A Predict-then-Optimize Job Assignment Framework for Efficient Trademark Application Evaluation

Abstract

Timely evaluation of trademark applications is critical for effective intellectual property protection, yet many national offices face rising workloads and limited staffing flexibility. At TURKPATENT, these pressures have contributed to growing backlogs and high variability in evaluation times. This study proposes a data-driven job assignment framework that integrates machine learning based prediction of examiner-specific completion times with a rolling-horizon mixed-integer programming model. A comprehensive feature set incorporating text indicators, classification codes, examiner effects, and workload characteristics is used to train predictive models, and predicted durations are subsequently embedded into an assignment model designed to minimize tardiness while maintaining workload balance. Multiple assignment policies are evaluated through extensive simulation under varying workload and prediction-quality scenarios. The results show that the predictive rolling-horizon model reduces tardy jobs by roughly 49.5% relative to the current system. A fully automated operational pipeline was implemented to enable daily deployment, and a real-world pilot with eight examiners achieved a 35.3% reduction in tardy jobs. The study demonstrates that integrating predictive analytics with optimization can substantially improve performance in administrative workflows and offers a scalable approach for modernizing trademark examination processes.

Keywords

• Predict-then-optimize
• Job Assignment Optimization
• Predictive Modeling
• Rolling Horizon Optimization
• Trademark Application

Veriye Dayalı Tahmin ve Optimizasyon Yaklaşımıyla Etkin Marka Başvurusu Değerlendirmesi

Abstract

Marka başvurularının zamanında değerlendirilmesi, etkin fikri mülkiyet koruması için kritik öneme sahiptir; ancak birçok ulusal ofis artan iş yükleri ve sınırlı personel esnekliği nedeniyle önemli zorluklarla karşılaşmaktadır. TÜRKPATENT’te bu sıkıntılar, büyüyen iş birikmelerine ve değerlendirme sürelerinde yüksek değişkenliğe yol açmıştır. Bu çalışma, değerlendirme sürelerini makine öğrenimi ile tahmin eden bir modeli, bir karma tamsayılı programlama modeliyle bütünleştiren veri odaklı bir iş atama çerçevesi önermektedir. Metin göstergeleri, sınıflandırma kodları, değerlendirici etkileri ve iş yükü özelliklerini içeren kapsamlı bir öznitelik seti ile tahmin modelleri eğitilmiş; tahmin edilen süreler ise gecikmeleri azaltırken iş yükü dengesini koruyacak şekilde tasarlanmış atama modeline entegre edilmiştir. Farklı iş yükü ve tahmin doğruluğu senaryoları altında farklı atama politikaları simülasyon modeli aracılığı ile değerlendirilmiştir. Sonuçlar, önerilen tahminle-ardından-eniyile modelinin mevcut sisteme kıyasla geciken iş sayısını yaklaşık %49,5 oranında azalttığını göstermektedir. Sistemin günlük kullanımını mümkün kılmak için tam otomatik bir operasyonel altyapı uygulanmış ve sekiz incelemeciyle yapılan gerçek dünya pilot çalışmasında geciken işlerde %35,3’lük bir azalma elde edilmiştir. Bu çalışma, öngörüsel analitik ile eniyilemenin bütünleştirilmesinin idari iş akışlarında performansı önemli ölçüde artırabileceğini ve marka inceleme süreçlerini modernize etmek için ölçeklenebilir bir yaklaşım sunduğunu göstermektedir.

Keywords

• veriye dayalı
• marka başvurusu
• iş atama optimizasyonu
• tahminle-ardından-eniyile

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