The present invention has given systems and methods for virtual recruitment of different candidates based on state model and, more specifically, candidate responses received during conversation session are recorded to create general pattern model that would be applied in the future session to give candidates a better personalized and immersive experience. The method comprises of providing a system-input to a user via at least one output unit of a system during a conversation session initiated by the user. The system-input being one of: information, question, and query. Receiving a user-input from the user in the conversation session from one or more one or more input devices coupled to the system. The input can be combination of audio, video, and text. After that, deduce the intent of the user-input. Then determining a response based at least in part on the query and the deduced intent and providing the response on the output unit within the conversation session.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving a user-input from a user in a conversation session from one or more input units coupled to a user-device, the user-input being a combination of audio, video, and text, and the conversation session being initiated by the user; deducing an intent of the user-input; determining a response based at least in part on the deduced intent; and providing the response on the output unit within the conversation session. 2. The method as claimed in claim 1, further comprises: providing a system-input to the user via at least one output unit of the user-device prior to receiving the user-input in the conversation session, the system-input being one of: information, question, and query. 3. The method as claimed in claim 2, wherein the system-input is selected from a script pre-associated with the conversation session, the script including a list of questions and mapped answers and actions. 4. The method as claimed in claim 2, wherein deducing the intent of the user-input comprises: analysing the user-input and the system-input to identify one or more parameters indicative of the intent; assigning a predetermined weightage to each of the one or more identified parameters; and deducing the intent based on an aggregation of the predetermined weightage of each of the one or more identified parameters. 5. The method as claimed in claim 4, wherein the one or more parameters include similarity of concept between the user-input and the system-input, sentiment of the user, emotion of the user, gesture of the user, tone of the user, body language of the user, expression of the user, code of conduct of the user, environmental factors, and a duration from said providing until receiving of the user-input from the user. 6. The method as claimed in claim 1, wherein the determination of the response is further based on the user-input, intent deduced from user-input corresponding to prior system-input within the conversation session, intent deduced from user-input corresponding system-input within a plurality of pre-stored conversation sessions, and the script. 7. The method as claimed in claim 6, wherein the determination of the response comprises: determining a current state in a state machine model based on the system-input, the state-machine model being pre-associated with the conversation session; and traversing the state-machine model based on the user-input, the deduced intent, and one or more pre-stored rules to determine next state. 8. The method as claimed in claim 7, wherein the next state comprises one of: a secondary question from the pre-stored script, the secondary question being one of a follow-on question and different question; a repetition of the system-input; a resolution related to a user-query; a follow-up message; a termination message; and a repetition of the conversation session. 9. The method as claimed in claim 1, wherein the conversation session is pre-associated with a plurality of context factors and a profile of the user. 10. The method as claimed in claim 9, further comprises, upon termination of the conversation session: selecting a set of conversation sessions from amongst a plurality of pre-stored conversation sessions based on the plurality of context factors and the profile of the user; identifying a repetitive pattern in the set of conversation sessions based at least in part on deduced intent of user-input in the set of conversation sessions and learned behaviour of users in the set of conversation sessions; and determining a pattern model, generic context factors, and a generic profile of user based on an analysis of repetitive pattern. 11. The method as claimed in claim 10, further comprising detecting an initiation of a new conversation session for a second user, the new conversation session being pre-associated with a plurality of context factors and a profile of the second user; determining if the plurality of context factors and the profile of the second user matches with the generic context factors and the generic profile; and applying the pattern model to the new conversation session based on the determination. 12. The method as claimed in claim 1, further comprises: recording and storing the conversation session; and enabling a search for the stored conversation by user. 13. The method as claimed in claim 1, further comprises: generating a feedback report upon termination of the conversation session, said report based on intent deduced from user-input received during the conversation session. 14. A system comprising: a receiving unit to receive a user-input from a user in a conversation session from one or more input units coupled to a user-device, the input being a combination of audio, video, and text, and the conversation session being initiated by the user; an intent analysis unit to deduce an intent of the user-input; a response analysis unit to determine a response based at least in part on the user-input and the deduced intent; and the transmitting unit to provide the response within the conversation session to the at least one output unit. 15. The system as claimed in claim 14, further comprises a transmitting unit to provide a system-input to the user, prior to receiving the user-input, via at least one output unit of the user-device in the conversation session, the system-input being one of: information, question, and query. 16. The system as claimed in claim 14, wherein the intent analysis unit to deduce the intent further: analyses the user-input and the system-input to identify one or more parameters indicative of the intent; assigns a predetermined weightage to each of the one or more identified parameters; and deduces the intent based on an aggregation of the predetermined weightage of each of the one or more identified parameters. 17. The system as claimed in claim 15, wherein the one or more parameters include similarity of concept between the user-input and the system-input, sentiment of the user, emotion of the user, gesture of the user, tone of the user, body language of the user, expression of the user, code of conduct of the user, environmental factors, and a duration from said providing until receiving of the user-input from the user. 18. The system as claimed in claim 14, wherein the determination of the response is further based on the user-input, intent deduced from user-input corresponding prior system-input within the conversation session, intent deduced from user-input corresponding system-input within a plurality of pre-stored conversation sessions, and a script pre-associated with the conversation session. 19. The system as claimed in claim 17, wherein the response analysis unit to determine the response: determines a current state in a state machine model based on the system-input, the state-machine model being pre-associated with the conversation session; and traverses the state-machine model based on the user-input, the deduced intent, and one or more pre-stored rules to determine next state. 20. The system as claimed in claim 14, wherein the conversation session is pre-associated with a plurality of context factors and a profile of the user. 21. The system as claimed in claim 19, further comprises a pattern generation unit, wherein the pattern generation unit upon termination of the conversation session: selects a set of conversation sessions from amongst a plurality of pre-stored conversation sessions in a storing unit based on the plurality of context factors and the profile of the user; identifies a repetitive pattern in the set of conversation sessions based at least in part on deduced intent of user-input in the set of conversation sessions and learned behaviour of users in the set of conversation sessions; and determines a pattern model, generic context factors, a generic profile of user based on an analysis of repetitive pattern. 22. The method as claimed in claim 20, wherein the pattern generation unit further detects an initiation of a new conversation session for a second user, the new conversation session being pre-associated with a plurality of context factors and a profile of the second user; determines if the plurality of context factors and the profile of the second user matches with the generic context factors and the generic profile; and applies the pattern model to the new conversation session based on the determination. 23. The system as claimed in claim 14, further comprises: a recording unit to record and store the conversation session in a storage unit; and a searching unit to enable a search of the stored conversation session in the storage unit. 24. The system as claimed in claim 14, further comprises: a feedback generating unit to generate a feedback report upon termination of the conversation session, said report based on intent deduced from user-input received during the conversation session. 25. The system as claimed in claim 14, wherein the at least one output unit comprises: display unit and audio output unit. 26. The system as claimed in claim 14, wherein the one or more input units include high-definition video camera, microphone, touch-based display unit, non-touch based display unit, sensor, wearable device, keyboard, stylus, and mouse.