台州模具产业相关专利价值评价数据

数据包中主要包括了台州模具产业相关的专利，根据自有算法对专利数据进行了评价，可用于横向、纵向了解台州模具产业相关专利数据变化情况，有助于了解台州模具产业知识产权工作成果；通过现有专利价值的评估，帮助企业及早做好专利保护；了解台州模具产业的技术发展趋势，有助于企业避免重复研发，避免企业开发的技术侵犯他人的知识产权。1.数据采集：通过第三方知识产权检索网站，对台州模具产业相关的发明专利数量、实用新型数量、外观设计数量进行采集。2.数据处理：对采集到的原始数据进行处理，通过去除重复和有缺失的数据，对数据进行清理分类。3.数据计算：专利价值度评价模型采用层次分析法，基于层次分析（AHP）的主观赋值方法，对各个指标的主观确定权重，技术/法律/市场/战略/经济价值得分=∑各方面对应各个指标 * 对应权重，技术价值：被审查员引证数，引用专利国别数，引用非专利文献数，引证专利数，IPC部数，IPC小类数；法律价值：独权数、主权项数字数、权项数、说明书页数、布局国家数、PCT国际申请、存活期；市场价值：同族数、剩余有效期；战略价值：无效次数、ETSI标准、专利奖；经济价值：转让次数、许可次数、质押次数算；专利得分为五个方面的价值得分累加总和。利用IFS函数对专利进行价值判定，专利价值度=IFS(专利得分>45, "超高价值专利",专利得分>20, "高价值专利",专利得分>10, "一般价值专利",专利得分>0, "无价值专利",) ，为本领域技术人员提供研发决策依据和技术规避作优先参考。

This dataset primarily contains patents related to the Taizhou mold industry. The patent data were evaluated using a self-developed algorithm, enabling horizontal and longitudinal analysis of the changes in patent data relevant to the Taizhou mold industry, and facilitating understanding of the achievements of intellectual property work in the Taizhou mold industry. It also helps enterprises proactively carry out patent protection through existing patent value assessments, grasp the technological development trends of the Taizhou mold industry, avoid redundant R&D, and prevent the technologies developed by enterprises from infringing others' intellectual property rights.
1. Data Collection: Relevant quantities of invention patents, utility model patents and design patents related to the Taizhou mold industry were collected via third-party intellectual property retrieval websites.
2. Data Processing: The collected raw data were processed by removing duplicate and incomplete entries, followed by data cleaning and classification.
3. Data Calculation: The patent value evaluation model adopts the Analytic Hierarchy Process (AHP), where subjective weights are assigned to each indicator through the AHP-based subjective weighting method. The scores for technical, legal, market, strategic and economic value are calculated as ∑(each corresponding indicator in each dimension × its corresponding weight). The specific indicators are as follows:
- Technical value: Number of citations by examiners, number of countries of cited patents, number of cited non-patent literatures, number of cited patents, number of IPC sections, number of IPC subclasses;
- Legal value: Number of independent claims, number of words in independent claims, number of claims, number of pages in the specification, number of filing countries, PCT international applications, term of validity;
- Market value: Number of patent families, remaining validity period;
- Strategic value: Number of invalidation proceedings, ETSI standards, patent awards;
- Economic value: Number of transfers, number of licenses, number of pledges.
The total patent score is the sum of the scores from the five dimensions above. The IFS function is used to determine the patent value: Patent Value = IFS(Patent Score>45, "Ultra-high Value Patent", Patent Score>20, "High Value Patent", Patent Score>10, "General Value Patent", Patent Score>0, "No Value Patent"). This dataset provides R&D decision-making basis and priority reference for technology avoidance for technical personnel in this field.