Patent Document

CROSS-REFERENCE TO RELATED APPLICATIONS 
       [0001]    This U.S. non-provisional patent application claims priority under 35 U.S.C. §119 of Korean Patent Application No. 10-2016-0034779, filed on Mar. 23, 2016, the entire contents of which are hereby incorporated by reference. 
       BACKGROUND 
       [0002]    The present disclosure herein relates to an interaction device capable of performing an interaction with a human, and more particularly, to an interaction device capable of performing an interaction with a plurality of participants. 
         [0003]    Techniques for interaction between human and robot have been studied. Human-robot interaction technology refers to a technology that allows a robot to communicate and collaborate with humans by determining a user&#39;s intent and performing appropriate responses and behaviors. One of the important techniques in the interaction between robot and human is turn-taking technology. Turn taking means exchanging a sequence of interactions such as conversation between participants in an interaction. 
         [0004]    However, conventional technologies for interaction between human and robot, including the turn taking technology, control interaction based on one-to-one interaction between human and robot. Therefore, it is difficult to perform natural interaction when a robot interacts with two or more humans. 
       SUMMARY 
       [0005]    The present disclosure provides an interactive device and method capable of performing a natural interaction with at least two participants. 
         [0006]    An embodiment of the inventive concept provides a interaction device including: a role classifying unit configured to classify a role for each of a plurality of participants based on an external stimulus signal for each of the plurality of participants; and an action adjusting unit configured to perform different interaction operations for each of the plurality of participants based on the role for each of the plurality of participants. 
         [0007]    In an embodiment, the role classifying unit may include: a stimulus analyzing unit configured to analyze external stimulus information on each of the plurality of participants; a participation degree calculating unit configured to calculate a participation degree value for each of the plurality of participants based on the external stimulus information; and a role determining unit configured to determine a role for each of the plurality of participants based on the participation degree value of each of the plurality of participants. 
         [0008]    In an embodiment of the inventive concept, an interaction method includes: receiving external stimulus information on each of a plurality of participants from a multi-modal sensor unit; determining a participation action state of each of the plurality of participants based on the external stimulus information on each of the plurality of participants; determining a role of each of the plurality of participants based on the external stimulus information on each of the plurality of participants; and performing different interaction operations for the plurality of participants based on at least one of a participation action state of each of the plurality of participants or a role of each of the plurality of participants. 
         [0009]    In an embodiment, the determining of the participation action state of each of the plurality of participants and the determining of the role of each of the plurality of participants may be performed in parallel. 
     
