Patent Publication Number: US-2010121804-A1

Title: Personality-sensitive emotion representation system and method thereof

Description:
This application claims the benefit of Taiwan application Serial No. 97143591, filed Nov. 11, 2008, the subject matter of which is incorporated herein by reference. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates in general to an emotion representation system and method thereof, and more particularly to a personality-sensitive emotion representation system and method thereof. 
     2. Description of the Related Art 
     Interactive toys have been provided for a period of time. The current best sale “electronic pet” is among one of the interactive toys. Despite the interactive toy has gained a great popularity now, all interactive toys are facing the same problem, that is, the behavior of the interactive toy is rigid either being one command one action or a monotonous response to a fixed behavior. As personalized effect is absent, the toy is not so enjoyable. As the electronic toy is normally an embedded system, complicated operations cannot be achieved and the behavior is limited to a monotonous response. Thus, how to provide an interactive toy with personalized effect has become an imminent issue to the manufacturers. 
     SUMMARY OF THE INVENTION 
     The invention is directed to a personality-sensitive emotion representation system and method thereof. Emotions and personality are applied to the electronic device using the same through simple calculation, not only creating more personalized effect to the electronic device using the same but also making the user enjoying more fun during operation and making the electronic device more enjoyable. 
     According to a first aspect of the present invention, a personality-sensitive emotion representation system is provided. The emotion representation system comprises a behavior database (BDB), a behavior selection (BS) module and a behavior modification (BM) module. The behavior selection module selects a set of behavior parameters from the behavior database according to emotion parameter which represents an input emotion. The behavior modification module modifies the set of behavior parameters according to the personality parameter so as to output a set of personality-sensitive behavior parameters. 
     According to a second aspect of the present invention, a personality-sensitive emotion representation method is provided. The emotion representation method comprises the following steps. Firstly, a set of behavior parameters is selected from a behavior database according to an emotion parameter, which represents an input emotion. Next, the set of behavior parameters is modified according to a personality parameter so as to output a set of personality-sensitive behavior parameters. 
     The invention will become apparent from the following detailed description of the preferred but non-limiting embodiments. The following description is made with reference to the accompanying drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a PAD 3-D emotion model; 
         FIG. 2  shows a personality-sensitive emotion representation system according to a preferred embodiment of the invention; 
         FIG. 3  shows an input emotion in a PAD 3-D emotion model; 
         FIG. 4  shows a data format of the behavior database; 
         FIG. 5  shows a comparison table of behavior parameters for the same emotion under different personalities, 
         FIG. 6  shows a flowchart of a personality-sensitive emotion representation method. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Referring to  FIG. 1 , a PAD 3-D emotion model is shown. The PAD 3-D emotion model was disclosed by Mehrabian and Russell in 1974. The dimension P, dimension A, dimension D of the PAD 3-D emotion model respectively denote pleasure, arousal and dominance. The value of each dimension ranges between −1 and +1, wherein +1 denotes the maximum in the dimension and −1 denotes the minimum in the dimension. Therefore, each point in the PAD 3-D emotion model can be represented by the emotion parameters constituted by the values in the three dimensions. Besides, each emotion has a spacial distribution in the PAD 3-D emotion model, and the spatial distribution of the emotion can be denoted by its average value and standard error. For example, if the average value for pleasure is (0.81, 0.51, 0.46) and the standard error is (0.21, 0.26, 0.38), then the spatial distribution of pleasure in the PAD 3-D emotion model is as indicated in  FIG. 1 . If an emotion parameter falls within the spatial distribution of  FIG. 1 , then the emotion denoted by the emotion parameter may be pleasure. 
     Referring to  FIG. 2 ,  FIG. 3  and  FIG. 4 .  FIG. 2  shows a personality-sensitive emotion representation system according to a preferred embodiment of the invention.  FIG. 3  shows an input emotion in a PAD 3-D emotion model.  FIG. 4  shows a data format of the behavior database. The personality-sensitive emotion representation system  20  comprises a behavior database  210  (BDB), a behavior selection (BS) module  220  and a behavior modification (BM) module  230 . The behavior database  210  is used for storing the average value (M Pj , M Aj , M Dj ) of the emotion Ej, the standard error (S Pj , S Aj , S Dj ) of the emotion Ej, and a set of behavior parameters Bj corresponding to the emotion Ej, wherein j equals 1˜n. Each set of behavior parameter Bj is constituted by many behavior parameters. The behavior parameter denotes the speed rate, the response time or the behavior size of a behavior corresponding to the emotion Ej. The behavior selection module  220  receives the emotion parameter (Pi, Ai, Di), which represent an input emotion Ei. The behavior selection module  220  selects a set of behavior parameters Bj from the behavior database  210  according to the emotion parameter (Pi, Ai, Di). The behavior modification module  230  modifies the set of behavior parameters Bj according to the personality parameter (TP, TA, TD) so as to output a set of personality-sensitive behavior parameters Bj′. 
     There are many ways for inputting the above emotion parameter (Pi, Ai, Di). For example, changes in the surrounding or interaction with the user are sensed by a sensor, and then the sensed results are converted into a corresponding emotion parameter (Pi, Ai, Di). Or, the user directly sets the mode of the to-be-inputted emotion through an emotion input module. 
     Furthermore, the emotion parameter (Pi, Ai, Di) may fall within many emotions. Firstly, the behavior selection module  220  locates all the emotions relevant to the emotion parameter (Pi, Ai, Di) from the behavior database  210  according to emotion parameter (Pi, Ai, Di). Next, the emotion closest to the input emotion Ei is located from all the emotions relevant to the emotion parameter (Pi, Ai, Di). Lastly, the behavior parameter corresponding to the emotion closest to the input emotion Ei is selected. For example, the emotion parameter (Pi, Ai, Di) falls within the spatial distribution of emotion E 0  and emotion E 1  at the same time. The behavior selection module  220 , first of all, locates the emotion E 0  and emotion E 1  from the behavior database  210  according to emotion parameter EP. Next, the behavior selection module  220  determines that the input emotion Ei corresponding to the emotion parameter (Pi, Ai, Di) is closest to the emotion E 1 . Lastly, the behavior selection module  220  selects the behavior parameter B 1  corresponding to the emotion E 1 . 
     The behavior selection module  220  locates all the emotions relevant to the emotion parameter (Pi, Ai, Di) through the average value (M Pj , M Aj , M Dj ) and the standard error (S Pj , S Aj , S Dj ) of the emotion Ej stored in the behavior database  210  and the following formula (1): 
     
