Client Network Expansion Using AI Attribute Screening
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Solution Overview
Problem
Current technologies lack the ability to identify dynamic business strategies and attributes that impact a client network's robustness, leading to inconsistent and error-prone initiatives for expanding client networks.
Innovation Solution
A method and system utilizing AI/ML models, specifically explainable AI/ML and distance metric learning models, to transform client details into quantifications that meet criteria for network expansion, ensuring robustness by adding or rejecting clients based on predefined criteria, and utilizing databases for attribute and parameter values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional resource network clientele expansion initiatives are implemented without technology support, then network expansion can occur, but the process is prone to inconsistencies and errors due to absence of relevant resources
Solution Approach 1:
The patent replaces manual, conventional expansion processes with an automated AI/ML-based system. The mechanical system of human-driven client evaluation and onboarding is substituted with an intelligent system that automatically transforms client details into quantified attributes, applies criteria-based filtering, and makes expansion decisions, thereby eliminating inconsistencies and errors associated with manual processes.
Solution Approach 2:
The system enables self-service expansion by automatically processing client requests without requiring manual intervention. The AI/ML model autonomously evaluates incoming clients, transforms their attributes, applies expansion criteria, and executes onboarding or rejection decisions, allowing the network to expand itself reliably without external coordination for each client.
2Quantity of substance
If new clients are added to expand the network, then network size increases, but network robustness may be diminished if clients do not meet integrity criteria
Solution Approach 1:
The system performs preliminary action by pre-establishing expansion criteria and integrity thresholds before onboarding any new client. The AI/ML model transforms client details into quantified attributes and evaluates them against predetermined criteria, ensuring that only clients meeting the required integrity standards are added to the network, thus preserving robustness while enabling expansion.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring client attributes and network robustness metrics. The AI/ML model uses feedback from evaluated client characteristics to adjust and refine expansion decisions, ensuring that network growth maintains or improves overall robustness rather than diminishing it.
3Measurement precision
If AI/ML models are used to transform client details into quantified attributes, then expansion decisions become more accurate, but system complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming qualitative client details into quantitative attributes through AI/ML models. This transformation converts non-measurable client characteristics into numerical values that can be precisely measured, compared, and evaluated against expansion criteria, thereby improving measurement precision while managing system complexity through automated computation.
Data Source
AI summary
A client network expansion system that: converts at least one from among a first rule and a first trend into a first set of code; stores the first set of code within a first repository of strategies; receives a first request that is associated with a new set of client details; based on the first repository of strategies, transforms a new set of client details into at least one new quantification of a first set of client attributes; determines whether the at least one new quantification meets a first set of criteria for a first client network; and adds a new client to the first client network when the determination is made that the at least one new quantification meets the first set of criteria.