Automatic classification of patent documents for TRIZ users

Loh, Han Tong; Cong, He; Shen, Lixiang

doi:10.1016/j.wpi.2005.07.007

Cited by 69 publications

(30 citation statements)

References 8 publications

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“…However, focusing on strategic foresight and the dynamic capabilities of a firm we need to be able to quantify the knowledge resources embedded in an organization. The existing information retrieval based classification system has a limited value in this effort [14] and methods that can go beyond using patent classes can open a more dynamic view on the knowledge landscape.…”

Section: Discussionmentioning

confidence: 99%

“…Different patent classification systems struggle to establish a tool to clearly distinguish industries to specific classes, limiting the applicability of classifications for sectoral analysis. Classifications are also of limited value in directing inventive effort [14], which is understandable due to the information retrieval nature of patent classifications. Patent classifications are a tool for the patent process.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised learning based patent landscapes using full-text patent data

Suominen

Toivanen²

2015

2015 Portland International Conference on Management of Engineering and Technology (PICMET)

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The complexity technologies require that companies have in-depth knowledge of the nature and effect of knowledge -its depth and breadth. Companies need to master expanding technological knowledge bases creating tensions for MOT. We examine how big data in patent landscaping creates insights into MOT. Using big data to manage Competitive Technical Intelligence, companies can foster new forms of adaptive learning processes in MOT. This however requires that managers augment human judgment with machine-learning tools, prompting challenges to management traditions. We demonstrate how unsupervised learning creates insight into MOT by identifying topical knowledge foci and showing the dynamics of knowledge domains among companies. Using unsupervised learning and network analysis; we show how a semantic analysis leads to the identification of opportunities in complex environments. We illustrate this using a case in globally operating telecommunication companies using a full-text copy of USPTO-database with approximately 6 million patents data. Our results show the landscape of the companies and the underlying knowledge embedded in the companies. We discuss how managers can evaluate their technological knowledge against competitors, balancing current needs with the adoption of new knowledge. We further discuss how a semantic analysis can lead to the discovery of latent patterns and identification of opportunities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Unsupervised learning based patent landscapes using full-text patent data

Suominen

Toivanen²

2015

2015 Portland International Conference on Management of Engineering and Technology (PICMET)

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“…This question becomes a new problem and the process is referred back to the 6th step, ''Developing functional solutions". In the stage, TRIZ, providing a pathway to get around personal restrictions and psychological inertia of the designers, and directing the designers to access the knowledge and experiences of the world's finest inventive minds [71], again served as a knowledge-based tool for finding solutions. According to the matrix, ''33.…”

Section: Advanced Experts' Interviewsmentioning

confidence: 99%

A vision-oriented approach for innovative product design

Lin

Luh

2009

Advanced Engineering Informatics

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“…Many companies are engaged in developing computer aided innovation software with TRIZ as the core. Currently popular software includes Pro/Innovator [7] [10]. One reason is that it consumes great time and man-power to manually classify patents.…”

Section: B Systematic Innovation Softwarementioning

confidence: 99%

“…Patent documents are divided into different areas according to technology fields, which is helpful to search the prior art for traditional inventors. However, it is inadequate for TRIZ users since TRIZ users are interested in previous patents that have solved the same Contradiction and used the same Inventive Principles, which may come from different fields [10,17]. Patent documents, which are classified according to Contradictions, will provide a broader view for TRIZ users and TRIZ software developers, by helping them find possible inspiration from the field that may be totally different from others.…”

Section: Patent Analysismentioning

confidence: 99%

Patent analysis with text mining for TRIZ

Liang

Tan

2008

2008 4th IEEE International Conference on Management of Innovation and Technology

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Patent is an important knowledge source for industrial research and product development on account of its innovation and practicability. In TRIZ theory, a product design problem can be considered as one or more Contradictions. But the Contradictions and corresponding Inventive Principles are rather abstract and general. It will be more helpful for innovators to solve the problem if they obtain related examples from published patents that have solved the Contradictions. In order to search the relevant patens according to Contradictions and Inventive Principles automatically, text mining is used to analyze patent documents. In this paper, a computer-aided methodology to classify patents according to Contradictions and Inventive Principles is proposed.

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Automatic classification of patent documents for TRIZ users

Cited by 69 publications

References 8 publications

Unsupervised learning based patent landscapes using full-text patent data

Unsupervised learning based patent landscapes using full-text patent data

A vision-oriented approach for innovative product design

Patent analysis with text mining for TRIZ

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