Patent Maintenance Recommendation with Patent Information Network Model

Jin, Xin; Spangler, Scott; Chen, Ying; Cai, Kefeng; Ma, Rui; Zhang, Li; Wang, Xian

doi:10.1109/icdm.2011.116

Cited by 31 publications

(22 citation statements)

References 19 publications

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“…Using some extracted contextual features (e.g., word frequency, word age and syntactic complexity), it makes recommendations for patentability decision. [12] works on patent maintenance recommendations, by extracting a set of patent features (e.g., number of patent citations and patent writing quality) to make maintenance recommendations. Patent Prior Arts Search.…”

Section: Related Workmentioning

confidence: 99%

Exploring Legal Patent Citations for Patent Valuation

Wang

Lei

Lee

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Effective patent valuation is important for patent holders. Forward patent citations, widely used in assessing patent value, have been considered as reflecting knowledge flows, just like paper citations. However, patent citations also carry legal implication, which is important for patent valuation. We argue that patent citations can either be technological citations that indicate knowledge transfer or be legal citations that delimit the legal scope of citing patents. In this paper, we first develop citation-network based methods to infer patent quality measures at either the legal or technological dimension. Then we propose a probabilistic mixture approach to incorporate both the legal and technological dimensions in patent citations, and an iterative learning process that integrates a temporal decay function on legal citations, a probabilistic citation network based algorithm and a prediction model for patent valuation. We learn all the parameters together and use them for patent valuation. We demonstrate the effectiveness of our approach by using patent maintenance status as an indicator of patent value and discuss the insights we learned from this study.

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Section: Related Workmentioning

confidence: 99%

Exploring Legal Patent Citations for Patent Valuation

Wang

Lei

Lee

2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…The writing features are more complex when the number of the different words, named entities, long VP, and long NP is bigger. This typical phenomenon is also appeared in the scientific papers and patent texts [3]. Therefore, the paper The concepts are often distributed widely when they are appeared in more articles in the background set of documents, which expresses they can be widely supported by other articles according to the extractions.…”

Section: The Importance Evaluation For Electronic Documents Using Mulmentioning

confidence: 99%

Mining based on Extraction and Importance Evaluation Using Multi-Measures Methods for Electronic Documents

Xiong

Ding

2017

ITM Web Conf.

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Abstract-Mining the implicit knowledge in the electronic documents is a critical task in text analysis and data mining. To attain a knowledge-based view of the electronic documents, the clustering method based upon the topic cannot only be used, but also that based upon the extraction can be done. Therefore, a novel method for the clustering of the electronic documents, summarizing of the full text based on the extracted segments, and an evaluation using multi-measures for the importance to the document were presented. In the method, eighteen kinds of named entities and two kinds of syntactical phrases were extracted, and exploited for the text clustering. Then, a novel similarity equation was proposed for the calculation about the extractions. Meantime, three measures for the importance to the document were proposed, which provided a different view for the document's content, and recommended a prior checking for the users. Therefore, the method can improve the efficiency of the knowledge discovery, and enhance the management of the document on the large scale of document collection.

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“…The problem formulation is different from existing work on patent quality analysis [11,18,19,26], which has focused on mining patent content. It is also different from existing works on collaborator recommendations [13] and friends suggestions [22].…”

Section: Problem Definitionmentioning

confidence: 99%

“…For research on the patent data, Tang et al [26] propose a topicdriven patent analysis and mining method. Jin et al [11] proposed a method to evaluate the quality of patents. Liu et al [18] and Mann [19] studied how to estimate patent quality from the perspective of court validity rulings or the number of forward citations.…”

Section: Related Workmentioning

confidence: 99%

Patent partner recommendation in enterprise social networks

Sun²,

Tang

2013

Proceedings of the Sixth ACM International Conference on Web Search and Data Mining

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It is often challenging to incorporate users' interactions into a recommendation framework in an online model. In this paper, we propose a novel interactive learning framework to formulate the problem of recommending patent partners into a factor graph model. The framework involves three phases: 1) candidate generation, where we identify the potential set of collaborators; 2) candidate refinement, where a factor graph model is used to adjust the candidate rankings; 3) interactive learning method to efficiently update the existing recommendation model based on inventors' feedback. We evaluate our proposed model on large enterprise patent networks. Experimental results demonstrate that the recommendation accuracy of the proposed model significantly outperforms several baselines methods using content similarity, collaborative filtering and SVM-Rank. We also demonstrate the effectiveness and efficiency of the interactive learning, which performs almost as well as offline re-training, but with only 1 percent of the running time.

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Patent Maintenance Recommendation with Patent Information Network Model

Cited by 31 publications

References 19 publications

Exploring Legal Patent Citations for Patent Valuation

Exploring Legal Patent Citations for Patent Valuation

Mining based on Extraction and Importance Evaluation Using Multi-Measures Methods for Electronic Documents

Patent partner recommendation in enterprise social networks

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