  Advanced language-processing and machine-learning techniques promise massive
efficiency improvements in the previously widely manual field of patent and
technical knowledge management. This field presents large-scale and complex
data with very precise contents and language representation of those contents.
Particularly, patent texts can differ from mundane texts in various aspects,
which entails significant opportunities and challenges. This paper presents a
systematic overview of patent-related tasks and popular methodologies with a
special focus on evolving and promising techniques. Language processing and
particularly large language models as well as the recent boost of general
generative methods promise to become game changers in the patent field. The
patent literature and the fact-based argumentative procedures around patents
appear almost as an ideal use case. However, patents entail a number of
difficulties with which existing models struggle. The paper introduces
fundamental aspects of patents and patent-related data that affect technology
that wants to explore or manage them. It further reviews existing methods and
approaches and points out how important reliable and unbiased evaluation
metrics become. Although research has made substantial progress on certain
tasks, the performance across many others remains suboptimal, sometimes because
of either the special nature of patents and their language or inconsistencies
between legal terms and the everyday meaning of terms. Moreover, yet few
methods have demonstrated the ability to produce satisfactory text for specific
sections of patents. By pointing out key developments, opportunities, and gaps,
we aim to encourage further research and accelerate the advancement of this
field.