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         [0010]    The accompanying drawings are included to provide a further understanding of the inventive concept, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the inventive concept and, together with the description, serve to explain principles of the inventive concept. In the drawings: 
           [0011]      FIG. 1  is a view illustrating a multi-party interaction system according to an embodiment of the inventive concept; 
           [0012]      FIG. 2  is a block diagram illustrating an interaction device according to the technical idea of the present application; 
           [0013]      FIG. 3  is a block diagram illustrating a configuration of an action state classifying unit of  FIG. 2  in detail; 
           [0014]      FIG. 4  is a block diagram illustrating a configuration of a role classifying unit of  FIG. 2  in detail; 
           [0015]      FIG. 5  is a flowchart illustrating an operation of an interaction device of  FIG. 2 ; 
           [0016]      FIG. 6  is a flowchart illustrating an operation of a role classifying unit of  FIG. 4 ; 
           [0017]      FIG. 7  is a view illustrating an interaction device according to another embodiment of the technical ideal of the inventive concept; and 
           [0018]      FIG. 8  is a view illustrating an interaction device according to another embodiment of the technical ideal of the inventive concept. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    Hereinafter, the technical idea of the present application will be described in more detail with reference to the accompanying drawings so that those skilled in the art easily understand the technical idea of the present application. 
         [0020]      FIG. 1  is a view illustrating a multi-party interaction system according to an embodiment of the inventive concept. Referring to  FIG. 1 , the multi-party interaction system includes a robot  10 , an active participant  20 , an addressee  30 , a side-participant  40 , and a bystander  50 . 
         [0021]    The robot  10  performs an interaction operation with a plurality of participants. For natural interaction with a plurality of participants, the robot  10  mounts an interaction device according to an embodiment of the technical idea of the present application, which will be described below with reference to  FIGS. 2 to 8 . 
         [0022]    The interaction device mounted on the robot  10  may classify the roles of a plurality of participants according to a participation degree and/or a participation action state. 
         [0023]    For example, the interaction device mounted on the robot  10  may classify a plurality of participants in the order of the active participant  20 , the addressee  30 , the side-participant  40  and the bystander  50  according to the size order of a participation degree value corresponding to each participant. 
         [0024]    As another example, the interaction device mounted on the robot  10  classifies a participant with the highest participation degree among a plurality of participants as the active participant  20 , classifies a participant in a participation action state having a high participation degree among the plurality of participants but waiting for an interaction order as the addressee  30 , classifies a participant with a relatively low participation degree among the plurality of participants as the side-participant  40 , and classifies a participant with the lowest participation degree among the plurality of participants as the bystander  50 . 
         [0025]    The interaction device mounted on the robot  10  may control the robot  10  to provide a customized interaction for each participant according to the classified roles. 
         [0026]    For example, the interaction device may control the flow of turn taking so that the interaction is progressed based on the active participant  20  and the addressee  30 . In addition, the interaction device may alter the subject of conversation or create a question to induce the side-participant  40  and the bystander  50  to participate in the interaction. 
         [0027]    In such a way, an interaction device according to the technical idea of the present application and the robot  10  equipped with the interaction device may perform natural interaction operations by classifying the roles of a plurality of participants according to a participation degree and/or a participation action state and providing customized interactions for each participant according to the classified roles. 
         [0028]    Moreover, it should be understood that the above description is exemplary and that the technical idea of the present application is not limited thereto. For example, it is assumed in  FIG. 1  that the number of participants interacting with a robot is four. However, this is exemplary, and an interaction device according to the technical idea of the present application and a robot equipped with the interaction device may perform an interaction operation with two or three participants and also may perform an interaction operation with five or more participants. 
         [0029]      FIG. 2  is a block diagram illustrating an interaction device  100  according to the technical idea of the present application. Referring to  FIG. 2 , the interaction device  100  includes a multi-modal sensor unit  110 , a participation action state classifying unit  120 , a role classifying unit  130 , an action adjusting unit  140 , and a control unit  150 . 
