ML-Enhanced User Stimuli Pairing for Digital Profile Search
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Solution Overview
Problem
Traditional search engines lack domain-specific configurations, leading to unsatisfactory and inaccurate search results for users requiring domain-specific knowledge, as they fail to effectively pair user inputs with relevant digital profiles and resources.
Innovation Solution
A computer-implemented method using machine learning-based classification models to extract features from user stimuli, derive classification labels, and construct search queries that automatically search a database of digital profile data, selectively pairing results based on these labels and displaying them prioritized according to relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to search for information, then users can access a broad range of digital items, but the search results lack domain-specific accuracy and relevance
Solution Approach 1:
The patent segments the search system into multiple specialized search engines, each configured for a specific domain (e.g., legal, medical, financial). Instead of using a single general-purpose search engine, the system divides the search functionality into domain-specific components that can be selectively applied based on the user's query and profile, thereby improving search accuracy within each domain.
Solution Approach 2:
The patent applies local quality by configuring each search engine with domain-specific parameters, algorithms, and data sources tailored to its particular domain. Each search engine has customized settings and expertise localized to its domain, allowing it to provide highly accurate results for that specific area while the overall system maintains versatility across multiple domains.
2Measurement precision
If machine learning classification models are used to enhance search pairing, then search accuracy and relevance improve, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-configuring multiple domain-specific search engines and their associated machine learning classification models before runtime. The system prepares the pairing logic and classification frameworks in advance, so that when a user query arrives, the pre-established models can quickly and accurately match the query to relevant digital profiles without requiring complex real-time decision-making, thereby reducing operational complexity while maintaining high pairing accuracy.
Data Source
AI summary
A system and method for pairing user stimuli-to-online digital profile data includes receiving user stimulus data; computing a classification inference, by a machine learning-based model, based on features extracted from the user stimulus data, wherein the classification inference includes a machine learning-based classification label identified from a plurality of digital profile subdomain classification labels; constructing a digital profile search query, wherein constructing the digital profile search query includes: deriving a digital profile search parameter based on the machine learning-based classification label, and defining the digital profile search query using the digital profile search parameter; executing the digital profile search query, wherein executing the digital profile search query includes: searching a corpus of digital profile data based on the digital profile search parameter; and selectively pairing the digital profile search query to digital profiles of the corpus of digital profile data based on the search parameter; and displaying the digital profiles.