Document analysis method for qualification review and similarity comparison

By combining multimodal fusion technology and a rule engine, the problems of insufficient generalization and human-machine collaboration in the bidding process have been solved, realizing intelligent parsing and review of bid documents and improving the efficiency and accuracy of the bidding process.

CN121860733APending Publication Date: 2026-04-14THREE GORGES SMART WATER TECH CO LTD
0 Cites 3 Cited by

Patent Information

Application Number
CN202511859488.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-10
Publication Date
2026-04-14

AI Technical Summary

Technical Problem

Existing bid clearing technologies lack generalizability and human-machine collaboration capabilities in the bidding and tendering field, and are unable to effectively identify and extract key information, resulting in low efficiency, poor accuracy, and insufficient information transparency.

Method used

It employs multimodal fusion technologies of OCV, CV, and NLP to perform document parsing and semantic association, constructs a multi-dimensional similarity assessment system, combines a rule engine to achieve dynamic risk judgment, supports user-defined review rules, and performs automated parsing and review through human-machine collaboration.

Benefits of technology

It has enabled automated, intelligent, and in-depth analysis and review of tender documents, improving the efficiency and accuracy of bid clearing, reducing the workload of manual review, and enhancing information transparency.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a document analysis method for qualification review and similarity comparison, and relates to the field of document intelligent analysis, the method adopts an OCR, CV and NLP multi-modal fusion technology to realize bidding document deep analysis, fuses text, layout and image features through an attention mechanism, and converts unstructured data into a structured semantic map. And fusing text, image and quotation multi-dimensional similarities, and dynamically generating a comprehensive risk score through a learnable function. And qualification review adopts a rule engine and NLP double-engine cooperation, a rigid rule guarantees a review base line, flexible semantic reasoning processes fuzzy conditions, and NLP weights are dynamically adjusted according to historical behaviors of an enterprise. Manual auditing feedback is used as continuous learning data, and the model and the rule base are optimized. According to the method, full-process automation of bidding clearing is realized, the review efficiency and accuracy are improved, dynamic configuration of rules is supported, the risk of range bidding is effectively prevented, the bidding clearing cost is reduced, and continuous evolution of the system is realized.
Need to check novelty before this filing date? Find Prior Art