Topical Data Feed Aggregation Service for Scalable Monitoring
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Solution Overview
Problem
Users face inefficiencies in monitoring and filtering large volumes of data items from multiple data feeds, as existing methods are computationally intensive, may violate acceptable use policies, and incur high network transport costs, leading to missed topically related items and redundant data.
Innovation Solution
A system that aggregates topical interests of multiple users to perform sophisticated analysis on a large set of data items, generating topical data feeds that are scalable and cost-effective, allowing for automated polling and reducing bandwidth usage, while providing accurate and personalized content to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a user monitors multiple data feeds individually to stay informed about a topic, then the user can access topically related data items, but the user must visit various data sources frequently and review large volumes of data items, resulting in loss of time and inefficient use of resources
Solution Approach 1:
The patent combines multiple data feeds from different data sources into a single consolidated data feed. The system receives data items from multiple data feeds, identifies topics associated with each data item, and generates a consolidated data feed that aggregates topically related data items. This merging approach allows users to access all topically related data items from multiple sources in one location, eliminating the need to visit various data sources individually and significantly reducing the time required to stay informed about a topic.
2Quantity of substance
If a user applies filtering mechanisms to data items using keywords, topical identifiers, or webpage links, then the user can reduce the volume of data items to review, but the filtering may be inefficient and miss topically related items that use synonyms, acronyms, or contextual references
Solution Approach 1:
The patent introduces a topic identification component as an intermediary between data item reception and user delivery. This component performs sophisticated topical analysis including natural-language parsing, language translation, and contextual analysis to accurately identify topics associated with data items. By using this intermediary layer with advanced analysis capabilities, the system can accurately identify topically related items even when they use synonyms, acronyms, or contextual references, without requiring users to manually filter through large volumes of data.
3Measurement precision
If a user performs computationally intensive contextual analysis on data items through natural-language parsing and language translation, then the accuracy of topic identification improves, but the computational resources and time required increase significantly
Solution Approach 1:
The patent segments the computational workload by separating topic identification into a dedicated component that processes data items independently of user-specific filtering operations. The system performs sophisticated topical analysis including natural-language parsing and language translation at the data feed consolidation level, before distributing consolidated feeds to multiple users. This segmentation allows computationally intensive analysis to be performed once for all users rather than individually for each user, significantly reducing total computational resource consumption while maintaining high accuracy in topic identification.
4Adaptability or versatility
If a user allocates sufficient bandwidth to receive a large number of data feeds with many data items, then the user can monitor more data sources, but the network transport costs become unacceptable
Solution Approach 1:
The patent combines data feeds from multiple data sources into a single consolidated data feed, which dramatically reduces network transport requirements. Instead of requiring separate bandwidth allocation for each data feed, the system receives data items from multiple feeds, performs topic identification and consolidation, and delivers a single aggregated feed to each user. This merging approach enables the system to monitor a large number of data sources while keeping network transport costs acceptable, as the consolidated feed contains only topically relevant items rather than duplicate or redundant data.
Data Source
AI summary
Data items of various data feeds (such as articles posted to a website or entries in an RSS feed) may be associated with various topics, but a user may be unable to monitor a large number of data feeds, and to avoid previously reviewed data items while searching for new data items. An aggregation service may monitor many data feeds, perform an automated topical evaluation of the data items, and generate a set of topical data feeds. Additional services may also be applied, such as filtering the topical data feeds by various criteria, translating data items from a native language into another language, and removing equivalent data items, such as articles redundantly covering the same news story. A centralized or cooperatively distributed service may scale for improved efficiency and value, since each data feed may be monitored and each data item received and evaluated on behalf of many users.