         [0030]    The multi-modal sensor unit  110  senses an external stimulus for each of a plurality of participants and provides the sensed external stimulus to the participation action state classifying unit  120  and the role classifying unit  130 . The multi-modal sensor unit  110  includes a voice sensing module  111 , a gaze sensing module  112 , a movement sensing module  113 , a head direction sensing module  114 , and a touch sensing module  115 . 
         [0031]    The voice sensing module  111  senses the voice of each of a plurality of participants. The voice sensing module  111 , for example, may be implemented using a voice recognition sensor connected to a microphone or a micro-microphone. The voice sensing module  111  provides voice information on each of a plurality of participants to the participation action state classifying unit  120  and the role classifying unit  130 . 
         [0032]    The gaze sensing module  112  senses the gaze direction of each of a plurality of participants. The movement sensing module  113  senses movements such as hand movements, gestures, body postures, and the like for each of a plurality of participants. The head direction sensing module  114  senses to which direction the head direction of each of the plurality of participants points with respect to a robot. 
         [0033]    Each of the gaze sensing module  112 , the movement sensing module  113 , and the head direction sensing module  114  may be implemented by a camera, a 3D Depth camera, or they may be implemented integrally by a single camera or a single 3D Depth camera. Each of the gaze sensing module  112 , the movement sensing module  113 , and the head direction sensing module  114  provides sensed gaze direction information, movement information, and head direction information to the participation action state classifying unit  120  and the role classifying unit  130 . 
         [0034]    The touch sensing module  115  senses whether each of a plurality of participants directly touches the interaction device  100  or a robot with the interaction device  100 . The touch sensing module  115 , for example, may be implemented using a tactile sensor. The touch sensing module  115  provides touch information on each of a plurality of participants to the participation action state classifying unit  120  and the role classifying unit  130 . 
         [0035]    The participation action state classifying unit  120  receives external stimulus information on each of a plurality of participants from the multi-modal sensor unit  110 . For example, the participation action state classifying unit  120  receives voice information, gaze direction information, movement information, head direction information, and touch information on each of a plurality of participants from the multi-modal sensor unit  110 . 
         [0036]    The participation action state classifying unit  120  determines the participation action state of each of a plurality of participants based on the received external stimulus information. The participation action state classifying unit  120  may classify the participation action state of each of a plurality of participants into one of a Grab state, a Release state, a Wait state, and a Keep state. An operation and configuration of the participation action state classifying unit  120  will be described in more detail with reference to  FIG. 3 . 
         [0037]    The role classifying unit  130  receives external stimulus information on each of a plurality of participants from the multi-modal sensor unit  110 . For example, the role classifying unit  130  receives voice information, gaze direction information, movement information, head direction information, and touch information on each of a plurality of participants from the multi-modal sensor unit  110 . 
         [0038]    The role classifying unit  130  determines the role of each of a plurality of participants based on the received external stimulus information. For example, the role classifying unit  130  may classify the role of each of a plurality of participants into one of an active participant, an addressee, a side-participant, and a bystander. A configuration and operation of the role classifying unit  130  will be described in more detail with reference to  FIG. 4 . 
         [0039]    The action adjusting unit  140  receives participation action state information on each of a plurality of participants from the participation action state classifying unit  120  and receives role information on each of a plurality of participants from the role classifying unit  120 . Based on the received participation action state information and role information, the action adjusting unit  140  controls the interaction device  100  or a robot equipped with the interaction device  100  to perform a customized interaction operation with respect to each of a plurality of participants. 
         [0040]    For example, when receiving four participation action state information (e.g., Grab state, Release state, Wait state, and Keep state) from the participation action state classifying unit  120  and receiving four role information (e.g., active participant, addressee, side-participant, and bystander) from the role classifying unit  130 , the action adjusting unit  140  may provide various different customized interaction operations using the participation action state information and the role information. 
