Prior Art Search Using Multi-modal Embedding of Patent Documents

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.

[1]  Jieh Hsiang,et al.  PatentBERT: Patent Classification with Fine-Tuning a pre-trained BERT Model , 2019, ArXiv.

[2]  Yiming Yang,et al.  XLNet: Generalized Autoregressive Pretraining for Language Understanding , 2019, NeurIPS.

[3]  David Hunt,et al.  Patent searching : tools & techniques , 2007 .

[4]  Eunjeong Park,et al.  Deep Patent Landscaping Model Using Transformer and Graph Embedding , 2019 .

[5]  Junbyoung Oh,et al.  Examination workloads, grant decision bias and examination quality of patent office , 2017 .

[6]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[7]  Andrea Bonaccorsi,et al.  Functional Patent Classification , 2019, Springer Handbook of Science and Technology Indicators.

[8]  Jianjun Hu,et al.  A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification , 2018 .

[9]  Jason Hepburn.  Universal Language Model Fine-tuning for Patent Classification , 2018, ALTA.

[10]  Kevin Gimpel,et al.  ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.

[11]  Yuxin Peng,et al.  Fine-Grained Image Classification via Combining Vision and Language , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Tung Tran,et al.  Supervised Approaches to Assign Cooperative Patent Classification (CPC) Codes to Patents , 2017, MIKE.

[13]  Jinho Kim,et al.  EEUM: Explorable and Expandable User-Interactive Model for Browsing Bibliographic Information Networks , 2018 .

[14]  Senthil Mani,et al.  DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers , 2018, AAAI.

[15]  F. Schuett.  Patent quality and incentives at the patent office , 2013 .

[16]  Thomas Wolf,et al.  DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter , 2019, ArXiv.

[17]  Yongjin Kwon,et al.  IPC Multi-label Classification Based on the Field Functionality of Patent Documents , 2016, ADMA.

[18]  Kristin L. Wood,et al.  TechNet: Technology semantic network based on patent data , 2019, Expert Syst. Appl..

[19]  Yuxin Cui,et al.  DeepPatent: patent classification with convolutional neural networks and word embedding , 2018, Scientometrics.

[20]  Stephan Günnemann,et al.  An LSTM Approach to Patent Classification based on Fixed Hierarchy Vectors , 2018, SDM.