Spoiler Suppression via Content Consumption History
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Solution Overview
Problem
Users accessing the internet may encounter information about media content they have not previously accessed, which can be irrelevant or spoil their experience by revealing unexperienced portions of the media, leading to a lack of enjoyment.
Innovation Solution
A system that generates spoiler data from media content using natural language processing, speech-to-text processing, and image recognition, and compares it with user-specific content consumption data to suppress or modify network content on user devices, ensuring that only relevant information is displayed, preventing spoilers from being output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If network content is provided to users without filtering, then information availability is improved, but user experience deteriorates due to spoilers and irrelevant information
Solution Approach 1:
The system performs preliminary analysis of network content to identify spoilers and irrelevant information before presenting it to users. By pre-processing content to detect media content references and determine user exposure status, the system prevents harmful information from reaching users in the first place, rather than filtering it after delivery.
Solution Approach 2:
The system introduces an intermediary processing layer between network content sources and users. This intermediary analyzes content for spoilers, cross-references it with user consumption history, and selectively blocks or flags harmful information before it affects the user experience, while still allowing relevant information to pass through.
2Object-affected harmful factors
If content filtering is implemented to prevent spoilers, then user experience is improved, but device complexity increases due to multiple processing modules
Solution Approach 1:
The system employs multi-functional processing modules that perform multiple tasks. For example, the content analysis module not only identifies spoilers but also determines user exposure status, matches content against consumption history, and makes filtering decisions. This consolidation reduces the number of separate components needed while maintaining comprehensive filtering capability.
Solution Approach 2:
The system utilizes the user's own consumption history data, which is already stored and maintained by the media content management system, to perform the filtering function. Rather than requiring external databases or additional user profiles, the system serves itself by leveraging existing user data to automatically prevent spoilers, reducing the need for additional infrastructure.
3Loss of information
If spoiler detection and filtering is implemented, then relevant information delivery is improved, but processing time increases due to analysis requirements
Solution Approach 1:
The system performs spoiler detection and user exposure status determination in advance, before content is delivered to users. By pre-analyzing network content and comparing it against user consumption history beforehand, the system prepares filtered content ahead of time, reducing the processing delay that would occur if filtering were performed at the moment of delivery.
Data Source
AI summary
Described are techniques for associating messages with a particular portion of media content. A message received from a first device, associated with a portion of media content stored on the first device, may be provided to a second device and stored in association with a corresponding portion of media content on the second device. Content consumption data associated with the second device may indicate whether the second device has previously accessed the portion of the media content. The message may be suppressed from presentation if the second device has not previously accessed the corresponding portion of the media content. The message may be presented to the second device when the corresponding portion of the media content is accessed.