Systems and methods for pre-communicating shoppers' communication preferences to retailers

Abstract

Systems and methods are provided for providing a customized user experience. Providing a customized user experience may include receiving, from a graphical user interface displayed on a mobile device a user input indicative of a preferred level of interaction by the user, and monitoring, by a sensor, a facial expression of the user. Then, a first preference metric may be assigned to the user based on the monitored facial expression. Providing a customized user experience may further include monitoring, by a sensor, a behavior of the user, assigning a second preference metric based on the monitored behavior, and aggregating at least the user input, the first preference unit, and the second preference metric to generate a preference score of the user. The preference score may be stored in a central database, displayed on a remote device, and used to modify a customer service experience.

1 - 20 . (canceled) 
     
     
         21 . A computer-implemented method for providing a customized user experience, the method comprising:
 receiving a plurality of electromagnetic signals generated by a plurality of sensors;   determining a presence of a mobile device at a merchant location based on the electromagnetic signals, the mobile device being associated with a user;   upon determining the presence of the user at the merchant location, accessing a preference score indicating a preferred level of interaction;   determining an emotion of the user based on a video of the user in the merchant location;   assigning a first preference metric based on the emotion;   varying the preference score based on the first preference metric to generate an updated preference score; and   causing the updated preference score to be displayed on an agent mobile device.   
     
     
         22 . The computer-implemented method of  claim 21 , wherein the preference score is based on an input by the user via a graphical user interface displayed on the mobile device. 
     
     
         23 . The computer-implemented method of  claim 21 , wherein determining the emotion of the user based on the video of the user comprises matching, using a trained machine learning algorithm, at least one frame of the video to at least one frame of a facial expression stored in the database. 
     
     
         24 . The computer-implemented method of  claim 23 , wherein the machine-learning algorithm comprises a deep learning algorithm trained to match video frames to corresponding user emotions. 
     
     
         25 . The computer-implemented method of  claim 23 , wherein determining the emotion of the user based on the video of the user further comprises separating the video into a plurality of video frames, the plurality of video frames including the at least one frame of the video. 
     
     
         26 . The computer-implemented method of  claim 21 , wherein the video of the user is captured by an image sensor of the agent mobile device. 
     
     
         27 . The computer-implemented method of  claim 26 , wherein the method further comprises:
 comparing the preference score to a predetermined threshold; and   causing an indication to monitor the user from a distance to be displayed on the agent mobile device based on the comparison.   
     
     
         28 . The computer-implemented method of  claim 21 , wherein the method further comprises:
 monitoring a behavior of the user;   assigning a second preference metric based on the monitored behavior; and   aggregating the first preference metric and the second preference metric;   wherein the updated preference score is generated based on the aggregation of the first preference metric and the second preference metric.   
     
     
         29 . The computer-implemented method of  claim 28 , wherein the behavior of the user is determined based on the plurality of electromagnetic signals. 
     
     
         30 . The computer-implemented method of  claim 28 , wherein the behavior of the user is determined based on the video of the user. 
     
     
         31 . The computer-implemented method of  claim 28 , wherein the method further comprises:
 recording, by a microphone, a voice of the user;   assigning a third preference metric based on the recorded voice; and   aggregating the first preference metric, the second preference metric, and the third preference metric;   wherein the updated preference score is generated based on the aggregation of the first preference metric, the second preference metric, and the third preference metric.   
     
     
         32 . The computer-implemented method of  claim 31 , wherein aggregating the first preference metric, the second preference metric, and the third preference metric includes weighting the first preference metric, the second preference metric, and the third preference metric based on a predetermined hierarchy. 
     
     
         33 . The computer-implemented method of  claim 21 , wherein the method further comprises receiving a history of a plurality of transactions associated with the user, wherein the updated preference score is generated based on the transaction history. 
     
     
         34 . The computer-implemented method of  claim 33 , wherein the transaction history includes at least one of a time spent before making the plurality of transactions or a degree of assistance received before making the plurality of transactions. 
     
     
         35 . A system for providing a customized user experience, comprising:
 at least one processor; and   a memory storing instructions to cause the processor to perform operations comprising:
 receiving a plurality of electromagnetic signals generated by a plurality of sensors; 
 determining a presence of a mobile device at a merchant location based on the electromagnetic signals, the mobile device being associated with a user; 
 upon determining the presence of the user at the merchant location, accessing, a preference score indicating a preferred level of interaction; 
 determining an emotion of the user based on a video of the user in the merchant location; 
 assigning a first preference metric based on the emotion; 
 varying the preference score based on the first preference metric to generate an updated preference score; and 
 causing the updated preference score to be displayed on an agent mobile device. 
   
     
     
         36 . The system of  claim 35 , wherein the operations further comprise:
 monitoring a behavior of the user;   assigning a second preference metric based on the monitored behavior; and   aggregating the first preference metric and the second preference metric;   wherein the updated preference score is generated based on the aggregation of the first preference metric and the second preference metric.   
     
     
         37 . The system of  claim 36 , wherein the operations further comprise:
 recording, by a microphone, a voice of the user;   assigning a third preference metric based on the recorded voice; and   aggregating the first preference metric, the second preference metric, and the third preference metric;   wherein the updated preference score is generated based on the aggregation of the first preference metric, the second preference metric, and the third preference metric.   
     
     
         38 . The system of  claim 37 , wherein aggregating the first preference metric, the second preference metric, and the third preference metric includes weighting the first preference metric, the second preference metric, and the third preference metric based on a predetermined hierarchy. 
     
     
         39 . The system of  claim 35 , wherein the operations further comprise receiving a history of a plurality of transactions associated with the user, wherein the updated preference score is generated based on the transaction history. 
     
     
         40 . A non-transitory computer-readable medium including instructions that when executed by at least one processor cause the at least one processor to perform a method for providing a customized user experience, the method comprising:
 receiving a plurality of electromagnetic signals generated by a plurality of sensors;   determining a presence of a mobile device at a merchant location based on the electromagnetic signals, the mobile device being associated with a user;   upon determining the presence of the user at the merchant location, accessing, by the merchant device, a preference score indicating a preferred level of interaction;   determining an emotion of the user based on a video of the user in the merchant location;   assigning a first preference metric based on the emotion;   varying the preference score based on the first preference metric to generate an updated preference score; and   causing a notification indicating a suggested level of interaction be displayed on an agent mobile device.