Automated Marketing Model Generation Using Domain Theory Constraints
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Solution Overview
Problem
Current marketing analytics systems rely heavily on human analysts for model setup and quality control, limiting the frequency and quality of model building and raising privacy concerns due to human access to sensitive data.
Innovation Solution
A computer system that uses machine learning and statistical estimation methods to automatically create and optimize marketing models, conforming to domain theories and user specifications, without iterative human intervention, thus preserving data privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts are used for model setup and quality control, then model quality can be ensured through expert judgment, but model building frequency is limited and data privacy is compromised
Solution Approach 1:
The system enables automated model building where the computer system performs model setup, selection, and quality control independently without requiring human analysts. The automated system uses domain theories and statistical methods to self-evaluate and select models, eliminating the bottleneck of manual analysis while maintaining quality through systematic evaluation criteria.
Solution Approach 2:
The patent replaces the mechanical process of human analyst review with an automated computer-based system that uses domain theories, statistical estimation methods, and automated evaluation metrics. This substitution enables continuous model building while maintaining quality through systematic, repeatable automated assessment rather than manual expert judgment.
2Measurement precision
If human analysts access sensitive consumer data for model building, then model accuracy can be improved through expert insight, but data privacy concerns arise
Solution Approach 1:
The automated system performs model building and evaluation independently without requiring human analysts to access sensitive consumer data. The computer system directly processes data using automated statistical methods and domain theories, eliminating the privacy risk associated with human data access while maintaining model accuracy through systematic automated analysis.
Solution Approach 2:
The patent introduces an automated computer-based intermediary system that acts as a mediator between sensitive consumer data and model building processes. This intermediary handles all data processing automatically without human exposure, using domain theories and statistical methods to ensure model accuracy while protecting data privacy through automated, transparent evaluation criteria.
3Productivity
If automated systems are used for model building, then model building frequency and data privacy are improved, but model quality control becomes more challenging
Solution Approach 1:
The automated system incorporates feedback mechanisms where model performance is continuously evaluated against domain theories and statistical criteria. The system uses automated evaluation metrics to assess model quality, providing feedback that guides model selection and refinement, ensuring consistent quality control across high-frequency model building operations.
Solution Approach 2:
The patent employs systematic parameter changes and optimization through automated statistical estimation methods. The system evaluates multiple model parameters against domain theories and selects models based on optimized parameter values that satisfy quality criteria, enabling automated quality control through mathematical optimization rather than manual assessment.
Data Source
AI summary
A computer implemented system and process to determine a model for a domain includes identifying a schema that defines a possible causal element of a particular type of behavior. One or more concepts are determined, as well as one or more sub-concepts for each concept, where each concept and sub-concept are associated with a logical relationship. Multiple models are determined from the one or more concepts and the one or more sub-concepts. The multiple models may be calibrated using representative data collected from a real-world source. An optimal model is determined amongst a plurality of calibrated models.