         [0041]    For example, if any one of a plurality of participants corresponds to a Grab state and an addressee, the participation action state classifying unit  120  may control the interactive device  100  so that a turn taking operation which changes the dialogue order of a corresponding participant is performed. However, this is exemplary and may be applied variously according to a designer. For example, an interaction expression method and/or content of a customized interaction type and a robot&#39;s gaze, voice, gesture, and operation with respect to each participant may be variously changed according to a designer. 
         [0042]    Moreover, the control unit  150  is connected to the multi-modal sensor unit  110 , the participation action state classifying unit  120 , the role classifying unit  130 , and the action adjusting unit  140  and controls an overall operation of the interaction device  100 . 
         [0043]      FIG. 3  is a block diagram illustrating a configuration of the participation action state classifying unit of  FIG. 2  in detail. Referring to  FIG. 3 , the participation action state classifying unit  120  includes a clue generating unit  121  and a participation action state determining unit  122 . 
         [0044]    The clue generating unit  121  receives external stimulus information on each of a plurality of participants from the multi-modal sensor  110 , and generates a state determination clue for each of a plurality of participants using the external stimulus information. The clue generating unit  121  includes a voice clue module  121 _ 1 , a gaze clue module  121 _ 2 , a movement clue module  121 _ 3 , and a touch clue module  121 _ 4 . 
         [0045]    The voice clue module  121 _ 1  receives voice information from the voice sensing module  111 , and generates a voice state determination clue based on the received voice information. The voice state determination clue may include, for example, utterance, temporary pause (i.e., pause) of utterance, high intonation, and flat intonation. 
         [0046]    The gaze clue module  121 _ 2  receives gaze direction information from the gaze sensing module  112 , and generates a voice state determination clue based on the received gaze direction information. The gaze state determination clue, for example, may include information (i.e., eye contact) on whether a participant is looking at the interaction device  100  or a robot equipped with the interaction device  100 . 
         [0047]    The movement clue module  121 _ 3  receives movement information from the movement sensing module  113 , and generates a movement state determination clue based on the received movement information. The movement state determination clue may include, for example, information on whether there is a beck or a gesture, and information on the posture of a body. 
         [0048]    The touch clue module  121 _ 4  receives touch information from the touch module  114  and generates a touch state determination clue based on the received touch information. The touch state determination clue, for example, may include information on whether a participant has a clear touch to the interactive device  100  or a robot equipped with the interaction device  100 . 
         [0049]    The participation action state determining unit  122  receives a state determination clue such as a voice state determination clue, a gaze state determination clue, a movement state determination clue, and a touch state determination clue for each of a plurality of participants from the clue generating unit  121 , and determines a participation action state for each of a plurality of participants using the state determination clue. The participation action state may include, for example, a Grab state, a Release state, a Wait state, and a Keep state. 
         [0050]    The Grab state means a state to fetch the order of interaction. For example, when the state determination clues of a specific participant include clues such as utterance, eye contact, and touch, the participation action state determining unit  122  may determine the participation action state of a corresponding participant as the Grab state. 
         [0051]    The Release state means a state in which the order of interaction is to be handed over to another participant. For example, when the state determination clues of a specific participant include clues such as a pause, no beck, or no gesture, the participation action state determining unit  122  may determine the participation action state of a corresponding participant as the Release state. 
         [0052]    The Wait state means a state to wait for interaction order. For example, when the state determination clues of a specific participant include a voice state determination clue to agree with the opponent&#39;s speech, the participation action state determining unit  122  may determine the participation action state of a corresponding participant as the Wait state. 
         [0053]    The Keep state means a state having the order of interaction. For example, when the state determination clues of a specific participant include clues such as a high intonation, a constant intonation, a beck, or a gesture, the participation action state determining unit  122  may determine the participation action state of a corresponding participant as the Keep state. 
         [0054]    Meanwhile, the participation action state determining unit  122  provides the determined participation action state information on each of a plurality of participants to the action adjusting unit  140 , and the action adjusting unit  140  uses the provided participation action state information on each of the plurality of participants to support a customized interaction operation. 
         [0055]      FIG. 4  is a block diagram illustrating a configuration of the role classifying unit  130  of  FIG. 2  in detail. Referring to  FIG. 4 , the role classifying unit  130  includes a stimulus analyzing unit  131 , a participation degree calculating unit  132 , and a role determining unit  133 . 
