A patent search strategy based on machine learning for the emerging field of service robotics

Kreuchauff, Florian; Korzinov, Vladimir

doi:10.1007/s11192-017-2268-3

Cited by 16 publications

(8 citation statements)

References 40 publications

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“…We thus go beyond existing literature (e.g. Kreuchauff and Korzinov (2017)) that bases their SR patent identification strategy on expert judgment. Notice that patent classifications (such as International Patent Classification) also rely on expert knowledge and introduce new classes with a considerable time delay compared to first patents filed on that topic, thus being inherently weak to capture emerging trends in SR.…”

Section: Review Perspectives and Policy Implicationsmentioning

confidence: 99%

Tracing the evolution of service robotics: Insights from a topic modeling approach

Savin

Ott

Konop

2022

Technological Forecasting and Social Change

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Taking robotic patents between 1977 and 2017 and building upon the topic modeling technique, we extract their latent topics, analyze how important these topics are over time, and how they are related to each other looking at how often they are recombined in the same patents. This allows us to differentiate between more and less important technological trends in robotics based on their stage of diffusion and position in the space of knowledge represented by a topic graph, where some topics appear isolated while others are highly interconnected. Furthermore, utilizing external reference texts that characterize service robots from a technical perspective, we propose and apply a novel approach to match the constructed topics to service robotics. The matching procedure is based on frequency and exclusivity of words overlapping between the patents and the reference texts. We identify around 20 topics belonging to service robotics. Our results corroborate earlier findings, but also provide novel insights on the content and stage of development of application areas in service robotics. With this study we contribute to a better understanding of the highly dynamic field of robotics as well as to new practices of utilizing the topic modeling approach, matching the resulting topics to external classifications and applying to them metrics from graph theory.☆☆ Financial support from the Helmholtz Association (HIRG-0069) is gratefully acknowledged. Ivan Savin acknowledges support from the Russian Science Foundation . This work has benefited from presentations at workshops in Kiel, Strasbourg, Karlsruhe, the EMAEE conference in Brighton and the EAEPE conference in Bilbao. All remaining shortcomings are our responsibility.

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Section: Review Perspectives and Policy Implicationsmentioning

confidence: 99%

Tracing the evolution of service robotics: Insights from a topic modeling approach

Savin

Ott

Konop

2022

Technological Forecasting and Social Change

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“…The leaf node represents the output of the model, while the directed edge connects the nodes in the tree [19]. When building a decision tree, starting from the root, the sample data is divided into different sub nodes by setting criteria for a certain feature in the multi-dimensional features of the input data, and the process is repeated until it reaches the leaf node and outputs the classification [20].…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Machine Learning Algorithm based on Spatiotemporal Kriging Model in The Construction of National Stock Market Network

2020

IJETC

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Kriging in space-time has obvious advantages in analyzing the continuous change of natural characteristic quantity. This method can use the time correlation to improve the analysis accuracy. In the analysis of stock market network construction, Kriging is still not applied. The purpose of this paper is to construct the national stock market network based on the machine learning algorithm of spatiotemporal Kriging model. Firstly, the basic theory of the basic properties of the network is introduced. Secondly, the principle of machine learning algorithm is further studied. After distinguishing the classical Kriging model and the spatiotemporal Kriging model, the data of stock market network is constructed. Some problems related to the interpolation with spatiotemporal Kriging method are further studied, such as the reasons for the improvement of precision, the selection of database capacity in the process of spatiotemporal interpolation, etc. The experimental results show that the spatiotemporal Kriging method is used to calculate the spatiotemporal variation function, Cst(0,0) is estimated to be 0.062, and the spatiotemporal variation function is constructed. The average path of stock network is 2.0476, which is close to 2.6095 of random network. At the same time, the clustering coefficient of stock market network is 7619, which is much larger than that of random network. For any real network, if it satisfies the following two conditions at the same time, it can be called a small world effect.

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“…The analysis of patents is one of the core duties of technology and innovation management with varying purposes and perspectives, such as forecasting emerging technologies, assessing performances of regional/national innovation systems, mapping technologies, managing R&D activities, or evaluating the collaboration potential at company or policy level [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Sci. 2021, 11, 690 2 of 16 technologies [4] or a complete reclassification of patents beyond the official taxonomy systems is still indispensable [1].…”

Section: Introductionmentioning

confidence: 99%

On the Potential of Taxonomic Graphs to Improve Applicability and Performance for the Classification of Biomedical Patents

et al. 2021

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A core task in technology management in biomedical engineering and beyond is the classification of patents into domain-specific categories, increasingly automated by machine learning, with the fuzzy language of patents causing particular problems. Striving for higher classification performance, increasingly complex models have been developed, based not only on text but also on a wealth of distinct (meta) data and methods. However, this makes it difficult to access and integrate data and to fuse distinct predictions. Although the already established Cooperate Patent Classification (CPC) offers a plethora of information, it is rarely used in automated patent categorization. Thus, we combine taxonomic and textual information to an ensemble classification system comparing stacking and fixed combination rules as fusion methods. Various classifiers are trained on title/abstract and on both the CPC and IPC (International Patent Classification) assignments of 1230 patents covering six categories of future biomedical innovation. The taxonomies are modeled as tree graphs, parsed and transformed by Dissimilarity Space Embedding (DSE) to real-valued vectors. The classifier ensemble tops the basic performance by nearly 10 points to F1 = 78.7% when stacked with a feed-forward Artificial Neural Network (ANN). Taxonomic base classifiers perform nearly as well as the text-based learners. Moreover, an ensemble only of CPC and IPC learners reaches F1 = 71.2% as fully language independent and straightforward approach of established algorithms and readily available integrated data enabling new possibilities for technology management.

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A patent search strategy based on machine learning for the emerging field of service robotics

Cited by 16 publications

References 40 publications

Tracing the evolution of service robotics: Insights from a topic modeling approach

Tracing the evolution of service robotics: Insights from a topic modeling approach

Machine Learning Algorithm based on Spatiotemporal Kriging Model in The Construction of National Stock Market Network

On the Potential of Taxonomic Graphs to Improve Applicability and Performance for the Classification of Biomedical Patents

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