Personalized Premium Content Highlighting via Viewing History
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Solution Overview
Problem
Users often miss or are unaware of premium multimedia content offered by television systems due to ineffective advertising, resulting in wasted advertising efforts as many users are not interested in the advertised programs.
Innovation Solution
A method and system for providing highlights of premium multimedia programs to users based on their preferences, obtained from viewing history and user input, which records and presents these highlights without user input, using a television receiver with a microprocessor and computer-readable storage medium to identify and showcase relevant content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional advertising methods (television commercials, print ads) are used to promote premium content, then advertising reach is maximized, but advertising effectiveness deteriorates because many users are uninterested in the advertised programs
Solution Approach 1:
The system performs preliminary actions by analyzing user viewing history and preferences before premium content is advertised, pre-identifying which users are likely to be interested in specific premium content. This allows the system to proactively target advertisements to the right users before the advertising campaign begins, rather than using broad traditional advertising methods.
Solution Approach 2:
The system enables self-service by automatically analyzing user viewing patterns and generating personalized content recommendations without requiring manual user input or intervention. The television system autonomously processes viewing history data, identifies user preferences, and determines which premium content to advertise to which users, eliminating the need for manual advertising campaign management.
2Reliability
If premium content is made accessible only after purchase, then content value is protected, but user engagement deteriorates because users miss or are unaware of available content
Solution Approach 1:
The system introduces an intermediary mechanism that analyzes user viewing history and generates personalized recommendations as a bridge between the user and premium content. This intermediary process provides users with targeted information about premium content that matches their interests, enabling them to make informed purchasing decisions without compromising content protection or accessibility control.
3Measurement precision
If user preferences are obtained through manual input, then preference accuracy is improved, but system complexity increases due to requiring user interaction
Solution Approach 1:
The system implements feedback by continuously monitoring and analyzing user viewing history data to automatically determine user preferences. This feedback loop processes viewing patterns over time, refining preference accuracy without requiring direct user input. The system learns from user behavior patterns, such as what types of programs users watch and when, to generate increasingly accurate content recommendations.
Data Source
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AI summary
One embodiment may take the form of method for providing highlights of multimedia content that may be of interest to a user of a television system. The interest of a particular user of the television system may be obtained from a viewing history about the user. From this information, one or more available multimedia programs may be identified as being of interest to the user. Highlights of such content, such as pay-per-view or other premium content, may be gathered and sent to or recorded on the receiver associated with the user such that the highlights may be presented to the user during use of the television system. The highlights may be viewed by the user through the television system and, if the user is interested in the content, may also purchase or otherwise access the highlighted programs.