AI-based scheduling and risk prediction system for regulatory submissions

Abstract

A Cl-based scheduling and risk prediction system (100) for marketing authorisation applications, comprising: - a data integration and collection module configured to aggregate historical regulatory submission data, project timelines, and resource availability; - a preprocessing and data normalization module configured to clean and standardize the collected data; - a risk identification and analysis module configured to identify potential risks affecting the submission of marketing authorisation applications using artificial intelligence; - a timeframe prediction module configured to predict the submission timeframe based on the analyzed data and the identified risks; - a decision support and recommendation module configured to provide suggestions for workflow optimization and risk reduction; and - a user interface and reporting module configured to provide users with real-time status, risk levels, and predictive insights.

Description

[0001] The present invention relates to an AI-based regulatory submission system. More specifically, it is a scheduling and risk prediction system designed to optimize and manage regulatory submission processes. The system leverages artificial intelligence to improve accuracy, efficiency, and decision-making in regulatory compliance workflows.

[0002] Regulatory submissions in industries such as pharmaceuticals, biotechnology, and medical devices require strict adherence to deadlines and compliance standards. However, managing these submissions is often complex and prone to delays due to unforeseeable factors, such as evolving regulations, document reviews, and resource constraints. These challenges can lead to missed deadlines, increased costs, and potential regulatory penalties, impacting the overall success of product approvals.

[0003] Traditional project management and manual tracking methods are unable to accurately predict risks and schedule variances in regulatory processes. The lack of predictive insights prevents proactive action, leading to reactive problem-solving and inefficient resource allocation. This leads to increased uncertainty and difficulty meeting critical regulatory milestones within the specified timeframe.

[0004] To solve these problems, an intelligent system is needed that uses artificial intelligence to analyze historical data, identify potential risks, and predict realistic deadlines for regulatory submissions. Such a system can enable regulatory teams to make data-driven decisions, optimize workflows, and minimize delays, thereby improving regulatory compliance and accelerating product approval.

[0005] The aim of the present disclosure is to improve one or more problems of the prior art or at least to provide a useful alternative

[0006] One objective of the present disclosure is to improve the accuracy of regulatory filing deadlines through AI-driven predictions.

[0007] Another objective of this disclosure is to proactively identify and quantify risks to avoid delays and compliance issues.

[0008] Another objective of this disclosure is to integrate data from different sources for a comprehensive analysis of submissions.

[0009] Another objective of this disclosure is to provide actionable recommendations for optimizing workflows and resource allocation.

[0010] Another objective of this disclosure is to provide real-time monitoring and reporting through an intuitive user interface.

[0011] Another objective of this disclosure is to improve collaboration between regulatory teams with shared dashboards and alerts.

[0012] Another objective of this disclosure is to reduce delays in the submission of applications and thus accelerate product approval and market entry.

[0013] Another objective of this disclosure is to minimize the costs associated with non-compliance and project overruns.

[0014] The present invention generally provides an AI-based scheduling and risk prediction system tailored for managing complex regulatory submissions in industries such as pharmaceuticals and medical devices. It integrates diverse data to provide actionable insights.

[0015] In one embodiment of the present invention, the system uses the data integration and collection module to collect historical filing records, resource details, and regulatory updates to create a comprehensive dataset. This enables a holistic view of the filing process.

[0016] Another embodiment of the invention is the preprocessing and data normalization module, which ensures that all data is cleaned, standardized, and free of inconsistencies, increasing the accuracy of downstream analyses and predictions.

[0017] Another embodiment of the invention is that the risk identification and analysis module uses advanced AI techniques to identify potential risks such as delays, compliance gaps, and resource constraints, thus enabling proactive risk management.

[0018] Another embodiment of the invention is that the system, using the schedule prediction module, predicts realistic schedules by analyzing dependencies, historical trends, and identified risks, thus helping teams anticipate and mitigate bottlenecks.

[0019] Another embodiment of the invention is the decision support and recommendation module, which provides data-driven suggestions for workflow optimization, resource allocation, and contingency planning, thus improving the efficiency and quality of submission.

[0020] Another embodiment of the invention is the user interface and reporting module, which provides intuitive dashboards and customizable reports that promote transparency, real-time monitoring, and effective collaboration between stakeholders.