       
      
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     After all the emotions relevant to the emotion parameter (Pi, Ai, Di) are located according to formula (1), the behavior selection module  220  selects the emotion closest to the input emotion Ei according to the distance or Gaussian distribution. 
     For example, the behavior selection module  220  calculates the distance Dist, that is, the distance from all the emotions relevant to the emotion parameter (Pi, Ai, Di) to the input emotion according to the following distance formula (2). 
       Dist=√{square root over (( P   i   −M   pj ) 2 +( A   i   −M   Aj ) 2 +( D   i   −M   Dj ) 2 )}{square root over (( P   i   −M   pj ) 2 +( A   i   −M   Aj ) 2 +( D   i   −M   Dj ) 2 )}{square root over (( P   i   −M   pj ) 2 +( A   i   −M   Aj ) 2 +( D   i   −M   Dj ) 2 )}  (2) 
     After the behavior selection module  220  calculates the distance Dist from all the emotions relevant to the emotion parameter (Pi, Ai, Di) to the input emotion Ei according to the above distance formula (2), the behavior selection module  220  selects a set of behavior parameters Bj corresponding to the emotion with shortest distance. 
     Or, the behavior selection module  220  calculates the probability PP,A,D (P, A, D) of the input emotion Ei falling within all the emotions relevant to the emotion parameter (Pi, Ai, Di) according to the following formulae (3)˜(6), and then selects the emotion with maximum probability. 
     
       
         
           
             
               
                 
                   
                     
