Abstract: Apparatuses, systems, and methods for SoR list management and PLMN selection based on an SoR list. A UE may receive an SoR container which may include a list of OPLMNs. The UE may store a prior list of OPLMNs stored on a USIM of the UE on a memory of the UE. The UE replace the prior list of OPLMNs stored on the USIM with the list of OPLMNs included in the SoR container. The UE may consider the prior list of OPLMNs as a lower priority list as compared to the list of OPLMNs stored on the USIM. Additionally, in response to determining to move from a current PLMN, the UE may scan for a new PLMN based on the list of OPLMNs stored on the USIM.

Abstract: Apparatuses, systems, and methods for SoR list management and PLMN selection based on an SoR list. A UE may receive an SoR container which may include a list of OPLMNs. The UE may store a prior list of OPLMNs stored on a USIM of the UE on a memory of the UE. The UE replace the prior list of OPLMNs stored on the USIM with the list of OPLMNs included in the SoR container. The UE may consider the prior list of OPLMNs as a lower priority list as compared to the list of OPLMNs stored on the USIM. Additionally, in response to determining to move from a current PLMN, the UE may scan for a new PLMN based on the list of OPLMNs stored on the USIM.

Abstract: Apparatuses, systems, and methods for SoR list management and PLMN selection based on an SoR list. A UE may receive an SoR container which may include a list of OPLMNs. The UE may store a prior list of OPLMNs stored on a USIM of the UE on a memory of the UE. The UE replace the prior list of OPLMNs stored on the USIM with the list of OPLMNs included in the SoR container. The UE may consider the prior list of OPLMNs as a lower priority list as compared to the list of OPLMNs stored on the USIM. Additionally, in response to determining to move from a current PLMN, the UE may scan for a new PLMN based on the list of OPLMNs stored on the USIM.

Abstract: A natural language understanding system that can determine an overall score for a natural language hypothesis using hypothesis-specific component scores from different aspects of NLU processing as well as context data describing the context surrounding the utterance corresponding to the natural language hypotheses. The individual component scores may be input into a feature vector at a location corresponding to a type of a device captured by the utterance. Other locations in the feature vector corresponding to other device types may be populated with zero values. The feature vector may also be populated with other values represent other context data. The feature vector may then be multiplied by a weight vector comprising trained weights corresponding to the feature vector positions to determine a new overall score for each hypothesis, where the overall score incorporates the impact of the context data. Natural language hypotheses can be ranked using their respective new overall scores.

Abstract: A dock device connects participating devices such as a tablet device and an audio activated device, allowing them to operate as a single device. These participating devices may be associated with different accounts, each account being associated with particular “speechlets” or data processing functions. A natural language understanding (NLU) system uses NLU models to process text obtained from an automatic speech recognition (ASR) system to determine a set of possible intents. A second set of possible intents may then be generated that is limited to those possible intents that correspond to the speechlets associated with the docked device. The intents within the second set of possible intents are ranked, and the highest ranked intent may be deemed to be the intent of the user. Command data corresponding to the highest ranked intent may be generated and used to perform the action associated with that intent.