Prior Art Search Using Multi-modal Embedding of Patent Documents

Myungchul Kang · Suan Lee · Wookey Lee

7 citations0/0 files storedIEEE International Conference on Big Data and Smart Computing (BigComp)

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Intellectual Property and Patents Topic Modeling Information Retrieval and Search Behavior

Abstract

Due to the limitations of the existing prior art search methods, a new patent search paradigm can be innovated by the concepts based on a precise patent document embedding, and a real-time feedback. These concepts can be achieved by the following ideas. The latest language model BERT can be incorporated with the description drawing embedding so that the explorable user interactive model can be adopted to the patent domain for “Building an artificial intelligent patent search system." Therefore, these methodologies mainly with the help of deep learning can solve the traditional labor-intensive and time-consuming prior art search.

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References

2018DeepPatent: patent classification with convolutional neural networks and word embedding 2013Patent quality and incentives at the patent office 2007Patent searching: tools & techniques 2017Examination workloads, grant decision bias and examination quality of patent office 2019Functional Patent Classification 2019Deep Patent Landscaping Model Using Transformer and Graph Embedding

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