Exploratory Key Nodes in the Inventor-author Knowledge Diffusion Network

This paper aims to mine the key nodes in the process of knowledge flow from literatures of science and technology journals to technology patents on the community level. Based on the citation of technological patents to literatures of scientific journals and the cooperation among the researchers, this paper builds the knowledge flow network from the angle of spatial dimension. Then employing the extensity centrality-Newman and the commonly used degree indexes, this paper excavates and analyses the nodes which occupy important positions among communities in the knowledge flow network. After that, this paper puts forward suggestions on how to make full use of the key nodes’ role of bridge to promote knowledge flow from literatures of science and technology journals to technology patents

A hybrid method to trace technology evolution pathways: a case study of 3D printing

© 2017, Akadémiai Kiadó, Budapest, Hungary. Whether it be for countries to improve the ability to undertake independent innovation or for enterprises to enhance their international competitiveness, tracing historical progression and forecasting future trends of technology evolution is essential for formulating technology strategies and policies. In this paper, we apply co-classification analysis to reveal the technical evolution process of a certain technical field, use co-word analysis to extract implicit or unknown patterns and topics, and employ main path analysis to discover significant clues about technology hotspots and development prospects. We illustrate this hybrid approach with 3D printing, referring to various technologies and processes used to synthesize a three-dimensional object. Results show how our method offers technical insights and traces technology evolution pathways, and then helps decision-makers guide technology development

Predicting Technical Value Of Technologies Through Their Knowledge Structure

This thesis tests the hypothesis that the characteristics displayed by the knowledge structure of a high technical value invention is different from that of a low technical value invention. The knowledge structure crystalizes at the inception of the invention making it ideal for evaluating new inventions. More specifically, this research investigates two characteristics of the knowledge structure: knowledge accumulation and knowledge appropriation. Knowledge accumulation is defined as the collective body of knowledge gathered in a sector over time that has contributed to the creation of the invention. A higher degree of accumulated knowledge is more likely to be associated with high technical value inventions. Knowledge appropriation describes absorption of knowledge in the creation of the invention. From knowledge structure perspective knowledge absorption is observed by the emergence of edges that connect knowledge elements together. The robustness of this emergent knowledge structure is thus an indicator of the amount of knowledge appropriated by the invention. This research introduces a new metric for the measurement of knowledge accumulation and presents structural robustness as an indicator of knowledge appropriation. Knowledge accumulation and knowledge appropriation are hypothesized to be positively correlated with the technical value of the invention. This research tests the hypotheses by examining the citation networks of patents in four sectors: thin film photovoltaics, inductive vibration energy harvesting, piezoelectric energy harvesting, and carbon nanotubes. In total 152 base inventions and over 4000 patents are investigated. This research shows that knowledge accumulation is a significant predictor of the technical value of an invention and that high value inventions show a higher level of knowledge appropriation

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action

Deriving Technology Intelligence from Patents: Preposition-based Semantic Analysis

Patents are one of the most reliable sources of technology intelligence, and the true value of patent analysis stems from its capability of describing the content of technology based on the relationships between keywords. To date a number of techniques for analyzing the information contained in patent documents that focus on the relationships between keywords have been suggested. However, a drawback of the existing keyword approaches is that they cannot yet determine the types of relationships between the keywords. This study proposes a novel approach based on preposition semantic analysis network which overcomes the limitations of the existing keywords-based network analysis and demonstrates its potential through an application. A preposition is a word that defines the relationship between two neighboring words, and, in the case of patents, prepositions aid in revealing the relationships between keywords related to technologies. To demonstrate the approach, patents regarding an electric vehicle were employed. 13 prepositions were identified which could be used to define 5 relationships between neighboring technological terms: “inclusion (utilization),” “objective (purpose),” “effect,” “process,” and “likeness.” The proposed approach is expected to improve the usability of keyword-based patent analyses and support more elaborate studies on patent documents

Patent Information Retrieval: Approaching a Method and Analyzing Nanotechnology Patent Collaborations

ArticleThis is the final version of the article. Available from Springer Verlag via the DOI in this record.Many challenges still remain in the processing of explicit technological knowledge documents such as patents. Given the limitations and drawbacks of the existing approaches, this research sets out to develop an improved method for searching patent databases and extracting patent information to increase the efficiency and reliability of nanotechnology patent information retrieval process and to empirically analyse patent collaboration. A tech-mining method was applied and the subsequent analysis was performed using Thomson data analyser software. The findings show that nations such as Korea and Japan are highly collaborative in sharing technological knowledge across academic and corporate organisations within their national boundaries, and China presents, in some cases, a great illustration of effective patent collaboration and co-inventorship. This study also analyses key patent strengths by country, organisation and technology