基于稀疏交易的专利价值评估与应用

Patent Value evaluation and application based on sparse transaction

摘要:

合理定价与精准评估价格是专利交易服务发展的关键，而目前用于评估无形资产的传统方法已不适用于专利价格的评价。为此，本文从技术价值、法律价值和经济价值3个层面选取8个二级评估指标，通过专利各指标权重来反映专利间的可匹配性，利用熵权法构建专利价格预测模型，基于我国知识产权交易场所联盟2005－2021年的320件单件专利成交价格，引入市场已达成交易形成的实际成交价作为估值基础，运用专利评估指标综合权重匹配法构建专利价格评估指标体系，并通过稳健性检验定价模型的拟合程度。结果表明：基于熵权法的评价指标权重赋值合理，基于专利价格评估指标体系构建的专利价格预测模型有效，且在随机样本下具有较好的稳定性；在加入预测价格影响因素后，专利价格预测模型的解释力度提升36.5%，能够为专利及其衍生权利交易市场提供有价值的参考信息。

Abstract:

Reasonable pricing and accurate assessment of price is the key to the development of patent transaction services, while the traditional methods currently used to assess intangible assets are no longer applicable to the evaluation of patent price. For this reason, this paper selects 8 secondary assessment indicators from 3 levels of technical value, legal value and economic value, reflects the matchability between patents through the weights of patent indicators, utilizes entropy power method to construct the patent price prediction model, and introduces the actual transaction price that has been reached in the market based on the transaction price of 320 individual patents of China's Intellectual Property Rights Trading Places Alliance from 2005 to 2021, and uses the comprehensive weight matching of patent assessment indicators as the valuation basis. Based on the transaction price of 320 single patents in the Union of Intellectual Property Trading Places of China from 2005 to 2021, the actual transaction price formed by the market has been introduced as the basis of valuation, and the patent price assessment index system is constructed by utilizing the comprehensive weight matching method of patent assessment indexes, and the degree of fit of the pricing model is examined through the robustness. The results show that: the weight assignment of evaluation indexes based on entropy weight method is reasonable, the patent price prediction model constructed based on the patent price evaluation index system is effective and has good stability under random samples; the explanatory strength of the patent price prediction model is increased by 36.5% after adding the predictive price influencing factors, which can provide valuable reference information for the patent and its derivative rights trading market.