Keyword-based patent citation prediction via information theory

Abstract

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from the abstracts of selected patents. After applying three classes of RA (variable-based analysis without and with loops and state-based analysis), nine specific IV states of a predicting model are extracted. These states involve the four keywords of “chamber”, “hous”, “main”, and “return”. Lastly, the abstracts of the patents are examined to identify the technical subjects relevant to smart building technologies for which these keywords are proxies.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.