An AI-based intelligent text review system

CN121786178BActive Publication Date: 2026-06-30SHANGHAI NAIR IND CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI NAIR IND CO LTD
Filing Date
2025-12-15
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing technologies lack uniformity and flexibility in text review, failing to dynamically adjust review dimensions and standards according to the professional requirements of different fields. Furthermore, they lack deep semantic understanding and closed-loop optimization capabilities, resulting in insufficient review accuracy and weak adaptability.

Method used

An AI-based intelligent text review system is adopted. The system obtains review semantics through a sample collection module, generates preset text by using Markov chains and random selection and recombination, optimizes the template structure by combining domain knowledge base and frequent itemset mining, constructs a multi-dimensional review model, and continuously improves the review quality through a closed-loop iterative optimization mechanism.

Benefits of technology

It achieves dynamic adjustment of evaluation criteria and deep semantic analysis, which significantly improves the relevance and accuracy of the evaluation, ensures that the evaluation results truly reflect the text quality, and continuously adapts to new evaluation requirements through an iterative optimization mechanism, thereby improving the model's adaptability and growth.

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Abstract

This invention relates to the field of intelligent text review technology. Specifically, it relates to an artificial intelligence-based intelligent text review system. It includes a sample collection module, a preset text management module, a review model management module, and a review result management module. The sample collection module is used to acquire review topics and review semantics, and classify semantic requirements based on the review semantics. By integrating a domain knowledge base, multi-dimensional feature extraction, and a closed-loop iterative optimization mechanism, it effectively solves the problems of inconsistent review standards and insufficient accuracy in existing technologies. The system determines the technical field through in-depth analysis of review semantics, calls the corresponding domain knowledge base to construct structured review dimensions and templates to be filled, and optimizes the template structure by combining frequent itemset mining. This ensures that the review standards not only conform to domain professional norms but can also be dynamically adjusted according to the core components of the text, significantly improving the relevance and accuracy of the review and avoiding the drawbacks of the one-size-fits-all approach in existing technologies.
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