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     The formulae (3)˜(5) respectively denote the Gaussian probability density function (PDF) on the dimension P, the dimension A, and the dimension D, and formula (6) denotes a joint probability. As the probability density function of Gaussian distribution calculates the probability of one-dimensional distribution only, the behavior selection module  220  calculates the probability of respective dimension according to formulas (3)˜(5) first, and then calculates the joint probability of the three dimensions according to formula (6) so to obtain the probability of the input emotion Ei falling within emotion Ej. After the behavior selection module  220  calculates the probability of the input emotion Ei falling within all the emotions relevant to the emotion parameter (Pi, Ai, Di) according to the above formulae (3)˜(6), the behavior selection module  220  selects a set of behavior parameters Bj corresponding to the emotion with largest probability. 
     The behavior modification module  230  modifies the behavior parameter Bj as a modified behavior parameter according to the personality parameter (TP, TA, TD), and determines whether the modified behavior parameter is within a pre-determined range. If no, then personality-sensitive behavior parameter Bj′ equals the extremum of the modified behavior parameter. If so, the personality-sensitive behavior parameter Bj′ equals modified behavior parameter. 
     The behavior modification module  230  further comprises m behavior parameter modification units  230 ( 1 )˜ 230 ( m ). The behavior parameter modification units  230 ( 1 )˜ 230 ( m ) respectively modify each behavior parameter of the set of behavior parameters Bj according to the personality parameter (TP, TA, TD). For example, the behavior parameter Bj comprises a speed rate behavior parameter, a response time behavior parameter or a behavior size behavior parameter and the behavior parameter modification units  230 ( 1 )˜ 230 ( m ) comprise a speed modification unit used for modifying speed rate behavior parameter, a response time modification unit used for modifying response time behavior parameter or a behavior size modification unit used for modifying behavior size behavior parameter. 
     Referring to  FIG. 5 , a comparison table of behavior parameters for the same emotion under different personalities is shown. The personality-sensitive emotion representation system  20  can be used in an electronic device controlled by a servo motor, and the electronic device is an interactive toy for example. Let the emotion parameter (Pi, Ai, Di) denote pleasure by (0.81, 0.51, 0.46). The behavior selection module  220  selects the behavior parameter Bj according to the emotion parameter (0.81, 0.51, 0.46), wherein, the speed rate behavior parameter of the behavior parameter Bj is Bj speed =0.8, the response time behavior parameter of the behavior parameter Bj is Bj response =0.5 and the behavior size behavior parameter of the behavior parameter Bj is Bj motion =0.4. 
     The behavior modification module  230  can calculate the personality-sensitive behavior parameter Bj′ through the following modification function (7): 
         Bj′=f ( Bj, TP, TA, TD )   (7) 
     Wherein, the modification function (7) comprises the following formulae including formula (8) for modifying speed rate, formula (9) for modifying response time and formula (10) for modifying behavior size. 
         Bj′   speed   =Bj   speed +( TP× 0.5+ T× 0.5)   (8) 
         Bj′   response   =Bj   response +( TA× 1)   (9) 
         Bj′   motion   =Bj   motion +( TD× 1)   (10) 
     When personality is extrovert, the personality parameter (TP, TA, TD) denoting extroversion is (0.21, 0.17, 0.5), and the behavior modification module  230  respectively calculates Bj′ speed =0.8+(0.21×0.5+0.5×0.5)=1.15, Bj′ response =0.5+(0.17×1)=0.67, Bj′ motion =0.4+(0.5×1)=0.9 according to formulae (8)˜(10). In  FIG. 5 , the speed rate behavior parameter Bj′ speed , the response time behavior parameter Bj′ response  and the behavior size behavior parameter Bj′ motion  for extrovert personality are respectively denoted by approximated values 1, 0.7 and 0.9. 
     Similarly, when the personality is extrovert, the personality parameter (TP, TA, TD) denoting introversion is (−0.43, 0.29, −0.37), and the behavior modification module  230  respectively calculates 
         Bj′   speed =0.8+(−0.43×0.5+(−0.37×0.5))=0.4,  B′   response =0.5+(0.79×1), 
     Bj′ motion =0.4+(−0.37×1)=0.9 according to formulae (8)˜(10). In FIG. 5, the speed rate behavior parameter Bj′ speed , the response time behavior parameter Bj′ response  and the behavior size behavior parameter Bj′ motion  for extrovert personality are respectively denoted by approximated values 0.4, 0.8 and 0.1. 
     Thus, the personality-sensitive emotion representation system  20  can apply emotions and personality to the electronic device using the same through simple calculation, not only creating more personalized effect to the electronic device using the same but also making the user enjoying more fun during operation and making the electronic device more enjoyable. 
     Referring to  FIG. 6 , a flowchart of a personality-sensitive emotion representation method is shown. The emotion representation method applicable to the emotion representation system  20  at least comprises the following steps. Firstly, the method begins at step  610 , the behavior selection module  220  selects the behavior parameter Bj from the behavior database  210  according to the emotion parameter (Pi, Ai, Di), which represents an input emotion Ei. Next, the method proceeds to step  620 , the behavior modification module  230  modifies the behavior parameter Bj according to the personality parameter (TP, TA, TD) so as to an output personality-sensitive behavior parameter Bj′. 
     According to the personality-sensitive emotion representation system and method thereof disclosed in the above embodiment of the invention, emotions and personality are applied to the electronic device using the same through simple calculation, not only creating more personalized effect to the electronic device using the same but also making the user enjoying more fun during operation and making the electronic device more enjoyable. 
     While the invention has been described by way of example and in terms of a preferred embodiment, it is to be understood that the invention is not limited thereto. On the contrary, it is intended to cover various modifications and similar arrangements and procedures, and the scope of the appended claims therefore should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements and procedures.