Weighted Group Matching for Family and Community Interactions

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Solution Overview

Problem

Existing platforms fail to effectively match groups such as families, friend sets, households, and communities for social interactions and transactions, lacking suitability for sharing, lending, and borrowing, and do not optimize for identifying compatible parties beyond chance encounters.

Innovation Solution

A computer-implemented method for matching families, friend sets, households, and communities by storing datapoints, assigning point weights, and calculating matching scores using processors, mathematical algorithms, machine learning, and artificial intelligence to identify similarities and affinities.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If prior art methods are used for matching individuals, then two individuals can be matched for friendship or dating, but groups such as families, friend sets, households, and communities cannot be effectively matched for social interactions and transactions

Engineering Contradiction:

Improvematching capability for groupsVSAvoidefficiency of identifying compatible parties

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent segments the matching process into distinct components: data collection from multiple sources (surveys, social media, public records), data normalization and standardization, similarity calculation across multiple dimensions (demographics, interests, values, lifestyle), and ranked result generation. This segmentation enables the system to handle complex group matching scenarios that prior art could not address.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent creates a universal matching system that can handle multiple types of groups (families, friend sets, households, communities) and multiple matching criteria (demographics, interests, values, lifestyle) through a single platform. The system is designed to be multi-functional, supporting various interaction types (friendship, transactions, sharing, lending, borrowing) that prior art platforms did not provide.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If chance encounters are used to identify compatible groups, then some matches may occur, but the process is not organized, optimized, or digitized

Engineering Contradiction:

Improvequality of matchesVSAvoidtime to identify compatible groups

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent implements preliminary action by collecting and storing data about potential matchees before actual matching requests are made. The system pre-processes information from surveys, social media profiles, and public records, normalizing and standardizing data in advance. This allows the matching algorithm to quickly retrieve and compare pre-processed information when a matching request occurs, rather than gathering data in real-time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical system of chance encounters with an automated digital matching system. The algorithm automatically compares group attributes, calculates similarity scores, and generates ranked matches without requiring manual intervention or random chance. This substitution of automated computation for chance-based interaction dramatically improves both reliability and time efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Measurement precision

If manual identification of similar groups is attempted, then some compatibility can be assessed, but the process lacks automation and optimization

Engineering Contradiction:

Improveaccuracy of similarity assessmentVSAvoidcomplexity of matching system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms qualitative attributes (interests, values, lifestyle preferences) into quantifiable parameters that can be measured and compared. The system assigns weights to different data points and calculates numerical similarity scores, enabling precise measurement of group compatibility. This parameter transformation allows for accurate assessment while maintaining manageable system complexity through standardized calculation methods.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250298854A1Method for matching families, friend sets, households, neighbors, groups and communities for social interactions and transactions

Publication Date: 2025.09.25 BRESLOW PAUL

AI summary

An embodiment of the present invention is a computer-implemented method for matching families, friend sets, households, neighbors, and communities for social interactions and/or transactions, comprising: storing, on a computer memory device, a plurality of datapoints containing demographic, preference, and other descriptive or relevant information to matching families, friend sets, households, neighbors, and communities for social interactions and transactions; assigning point weights to the plurality of datapoints; and calculating, by a processor, a matching score, classification, or alternative algorithmic output determining the similarities for two or more families, friend sets, households, neighbors, and communities.