## 6 Conclusions  
We presented a scalable two-stage hierarchical ensemble neural network (ENN) model tuned for detecting the design rights a product is potentially infringing. We assumed a counterfeit is merely an identical copy of the existing product with a registered design right. We identify the violated design rights by classifying the image of a product shot under different settings such as lighting, packaging condition, focal length, and angles. This study focuses on the fact that thousands of design rights are registered, and many products pour into the market at the border. Classifying a product into the thousands of design rights with a single neural network is impractical due to heavy training costs and inefficient computer resource utilization. The ENN model is designed to address the scalability issue by having distributed backbone models trained on a unit-sized dataset independently and in parallel. The result of the backbone models is concatenated and passed through the ensemble DNN model that consists of fully connected residual blocks to output the model that returned the most similar class of a given product image. This novel structure could train the ENN model on incrementally added unit-sized datasets with constant time. Therefore, the ENN model can be scaled to classify many design rights. The ENN model was designed to enhance scalability. At the same time, the fine-tuned ENN model using UP-DETR as a backbone model showed Top-1 and Top-3 accuracies of 98.27% and 99.25%, respectively. Thus, we showed that the ENN model can be on a par with the single neural network model while having at least an order of magnitude lower training cost when given an incremental dataset to learn. The ENN model is the most appropriate neural network structure to adopt in the field, with thousands of products to examine. Customizing the ENN model is easy as we can plug in any neural network model in the backbone layer for further improvement in terms of accuracy. Conceptualization, Y.Y. ; methodology, C.J.L., S.H.J. and Y.Y. ; software, C.J.L. and S.H.J. ; validation, C.J.L., S.H.J. and Y.Y. ; formal analysis, C.J.L., S.H.J. and Y.Y. ; investigation, C.J.L., S.H.J. and Y.Y. ; resources, Y.Y. ; data curation, C.J.L., S.H.J. and Y.Y. ; writing--original draft preparation, C.J.L., S.H.J. and Y.Y. ; writing--review and editing, Y.Y. ; visualization, C.J.L. ; supervision, Y.Y. ; project administration, Y.Y. ; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the Ministry of Trade, Industry & Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT), under Grants P0014268 Smart HVAC demonstration support. This research was also supported by the MSIT (Ministry of Science and ICT), Korea under the ITRC (Information Technology Research Center) support program (RS-2023-00259099) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation), and supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. RS-2023-00240211). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The design rights information we used for this study is available on KIPRIS (Korea Intellectual Property Information Search, [[http://www.kipris.or.kr](http://www.kipris.or.kr)]([http://www.kipris.or.kr](http://www.kipris.or.kr))) (accessed on 15 September 2023. We made our code fully open-source, and it is available on [[https://github.com/neouyl-inc/ENN_ensemble](https://github.com/neouyl-inc/ENN_ensemble)]([https://github.com/neouyl-inc/ENN_ensemble](https://github.com/neouyl-inc/ENN_ensemble)) (accessed on 15 September 2023). We do not have permission to share the images of the products that are under the design rights. **Conflicts of Interest:** Author [PERSON] and [PERSON] is employed by NetcoreTech Co., Ltd.