Automatic contacts sorting

Abstract

To automatically sort multiple contacts of a user, in some examples, a system may be configured to monitor physiological signals, which reflect the emotional responses, of the user during communications between the user and his/her contacts and, further, to classify the contacts into multiple contact groups that may be sorted by the emotional responses of the user.

We claim: 
     
         1 . A method for categorizing contacts, comprising:
 generating a quality of experience (QoE) vector space for each of one or more contacts associated with a user;   monitoring multiple communications between the user and each of the one or more contacts;   collecting one or more physiological signals from the user during each of the multiple communications;   classifying each of the multiple communications into multiple predetermined classifications;   updating the QoE vector space for each of the one or more contacts based on the collected physiological signals;   categorizing the one or more contacts into multiple contact groups in accordance with the updated QoE vector spaces; and   sorting the contact groups based on the updated QoE vector spaces.   
     
     
         2 . The method of  claim 1 , wherein the QoE vector space includes multiple vectors in respective multiple dimensions, each of which corresponds to one of the multiple predetermined classifications. 
     
     
         3 . The method of  claim 1 , wherein the multiple communications include at least one of telephone conversation, a text message exchange, an email exchange, or a video chat. 
     
     
         4 . The method of  claim 1 , further comprising identifying at least one of the one or more contacts based on at least one of an account name, an IP address, a phone number, a user name, a facial image, and a voice of the user. 
     
     
         5 . The method of  claim 1 , wherein the multiple predetermined classifications include at least work, study, travel, and entertainment. 
     
     
         6 . The method of  claim 1 , wherein each of the multiple predetermined classifications is associated with one or more topical terms. 
     
     
         7 . The method of  claim 1 , wherein the one or more physiological signals include at least one of blood pressure, breath frequency, pulse, voice, facial expression, or brain activities. 
     
     
         8 . The method of  claim 2 , wherein each of the multiple vectors includes one or more elements, each of which corresponds to one of the one or more physiological signals. 
     
     
         9 . The method of  claim 6 , wherein the classifying includes extracting one or more key words from each of the multiple communications. 
     
     
         10 . The method of  claim 10 , wherein the classifying further includes calculating a semantic relatedness value between the one or more topical terms and the one or more extracted key words. 
     
     
         11 . A system, comprising:
 a QoE detector configured to collect one or more physiological signals from a user during each of multiple communications between the user and multiple contacts;   a communication monitor configured to monitor the multiple communications;   a vector generator configured to generate a QoE vector space for each of the multiple contacts;   a classifier configured to classify the multiple communications in accordance with multiple predetermined classifications;   a update manager configured to update the QoE vector space for each of the multiple contacts based on the collected one or more physiological signals;   a categorizer configured to categorize the multiple contacts into multiple friends groups in accordance with the updated QoE vector spaces; and   a sorter configured to sort the contact groups based on the updated vector spaces.   
     
     
         12 . The system of  claim 12 , wherein the QoE detector includes a camera, a microphone, a sphygmomanometer, an electroencephalography monitor, a heart rate monitor, or a combination thereof. 
     
     
         13 . The system of  claim 12 , wherein the QoE vector space includes multiple vectors in respective multiple dimensions, each of which corresponds to one of the multiple predetermined topics. 
     
     
         14 . The system of  claim 12 , wherein each of the multiple predetermined classifications is associated with one or more topical terms. 
     
     
         15 . The system of  claim 15 , wherein classifier is further configured to extract one or more key words from each of the multiple communications. 
     
     
         16 . The system of  claim 17 , wherein the classifier is further configured to calculate a semantic relatedness value between the one or more topical terms and the one or more extracted key words. 
     
     
         17 . A non-transitory computer-readable medium that stores executable-instructions that, when executed, cause one or more processors to perform operations comprising:
 monitoring multiple communications between a user and one or more contacts;   collecting one or more physiological signals from the users during each of the communications;   generating a QoE vector space for each of the one or more contacts based on the one or more physiological signals;   categorizing the one or more contacts into multiple contact groups in accordance with the QoE vector spaces;   sorting the contact groups based on the QoE vector spaces.   
     
     
         18 . The non-transitory computer-readable medium of  claim 19 , further comprising classifying the multiple communications in accordance with multiple predetermined classifications. 
     
     
         19 . The non-transitory computer-readable medium of claim  20 , further comprising:
 associating one or more topical terms with each of the multiple predetermined topics;   extracting one or more key words from each of the multiple communications; and   calculating a semantic relatedness value between the one or more topical terms and the one or more extracted key words.