AI Document Search Personalization for Faster Enterprise Retrieval
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Solution Overview
Problem
Existing document management systems struggle to provide personalized document search and creation tailored to individual users, making it difficult to manage and find documents efficiently within integrated information systems.
Innovation Solution
An apparatus utilizing artificial intelligence to personalize document search and creation by analyzing user search patterns, interests, and work characteristics, generating optimized search results and new documents through data preprocessing, learning, and model generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If documents are managed in an integrated manner using enterprise portal, then information integration and accessibility are improved, but document search personalization and user-specific optimization deteriorate
Solution Approach 1:
The system applies local quality by customizing document search results and creation assistance according to individual user characteristics, search patterns, and work contexts. Instead of uniform treatment for all users, the system tailors the information presentation and search optimization to each user's specific needs and behavior patterns.
Solution Approach 2:
The system enables self-service by automatically learning user preferences, search patterns, and work characteristics to personalize document management without requiring manual configuration. The system autonomously adapts to user needs through continuous learning from user interactions and behavioral data.
2Productivity
If comprehensive document management is implemented, then document accessibility is improved, but search efficiency for individual users deteriorates
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing documents based on user profiles and search patterns before actual search queries are made. It anticipates user needs by analyzing historical data and preparing relevant document recommendations in advance, reducing the time required for actual search operations.
Solution Approach 2:
The system implements feedback by continuously monitoring user interactions with documents and search results, using this information to refine and update user profiles. This feedback loop enables the system to improve search accuracy and relevance over time, making subsequent searches more efficient.
3Measurement precision
If AI-based personalization is introduced, then search result optimization is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by using a single AI-based personalization framework that serves multiple functions: optimizing search results, generating personalized document recommendations, assisting with document creation, and adapting to different user roles and contexts. This multi-functional approach consolidates complexity into a unified system rather than requiring separate specialized systems.
Data Source
AI summary
The present disclosure relates to an apparatus for personalizing document search and creating a new document using artificial intelligence and a method thereof, and according to the present disclosure, and includes obtaining first data including a search history of a user, a document viewing history, department information, and an electronic document through an input module; preprocessing the first data; learning the preprocessed first data; generating a document search personalization and new document creation model using the learning result; obtaining a request message through the input module; generating at least one of a document search result and a new document corresponding to the request message using the document search personalization and new document creation model; and controlling the display to display the generated result.