the profile identifier while using the video service. The method determines a channel identifier for a channel in which the asset is offered during the timestamp and determines whether a channel change occurred based on the channel identifier and a prior channel identifier determined from a prior message in the plurality of messages. When the channel change has occurred, the method stores the channel identifier and timestamp for the profile identifier in a data structure, wherein a list of channels is generated by querying the data structure to determine a set of channel identifiers and timestamps for the profile identifier.

Abstract: An innovative method is implemented to determine a robocall and blocks the incoming communication deemed to be a robocall. The method leverages blockchain's shared storage, memory, and ability to transact all information across a network and independently verified and stored on the immutable blockchain. The method takes advantage high-speed cellular network to process each communication with high-speed. Further, the method integrates blockchain encryption, swarm intelligence (SI), artificial intelligence (AI) and machine learning (ML) algorithms, a telecommunication expert knowledge graph (TEKG), and real-time parsing of records to block robocalls and reduce connection delays. All modules can evolve and update themselves with each use of the present invention through various SI, AI, and ML technologies.

Abstract: In one embodiment, a method receives a first bundle and a second bundle for a video based on metadata for the video. The method stores the second bundle with a dependency on the first bundle when the second bundle is associated with a first set of control data parameters found in the first bundle that control a first aspect of access to the video. The second bundle is stored with a second set of control data parameters to control a second aspect of access to the video. When the first bundle is selected, the method accesses the first set of control data parameters for the first bundle. When the second bundle is selected, the method accesses the first set of control data parameters in the first bundle via the dependency to the first bundle and the second set of control data parameters for the second bundle.

Abstract: In one embodiment, a method receives information for a video entity where a video delivery system delivers the video entity as a plurality of video assets using different video delivery methods. The method generates bundles for the video assets. Also, dependencies are generated between bundles when two bundles include control data parameters that are the same. The method then stores control data parameters for the bundles for the plurality of video assets based on the dependencies. A second bundle is stored with the dependency on a first bundle and the second bundle includes a second set of control data parameters for the second bundle that is different from the first set of control data parameters included in the first bundle. When the second bundle is selected, the method accesses the first set of control data parameters and the second set of control data parameters for the second bundle.

Abstract: In one embodiment, during execution of a current workload being processed by the kernel, the method searches policy storage for a similar workload that has been previously optimized, wherein the sensor is used to compare the current workload to workloads in the policy storage. When the similar workload is found, the method optimizes different parameter values in a parameter value range specified in the policy specification for the parameter while the current workload is being processed by the kernel, wherein a parameter setting for the similar workload is used in the optimizing. When the similar workload is not found, the method optimizes different parameter values based on the parameter value range specified in the policy specification for the parameter while the current workload is being processed by the kernel. The method then evaluates the optimizing based on the optimization target.

Abstract: In one embodiment, a method receives information for a video entity where a video delivery system delivers the video entity as a plurality of video assets using different video delivery methods. The method generates bundles for the video assets. Also, dependencies are generated between bundles when two bundles include control data parameters that are the same. The method then stores control data parameters for the bundles for the plurality of video assets based on the dependencies. A second bundle is stored with the dependency on a first bundle and the second bundle includes a second set of control data parameters for the second bundle that is different from the first set of control data parameters included in the first bundle. When the second bundle is selected, the method accesses the first set of control data parameters and the second set of control data parameters for the second bundle.

Abstract: In one embodiment, during execution of a current workload being processed by the kernel, the method searches policy storage for a similar workload that has been previously optimized, wherein the sensor is used to compare the current workload to workloads in the policy storage. When the similar workload is found, the method optimizes different parameter values in a parameter value range specified in the policy specification for the parameter while the current workload is being processed by the kernel, wherein a parameter setting for the similar workload is used in the optimizing. When the similar workload is not found, the method optimizes different parameter values based on the parameter value range specified in the policy specification for the parameter while the current workload is being processed by the kernel. The method then evaluates the optimizing based on the optimization target.