Predicting Patent Quality Based on Machine Learning Approach

Erdogan, Zulfiye; Altuntaş, Serkan; Dereli, Türkay

doi:10.1109/tem.2022.3207376

Cited by 9 publications

(4 citation statements)

References 109 publications

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“…The calculation of the hidden state ℎ t b of the backward LSTM unit is similar to that of ℎ t f . The final output ℎ t is formed by concatenating the hidden states from both the forward and backward directions: 4) The output of the Linear Layer is used for the probability distribution of labeling sequences, representing scores for each label sequence. Its shape is [n, k], where n is the length of the input sequence, and k is the number of labels.…”

Section: Maintaining the Integrity Of The Specificationsmentioning

confidence: 99%

“…Liu [1] applied knowledge graphs to the analysis of railway operational accidents, revealing potential patterns in accidents by describing incidents and hazards within a heterogeneous network. Wu [2] introduced a method using BiLSTM and CRF [3] to extract CTCS-3 knowledge from unstructured data, generating entity relationship triplets to aid machines in comprehending CTCS-3 knowledge efficiently and specifically.In the realm of technical assessment research, Zhu [4] introduced patent citation quality as a measure to assess the quality of invention patents and validated its effectiveness and necessity within the context of the new energy industry. Gu [5] established a comprehensive indicator system based on three aspects: technical benefits, scope of protection, and technical content.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

Liu,

Wang,

Wang

2024

AETR

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This paper introduces an intelligent technology assessment framework for high-speed railways based on a knowledge graph approach. We employ rule-based knowledge extraction algorithms and a Bert-BiLSTM-CRF model for entity extraction from technical texts. Subsequently, we establish relationships among various entities, constructing a knowledge graph specific to high-peed railways. The knowledge graph is stored in a Neo4j graph database in triple format. Furthermore, we establish a comprehensive evaluation metric system, integrating knowledge graph insights to assess the utility of enabling technologies. We employ the CRITIC weighting method to calculate the value assessment results for target technologies. Simulation results indicate that the training results for word segmentation and sentence splitting are favorable, and the Bert-BiLSTM-CRF model achieves convergence in accuracy, recall, and F1 score after 30 iterations. The technical assessment results in this paper align closely with the actual technological value assessment.

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Section: Maintaining the Integrity Of The Specificationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

Liu,

Wang,

Wang

2024

AETR

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“…Indeed, patents in the top 1% of citation counts are defined as impactful breakthrough inventions relevant to commercialization and future technology development [51]. Researchers have also used machine learning to classify utility through classification algorithms such as SOM, KPCA, and SVM [52]; predict patent citation counts using boosting algorithms (e.g., XGBoost classifier) [53]; predict utilizable patents for research and development investment decisions in companies [54]. Therefore, machine learning tools can be considered appropriate to measure and evaluate the utilization value of intangible assets such as patents.…”

Section: Research On Machine Learning Applicationmentioning

confidence: 99%

Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

Jeong,

Cho

2024

Preprint

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As the digital transformation accelerates in our society, open data is being increasingly recognized as a key resource for digital innovation in the public sector. This study explores the following two research questions: 1) Can a machine learning approach be appropriately used for measuring and evaluating open data utilization? 2) Should different machine learning models be applied for measuring open data utilization depending on open data attributes (field and usage type)? This study used single-model (Random Forest, XGBoost, LightGBM, CatBoost) and multi-model (Stacking Ensemble) machine learning methods. A key finding is that the best-performing models differed depending on open data attributes (field and type of use). The applicability of the machine learning approach for measuring and evaluating open data utilization in advance was also confirmed. This study contributes to open data utilization and to the application of its intrinsic value to society.

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“…With the development of artificial intelligence technology, an increasing number of scholars have begun to apply machine learning techniques to the identification of high-value patents. Erdogan et al (2022) believe that, against the backdrop of a rapid increase in patent applications, identifying high-value patents is crucial for enterprises to make precise investments. Therefore, he constructed a predictive model combining supervised algorithms with the analytic hierarchy process to identify high-value patents.…”

Section: The Identification Of High-value Patentmentioning

confidence: 99%

Research on High-Value Patent Identification Model from Perspective of Patent Transfer

Wu,

Li,

Han

et al. 2024

Preprint

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Accurately identifying high-value patents can be difficult with the dramatic increase in the number of patent applications. This leads to a low rate of commercialization of patent achievements. Whether a patent is transferred or not is an important reflection of the value of the patent. In order to solve above problems, we proposed a high-value patent identification model that combines hybrid sampling technology and ensemble learning algorithm. First, we add technical capacity of patentees based on traditional high-value patent identification indicators to reconstruct the indicator system. Then we reduce the identification indicator system for high-value patents to eliminate redundant indicators. Second, we use Adaptive Synthetic Sampling - Local Outlier Factor (ADASYN-LOF) to expand minority samples to balance the data. Finally, we use Genetic Algorithm (GA) to optimise the parameters of AdaBoost. For clarity, this model is called the ADASYN-LOF-GA-AdaBoost model. To test the effectiveness of above model, we use patent data in field of scientific instruments. The results demonstrate that the proposed model achieves ACC of 94.47%, AUC of 94.87%, recall of 97.54%, and F1-score of 95.23%. The results show that ADASYN-LOF-GA-AdaBoost model performs better than other models. Therefore, this model can effectively identify high-value patents with transfer potential.

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Predicting Patent Quality Based on Machine Learning Approach

Cited by 9 publications

References 109 publications

Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

Intelligent Technology Assessment of High-Speed Railway Based on Knowledge Graphs

Proposing Machine Learning Models Suitable for Predicting Open Data Utilization

Research on High-Value Patent Identification Model from Perspective of Patent Transfer

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