ML Content Supervision via Intermediary Mediator
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Solution Overview
Problem
There is a need to effectively supervise and monitor content displayed on computing devices, particularly for users like children and employees, to prevent exposure to harmful or inappropriate content such as violence, gambling, and sexually explicit imagery, which existing technologies have not adequately addressed.
Innovation Solution
The method involves using machine learning models to identify designated content types on user interfaces and generate a graphical user interface (GUI) that provides educational counseling and restricts access, while also presenting data representing the detection of such content, incorporating educational material to counsel users on the impact of their exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to identify designated content types on user interfaces, then content supervision effectiveness is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary content supervision system that acts as a mediator between the user and the content displayed on computing devices. This system includes a content supervisor component that uses machine learning models to analyze content and determine whether it matches designated content types, thereby improving supervision effectiveness without requiring the end-user device to become more complex. The intermediary system handles the complexity of ML model execution separately.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based content filtering systems with machine learning models. Instead of using predefined keywords or simple pattern matching, the system employs ML models to intelligently identify and classify content types, significantly improving the reliability of content supervision. The ML models can understand context, semantics, and nuanced content characteristics that rule-based systems cannot detect.
2Object-affected harmful factors
If access to interfaces containing inappropriate content is restricted, then user safety is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the content supervision system provides information to users about why certain content is being restricted. The system generates notifications that explain the rationale for blocking specific content, allowing users to understand the supervision decisions. This feedback loop maintains user safety while improving ease of operation by making the system's behavior transparent and predictable, reducing user frustration.
Solution Approach 2:
The system performs preliminary content analysis and restriction decisions before users encounter inappropriate content. By proactively identifying and blocking harmful content before exposure, the system prevents potential harm without requiring users to navigate through problematic material. The preliminary action approach ensures user safety is maintained while minimizing disruption to legitimate user activities.
3Loss of information
If educational material is provided to counsel users on content impact, then user awareness is improved, but loss of time increases
Solution Approach 1:
The patent applies partial action by providing educational material selectively rather than universally. The system analyzes the specific content that triggered the restriction and provides targeted educational information relevant to that particular content type and the user's context. This approach improves user awareness about specific harmful content categories without requiring users to consume excessive amounts of generic educational material, thereby reducing time loss while maintaining effectiveness.
Data Source
AI summary
The present disclosure is directed to counseling a user with respect to supervised content. In particular, the methods and systems of the present disclosure may: determine, based at least in part on one or more machine learning (ML) models, that one or more interfaces displayed to a user include content of a content type designated by a content supervisor of the user for identification; and, responsive to determining that the interface(s) include content of the content type, generate data representing a graphical user interface (GUI) for presentation to the user, the GUI indicating detection of the content of the content type and comprising educational material counseling the user with respect to the content type.