US-12621353-B2 - System and method for automated affinity-based network expansion through intelligent relationship discovery and compatibility matching

Abstract

A system for affinity-based user acquisition is disclosed. An affinity analysis agent analyzes affinity patterns from existing customers and automatically identifies potential referrals without accessing message content. A relationship mapping agent calculates relationship strength between customers and non-customers based on communication frequency, interaction duration, timing patterns, and automatically finds the high-probability network candidates by scoring these relationships. An outreach and engagement agent creates personalized referral offers using relationship information and delivers them through optimal communication channels at ideal times. When someone accepts a referral offer, a billing integration AI agent may be initiated for automated account creation and service activation through identity verification, service selection, and account activation. Each new individual becomes part of the network. The AI communication agents analyze affinity patterns associated with the new individuals for discovering additional compatible individuals through graph traversal algorithms and centrality analysis.

Inventors

• Raymond J. Sheppard
• Alan W. McCord
• Gustavo Manuel Damil Marin

Assignees

• Intelligent Communication Assistant, Inc.

Dates

Publication Date

20260505

Application Date

20250813

Claims (18)

1. 1 . A system for automated affinity-based user acquisition service for service providers, the system comprising: a communication management server comprising one or more processors, a memory, and a plurality of programming instructions stored in the memory, the plurality of programming instructions when executed by the one or more processors causes the one or more processors to: process, using a pattern analysis engine, communication metadata from an existing user to identify affinity patterns without accessing communication content, wherein the affinity patterns comprises temporal interaction frequencies, communication duration patterns, and bidirectional engagement metrics; generate an affinity compatibility strength score between the existing user and non-users using the identified affinity patterns; responsive to the affinity compatibility score being greater than a predetermined threshold, identify compatible candidates from the non-users; generate and deliver a personalized referral offer to each of the identified compatible candidates, wherein the personalized offer is generated based on relationship analysis between the user and the respective non-users; responsive to acceptance of the personalized referral offers, initiate user provisioning to activate the non-user as a new customer; and track an affinity-based user acquisition coefficient metrics to measure affinity group growth, wherein the pattern analysis engine coordinates a plurality of artificial intelligence (AI) communication agents using a message parsing architecture to drive the affinity-based acquisition service, and wherein the plurality of AI communication agents comprises an affinity analysis agent, a relationship mapping agent, an outreach and engagement agent, a conversion optimization agent, and a billing integration agent.
2. 2 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: calculate, using the affinity analysis agent, affinity compatibility scores, wherein the affinity analysis agent uses collaborative filtering algorithms and communication pattern analysis.
3. 3 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: create, using the relationship mapping agent, network graphs, and identify propagation pathways using network graphs.
4. 4 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: generate, using the outreach and engagement agent, content for the personalized referral offer using natural language processing for each relationship, and determine an optimal time and communication channel for the delivery of the personalized referral offer.
5. 5 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: optimize, using the conversion optimization agent, parameters of the affinity-based acquisition service, wherein the conversion optimization agent monitors the performance of the affinity-based user acquisition service and continuously optimizes system parameters.
6. 6 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: process, using the billing integration agent, automated account creation and service activation upon the referral offer acceptance.
7. 7 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: calculate affinity-based user acquisition coefficients in real-time using cohort analysis to measure exponential growth effectiveness; implement propagation pathway optimization that identifies multi-hop referral sequences through user networks; maintain affinity-based user acquisition coefficient thresholds above predetermined values through automated parameter adjustment; and generate affinity-based user acquisition effects where successful referrals automatically become new referral sources without manual intervention.
8. 8 . The system of claim 1 , wherein each of the plurality of AI communication agents operates as an independent microservice with dedicated computational resources.
9. 9 . The system of claim 1 , wherein the plurality of programming instructions, when executed by the one or more processors, causes the one or more processors to: enable parallel processing of the relationship analysis and referral generation using asynchronous message queues between the plurality of AI communication agents.
10. 10 . A computer-implemented method for driving an automated affinity-based acquisition service for service providers, the method comprising: processing, using a pattern analysis engine, communication metadata from an existing user to identify affinity patterns without accessing communication content, wherein the affinity patterns comprises temporal interaction frequencies, communication duration patterns, and bidirectional engagement metrics; generating an affinity compatibility score between the existing user and non-users using the identified affinity patterns; responsive to the affinity compatibility score being greater than a predetermined threshold, identifying a referral candidate from the non-users; generating and delivering a personalized referral offer to the identified referral candidate, wherein the personalized offer is generated based on relationship analysis between the user and the respective non-users; responsive to acceptance of the personalized referral offer, initiating user provisioning to activate the non-user as a new customer; and tracking an affinity-based user acquisition coefficient metrics to measure exponential growth, wherein the pattern analysis engine coordinates a plurality of artificial intelligence (AI) communication agents using a message parsing architecture to drive the affinity-based acquisition service, and wherein the plurality of AI communication agents comprises an affinity analysis agent, a relationship mapping agent, an outreach and engagement agent, a conversion optimization agent, and a billing integration agent.
