Genetic Modeling for Client Cluster Attribute Selection

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Solution Overview

Problem

The challenge lies in optimizing electronic communication channels to effectively deliver offers by aligning messages with the optimal recipients, as existing methods struggle with the complexity of calibrating multiple aspects of messaging, leading to inefficient data analysis and resource utilization.

Innovation Solution

A combination of clustering algorithms and genetic algorithms is employed to analyze and update message attributes based on transmission and reception patterns, retaining successful attributes and discarding unsuccessful ones, thereby optimizing message delivery by categorizing clients and generating new offer combinations that increase the likelihood of response.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If clustering algorithms and genetic algorithms are used to optimize message delivery, then the effectiveness of electronic delivery channels is improved, but the device complexity increases

Engineering Contradiction:

Improveeffectiveness of electronic delivery channelsVSAvoidcomplexity of algorithmic engine

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system segments the client population into distinct clusters based on shared characteristics and attributes. By dividing the overall population into smaller, more homogeneous groups, the system can apply targeted genetic algorithms to each cluster independently, improving delivery effectiveness while managing complexity through modular processing of segmented data sets.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The genetic algorithms dynamically adjust message parameters such as timing, channel selection, and content attributes based on cluster characteristics and performance feedback. By changing these parameters iteratively through selection, crossover, and mutation operations, the system optimizes delivery effectiveness without requiring complete redesign of the entire messaging infrastructure.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If multiple aspects of messaging are calibrated to align with optimal recipients, then the likelihood of successful transmission is improved, but the difficulty of detecting and measuring increases

Engineering Contradiction:

Improvelikelihood of successful transmissionVSAvoidcomplexity of data analysis

Core Design Contradiction:

ReliabilityVSDifficulty of detecting and measuring

Solution Approach 1:

The system implements feedback loops that track message transmission outcomes, recipient responses, and channel performance metrics. This feedback information is fed back into the genetic algorithms to continuously refine clustering assignments and message parameters, improving transmission success while automating the detection and measurement of complex interactions through systematic data collection and analysis.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces manual analysis and calibration of messaging parameters with automated genetic algorithms that perform optimization computations. This substitution of mechanical human analysis with computational systems reduces the difficulty of detecting and measuring complex patterns by leveraging algorithmic processing capabilities to handle the analytical complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If genetic algorithms retain successful attributes and discard unsuccessful ones, then the productivity of message delivery is improved, but the loss of information may increase

Engineering Contradiction:

Improveefficiency of message deliveryVSAvoidloss of message attributes

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The genetic algorithms discard message attributes and parameters that demonstrate poor performance across clusters, while retaining and propagating successful attributes through the population. By systematically eliminating ineffective elements and preserving successful ones, the system improves delivery productivity while the loss of information is mitigated through the retention of beneficial attributes and the ability to recover successful patterns across generations of optimized messages.

Inventive Principle:

Principle #34Discarding and recovering

Data Source

PatentUS11748631B2Genetic modeling for attribute selection within a cluster

Publication Date: 2023.09.05 CAPITAL ONE SERVICES LLC

AI summary

Genetic modeling is used to generate new term sets from existing term sets within a population cluster. Term attributes corresponding to a first plurality of term sets are encoded as genes for a computer-executed genetic algorithm. Clients are clustered in categories based on client attributes. The genetics algorithm is applied to a category of clustered clients to distribute the term sets to clients in the category. A first subset of terms sets is removed after receiving, within a first duration of time, a number of client responses that falls below a first threshold. A second subset of term sets is retained after receiving, within a second duration of time, a number of client responses above a second threshold. The second subset is bred using the genetic algorithm and a second plurality of term sets is generated based on the results.