System and method for real-time geo-physical social group matching and generation
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 not 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.
1 . A system for real-time geophysical social group matching, comprising:
a computing system comprising a plurality of processors, memory, and a network interface;
a customer portal comprising a first plurality of programming instructions that, when operating on at least one of the plurality of processors, cause the computing system to:
receive and store customer profile data;
receive real-time sensor data from a customer's mobile device;
process the sensor data to determine the customer's current state by applying an activity recognition algorithm using overlapping time windows to classify real-time movement patterns and infer availability for group formation; and
a geospatial processing subsystem comprising a second plurality of programming instructions that, when operating on at least one of the plurality of processors, cause the computing system to:
implement spatial indexing for proximity queries;
utilize adaptive location hashing that dynamically adjusts spatial resolution based on population density while maintaining precision sufficient for proximity-based group matching; and
a social grouping and recommendation subsystem comprising a third plurality of programming instructions that, when operating on at least one of the processors, cause the computing system to:
receive a group formation request from a customer;
retrieve relevant customer and venue profiles;
process customer profiles and real-time data through an ensemble machine learning algorithm that combines collaborative filtering using matrix factorization with content-based filtering using neural network feature extraction to identify similarities, wherein the ensemble machine learning algorithm weights predictions based on temporal dynamics of user interactions;
match customers based on the identified similarities to create a proposed social group;
facilitate communication between matched customers; and
establish a final social group upon receiving acceptances.
2 . The system of claim 1 , wherein the real-time sensor data comprises at least one of GPS, accelerometer, and gyroscope data.
3 . The system of claim 1 , wherein the spatial indexing implements a quad-tree based structure for efficient proximity queries.
4 . The system of claim 1 , wherein the adaptive location hashing adjusts hash precision based on population density and privacy requirements.
5 . The system of claim 1 , wherein facilitating communication comprises sending privacy-preserving electronic messages to the customers in the proposed social group.
6 . The system of claim 1 , wherein establishing the final social group comprises enabling end-to-end encrypted chat communications between the customers of the final social group.
7 . The system of claim 1 , further comprising generating, using a generative Al model, personalized group activity suggestions based on the group customer's profiles and real-time contextual data.
8 . The system of claim 1 , wherein the group formation request is a spontaneous dinner request.
9 . The system of claim 8 , wherein processing the spontaneous dinner request comprises:
dynamically determining a search radius based on the customer's location, activity state, and local population density;
identifying potential dinner companions within the search radius; and
ranking venues based on group preferences and real-time factors.
10 . The system of claim 1 , wherein the geospatial processing subsystem creates a non-circular search area that follows transport routes.
11 . The system of claim 1 , wherein matching customers includes implementing a genetic algorithm to optimize group composition based on multiple objectives and constraints.
12 . The system of claim 1 , further comprising implementing a cascading invitation system that recalculates and invites next-best matches if initial invitations are declined.
13 . The system of claim 1 , wherein facilitating communication comprises generating personalized invitation messages using a language model.
14 . The system of claim 1 , further comprising providing real-time updates on venue waiting times and table readiness to the final social group.
15 . A method for real-time geophysical social group matching executed by a computing system comprising a plurality of processors, memory, and a network interface, comprising the steps of:
operating a customer portal to receive and store customer profile data;
receiving, via the customer portal, real-time sensor data from a customer's mobile device;
processing, by the computing system, the sensor data to determine the customer's current state by applying an activity recognition algorithm using overlapping time windows to classify real-time movement patterns and infer availability for group formation;
executing, by a geospatial processing subsystem, spatial indexing for proximity queries;
utilizing, by the geospatial processing subsystem, adaptive location hashing that dynamically adjusts spatial resolution based on population density while maintaining precision sufficient for proximity-based group matching;
receiving a group formation request from a customer;
retrieving, by the social grouping and recommendation subsystem, relevant customer and venue profiles;
processing customer profiles and real-time data through an ensemble machine learning algorithm that combines collaborative filtering using matrix factorization with content-based filtering using neural network feature extraction to identify similarities, wherein the ensemble machine learning algorithm weights predictions based on temporal dynamics of user interactions;
matching, by the social grouping and recommendation subsystem, customers based on the identified similarities to create a proposed social group;
facilitating, by the social grouping and recommendation subsystem, communication between matched customers; and
establishing, by the computing system, a final social group upon receiving acceptances.
16 . The method of claim 15 , wherein the real-time sensor data comprises at least one of GPS, accelerometer, and gyroscope data.
17 . The method of claim 15 , wherein the spatial indexing implements a quad-tree based structure for efficient proximity queries.
18 . The method of claim 15 , wherein the adaptive location hashing adjusts hash precision based on population density and privacy requirements.
19 . The method of claim 15 , wherein facilitating communication comprises sending privacy-preserving electronic messages to the customers in the proposed social group.
20 . The method of claim 15 , wherein establishing the final social group comprises enabling end-to-end encrypted chat communications between the customers of the final social group.
21 . The method of claim 15 , further comprising generating, using a generative AI model, personalized group activity suggestions based on the group customer's profiles and real-time contextual data.
22 . The method of claim 15 , wherein the group formation request is a spontaneous dinner request.
23 . The method of claim 22 , wherein processing the spontaneous dinner request comprises the steps of:
dynamically determining a search radius based on the customer's location, activity state, and local population density;
identifying potential dinner companions within the search radius; and
ranking venues based on group preferences and real-time factors.
24 . The method of claim 15 , further comprising the step of creating a non-circular search area that follows transport routes.
25 . The method of claim 15 , wherein matching customers comprises implementing a genetic algorithm to optimize group composition based on multiple objectives and constraints.
26 . The method of claim 15 , further comprising implementing a cascading invitation system that recalculates and invites next-best matches if initial invitations are declined.
27 . The method of claim 15 , wherein facilitating communication comprises generating personalized invitation messages using a language model.
28 . The method of claim 15 , further comprising providing real-time updates on venue waiting times and table readiness to the final social group.