Contents Receiving Device Automating Downloads via Agent Preference Weights
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Solution Overview
Problem
Existing content downloading methods require users to manually search for and manage downloads, lacking automation in satisfying user preferences, especially in server-client and P2P networks.
Innovation Solution
A contents receiving device and storage medium that utilize a contents table and agent table to prioritize content downloads based on user preference weights and deviation, automatically selecting contents from servers or P2P nodes with the highest weights and least preference deviation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual content search and download management is used, then user control over content selection is maintained, but user time and operational effort are significantly consumed
Solution Approach 1:
The system enables self-service automation where the content receiving device automatically searches for, selects, and downloads contents based on user-defined preferences without requiring manual user intervention. The automated agent continuously monitors content sources, evaluates available contents against stored preference criteria, and executes downloads autonomously, eliminating the need for users to manually search and manage content downloads.
Solution Approach 2:
An automated agent acts as an intermediary between the user and content providing servers/P2P networks. The agent stores and processes user preferences, autonomously evaluates available contents, and executes download decisions on behalf of the user, thereby reducing both time consumption and operational complexity while maintaining user control over selection criteria.
2Ease of operation
If automated content downloading based on user preference is implemented, then user time and effort are reduced, but system complexity increases due to preference management mechanisms
Solution Approach 1:
The system creates simplified copies or representations of user preferences in the form of structured preference data stored in the agent. Instead of managing complex user behaviors and decisions, the system stores essential preference parameters (content types, quality requirements, source preferences) that can be automatically processed and matched against available contents, reducing operational complexity while maintaining automation capability.
3Productivity
If content selection is automated based on preference weights, then downloading efficiency is improved, but precision in matching user preferences requires complex evaluation mechanisms
Solution Approach 1:
The system transforms qualitative user preferences into quantifiable parameters with assigned weights and priorities. Each content attribute (type, quality, source) is converted into measurable parameters that can be systematically evaluated and compared. The automated agent calculates matching scores based on these parameters, enabling precise preference matching through mathematical evaluation rather than complex qualitative assessment.
Data Source
AI summary
A contents receiving device may automatically receive contents, and a recording medium may store a contents receiving program. The contents receiving device may refer to an agent table, receive contents having the greatest weight of the agent having the least preference deviation as a top priority from a contents providing server or a P2P network, and store the contents in a contents storage unit. Therefore, the contents receiving device and a device for operating the contents receiving program may automatically download contents satisfying the user's preference. Further, the devices may allow easy finding of user preferences on the contents and similarities on contents preference with respective agents.