Streaming Media Selection Data Aggregation for Personalized Content
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Solution Overview
Problem
Viewers of digital content often face difficulties in efficiently skipping or accessing relevant portions, as existing media player functions like fast-forward and scroll bars do not effectively aggregate user preferences or ratings, leading to a lack of personalized content selection.
Innovation Solution
A system and method for generating and retrieving selection data that allows users to select and rate digital content portions, which are then stored and transmitted across a network, enabling cumulative usage data representation and graphical display, allowing users to view the most popular or highly-rated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional media player functions (fast-forward, scroll bar) are used to skip or access content portions, then users can navigate digital content, but user preferences and ratings are not aggregated, resulting in lack of personalized content selection
Solution Approach 1:
The patent introduces a selection data aggregation system that acts as an intermediary between users and digital content. This system collects selection data from multiple users, aggregates it, and uses it to generate personalized content recommendations. The intermediary layer enables personalized content selection without requiring direct complex interactions between individual users and the content delivery system.
Solution Approach 2:
The system automatically aggregates user selection data and generates personalized content recommendations without requiring manual input or configuration from users. The aggregation process operates autonomously, collecting data from media players, storing it in databases, and generating recommendations based on aggregated patterns, thereby providing self-service personalized content selection.
2Adaptability or versatility
If selection data is collected and aggregated from multiple users, then personalized content selection is enabled, but data storage and processing requirements increase
Solution Approach 1:
The patent extracts only the essential selection data elements needed for aggregation - specifically, information about which content portions were selected and by how many users. By focusing on extracting only the necessary data components (selection events, user identifiers, content identifiers) rather than all possible user behavior data, the system reduces data storage requirements while still enabling effective personalized content selection.
Solution Approach 2:
The system performs preliminary data aggregation and processing in the background, continuously collecting and storing selection data from media players before users need personalized recommendations. This preliminary action involves pre-processing the data structure, organizing it in databases, and preparing aggregated statistics, so that when users access content, the system can quickly provide personalized recommendations without requiring large amounts of real-time data processing.
Data Source
AI summary
In one embodiment, a computer system is described as including a receiver to receive selection data, the selection data relating to a number of times a particular portion of digital content has been accessed, and an updating module to update selection data contained in a database record relating to digital content, the database record containing a sum of selection data. Further, a Graphical User Interface (GUI) is described as including a display and a selection device, a method of providing and selecting from a pane on the display, the method comprising retrieving a set of data to be displayed in the pane, displaying the data in the pane, receiving a selection input signal indicative of the selection device pointing at a selected position within the pane, and in response to the input signal, playing a portion of a data file.