System and method for real-time geo-physical social group matching and generation

Abstract

A system and method for real-time geophysical social grouping comprising customer profiles and venue profiles, wherein the profiles comprise expressed and inferred attributes, and a social grouping and recommendation server which utilizes machine learning algorithms on the profiles to generate recommendations for social group pairing, venues, and activities. Attribute matching provides optimized grouping between customers who share certain commonalities while also providing venues a system for locating and attracting ideal customers. Machine learning algorithms may be used to analyze profile attributes and identify patterns of commonality that would riot otherwise be recognized. This system allows patrons to meet, dine, and socialize with one or more matched individuals at a venue that satisfies all participants preferences and attributes.

What is claimed is: 
     
         1 . A system for real-time geophysical social group matching, comprising:
 a computing device comprising a memory, a processor, and a non-volatile data storage device;   a database residing on the non-volatile data storage device, the database comprising profiles of customers and restaurants;   a customer portal comprising a first plurality of programming instructions stored in the memory, and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
 receive profile attributes for a first customer; 
 store the information in the first customer's profile in the database; 
 receive a connection from a first customer's mobile device via a network; and 
 identify the first customer from an identifier received from the customer's mobile device; and 
   a social grouping and recommendation engine comprising a second plurality of programming instructions stored in the memory, and operating on the processor, wherein the second plurality of programing instructions, when operating on the processor, cause the computing device to:
 receive a search query; 
 retrieve the customer's profile in the database; 
 retrieve a plurality of the restaurant profiles the database; 
 select and retrieve from the set of all customers who have enabled social dining, a subset of customer profiles whose location attributes fall within the range of the location attribute selected by the first customer; 
 retrieve the subset of customer profiles in the database; 
 process the first customer's profile and the selected subset of customer profiles through a machine learning algorithm to identify a pattern of similarities between the first customer and one or more of the selected subset of customers; and 
 match the first customer to one or more of the subset of customers based on the pattern of similarities. 
   
     
     
         2 . The system of  claim 1 , wherein the database further comprises information from external factors, and the external factors are also processed through the one or more machine learning algorithms during the social group matching process. 
     
     
         3 . The system of  claim 1 , comprising a restaurant portal comprising a third plurality of programming instructions stored in the memory, and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to match customers and the restaurants by:
 receiving information for each of a plurality of restaurants; and   storing the information for each of the restaurants in the respective restaurant's profile in the database;   processing the first customer's profile, the selected subset of customer profiles, and the restaurant profiles through one or more machine learning algorithms to identify a pattern of similarities between the first customer, the selected subset of customers, and one or more restaurants; and   matching the first customer, the selected subset of customers, and one or more of the restaurants based on the pattern of similarity.   
     
     
         4 . The system of  claim 1 , wherein a portion of the information for the first customer, the selected subset of customers, or restaurant profiles is received from a social media account. 
     
     
         5 . The system of  claim 1 , wherein the search query contains one or more search terms and one or more user selected situational attributes. 
     
     
         6 . A method for real-time geophysical social group matching, comprising the steps of:
 receiving profile attributes for a first customer;   storing the information in the first customer's profile in the database;   receiving a connection from a first customer's mobile device via a network;   identifying the first customer from an identifier received from the customer's mobile device;   receiving a search query;   retrieving the customer's profile in the database;   retrieving a plurality of the restaurant profiles in the database;   selecting and retrieving from the set of all customers who have enabled social dining, a subset of customer profiles whose location attributes fall within the range of the location attribute selected by the first customer;   retrieving the subset of customer profiles in the database;   processing the first customer's profile and the selected subset of customer profiles through a machine learning algorithm to identify a pattern of similarities between the first customer and one or more of the selected subset of customers; and   matching the first customer to one or more of the subset of customers based on the pattern of similarities.   
     
     
         7 . The method of  claim 6 , wherein the database further comprises information from external factors, and the external factors are also processed through the one or more machine learning algorithms during the social group matching process. 
     
     
         8 . The method of  claim 6 , further comprising the steps of:
 receiving information for each of a plurality of restaurants; and   storing the information for each of the restaurants in the respective restaurant's profile in the database;   processing the first customer's profile, the selected subset of customer profiles, and the restaurant profiles through one or more machine learning algorithms to identify a pattern of similarities between the first customer, the selected subset of customers, and one or more restaurants; and   matching the first customer, the selected subset of customers, and one or more of the restaurants based on the pattern of similarity.   
     
     
         9 . The method of  claim 6 , wherein a portion of the information for the first customer, the selected subset of customers, or restaurant profiles is received horn a social media account. 
     
     
         10 . The method of  claim 6 , wherein the search query contains one or more search terms and one or more user selected situational attributes.