People Highlight Selection and Suppression Engine
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Solution Overview
Problem
Personal computing devices struggle to effectively present people highlight information, as existing systems often display irrelevant or annoying information, leading to user fatigue and decreased utility.
Innovation Solution
The technology selectively determines which people highlight information to display on a user device based on past user behavior, personal settings, people highlight history, and presentation logic, using a suppression engine to remove unhelpful information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If people highlight information is displayed to provide contextual information about encountered people, then the user gains better understanding of who the person is and their connections, but the user may experience annoyance and distraction from irrelevant or excessive information
Solution Approach 1:
The system dynamically changes the parameter of information selection by using machine learning models to predict user interest and selectively display only those people highlights that are predicted to be relevant, transforming the static display approach into a dynamic, adaptive one that adjusts based on user behavior patterns
Solution Approach 2:
The system implements feedback loops where user interactions with people highlights (such as viewing, dismissing, or engaging with the information) are tracked and used to refine future selection decisions, allowing the system to learn from user responses and continuously improve the relevance of displayed information
2Quantity of substance
If all available people highlight information is presented to ensure completeness, then the user receives comprehensive information, but the system complexity and processing requirements increase
Solution Approach 1:
The system extracts only the essential and relevant people highlight information from the complete dataset by applying filtering criteria based on user profiles, context analysis, and predictive modeling, separating the useful information from the redundant or irrelevant data
Solution Approach 2:
The information selection process is segmented into multiple independent components including user profile analysis, context detection, machine learning prediction, and hierarchical filtering, allowing each component to handle specific aspects of the selection task and reducing overall system complexity
Data Source
AI summary
Technology is disclosed for selecting at least one people highlight, with respect to a person of interest, to be shown on one or more user devices. The user device is monitored to determine a user interest in a person of interest. A set of possible people highlights, with respect to the person of interest is determined. A determination is made as to whether any of the possible people highlights should be suppressed and not presented to the user on the user device. Any people highlights that were determined to be people highlights that should be suppressed and not presented are removed to create a remaining set of possible people highlights. At least one of the remaining set of possible people highlights are presented on the user device.