Abstract
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.
Highlights
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].An essential subtask within these processes is classifying patents into coherent groups of similar documents as base for further retrieval and assessment
After hy- After hyperparameter tuning, four different base classifiers compute their predictions from tuning, four different base classifiers compute their predictions from distinct features ofdisthe perparameter tuning, four different base classifiers compute their predictions from dissame object (Stage to be merged by various fusion methods providing the final tinct features of the same object (Stage to be merged by various fusion methods tinct features of the same object (Stage I) to be merged by various fusion methods prediction
By inserting a root node, the trees of all assigned class codes are unified in one tree structured graph, which facilitates the computing of the distance between the structured codes of Cooperate Patent Classification (CPC)/International Patent Classification (IPC) taxonomy using the tree-edit-distance [33]
Summary
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]. A plethora of fusion methods, e.g., rule-based approaches such as summing and averaging, or stacking with machine leaning algorithms acting as fusion classifiers, are available to customize ensemble classification systems to specific tasks. Despite of these advantages, so far patent categorization into user-defined groups via ensemble classifier systems has rarely been studied. The IPC and especially the much more detailed CPC are transformed by means of DSE, an established method that—to the best of our knowledge—is applied to patent taxonomies for the first time Both textual and taxonomic features serve as input to four different machine learning base classifiers to assign patents into six classes of future biomedical innovation. The conclusions point out the major findings and important future perspectives
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