Patron-Server Matchmaking System Using ML Profiles

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current table management software in restaurants focuses solely on logistics and customer experience is left to servers, lacking a system to build lasting relationships between customers and establishments, due to high server turnover rates and scarce staff retention.

Innovation Solution

A system and method using machine learning algorithms and location data to generate profiles of patrons, servers, and restaurants, enabling optimized matchmaking and balancing table management operations by identifying commonalities and affinities, allowing patrons to choose servers and restaurants based on shared traits and preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If current table management software is used, then logistics management is improved, but customer experience and relationship building deteriorate

Engineering Contradiction:

Improvelogistics managementVSAvoidcustomer relationship stability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent combines table management functionality with customer experience management into a single integrated system. The server assignment system merges operational logistics with relationship building by automatically assigning servers based on compatibility metrics that incorporate both service efficiency and customer-server rapport potential, thereby simultaneously improving logistics and customer relationship stability

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system performs multiple functions through a single platform: it manages table logistics, assigns servers, builds customer profiles, tracks server performance, and facilitates relationship building. This multi-functional approach eliminates the need for separate systems and ensures that logistics management and customer relationship management work together harmoniously

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If servers have high turnover rates, then operational flexibility is improved, but customer relationship continuity deteriorates

Engineering Contradiction:

Improveoperational flexibilityVSAvoidcustomer relationship duration

Core Design Contradiction:

Adaptability or versatilityVSDuration of action of stationary object

Solution Approach 1:

The system implements continuous feedback loops where customer preferences, server performance, and interaction history are constantly tracked and fed back into the server assignment algorithm. This feedback mechanism ensures that even with server turnover, the system learns from past interactions and maintains relationship continuity by assigning compatible servers based on accumulated data

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system builds and maintains customer profiles and server profiles in advance, storing compatibility metrics and relationship history before actual service interactions occur. This preliminary data collection and analysis enables the system to quickly reassign compatible servers when turnover occurs, maintaining relationship continuity without disruption

Inventive Principle:

Principle #10Preliminary action

3Ease of operation

If manual server assignment is used, then operational control is improved, but matching precision deteriorates

Engineering Contradiction:

Improveoperational controlVSAvoidpatron-server matching accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system performs self-service by automatically analyzing customer profiles, server profiles, and compatibility metrics to make optimal server assignments without requiring manual intervention. This automated matching process achieves high precision by processing multiple data points simultaneously, while managers retain oversight capabilities for operational control when needed

Inventive Principle:

Principle #25Self-service

4Measurement precision

If profile mapping and recommendation engine is implemented, then matching precision is improved, but system complexity increases

Engineering Contradiction:

Improvematchmaking accuracyVSAvoidsystem structure

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent introduces a profile mapping and recommendation engine as an intermediary component that handles the complex data processing and matching algorithms. This intermediary layer processes customer and server profiles, calculates compatibility metrics, and generates recommendations, thereby achieving high matching precision while shielding the rest of the system from the complexity of the underlying algorithms

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11699122B2System and method for matching patrons, servers, and restaurants within the food service industry

Publication Date: 2023.07.11 ROCKSPOON INC

AI summary

A system and method of combining table management software and customer experience skills by generating profiles of patrons, servers, and restaurants by using machine learning algorithms on and location data in those profiles to build more intimate relationships between patrons, food service establishments, and food service professionals. Trait matching provides optimized matchmaking between patrons and servers who share certain commonalities or affinities while also balancing the table management operations. Machine learning algorithms may be used to identify patterns of commonality that would not otherwise be recognized. This system allows patrons to choose servers at an establishment over a plurality of electronic devices by using the cluster analysis results. It provides servers more lateral work experience between participating restaurants, and restaurants more power to operate smoothly and build highly cohesive teams.