A Comprehensive Framework for Efficient Court Case Management and Prioritization

TL;DR

The paper tackles the backlog of India's court cases by proposing a cloud-based framework that uses AI-driven prioritization to classify and schedule hearings. It employs a regression-based weighting scheme that factors in case age, severity, input priority, and applicable legal provisions, with automated hearing-date allocation and stakeholder notifications, integrating data from appeals and existing eCourts infrastructure. In simulations with 10,000 dummy cases, the system demonstrates efficient processing and high notification reliability, with the weighting model achieving $F1=99.7\%$, $precision=99.8\%$, and $recall=99.4\%$. This work advances court efficiency while addressing ethical concerns through expert-validated weights and human oversight, and outlines a pathway toward real-world deployment with NLP-enabled analysis and adaptive scheduling.

Abstract

The Indian judicial system faces a critical challenge with approximately 52 million pending cases, causing significant delays that impact socio-economic stability. This study proposes a cloud-based software framework to classify and prioritize court cases using algorithmic methods based on parameters such as severity of crime committed, responsibility of parties involved, case filing dates, previous hearing's data, priority level (e.g., Urgent, Medium, Ordinary) provided as input, and relevant Indian Penal Code (IPC), Code of Criminal Procedure (CrPC), and other legal sections (e.g., Hindu Marriage Act, Indian Contract Act). Cases are initially entered by advocates on record or court registrars, followed by automated hearing date allocation that balances fresh and old cases while accounting for court holidays and leaves. The system streamlines appellate processes by fetching data from historical case databases. Our methodology integrates algorithmic prioritization, a robust notification system, and judicial interaction, with features that allow judges to view daily case counts and their details. Simulations demonstrate that the system can process cases efficiently, with reliable notification delivery and positive user satisfaction among judges and registrars. Future iterations will incorporate advanced machine learning for dynamic prioritization, addressing critical gaps in existing court case management systems to enhance efficiency and reduce backlogs.

Figures (3)

• Figure 1: Workflow of the System
• Figure 2: Case Prioritization Flowchart
• Figure 3: System Architecture for Court Case Management and Prioritization