Asynchronous Component Selection for Targeted Advertising
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Solution Overview
Problem
Existing advertising methods are inefficient due to lack of personalization and often irritate users, leading to reduced effectiveness and legal risks, as they are not targeted effectively towards specific user groups based on behavior or needs.
Innovation Solution
A system for asynchronous selection of compatible components that monitors user environments and detects anomalies or needs, providing targeted information and solutions through a preventive notification system, which selects appropriate products or services based on user preferences and needs without intrusive advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional advertising methods are used to promote products, then product visibility is improved, but user irritation increases and advertising efficiency decreases
Solution Approach 1:
The system performs preliminary analysis of user needs and system anomalies before presenting any promotional information. By detecting anomalies in system operation and predicting future needs, the system prepares targeted product information in advance, ensuring it is presented only when relevant to the user's actual requirements rather than as intrusive advertising.
Solution Approach 2:
The system enables users to receive information about products and services that automatically match their detected needs without active seeking. By monitoring system parameters and anomaly states, the system self-determines when and what information to present, eliminating the need for users to filter through irrelevant advertising content.
2Adaptability or versatility
If targeted advertising based on user behavior is implemented, then advertising relevance is improved, but information collection complexity and legal risks increase
Solution Approach 1:
The system uses a multi-functional anomaly detection framework that serves both system monitoring and user preference identification purposes. The same state model interrogation that detects system anomalies also identifies user needs and preferences, eliminating the need for separate complex information collection systems while achieving personalized information delivery.
Solution Approach 2:
The system continuously monitors system parameters and user interactions, using feedback from anomaly detection to refine understanding of user needs. This feedback loop allows the system to adapt to changing user preferences and system states dynamically, achieving personalization through operational data rather than extensive separate information collection.
3Productivity
If intrusive advertising is displayed to maximize product exposure, then product visibility is improved, but user experience deteriorates and customers may redirect to competitors
Solution Approach 1:
The system delivers information with local quality by tailoring content specifically to each user's detected needs and system state. Rather than uniform advertising exposure, the system presents product information only in contexts where it locally matches user requirements, ensuring high relevance and value for each individual user interaction.
Solution Approach 2:
The system converts the potential harm of irrelevant advertising into benefit by using anomaly detection to identify genuine user needs. What would traditionally be wasted advertising exposure is transformed into targeted information delivery that benefits both users (relevant information) and businesses (effective promotions), eliminating the harm of user irritation while maintaining advertising effectiveness.
Data Source
AI summary
Systems and methods are presented for selection of compatible components for an observed system. An exemplary method comprises collecting parameters of one or more components of the system, assessing conformity of the one or more components of the system with a required state of the system, identifying one or more anomalies based on the assessment of conformity, analyzing the one or more anomalies to identify a class and parameters of the system corresponding to the one or more anomalies, determining one or more models of methods of restoration of the system, selecting one or more components that meets requirements of the one or more models of methods of restoration and implementing the one or more components in the system that are compatible with the system to eliminate the one or more anomalies.