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.

翻译：印度司法系统面临严峻挑战，积压案件约达5200万件，由此产生的严重延误影响了社会经济稳定。本研究提出一种基于云的软件框架，通过算法方法对法院案件进行分类与优先级排序，依据参数包括：犯罪严重程度、相关方责任、案件立案日期、先前听证数据、输入提供的优先级级别（如紧急、中等、普通），以及相关《印度刑法典》（IPC）、《刑事诉讼法》（CrPC）与其他法律条款（如《印度教婚姻法》《印度合同法》）。案件首先由记录在案的律师或法院书记员录入，随后系统自动分配听证日期，在平衡新旧案件的同时兼顾法院节假日安排。该系统通过从历史案件数据库提取数据，简化了上诉流程。我们的方法整合了算法优先级排序、鲁棒的通知系统与司法交互功能，支持法官查看每日案件数量及其详细信息。模拟实验表明，该系统能高效处理案件，实现可靠的通知送达，并获得法官与书记员群体的积极用户反馈。未来迭代版本将引入先进机器学习技术以实现动态优先级排序，弥补现有法院案件管理系统的关键不足，从而提升效率并减少案件积压。