Trend Prediction Through Multi-Source Data Normalization
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Solution Overview
Problem
Conventional trend data processors are inefficient and fail to recognize or distinguish consumer desires due to processing limitations, making it difficult for marketers to effectively gather, aggregate, and present useful trend data from heterogeneous social platforms.
Innovation Solution
A predictive/aggregation component that obtains trend data from multiple sources, cleans and normalizes it, applies predictive algorithms, and utilizes statistical techniques to determine significance, allowing for the generation and sale of trends based on consumer demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional trend data processors are used to gather and process data from heterogeneous social platforms, then data aggregation is attempted, but processing efficiency is poor and consumer desires are not recognized or distinguished
Solution Approach 1:
The patent segments the trend data processing system into multiple specialized components: a data collection module that gathers data from heterogeneous social platforms, a data cleaning module that filters and normalizes the collected data, a trend identification module that uses machine learning algorithms to recognize consumer desires, and a trend presentation module that displays processed trends. This segmentation allows each component to specialize in specific tasks, improving overall processing efficiency while preserving consumer desire information.
Solution Approach 2:
The patent introduces an intermediary processing layer between raw social media data and final trend presentation. This intermediary includes data normalization components that standardize heterogeneous data formats, and machine learning models that act as mediators to interpret consumer desires from unstructured social media content. These intermediaries transform raw data into meaningful trend information without losing critical consumer preference signals.
2Adaptability or versatility
If data from multiple heterogeneous social platforms is gathered and aggregated, then more comprehensive trend information is obtained, but data processing complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple heterogeneous social platforms through a single standardized interface. The system includes universal data collection protocols that adapt to different platform formats, and a standardized data normalization layer that converts diverse social media data structures into a unified internal representation. This multi-functional approach enables comprehensive multi-platform aggregation while maintaining manageable system complexity through standardization.
3Reliability
If conventional processing methods are used, then existing trends can be displayed, but new consumer desires cannot be distinguished or predicted
Solution Approach 1:
The patent applies preliminary action by implementing continuous background processing that pre-analyzes social media data streams using machine learning models. The system performs preliminary trend detection and consumer desire recognition on incoming data before formal trend compilation, allowing it to identify emerging patterns early. This preliminary analysis enhances both the accuracy of established trends and the detection of new consumer desires that conventional methods would miss.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously monitors the performance of its trend identification algorithms and adjusts its processing parameters based on observed patterns. Machine learning models are trained on historical trend data and refined through feedback from actual consumer behavior patterns, improving both the reliability of trend accuracy and the detection capability for emerging consumer desires over time.
Data Source
AI summary
Predicting trends may include obtaining trend data from one or more sources, extracting a plurality of trends from the trend data, and producing permutations combining terms or concepts appearing in the plurality of trends to create trend candidates. A first term from a first trend or concept in the plurality of trends may be combined with a second term or concept from a second trend in the plurality of trends.