[0021] Another embodiment of the invention is that this intelligent system reduces uncertainties, streamlines regulatory submission processes, and supports timely regulatory compliance, ultimately accelerating product approval and minimizing costs. Data normalization module

[0022] This module is used for data acquisition and processing raw data to remove inconsistencies, errors, and redundancies. It normalizes data formats and fills missing values using statistical methods or imputation through machine learning. This ensures that the input data is clean, standardized, and ready for effective use by downstream AI algorithms, improving prediction accuracy and system reliability. Risk identification and analysis module

[0023] This module applies AI-driven analytics to identify potential risks that could delay or fail regulatory submissions. It analyzes historical risk factors, such as document rejections, resource constraints, compliance gaps, and external regulatory changes. By classifying and quantifying risks, this module helps project managers prioritize risk mitigation strategies and allocate resources efficiently. Schedule prediction module

[0024] Using machine learning models trained on past submission data, this module forecasts realistic schedules for each phase of the regulatory process. It considers dependencies between tasks, regulatory review cycles, and potential risks to create dynamic and adaptive time predictions. These predictions enable teams to identify delays early and proactively adjust project plans. Module for decision support and recommendations

[0025] This module provides actionable insights and recommendations based on risk and schedule predictions. It suggests optimized workflows, resource reallocation, and contingency plans to minimize delays and improve submission quality. The system also offers scenario analysis, allowing users to evaluate the impact of various decisions before implementation. User interface and reporting module

[0026] For ease of use, this module provides an intuitive dashboard with real-time schedules, risk assessments, and progress tracking. It generates customizable reports for stakeholders highlighting key metrics, potential issues, and recommendations. The interface supports collaboration by allowing multiple users to access, update, and comment on submission status, improving transparency and communication.

[0027] The invention is explained again below with reference to the figure. It shows: Fig. a Class-based scheduling and risk prediction system (100) for marketing authorisation applications.

[0028] Fig.shows an AI-based scheduling and risk prediction system (100) for regulatory submissions. The system first works with the data integration and ingestion module to collect comprehensive data from various sources, including previous regulatory submissions, project schedules, resource availability, and relevant regulatory updates. Once ingested, this raw data is processed with the preprocessing and data normalization module, which cleans and standardizes the data to ensure accuracy and consistency. The refined data is then analyzed by the risk identification and analysis module, which applies advanced AI algorithms to identify potential risks such as document delays, regulatory compliance issues, and resource conflicts that could impact submission deadlines.At the same time, the schedule prediction module leverages machine learning models trained on historical data to predict realistic deadlines and potential bottlenecks throughout the regulatory process, creating a dynamic and adaptable schedule. Based on these insights, the decision support and recommendation module generates actionable suggestions for optimizing workflows, reallocating resources, and creating contingency plans aimed at mitigating identified risks and avoiding delays.Throughout the process, the user interface and reporting module presents all relevant information in an easy-to-understand dashboard, allowing users to monitor progress, view risk levels, and receive automated reports that facilitate informed decision-making and seamless collaboration between supervisory teams. Together, these modules form a cohesive, intelligent system that increases efficiency, reduces uncertainty, and supports the timely and compliant submission of applications.

Claims

[1] A Cl-based scheduling and risk prediction system (100) for marketing authorisation applications, comprising: - a data integration and collection module configured to aggregate historical regulatory submission data, project timelines, and resource availability; - a preprocessing and data normalization module configured to clean and standardize the collected data; - a risk identification and analysis module configured to identify potential risks affecting the submission of marketing authorisation applications using artificial intelligence; - a timeframe prediction module configured to predict the submission timeframe based on the analyzed data and the identified risks; - a decision support and recommendation module configured to provide suggestions for workflow optimization and risk reduction; and - a user interface and reporting module configured to provide users with real-time status, risk levels, and predictive insights. [2] The system (100) of claim 1, wherein the data integration and collection module collects data from multiple sources, including regulatory agency databases, project management tools, and internal document repositories. [3] The system (100) of claim 1, wherein the preprocessing and data normalization module uses machine learning techniques to fill in missing data and remove inconsistencies in the collected data set. [4] The system (100) of claim 1, wherein the risk identification and analysis module uses historical submission results and external regulatory updates to classify and quantify risks associated with a particular regulatory submission. [5] The system (100) of claim 1, wherein the timeline prediction module uses a machine learning model trained on previous regulatory submission projects to dynamically adjust the timeline prediction based on current project parameters and identified risks. [6] The system (100) of claim 1, wherein the decision support and recommendation module generates actionable insights including resource reallocation, schedule adjustments, and contingency planning to minimize submission delays. [7] The system (100) of claim 1, wherein the user interface and reporting module provides customizable dashboards and automated reports to facilitate collaboration between regulatory team members and stakeholders. [8] The system (100) of claim 1, wherein the system further comprises an alert mechanism configured to notify users of high-risk events or significant deviations from predicted schedules in real time.

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