  Diversity in patent language is growing and makes finding synonyms for
conducting patent searches more and more challenging. In addition to that, most
approaches for dealing with diverse patent language are based on manual search
and human intuition. In this paper, a word embedding approach using statistical
analysis of human labeled data to produce accurate and language independent
word vectors for technical terms is introduced. This paper focuses on the
explanation of the idea behind the statistical analysis and shows first
qualitative results. The resulting algorithm is a development of the former
EQMania UG (eqmania.com) and can be tested under eqalice.com until April 2021.
