Adjustable Automated Forecasting System for Promotions
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Solution Overview
Problem
Existing data processing methods struggle to provide understandable and adjustable forecasts for promotions, often 'black-boxing' data manipulations and failing to account for user-specific adjustments, which limits their usability for laypersons.
Innovation Solution
A computer-implemented method and system using a machine learning model to generate adjustable automated forecasts for promotions by receiving historical data and user input parameters, determining optimized parameters, and allowing users to adjust these parameters for real-time outcome visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated forecasting systems use complex machine learning models to generate accurate promotion forecasts, then forecast accuracy is improved, but the system becomes a 'black box' that is difficult for laypersons to understand and adjust
Solution Approach 1:
The patent introduces an intermediary layer between the complex machine learning model and the user. This layer includes a graphical user interface that displays forecast results in an understandable format and allows users to adjust input parameters without needing to understand the underlying complex algorithms. The system mediates between the black-box model and the layperson user, making the system both accurate and usable.
Solution Approach 2:
The system automatically handles the complexity of the machine learning model internally, requiring minimal user intervention. Users can simply input parameters and receive optimized forecasts without needing to understand or configure the complex model itself. The system serves itself by managing the computational complexity while providing simple user interactions.
2Loss of information
If the system provides detailed data manipulations and model parameters to users, then transparency is improved, but the complexity of the system increases making it harder to use
Solution Approach 1:
The patent applies local quality by providing different levels of information to different users or at different stages of interaction. The system maintains full transparency of data manipulations internally while presenting simplified information to users through the graphical interface. Users can access detailed information when needed but are not overwhelmed by it during normal operation, creating locally optimized information presentation at different system layers.
3Adaptability or versatility
If the system allows extensive user adjustments to parameters, then adaptability is improved, but the computational resources and time required to recalculate forecasts increase
Solution Approach 1:
The patent implements partial action by allowing users to adjust only specific parameters they are interested in rather than requiring complete re-evaluation of all model parameters. The system selectively recalculates forecasts based on the specific adjustments made, rather than performing exhaustive recomputations, thus maintaining fast response times while providing extensive adaptability.
Data Source
AI summary
A system and method generation of adjustable automated forecasts for a promotion. The method includes: determining, using a machine learning model, a set of forecasts each based on different parameters; determining at least one set of optimized parameters that maximize an outcome measure of the forecast for the promotion; generating a graphical representation of the forecast; receiving an adjustment to at least one parameter from a user; determining an adjusted outcome measure of the forecast for the promotion by applying the adjustment to the machine learning model; generating an adjusted graphical representation of the forecast; and displaying the adjusted graphical representation to the user.