         [0056]    The stimulus analyzing unit  131  receives external stimulus information on each of a plurality of participants from the multi-modal sensor  110 , and generates a participation degree factor for each of a plurality of participants using the external stimulus information. The stimulus analyzing unit  131  includes a voice analyzing module  131 _ 1 , a gaze analyzing module  131 _ 2 , a movement analyzing module  131 _ 3 , and a head direction analyzing module  131 _ 4 . 
         [0057]    The voice analyzing module  131 _ 1  receives voice information from the voice sensing module  111 , and generates a voice participation degree factor based on the received voice information. The voice participation degree factor may include, for example, a state of speaking, a state of no speaking for a certain period of time, or a state of no speaking beyond a certain period of time. 
         [0058]    The gaze analyzing module  131 _ 2  receives gaze direction information from the gaze sensing module  112 , and generates a gaze participation degree factor based on the received gaze information. The gaze participation degree factor, for example, may include information on whether a participant is looking at the interaction device  100  or a robot equipped with the interaction device  100  (i.e., eye contact). 
         [0059]    The movement analyzing module  131 _ 3  receives movement information from the movement sensing module  113 , and generates a movement participation degree factor based on the received movement information. The movement participation degree factor may include, for example, information on whether there is a beck or a gesture, or information on the posture of a body. 
         [0060]    The head direction analyzing module  131 _ 4  receives head direction information from the touch module  114 , and generates a head direction participation degree factor based on the received head direction information. The head direction participation degree factor, for example, may include information on whether the head of a participant is facing upward, downward, leftward or rightward with respect to a robot. 
         [0061]    The participation degree calculating unit  132  receives a voice participation degree factor, a gaze participation degree factor, a movement participation degree factor, and a head direction participation degree factor for each of a plurality of participants from the stimulus analyzing unit  131 , and determines a participation degree for each of the plurality of participants using these factors. 
         [0062]    More specifically, the participation degree of a particular participant may be expressed as P d (i). The voice participation degree factor, the gaze participation degree factor, the movement participation degree factor, the head direction participation degree factor, and the participation action state factor, each of which is a factor to determine the participation degree P d (i), may be expressed as V s (i), G s (i), M s (i), H p (i), and A s (i), respectively. The participation degree of a particular participant P d (i) may be determined by a set of the voice participation degree factor V s (i), the gaze participation degree factor G s (i), the movement participation degree factor M s (i), the head direction participation degree factor H s (i), and the participation action state factor A s (i). 
         [0063]    Specifically, for example, if a particular participant is in a talking state, this positively affects the voice participation degree factor V s (i) and the participation degree P d (i). On the other hand, if a particular participant belongs to a state without speech for a certain period of time or beyond a certain period of time, this negatively affects the voice participation degree factor V s (i) and the participation degree P d (i). 
         [0064]    If the gaze of a particular participant is directed to the interaction device  100  or a robot equipped with the interaction device  100 , this positively affects the gaze participation degree factor G s (i) and the participation degree P d (i). On the other hand, if the gaze of a particular participant is not directed to the interaction device  100  or a robot equipped with the interaction device  100 , this negatively affects the gaze participation degree factor G s (i) and the participation degree P d (i). 
         [0065]    If a particular participant&#39;s beck or gesture is sensed, this positively affects the movement participation degree factor M s (i) and the participation degree P d (i). On the other hand, if a particular participant&#39;s beck or gesture is not sensed, this negatively affects the movement participation degree factor M s (i) and the participation degree P d (i). 
         [0066]    If the head direction of a particular participant is directed to the front based on the interaction device  100  or a robot equipped with the interaction device  100 , this positively affects the head direction participation degree factor H s (i)and the participation degree P d (i). On the other hand, if the head direction of a particular participant is directed to another direction based on the interaction device  100  or a robot equipped with the interaction device  100 , this negatively affects the head direction participation degree factor H s (i) and the participation degree P d (i). 
         [0067]    The calculation method of the participation degree P d (i) may be expressed as Equation 1 below. 
         [0000]    
       
