Transformer-Based Patent Novelty Search by Training Claims to Their Own Description

In this paper we present a method to concatenate patent claims to their own description. By applying this method, bidirectional encoder representations from transformers (BERT) train suitable descriptions for claims. Such a trained BERT could be able to identify novelty relevant descriptions for patents. In addition, we introduce a new scoring scheme: relevance score or novelty score to interprete the output of BERT. We test the method on patent applications by training BERT on the first claims of patents and corresponding descriptions. The output is processed according to the relevance score and the results compared with the cited X documents in the search reports. The test shows that BERT score some of the cited X documents as highly relevant.

Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality.

Population-Based Novelty Searches Can Converge

Novelty search is a powerful tool for finding sets of complex objects in complicated, open-ended spaces. Recent empirical analysis on a simplified version of novelty search makes it clear that novelty search happens at the level of the archive space, not the individual point space. The sparseness measure and archive update criterion create a process that is driven by a clear pair of objectives: spread out to cover the space, while trying to remain as efficiently packed as possible driving these simplified variants to converge to an