A Comprehensive Framework for Efficient Court Case Management and Prioritization
- URL: http://arxiv.org/abs/2510.14892v1
- Date: Thu, 16 Oct 2025 17:11:38 GMT
- Title: A Comprehensive Framework for Efficient Court Case Management and Prioritization
- Abstract summary: The Indian judicial system faces a critical challenge with approximately 52 million pending cases.<n>This study proposes a cloud-based software framework to classify and prioritize court cases.<n>Cases are initially entered by advocates on record or court registrars, followed by automated hearing date allocation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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.
Related papers
- Cassette: Case-to-Case Structural Distillation for Efficient Legal Case Retrieval [60.39519301815861]
High computational cost during inference on large-scale legal databases limits its practical use in real-world settings.<n>In this paper, Cassette framework is proposed with a ranking objective and eigen-matching objective for an effective transfer of knowledge from a powerful and well-trained heavy teacher retriever to a lightweight and efficient hybrid student dual encoder.
arXiv Detail & Related papers (2026-09-08T03:16:20Z) - IBPS: Indian Bail Prediction System [3.1254215477461553]
Bail decisions are among the most frequently adjudicated matters in Indian courts.<n>Over 75% of India's prison population comprises undertrial prisoners.<n>We present the Indian Bail Prediction System (IBPS), an AI-powered framework designed to assist in bail decision-making.
arXiv Detail & Related papers (2025-08-11T03:44:17Z) - ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework [21.003203706712643]
Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines.<n>Current datasets, derived from authentic cases, suffer from high human annotation costs and imbalanced distributions.<n>We propose an Adversarial Self-Play Lawyer Augmented Legal Judgment Framework, called ASP2LJ.<n>Our framework enables a judge to reference evolved lawyers' arguments, improving the objectivity, fairness, and rationality of judicial decisions.
arXiv Detail & Related papers (2025-06-11T06:55:40Z) - RLJP: Legal Judgment Prediction via First-Order Logic Rule-enhanced with Large Language Models [58.69183479148083]
Legal Judgment Prediction (LJP) is a pivotal task in legal AI.<n>Existing LJP models integrate judicial precedents and legal knowledge for high performance.<n>But they neglect legal reasoning logic, a critical component of legal judgments requiring rigorous logical analysis.<n>This paper proposes a rule-enhanced legal judgment prediction framework based on first-order logic (FOL) formalism and comparative learning (CL)
arXiv Detail & Related papers (2025-05-27T14:50:21Z) - AppealCase: A Dataset and Benchmark for Civil Case Appeal Scenarios [47.83822985839837]
We present the AppealCase dataset, consisting of 10,000 pairs of real-world, matched first-instance and second-instance documents across 91 categories of civil cases.<n>The dataset also includes detailed annotations along five dimensions central to appellate review: judgment reversals, reversal reasons, cited legal provisions, claim-level decisions, and whether there is new information in the second instance.<n> Experimental results reveal that all current models achieve less than 50% F1 scores on the judgment reversal prediction task, highlighting the complexity and challenge of the appeal scenario.
arXiv Detail & Related papers (2025-05-22T10:50:33Z) - AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction [56.797874973414636]
AnnoCaseLaw is a first-of-its-kind dataset of 471 meticulously annotated U.S. Appeals Court negligence cases.<n>Our dataset lays the groundwork for more human-aligned, explainable Legal Judgment Prediction models.<n>Results demonstrate that LJP remains a formidable task, with application of legal precedent proving particularly difficult.
arXiv Detail & Related papers (2025-02-28T19:14:48Z) - End-to-End Optimization and Learning of Fair Court Schedules [46.76273076646004]
Criminal courts across the United States handle millions of cases every year.
Defendants' scheduling preferences often take the least priority.
This paper proposes a joint optimization and learning framework that combines machine learning models trained end-to-end with efficient matching algorithms.
arXiv Detail & Related papers (2024-10-22T20:40:53Z) - PILOT: Legal Case Outcome Prediction with Case Law [43.680862577060765]
We identify two unique challenges in making legal case outcome predictions with case law.
First, it is crucial to identify relevant precedent cases that serve as fundamental evidence for judges during decision-making.
Second, it is necessary to consider the evolution of legal principles over time, as early cases may adhere to different legal contexts.
arXiv Detail & Related papers (2024-01-28T21:18:05Z) - GERE: Generative Evidence Retrieval for Fact Verification [57.78768817972026]
We propose GERE, the first system that retrieves evidences in a generative fashion.
The experimental results on the FEVER dataset show that GERE achieves significant improvements over the state-of-the-art baselines.
arXiv Detail & Related papers (2022-04-12T03:49:35Z) - Sequential Multi-task Learning with Task Dependency for Appeal Judgment
Prediction [28.505366852202794]
Legal Judgment Prediction (LJP) aims to automatically predict judgment results, such as charges, relevant law articles, and the term of penalty.
This paper concerns a worthwhile but not well-studied LJP task, Appeal judgment Prediction (AJP), which predicts the judgment of an appellate court on an appeal case.
We propose a Sequential Multi-task Learning Framework with Task Dependency for Appeal Judgement Prediction (SMAJudge) to address these challenges.
arXiv Detail & Related papers (2022-03-09T08:51:13Z) - Learning to Limit Data Collection via Scaling Laws: Data Minimization
Compliance in Practice [62.44110411199835]
We build on literature in machine learning law to propose framework for limiting collection based on data interpretation that ties data to system performance.
We formalize a data minimization criterion based on performance curve derivatives and provide an effective and interpretable piecewise power law technique.
arXiv Detail & Related papers (2021-07-16T19:59:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.