         
           
             
               
                 
                   
                     
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                   = 
                   
                     
                       
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                           1 
                         
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                        
                       
                           
                       
                        
                       
                         
                           W 
                           j 
                         
                         · 
                         
                           
                             F 
                             s 
                           
                            
                           
                             ( 
                             i 
                             ) 
                           
                         
                       
                     
                     N 
                   
                 
               
               
                 
                   [ 
                   
                     Equation 
                      
                     
                         
                     
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                     1 
                   
                   ] 
                 
               
             
           
         
       
     
         [0068]    Herein, P d (X) represents a participation degree value for a participant X. F s (i) represents a set of the voice participation degree factor V s (i), the gaze participation degree factor G s (i), the movement participation degree factor M s (i), the head direction participation degree factor H s (i), and the participation action state factor A s (i). Because F s (i) uses five characteristic values, n=5. In addition, a weight for each participation degree factor may be used in the calculation of the participation degree P d (i). The weight may be expressed by W i  as shown in Equation 1. 
         [0069]    Referring to  FIG. 4 , the role determining unit  133  receives a participation degree value for each of a plurality of participants from the participation degree calculating unit  132 . The role determining unit  130  may determine the role of each of a plurality of participants as one of an active participant, an addressee, a side-participant, and a bystander based on the received participation degree value. 
         [0070]    For example, the role determining unit  133  may classify a participant having the highest participation degree value among a plurality of participants as an active participant. In this case, the role determining unit  133  may determine an active participant according to an Equation 2 below. 
         [0000]        P   a ( X )=Max{ P   d (1), P   d (2), P   d (3) . . .  P   d ( k )}  [Equation 2]
 
