## 3 Methodology
### MIRA System Architecture
Moreover, MIRA checks the validity of a corresponding session upon receiving an input request. In the case of timeout (idle for 60 minutes), the renewal request is forwarded to the session manager. The dialogue manager is responsible for scenario understanding, state tracking and managing the flow of the conversation. This module identifies the conversational context from the natural language input and generates an appropriate response. It may be possible that the user starts another conversation without terminating or concluding the previous one. This type of conversation handling is not in the scope of this study. Moreover, the dialogue manager consists of six sub-modules. (i) The story data are used to train the dialogue management model. A story is the representation of a complete dialogue between the user and virtual assistant. We designed the story data manually from the recorded dialogue corpora that facilitate MIRA to make the conversation real and natural. (ii) The state tracking is the core module of MIRA that predicts the user goal (represented by slot-value pairs) at every dialogue turn. It maintains the conversation state, performs an action based on policy, and generates a relevant response after analyzing the natural language input. (iii) The dialogue templates consist of predefined statements that can be used by filling in the keyword. Although we trained a model to understand conversation and response generation, some statements are similar and common except for the keyword. Consider the statements 'Do you feel hungry?' and 'Do you feel tired?'. Both sentences are similar except for the keywords 'hungry' and 'tired'. To improve the performance and response generation of MIRA, we used templates for these kinds of statements that have similar semantics. (iv) The chief complaint data is the knowledge source that helps identify the conversation context. Based on the identified context, MIRA analyzes the dialogue corpora and asks a follow-up question. (v) The medical history consists of the health record that a user provided during registration. It also stores each recommendation along with the key attributes (sign and symptom) that results from the conversation between MIRA and the user. Keeping these health records helps the MIRA to generate a personalized decision for future conversations. (vi) The response formulation has a challenging role in the interaction because it generates a relevant response based on the input query. Therefore, this module takes the necessary information from different sub-modules of the dialogue manager and generates an appropriate text-based statement. The text to speech synthesis analyzes and processes the text-based statement using natural language processing. Then, it converts the processed text into synthesized speech using digital signal processing and conveys it to the end-user in a polite female voice. MIRA deals with healthcare data and directs the user to a nearby appropriate medical specialist based on the chief complaints. This kind of dialogue contains sensitive information and its leakage may lead to serious consequences such as a masquerading and ransomware attacks. The security event logging and monitoring module continuously monitors the communication channels for anomalies. Also, it collects the information, which can be used as an audit trail for intrusion prevention and event management. With the proposed system architecture, MIRA understands single and multiple intent statements, supports adaptability, and provides data control.