ML Family Network Building Through Event Invitee Relationship Mapping

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Solution Overview

Problem

Existing family networking systems lack mechanisms to facilitate ongoing engagement with event invitees, making it challenging to integrate them into the family network and foster meaningful connections.

Innovation Solution

A system and method using a machine-learning model to generate event invites, connect invitees with the family network based on relationships, and automatically suggest relationship types, generate seating plans, and send personalized messages, enhancing interaction and integration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If invitees are invited to family events, then family members feel included and valued, but invitees have limited interaction with other family members during the event

Engineering Contradiction:

Improveinclusivity of inviteesVSAvoidinteraction mechanism complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-establishing family network profiles, relationship maps, and interaction guidelines before the event. The machine learning model pre-processes invitee information and automatically generates personalized interaction suggestions, so that when the event occurs, invitees already have structured interaction pathways prepared in advance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary digital platform that mediates between invitees and family members. This intermediary system includes automated messaging, relationship-matched pairing suggestions, and interaction tracking tools that facilitate meaningful connections without requiring invitees to navigate complex social dynamics alone.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Duration of action of moving object

If existing family networking systems are used, then basic connection maintenance is possible, but there is no established mechanism to facilitate ongoing engagement with invitees beyond the initial event

Engineering Contradiction:

Improveengagement duration with inviteesVSAvoidnetworking system complexity

Core Design Contradiction:

Duration of action of moving objectVSDevice complexity

Solution Approach 1:

The system ensures continuity of useful action by maintaining active engagement pathways between events. The machine learning model continuously processes interaction data, relationship developments, and family network changes, generating ongoing personalized suggestions for re-engagement that extend the meaningful connection well beyond the initial event duration.

Inventive Principle:

Principle #20Continuity of useful action

Solution Approach 2:

The system implements continuous feedback loops where invitee interactions, responses, and relationship developments are monitored and fed back into the machine learning model. This feedback mechanism enables the system to dynamically adjust engagement strategies and personalize future interactions based on actual relationship evolution, extending engagement duration adaptively.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If manual methods are used to connect invitees with family network, then relationship accuracy can be maintained, but the process requires significant time and effort

Engineering Contradiction:

Improverelationship type accuracyVSAvoidtime required for connection process

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables self-service by allowing the machine learning model to autonomously process invitee information, determine relationship types, and generate connection suggestions without requiring manual verification for each connection. The system self-calibrates using provided family network data and automatically maintains relationship accuracy through continuous learning from interaction patterns.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system replaces manual mechanical processes of relationship determination with automated machine learning algorithms. The ML model substitutes human manual classification and verification with computational pattern recognition, significantly reducing time requirements while maintaining or improving relationship type accuracy through data-driven classification.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250363463A1System and method for building family network with invitees to an event using machine-learning model

Publication Date: 2025.11.27 NEELAMEGAM VETHARAMAN GURUNATH SANTHOSH

AI summary

A processor-implemented method for building a family network with invitees to an event using a machine-learning model is provided. The method includes (i) generating an event invite using event information obtained from the user of the family network, (ii) sending the event invite to each of the invitees and a request for adding the invitee to the family network of the user, and (iii) automatically connecting the invitees with the family network of the user using the machine learning model. The machine learning model is configured to add the invitee who is the family member of the user to a family tree of the user, add the invitee who is a relative of the user to a relative section of the user's family network, and (iii) add the invitee who is the family friend of the user to a family friend section of the user's family network.