         [0071]    Here, P d ( 1 ) and P d ( 2 ) indicate a first participant and a second participant, respectively. P a (X) indicates an active participant, and k indicates the number of participants. 
         [0072]    As another example, the role determining unit  133  may classify a plurality of participants as the order of an addressee, a side-participant, and a bystander according to the size order of a participation degree value. However, this is exemplary and it should be understood that the role determining unit  133  may classify the participants&#39; roles in various ways based on a participation degree value for each of a plurality of participants. 
         [0073]    Meanwhile, the role determining unit  133  provides the determined role information on each of a plurality of participants to the action adjusting unit  140  (see  FIG. 2 ), and the action adjusting unit  140  uses the provided role information on each of the plurality of participants together with the participation action state information to support a customized interaction operation. 
         [0074]      FIG. 5  is a flowchart illustrating an operation of the interaction device  100  of  FIG. 2 . 
         [0075]    In operation S 110 , the multi-modal sensor unit  110  senses an external stimulus for each of a plurality of participants and provides the sensed external stimulus to the participation action state classifying unit  120  and the role classifying unit  130 . For example, the multi-modal sensor unit  110  provides voice information, gaze direction information, movement information, head direction information, and/or touch information on each of a plurality of participants to the participation action state classifying unit  120  and the role classifying unit  130 . 
         [0076]    In operation S 120 , the participation action state classifying unit  120  determines the participation action state of each of a plurality of participants based on the received external stimulus information and the role classifying unit  130  determines the role of each of a plurality of participants based on the received external stimulus information. In this case, the participation action state classifying unit  120  and the role classifying unit  130  may perform a participation action state determining operation and a role determining operation in parallel. 
         [0077]    For example, the participation action state classifying unit  120  may classify the participation action state of each of a plurality of participants into one of a Grab state, a Release state, a Wait state, and a Keep state (operation S 121 ). The role classifying unit  130  may classify the role of each of a plurality of participants into one of an active participant, an addressee, a side-participant, and a bystander. Each of the participation action state classifying unit  120  and the role classifying unit  130  provides participation action state information and role information to the action adjusting unit  140 . 
         [0078]    In operation S 130 , based on the received participation action state information and role information, the action adjusting unit  140  controls the interaction device  100  or a robot equipped with the interaction device  100  to perform a customized interaction operation with respect to each of a plurality of participants. 
         [0079]      FIG. 6  is a flowchart illustrating an operation of the role classifying unit  130  of  FIG. 4 . 
         [0080]    In operation S 210 , the stimulus analyzing unit  131  receives external stimulus information on each of a plurality of participants from the multi-modal sensor unit  110 . 
         [0081]    In operation S 220 , the stimulus analyzing unit  131  generates a participation degree factor for each of a plurality of participants by using the received external stimulus information. The stimulus analyzing unit  131 , for example, may generate a voice participation degree factor, a gaze participation degree factor, a movement participation degree factor, and a head direction participation degree factor for each of a plurality of participants. 
         [0082]    In operation S 230 , the participation degree calculating unit  132  calculates a participation degree value for each of a plurality of participants using a voice participation degree factor, a gaze participation degree factor, a movement participation degree factor, and a head direction participation degree factor for each of a plurality of participants from the stimulus analyzing unit  131 . 
         [0083]    In operation S 240 , the role determining unit  130  may determine the role of each of a plurality of participants as one of an active participant, an addressee, a side-participant, and a bystander based on a participation degree value for each of a plurality of participants. For example, the role determining unit  133  may classify a plurality of participants as the order of an addressee, a side-participant, and a bystander according to the size order of a participation degree value. 
         [0084]    As described above, the interaction device  100  according to the technical idea of the present application classifies the roles of a plurality of participants according to a participation degree value, and provides a customized interaction for each of a plurality of participants based on a role and a participation action state for each of a plurality of participants. Thus, the interaction device  100  may perform a natural interaction. 
         [0085]      FIG. 7  is a view illustrating an interaction device  200  according to another embodiment of the technical ideal of the inventive concept. The interaction device  200  of  FIG. 7  is similar to the interaction device  100  of  FIG. 2 . Thus, for clear description, like elements are described using like reference numerals. Further, for simple description, components identical or similar to those of the interaction device  100  of  FIG. 2  will not be described. 
         [0086]    Referring to  FIG. 7 , the interaction device  200  includes a multi-modal sensor unit  210 , a participation action state classifying unit  220 , a role classifying unit  230 , an action adjusting unit  240 , and a control unit  250 . 
         [0087]    Unlike the participation action state classifying unit  120  of the interaction device  100  of  FIG. 2 , the participation action state classifying unit  220  of  FIG. 7  provides participation action state information to the role classifying unit  230 . The role classifying unit  230  includes a role determining unit  233 , and the role determining unit  233  determines the role for each of a plurality of participants referring to the received participation action state information. 
         [0088]    In other words, the role determining unit  233  may determine the role for each of a plurality of participants using not only a participation degree value for each of a plurality of participants calculated by the role classifying unit  230 , but also participation action state information on each of a plurality of participants received from the participation action state classifying unit  220 . 
         [0089]    For example, the role determining unit  233  classifies a participant in a participation action state having a high participation degree among a plurality of participants but waiting for an interaction order as an addressee  30  and classifies a participant in a participation action state having a low participation degree among a plurality of participants but waiting for an interaction order as a side-participant  40 . However, it should be understood that the role determining unit  233  may classify the roles of a plurality of participants in various ways by using various combinations of the participation action state information and the role information. 
         [0090]      FIG. 8  is a view illustrating an interaction device  300  according to another embodiment of the technical ideal of the inventive concept. The interaction device  300  of  FIG. 8  is similar to the interaction device  100  of  FIG. 2 . Thus, for clear description, like elements are described using like reference numerals. Further, for simple description, components identical or similar to those of the interaction device  100  of  FIG. 2  will not be described. 
         [0091]    Referring to  FIG. 8 , the interaction device  300  includes a participation action state classifying unit  320 , a role classifying unit  330 , an action adjusting unit  340 , and a control unit  350 . 
         [0092]    Unlike the participation action state classifying unit  120  of the interaction device  100  of  FIG. 2 , the interaction device  300  of  FIG. 8  does not include a multi-modal sensor unit. In other words, the interaction device  300  of  FIG. 8  receives external stimulus information on each of a plurality of participants from an externally installed multi-modal sensor. In this case, the participation action state classifying unit  320  and the role classifying unit  330  may receive external stimulus information on each of a plurality of participants from outside via wired or wireless communication. 
         [0093]    An interaction device according to an embodiment of the present application classifies the roles of a plurality of participants according to a participation degree and/or an action state and provides a customized interaction operation for each of participants according to the classified roles. Accordingly, an interaction device according to an embodiment of the present application may perform a natural interaction operation with a plurality of participants. 
         [0094]    Although the exemplary embodiments of the present invention have been described, it is understood that the present invention should not be limited to these exemplary embodiments but various changes and modifications can be made by one ordinary skilled in the art within the spirit and scope of the present invention as hereinafter claimed.

Technology Category: 3