Trend Prediction System Using AI Aggregation for Social Data
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Solution Overview
Problem
Marketers face challenges in gathering and processing data from diverse social platforms to identify and present consumer trends effectively, as conventional trend data processors are inefficient and fail to recognize consumer desires accurately.
Innovation Solution
The system includes a predictive/aggregation component that obtains trend data from multiple sources, cleans and normalizes it, uses predictive algorithms like AI and machine learning to combine and predict trends, and presents them to users and business owners, allowing consumer demand to influence which trends become popular.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional trend data processors are used to process data from social platforms, then data processing can be performed, but the processing efficiency is low and consumer desires are not accurately recognized
Solution Approach 1:
The system segments the data processing function into multiple specialized components: data collection module, data cleaning module, trend identification module, and prediction module. Each component handles a specific aspect of data processing, improving both efficiency and accuracy of consumer desire recognition.
Solution Approach 2:
The patent introduces an intermediary processing layer that collects data from multiple social platforms, standardizes and cleans the data, then feeds it to trend analysis algorithms. This intermediary layer acts as a bridge between raw social media data and consumer desire insights, improving processing efficiency while maintaining accuracy.
2Quantity of substance
If data is gathered from multiple heterogeneous social platforms, then more comprehensive trend data can be obtained, but data aggregation and synthesis become more difficult
Solution Approach 1:
The system employs a universal data processing framework that can handle multiple types of social media data from different platforms through standardized interfaces. The data cleaning and normalization module applies consistent processing rules across heterogeneous data sources, reducing system complexity while maintaining comprehensive data collection.
Solution Approach 2:
The patent transforms heterogeneous social media data into a standardized format by changing parameters such as data structure, normalization scales, and classification categories. This parameter transformation enables efficient aggregation of diverse data sources without increasing system complexity proportionally.
3Loss of information
If trend data is processed and synthesized, then useful information can be produced, but processing time increases
Solution Approach 1:
The system performs preliminary data cleaning, normalization, and validation during the data collection phase rather than after complete data gathering. By preparing data in advance, the system reduces the time required for subsequent trend synthesis and analysis while maintaining information quality.
Solution Approach 2:
The patent implements continuous data processing where trend analysis operates on incoming data streams in near-real-time rather than batch processing. This continuous action maintains information usefulness while minimizing processing delays through optimized algorithmic operations.
Data Source
AI summary
Predicting trends may include obtaining trend data from two or more sources, extracting meaning from the trend data including meaning from a plurality of trends, and grouping trends from the plurality of trends such that trends that have equivalent meaning but not identical expression are grouped together as an aggregated trend.