Hosted Content Data Stream Segmentation and Polarity Analysis
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Solution Overview
Problem
Conventional methods for capturing user content data are limited as they primarily receive closed-ended responses from enterprises, whereas user content data streams hosted on third-party platforms are more open-ended and diverse, lacking comprehensive analysis tools.
Innovation Solution
A system utilizing a Remote Host API to interface with third-party platforms, capturing and analyzing user content data streams using artificial intelligence and natural language processing, which filters, classifies, and determines polarity and sentiment, enabling enterprises to identify system or service issues and enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If user content data is captured through closed-ended data requests from enterprises, then the data structure is simple and easy to process, but the information conveyed is limited and lacks diversity
Solution Approach 1:
The patent introduces a third-party platform as an intermediary that hosts user content data streams. This intermediary enables enterprises to access diverse user-generated content without requiring enterprises to directly manage complex data collection systems. The third-party platform simplifies the architecture while providing rich, diverse information through its hosting capabilities.
Solution Approach 2:
The patent segments the data capture process into distinct components: a third-party platform for hosting diverse content, a remote host API for standardized data requests, and an analysis system for processing the data. This segmentation allows each component to specialize in specific functions, improving overall system capability while maintaining manageability.
2Adaptability or versatility
If user content data streams are hosted on third-party platforms, then the data is more open-ended and diverse, but the data is harder to capture and analyze
Solution Approach 1:
The remote host API serves as an intermediary layer between the third-party platform and the analysis system. This API standardizes the interaction with the third-party platform, providing consistent interfaces for data requests regardless of the platform's internal complexity. This abstraction reduces the difficulty of detecting and measuring data characteristics while maintaining access to versatile content.
Solution Approach 2:
The patent replaces manual data collection and analysis processes with automated AI-based analysis systems. Instead of requiring complex manual processes to handle diverse data formats, the system uses automated machine learning models to detect, measure, and analyze content characteristics, significantly reducing the difficulty of working with versatile data.
3Productivity
If automated AI analysis is applied to hosted content data, then the data processing efficiency is improved, but the system complexity increases
Solution Approach 1:
The patent introduces specialized analysis services as intermediaries between the raw data and the final analysis outputs. These services include sentiment analysis, topic modeling, and other AI-based processing functions that handle complexity centrally, allowing the main system to focus on data coordination and interpretation without managing the complexity of individual analysis algorithms.
Solution Approach 2:
The analysis system is designed with multi-functional capabilities that can handle various data types and analysis requirements through a unified architecture. The same core infrastructure supports multiple analysis tasks (sentiment detection, topic identification, trend analysis), reducing overall system complexity compared to having separate specialized systems for each function while maintaining high processing efficiency.
Data Source
AI summary
Disclosed are systems and methods that that automatically classify, filter, and reduce large volumes of hosted content data using artificial intelligence technology. The aggregated hosted content data is reduced by representing the hosted content data as sets of data polarity identifiers or data polarity values that correspond to one or more sequencing identifiers that are displayed on a graphical user interface. Hosted content data packets are segmented by labeling the hosted content data packets with a sequencing identifier. The hosted content data packets are processed utilizing neural network technology to classify the hosted content data according to a polarity identifier, polarity value, sentiment identifier, or one or more subject identifiers.