11. 11 . The method of claim 10 , wherein generating the affinity compatibility score further comprises the steps of: calculating, using the affinity analysis agent, affinity compatibility scores, wherein the affinity analysis agent uses collaborative filtering algorithms and communication pattern analysis.
12. 12 . The method of claim 10 , wherein the method comprises the steps of: creating, using the relationship mapping agent, network graphs, and identify propagation pathways using network graphs.
13. 13 . The method of claim 10 , wherein the method comprises the steps of: generating, using the outreach and engagement agent, content for the personalized referral offer using natural language processing for each relationship, and determining an optimal time and communication channel for the delivery of the personalized referral offer.
14. 14 . The method of claim 10 , wherein the method comprises the steps of: optimizing, using the conversion optimization agent, parameters of the affinity-based acquisition service, wherein the conversion optimization agent monitors the performance of the affinity-based user acquisition service and continuously optimizes system parameters.
15. 15 . The method of claim 10 , wherein the method comprises the steps of: processing, using the billing integration agent, automated account creation and service activation upon the referral offer acceptance.
16. 16 . The method of claim 10 , wherein the method further comprises the steps of: calculating affinity-based user acquisition coefficients in real-time using cohort analysis to measure exponential growth effectiveness; implementing propagation pathway optimization that identifies multi-hop referral sequences through user networks; maintaining affinity-based user acquisition coefficient thresholds above predetermined values through automated parameter adjustment; and generating affinity-based user acquisition effects where successful referrals automatically become new referral sources without manual intervention.
17. 17 . The method of claim 10 , wherein each of the plurality of AI communication agents operates as an independent microservice with dedicated computational resources.
18. 18 . The method of claim 10 , wherein the method comprises the steps of: enabling parallel processing of the relationship analysis and referral generation using asynchronous message queues between the plurality of AI communication agents.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. patent application Ser. No. 19/224,748, titled, “SYSTEM AND METHOD FOR MANAGING COMMUNICATION THROUGH SEAMLESS TRANSITIONS BETWEEN SYNCHRONOUS AND ASYNCHRONOUS COMMUNICATIONS” which claims the benefit and priority to U.S. patent application Ser. No. 19/006,192, titled, “SECURITY VALIDATION SYSTEM USING DYNAMIC RELATIONSHIP FINGERPRINTS” filed on Feb. 28, 2025, which claims the benefit, and priority to U.S. patent application Ser. No. 18/921,443, titled, “ADAPTIVE COMMUNICATION MANAGEMENT SYSTEM USING MODEL-BASED AND MODEL FREE REINFORCEMENT LEARNING” filed on Oct. 21, 2024 which claims the benefit of, and priority to, U.S. patent application Ser. No. 18/751,905, titled, “SYSTEMS AND METHOD FOR OPTIMIZING PERSONAL COMMUNICATIONS THROUGH OBJECTIVE ALIGNMENT” which claims the benefit of, and priority to, U.S. Provisional Application No. 63/601,645, titled, “SYSTEM AND METHOD FOR OPTIMIZING PERSONAL COMMUNICATIONS THROUGH OBJECTIVE ALIGNMENT” filed on Nov. 21, 2023, the specifications of which are hereby incorporated by reference in its entirety. BACKGROUND OF THE INVENTION Field of the Art The disclosure relates to the field of communications, and more particularly to the field of automated affinity-based network expansion and intelligent community formation for service providers. Discussion of the State of the Art Service providers across multiple industries face challenges in achieving sustainable customer acquisition. Current user acquisition systems implement static targeting mechanisms based on demographic segmentation, geographic proximity, and historical behavioral analytics. These conventional approaches suffer from fundamental limitations, including low conversion rates, high customer acquisition costs ranging from hundreds to thousands of dollars per customer, and inefficient resource allocation that fails to leverage the inherent network effects within service-based businesses. Another approach to customer acquisition is referral systems that enable existing customers to recommend a service or product to potential new customers, typically involving incentives or rewards for successful referrals that result in new customer acquisition. Referral systems across different industries, including healthcare, financial, professional, consulting, legal, and business services, are predominantly manual processes requiring active customer participation to identify potential referrals, manually generate and distribute referral codes, and personally manage promotional offer distribution. The participation rates for these are low. Service providers struggle with the inability to systematically convert existing customer satisfaction into new customer acquisition at scale. Current approaches fail to identify optimal timing for customer engagement or personalize outreach based on relationship characteristics and interaction patterns. Further, the technical complexity of coordinating between customer identification, engagement generation, referral management, and service activation across disparate systems creates operational bottlenecks that prevent efficient scaling of acquisition efforts. Hence, there is a need for a unified system that address the limitations of current user acquisition approaches and automatically identify acquisition opportunities between exist between individuals with shared interests and behavioral patterns. SUMMARY OF THE INVENTION Accordingly, the inventor has conceived and reduced to practice, in a preferred embodiment of the invention, a system and method for AI-driven affinity-based acquisition and automated self-provisioning in service providers. The system implements a pattern analysis engine that processes communication metadata from existing users to identify underlying affinity patterns while maintaining complete privacy protection. Specialized AI agents, including an affinity analysis agent, coordinate to generate affinity compatibility scores between existing users and potential new users using collaborative filtering algorithms and sophisticated communication pattern analysis. When affinity compatibility scores exceed predetermined thresholds, the system automatically identifies compatible candidates from non-users and executes automated affinity-based user acquisition workflows that create personalized referral offer while tracking affinity-based user acquisition coefficient metrics to ensure exponential growth. Upon acceptance of the personalized referral offer, the system seamlessly implements automated user provisioning workflows that activate services without manual intervention, creating a complete end-to-end growth engine. According to a preferred embodiment of the invention, a multi-stage automated acquisition process begins with privacy-preserving affinity pattern analysis, where sophisticated algorithms process metadata including timestamps, duration, frequency, and channel prefere