Big data-driven intellectual property value dynamic evaluation and transaction matching system

Abstract

The invention discloses a big data-driven intellectual property value dynamic evaluation and transaction matchmaking system, and relates to the fields of big data analysis, artificial intelligence, financial science and technology and intellectual property management. Aiming at the problems of high subjectivity, update lag, low transaction matching efficiency and the like of traditional intellectual property evaluation, the system integrates multi-source heterogeneous data acquisition and fusion functions, a dynamic value evaluation model is constructed based on an AI technology, and real-time value quantification is realized by integrating multi-dimensional factors; supply and demand parties are accurately matched through an intelligent matching algorithm, risk early warning, a credit evaluation mechanism and an automatic transaction management process are matched, and visual decision support is provided. Experiments show that the correlation between the system evaluation result and the market trading price is improved by more than 20%, the matching success rate is more than 70%, the period is shortened by 40%, and the risk early warning accuracy is significantly superior to that of a traditional method. The system can be widely applied to the fields of intellectual property operation, investment and financing, scientific and technological achievement transformation and the like, evaluation objectivity and transaction efficiency are effectively improved, and market resource allocation is optimized.

Description

Technical Field

[0001] This invention relates to the fields of big data analytics, artificial intelligence, fintech, and intellectual property management, and in particular to a big data-driven dynamic valuation and transaction matching system for intellectual property. Background Technology

[0002] With the development of the innovation economy, intellectual property (such as patents, trademarks, and copyrights) has become an important intangible asset for enterprises and individuals. The demand for intellectual property valuation and transactions is growing, but traditional valuation methods rely heavily on expert experience and static data, resulting in strong subjectivity, outdated information, and difficulty in quantification. This leads to insufficient liquidity in the intellectual property market and low transaction matching efficiency. In recent years, although some platforms have attempted to introduce automated valuation and matching mechanisms, they generally lack in-depth mining and dynamic analysis of multi-source heterogeneous big data, making it difficult to achieve real-time, objective, and accurate valuation of intellectual property value and efficient transaction matching. Summary of the Invention

[0003] This invention provides a big data-driven dynamic evaluation and transaction matching system for intellectual property value. It utilizes multi-source big data collection, intelligent analysis, and dynamic modeling technologies to achieve real-time dynamic evaluation of intellectual property value, and uses intelligent matching algorithms to achieve efficient transaction matching between intellectual property supply and demand sides, thereby improving the liquidity and resource allocation efficiency of the intellectual property market.

Claims

1. A big data-driven dynamic evaluation and transaction matching system for intellectual property value, characterized in that, include: a) Multi-source data acquisition and fusion module, used to automatically collect and fuse heterogeneous data from multiple sources such as patent, trademark, copyright databases, market transactions, litigation, licensing, industry chain, scientific and technological literature, and social media to form a comprehensive intellectual property information database; b) Dynamic value assessment module, used to perform multi-dimensional feature analysis on the intellectual property in the database based on machine learning and deep learning algorithms, and to calculate the value of intellectual property in real time by taking into account factors such as legal status, technological influence, market performance, industrial application, and competitive landscape. c) Intelligent transaction matching module, which uses intelligent matching algorithms to achieve accurate and efficient matching between intellectual property supply and demand parties based on characteristics such as intellectual property value, transaction intentions of supply and demand parties, industry needs, and regional preferences, and pushes the matching results; d) Risk warning and credit assessment module, used to automatically analyze the legal risks, market risks and historical transaction credit of intellectual property rights, generate risk warnings and credit scores to assist in transaction decisions; e) Visual decision support module, used to display information such as changes in intellectual property value, distribution of market demand, and matching results in the form of charts, trend graphs, heat maps, etc., to assist users in decision-making; f) An automated transaction management module to support automated management of the entire process, including online communication of intent, contract generation, and transaction tracking.

2. The system according to claim 1, wherein the multi-source data acquisition and fusion module further includes a data cleaning and standardization unit for performing format conversion, deduplication, missing data completion, and consistency verification on the acquired heterogeneous data.

3. The system according to claim 1, wherein the dynamic value assessment module adopts a self-learning AI model, which can continuously optimize the assessment algorithm based on historical transaction data and user feedback.

4. The system according to claim 1, wherein the intelligent transaction matching module supports multi-dimensional custom matching conditions, including but not limited to intellectual property type, value range, industry sector, geographical scope, etc.

5. The system according to claim 1, wherein the risk warning and credit assessment module can automatically link external data sources such as judicial and administrative penalties and records of dishonesty, thereby improving the comprehensiveness and accuracy of risk identification.