Predictive Model Framework for Media Planning Adaptability
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Solution Overview
Problem
Conventional methods in media planning lack a universal framework for generating customized predictive models, making it difficult to reuse predictive solutions across different problems and industries due to data and industry-specific variations.
Innovation Solution
A framework comprising a characterizer, evaluator, and recommender module to identify problem characteristics, evaluate model feasibility, and recommend suitable predictive models, such as artificial neural networks or Fourier transform models, based on input data and prediction requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predictive solution is conceived to address a specific media planning problem, then the problem can be solved effectively, but it becomes difficult to reuse the solution for other problems due to data and industry variations
Solution Approach 1:
The patent creates a universal framework that can generate customized predictive models for different media planning problems. The framework includes modules for problem characterization, data evaluation, and model recommendation that can adapt to various data types and industry requirements, making the solution reusable across multiple problems while maintaining effectiveness
Solution Approach 2:
The framework allows customization of model parameters based on specific problem characteristics, data properties, and industry requirements. By adjusting parameters such as model type, training data selection, and evaluation metrics, the same framework can effectively address different predictive challenges without requiring completely separate solutions
2Measurement precision
If customized predictive models are generated for each specific problem, then accuracy can be improved, but the complexity of the system increases
Solution Approach 1:
The framework is divided into distinct functional modules: problem characterization module, data evaluation module, model recommendation module, and model generation module. Each module handles a specific aspect of the predictive modeling process, making the overall complex system manageable through clear separation of concerns while still delivering customized accurate models
3Adaptability or versatility
If a universal framework is created to generate customized predictive models, then adaptability across different problems is improved, but the complexity of model selection and configuration increases
Solution Approach 1:
The framework performs self-characterization and self-evaluation by automatically analyzing problem inputs and data properties to determine the most suitable predictive models. The system autonomously recommends models based on the characterized problem features and evaluated data characteristics, reducing the operational burden on users while maintaining high adaptability
Data Source
AI summary
Systems and methods for a framework for generating predictive models for media planning. In some aspects, control circuitry receives, from a database, reference data associated with a program. The control circuitry receives a future date for insertion of an advertisement during transmission of the program. The control circuitry determines a prediction period between a current date and the future date. The control circuitry determines whether the prediction period exceeds a threshold period. If the prediction period does not exceed the threshold period, the control circuitry selects a first type for a predictive model. If the prediction period exceeds the threshold period, the control circuitry selects a second type for the predictive model. The control circuitry trains the predictive model according to the selected type and based on the reference data. The control circuitry predicts, based on the predictive model, an average audience for insertion of the advertisement.