Double Blind ML Insight Interface for Ad Campaign Optimization
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Solution Overview
Problem
Current data anonymized machine learning systems face challenges in efficiently processing large datasets and generating insights without revealing underlying data, particularly in advertising campaign optimization, where traditional methods struggle to decouple machine learning models from engineering interfaces and effectively utilize proprietary data.
Innovation Solution
The Double Blind Machine Learning Insight Interface Apparatuses, Methods, and Systems (DBMLII) transform campaign configuration requests into top features and machine learning configured user interfaces, employing dynamic data pruning, a UI to machine learning bridge, and externalized optimization pipelines that do not require underlying data, allowing for independent work on machine learning models and leveraging proprietary data for enhanced insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning systems process large datasets to generate insights, then measurement precision and reliability improve, but device complexity and loss of time increase
Solution Approach 1:
The system segments the machine learning pipeline into distinct modular components: data preprocessing module, feature extraction module, model training module, and insight generation module. Each module operates independently with defined interfaces, allowing the system to maintain high measurement precision through specialized processing while reducing overall system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary layer between raw data and machine learning models that performs data anonymization and feature transformation. This intermediary processing layer enables the system to generate accurate insights while protecting proprietary data and simplifying the interface between data sources and models.
2Ease of operation
If machine learning models are tightly coupled with engineering interfaces, then ease of operation improves, but adaptability and ease of manufacture worsen
Solution Approach 1:
The system separates the machine learning model layer from the engineering interface layer through well-defined APIs and data contracts. The model layer can be developed, tested, and modified independently while the interface layer provides consistent user interaction, enabling both ease of operation and high adaptability.
Solution Approach 2:
The patent implements a universal interface layer that can serve multiple machine learning models and data sources through standardized protocols. This universal interface maintains ease of operation while accommodating different models and data formats, enhancing both adaptability and versatility.
3Measurement precision
If proprietary data is used to enhance insights, then measurement precision improves, but loss of information and device complexity increase
Solution Approach 1:
The system extracts only the essential features and patterns from proprietary data that are necessary for generating insights, while leaving the raw proprietary data protected and inaccessible. This extraction process maintains insight quality by preserving critical information while preventing data exposure through selective feature selection and transformation.
Data Source
AI summary
The Double Blind Machine Learning Insight Interface Apparatuses, Methods and Systems (“DBMLII”) transforms campaign configuration request, campaign optimization input inputs via DBMLII components into top features, machine learning configured user interface, translated commands, campaign configuration response outputs. A decoupled machine learning workflow generation request is obtained. A set of decoupled tasks specified via the decoupled machine learning workflow generation request is determined, wherein each decoupled task in the set of decoupled tasks is associated with a corresponding class. Dependencies among decoupled tasks in the set of decoupled tasks are determined. A decoupled machine learning workflow structure comprising the set of decoupled tasks and the determined dependencies is generated, wherein the decoupled machine learning workflow structure is executable via a decoupled machine learning workflow controller to produce machine learning results.