Abstract: A method comprising: obtaining a graph comprising a plurality of nodes and a plurality of edges, wherein: the nodes include behavior nodes that correspond to respective behavior reasons in a plurality of predefined behavior reasons, the edges correspond to transitions between the nodes and are associated with weights; obtaining one or more interaction records, wherein the one or more interaction records are records of interactions of representatives of an organization with a current client; applying a trained machine learning (ML) model to the one or more interaction records to identify one or more behavior reasons associated with the current client; generating a behavior vector based on the one or more behavior reasons associated with the current client; and performing a communication action based on the graph and the behavior vector.

Abstract: Present disclosure generally relates to energy saving technology, and more particularly relates to systems and methods for saving energy using r-Apps (112) and x-Apps (114B) in an Open Radio Access Network (O-RAN). The user (128) via first computing device (124) may input initial energy saving policies to “energy saving” r-Apps (112) which creates policies to start measurements in NR capacity booster cells and candidate cell. The r-Apps acts on various inputs and may decide to move NR booster cell into energy saving mode. Once all the mobile devices move to other cells, the NR capacity cell may move to energy saving mode. The r-Apps decide to de-activate energy saving mode. The Service Management and Orchestration (SMO) device (108) de-activate energy saving mode on capacity booster cell using O1 interface.

Abstract: Data associated with a user is input into a reinforcement learning model. The reinforcement learning model generates a target message that satisfies a target response level of the user. The target message is transmitted to a computing device for presentation to the user. The reinforcement learning model is trained by: predicting a first message for a first type of user; determining, based on training data, that the first message will not satisfy the target response level; obtaining, using a predefined reward function, rewards based on the determination; and iteratively updating parameters of the reinforcement learning model until a second message is predicted for the first type of user that will satisfy the target response level to maximize the rewards.

Abstract: A system, apparatus and method for access control in industrial environment is provided. The method includes generating, by processing unit, a first digital certificate for first entity, which includes information about authenticity of first entity and set of operational values of first entity. Next, receiving request from second entity of industrial environment to access first entity, which request includs a set of operational requirements of second entity. Next, generating second digital certificate comprising first set of access rights.

Abstract: Present disclosure generally relates to energy saving technology, and more particularly relates to systems and methods for saving energy using r-Apps (112) and x-Apps (114B) in an Open Radio Access Network (O-RAN). The user (128) via first computing device (124) may input initial energy saving policies to “energy saving” r-Apps (112) which creates policies to start measurements in NR capacity booster cells and candidate cell. The r-Apps acts on various inputs and may decide to move NR booster cell into energy saving mode. Once all the mobile devices move to other cells, the NR capacity cell may move to energy saving mode. The r-Apps decide to de-activate energy saving mode. The Service Management and Orchestration (SMO) device (108) de-activate energy saving mode on capacity booster cell using O1 interface.