Multi-Source Trend Prediction Through Heterogeneous Data Aggregation

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Solution Overview

Problem

Existing trend data processing systems are inefficient and fail to recognize consumer desires due to their inability to aggregate and process data from multiple heterogeneous social platforms, leading to a lack of effective trend prediction and marketing strategies.

Innovation Solution

A system that includes a predictive/aggregation component to obtain, clean, and normalize trend data from multiple sources, using predictive algorithms and statistical techniques to identify significant trends, generate new trends, and present them in a consumer-driven marketplace.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional trend data processors are used to process data from social platforms, then processing can be performed on single data sets, but they fail to aggregate data from multiple heterogeneous sources and result in inefficiencies that prevent recognition of consumer desires

Engineering Contradiction:

Improveability to process multiple heterogeneous data sourcesVSAvoidprocessing efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system employs a universal data processing architecture that can handle multiple heterogeneous data sources (social media platforms, search engines, news outlets, blogs) through a single integrated framework. The processing pipeline includes universal components such as data collectors, parsers, normalizers, and analyzers that work across different data types and sources, enabling the system to aggregate and process diverse trend data efficiently without requiring separate processing systems for each source.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of information

If data is gathered from multiple heterogeneous social platforms, then more comprehensive trend information can be obtained, but the data aggregation and processing becomes evasive and inefficient

Engineering Contradiction:

Improvecompleteness of trend dataVSAvoidcomplexity of data aggregation system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system segments the complex data aggregation process into distinct modular components: data collectors that gather raw data from various platforms, parsers that extract relevant information, normalizers that standardize data formats, and analyzers that process the normalized data. Each component handles a specific aspect of the aggregation pipeline, making the overall complex system manageable and maintainable while comprehensively gathering data from multiple heterogeneous sources.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces intermediary components such as data normalizers and standardizers that act as mediators between diverse data sources and the analysis engine. These intermediaries translate and harmonize data from different platforms into a unified format, enabling efficient processing while maintaining comprehensive data coverage from multiple heterogeneous sources.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Speed

If conventional processing methods are used, then processing speed may be maintained, but consumer desires are not recognized or distinguished due to inefficiencies

Engineering Contradiction:

Improvedata processing speedVSAvoidaccuracy in identifying consumer desires

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The system performs preliminary actions by pre-processing and normalizing data as it is collected, preparing it in advance for analysis. Data is cleaned, standardized, and structured during the collection phase rather than during the analysis phase, enabling faster and more accurate identification of consumer desires when the analysis is performed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system replaces traditional mechanical processing methods with智能化 (intelligent) processing techniques including machine learning algorithms and advanced analytics. These智能化 methods can rapidly process and analyze large volumes of normalized data to accurately identify consumer desires and trends, maintaining high processing speed while significantly improving measurement precision.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS12423719B2Trend prediction

Publication Date: 2025.09.23 ALPERT MARTIN A

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.