Modeling the Duration of Patent Examination at the European Patent Office

We analyze the duration of the patent examination process at the European Patent Office (EPO). Our data contain information related to the patent’s economic and technical relevance, EPO capacity and workload as well as novel citation measures which are derived from the EPO’s search reports. In our multivariate analysis we estimate competing risk specifications in order to characterize differences in the processes leading to a withdrawal of the application by the applicant, a refusal of the patent grant by the examiner or an actual patent grant. Highly cited applications are approved faster by the EPO than less important ones, but they are also withdrawn less quickly by the applicant. The process duration increases for all outcomes with the application’s complexity, originality, number of references (backward citations) in the search report and with the EPO’s workload at the filing date. Endogenous applicant behavior becomes apparent in other results: more controversial claims lead to slower grants, but faster withdrawals, while relatively well-documented applications (identified by a high share of applicant references appearing in the search report) are approved faster and take longer to be withdrawn

Learned Cardinalities: Estimating Correlated Joins with Deep Learning

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.Comment: CIDR 2019. https://github.com/andreaskipf/learnedcardinalitie