Grouping of TRIZ Inventive Principles to facilitate automatic patent classification

Cong, He; Tong, Loh Han

doi:10.1016/j.eswa.2006.10.015

Cited by 90 publications

(38 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The interpretation of the TRIZ inventive principles in a service related context varies from one author to another (Cong & Tong, 2008;Dourson, 2004;Mann & Domb, 1999). Each author's interpretation depends on imperial experience, their opinions, and using examples from other studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…TRIZ gives the problem solvers different tools. The most commonly used tool for eliminating contradiction problems is 40 IPs (Cong & Tong, 2008;Lin & Su, 2007). Eliminating the contradictions from the any system often leads to innovative solutions (Hsia & Pu, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…A few studies have attempted to classify the IPs in order to overcome this issue. For instance, Cong and Tong (2008) classified the 40 IPs using text similarity and meaning similarity into 22 classes to facilitate building an automatic patent classification expert system for TRIZ technical users. Gazem and Rahman (2012) grouped the 40 IPs using service characteristics developed by (L. Berry & Lampo, 2000).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The fundamental principle behind the TRIZ method is to find contradictions in a system and then eliminate them by using TRIZ tools such as the 40 Interactive Principles (IPs). These tools are important and are frequently used to eliminate contradictions (Cong & Tong, 2008;Lin & Su, 2007).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Interpretation of TRIZ Principles in a Service Related Context

Gazem¹,

Rahman²

2014

ASS

View full text Add to dashboard Cite

A systematic innovation method such as the Theory of Inventive Problem Solving (TRIZ) has powerful tools that can be used to solve contradiction problems in technical or non-technical systems. The most common tool used in TRIZ is 40 inventive principles (IPs). The purpose of this paper is to interpret these IPs from a service prospective. The data was collected from many resources found in the literature. A case study was conducted to prove the feasibility of interpretative IPs. The outcome of this study enhanced the usability of the 40 IPs by including new synonyms for some principles, comprehensive descriptions, and providing suggestions and examples for each principle. The interpretative principles focused on a service process used to fulfill customer demands. An Interpretation of TRIZ tool such as the 40 IPs in a service related context improves the understanding of these principles by researchers or service designers.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Interpretation of TRIZ Principles in a Service Related Context

Gazem¹,

Rahman²

2014

ASS

View full text Add to dashboard Cite

show abstract

“…It has attracted a lot of attentions of not only scholars from research communities but also practitioners from industries in the past few years due to its broad application scenarios (Madjarov et al 2012). For instance, each academic paper may have more than one author, and learning from this data could help to identify the academic interests of authors and recommend potential collaborators according to their interests Xuan et al 2015b); a patent may be associated with several categories, and automatically assigning large amount of new patents to correct categories could save the costs in human resources and time (Cong and Tong 2008); each gene may be associated with not one but a set of functional classes, and detecting functional classes of new genes could benefit the medicine design (Elisseeff and Weston 2001).…”

Section: Introductionmentioning

confidence: 99%

A Bayesian nonparametric model for multi-label learning

Xuan

Zhang

et al. 2017

Mach Learn

View full text Add to dashboard Cite

Multi-label learning has become a significant learning paradigm in the past few years due to its broad application scenarios and the everincreasing number of techniques developed by researchers in this area. Among existing state-of-the-art works, generative statistical models are characterized by their good data generalization and robustness on large number of labels through learning a low-dimensional label embedding. However, one issue of this branch of models is that the number of dimensions needs to be fixed in advance, which is difficult and inappropriate in many real-world settings. In this paper, we propose a Bayesian nonparametric model to resolve this issue. More specifically, we extend a Gamma-negative binomial process to three levels in order to capture the label-instance-feature structure. Furthermore, a mixing strategy for Gamma processes is designed to account for the multiple labels of an instance. The mixed process also leads to a difficulty in model inference, so an efficient Gibbs sampling inference algorithm is then developed to resolve this difficulty. Experiments on several real-world datasets show the performance of the proposed model on multi-label learning tasks, comparing with three state-of-the-art models from the literature.

show abstract

Hybrid-patent classification based on patent-network analysis

Liu

Shih

2010

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

Effective patent management is essential for organizations to maintain their competitive advantage. The classification of patents is a critical part of patent management and industrial analysis. This study proposes a hybrid-patent-classification approach that combines a novel patent-network-based classification method with three conventional classification methods to analyze query patents and predict their classes. The novel patent network contains various types of nodes that represent different features extracted from patent documents. The nodes are connected based on the relationship metrics derived from the patent metadata. The proposed classification method predicts a query patent's class by analyzing all reachable nodes in the patent network and calculating their relevance to the query patent. It then classifies the query patent with a modified k -nearest neighbor classifier. To further improve the approach, we combine it with content-based, citation-based, and metadata-based classification methods to develop a hybrid-classification approach. We evaluate the performance of the hybrid approach on a test dataset of patent documents obtained from the U.S. Patent and Trademark Office, and compare its performance with that of the three conventional methods. The results demonstrate that the proposed patent-network-based approach yields more accurate class predictions than the patent network-based approach. IntroductionPatents are valuable intellectual property and therefore require effective management to ensure that an organization maintains its competitive advantage (Guan & Gao, 2009;Su, Lai, Sharma, & Kuo, 2009). Because of developments in various technologies, the number of patents has increased rapidly in recent years. How to manage the constantly growing volume of patents is thus becoming an important issue. Patent classification is a key part of patent management; however, as the task is usually performed by patent analysts, categorizing new patent documents correctly is a laborious process. Hence, there is a pressing need for an effective patent-classification approach.Basically, patent classification can be regarded as a textcategorization problem that involves assigning a patent document to a particular class. Most existing studies have considered information content to classify patent documents, and several classification algorithms have been developed based on different content features (e.g., He & Lo, 2008;Fall, Torcsvari, Benzineb, & Karetka, 2003, 2004Kim & Choi, 2007;Larkey, 1999;Loh, He, & Shen, 2006;Trappey, Hsu, Trappey, & Lin, 2006). In addition, some approaches have utilized citation relationships to improve the performance of patent classification (Lai & Wu, 2005; Li, Chen, Zang, & Lie, 2007) while others have employed patent metadata (e.g., the inventor's name) to achieve improvements in the classification performance (Richter & MacFarlane, 2005).Since patent metadata provides rich information that can be used to infer possible relationships between patent documents, there exists the poten...

show abstract

Grouping of TRIZ Inventive Principles to facilitate automatic patent classification

Cited by 90 publications

References 6 publications

Interpretation of TRIZ Principles in a Service Related Context

Interpretation of TRIZ Principles in a Service Related Context

A Bayesian nonparametric model for multi-label learning

Hybrid-patent classification based on patent-network analysis

Contact Info

Product

Resources

About