Abstract: A method of clustering call stacks from a memory dumps resulting from out-of-memory errors includes accessing a memory dump resulting from an out-of-memory error; identifying call stacks in the memory dump that are associated with the out-of-memory error; accessing call stacks from one or more other memory dumps that were determined to be associated with other out-of-memory errors; generating clusters of call stacks based on a similarity score; and providing a cluster for an analysis of the out-of-memory error.

Abstract: This disclosure relates generally to human-machine interaction. In one embodiment, an interaction device for providing the interaction between the user and the ECA is disclosed. The interaction device comprises a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, causes the processor to receive conversation data of a user interacting with the ECA, wherein the ECA is presented on an interface of the interaction device. The processor further determines an emotional state of the user based on one or more behavioral parameters associated with the conversation data of the user. The processor identifies a response state for the ECA corresponding to the emotional state of the user, wherein the response state is identified from a plurality of response states based on a pre-defined probability for each response state. The processor further transitions behavior of the ECA based on the response state.

Abstract: This disclosure relates generally to human-machine interaction. In one embodiment, an interaction device for providing the interaction between the user and the ECA is disclosed. The interaction device comprises a processor and a memory communicatively coupled to the processor. The memory stores processor instructions, which, on execution, causes the processor to receive conversation data of a user interacting with the ECA, wherein the ECA is presented on an interface of the interaction device. The processor further determines an emotional state of the user based on one or more behavioral parameters associated with the conversation data of the user. The processor identifies a response state for the ECA corresponding to the emotional state of the user, wherein the response state is identified from a plurality of response states based on a pre-defined probability for each response state. The processor further transitions behavior of the ECA based on the response state.