Abstract: The present application relates to devices and components including apparatus, systems, and methods for reporting random access configuration information in wireless networks.

Abstract: Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

Abstract: This disclosure relates generally to data meta model and meta file generation for feature engineering and training of machine learning models thereof. Conventional methods do not facilitate appropriate relevant data identification for feature engineering and also do not implement standardization for use of solution across domains. Embodiments of the present disclosure provide systems and methods wherein datasets from various sources/domains are utilized for meta file generation that is based on mapping of the dataset with a data meta model based on the domains, the meta file comprises meta data and information pertaining to action(s) being performed. Further functions are generated using the meta file and the functions are assigned to corresponding data characterized in the meta file. Further functions are invoked to generate feature vector set and machine learning model(s) are trained using the features vector set. Implementation of the generated data meta-model enables re-using of feature engineering code.

Abstract: Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s).

Abstract: Embodiments of the present disclosure describe methods and apparatuses for UE capability reporting in mobile communication systems. UE capability reporting in LTE with high number of CCs (32) creates a great signalling overhead. An enhanced network enquiry message (UECapabilityEnquiry) is proposed which includes indications (e.g. maximum aggregated CC number or bandwidth class, MIMO/CSI-process capabilities) that indicate to a UE that the UE is to exclude, from its capability report (UECapabilityInformation), information related to functions that the network either does not support or is not interested in. The enhanced network enquiry message may include indications of a max. number of DL/UL CCs for which the UE is requested to provide supported CA band combinations and non-CA bands. The UE selects a subset of CA capabilities based on these indicator and generates a UE capability response (UECapabilityInformation) including information related to the selected subset of CA capabilities.

Abstract: Business to Consumer (B2C) systems face a challenge of engaging users since offers are created using static rules generated using clustering on large transactional data generated over a period of time. Moreover, the offer creation and assignment engine is disjoint to the transactional system which led to significant gap between history used to create offers and current activity of users. Systems and methods of the present disclosure provide a meta-model based configurable auto-tunable recommendation model generated by ensembling optimized machine learning and deep learning models to predict a user's likelihood to take an offer and deployed in real time. Furthermore, the offer given to the user is based on a current context derived from the user's recent behavior that makes the offer relevant and increases probability of conversion of the offer to a sale. The system achieves low recommendation latency and scalable high throughput by virtue of the architecture used.

Abstract: Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s).

Abstract: Embodiments of the present disclosure describe methods and apparatuses for UE capability reporting in mobile communication systems. UE capability reporting in LTE with high number of CCs (32) creates a great signalling overhead. An enhanced network enquiry message (UECapabilityEnquiry) is proposed which includes indications (e.g. maximum aggregated CC number or bandwidth class, MIMO/CSI-process capabilities) that indicate to a UE that the UE is to exclude, from its capability report (UECapabilityInformation), information related to functions that the network either does not support or is not interested in. The enhanced network enquiry message may include indications of a max. number of DL/UL CCs for which the UE is requested to provide supported CA band combinations and non-CA bands. The UE selects a subset of CA capabilities based on these indicator and generates a UE capability response (UECapabilityInformation) including information related to the selected subset of CA capabilities.