UNIFORM CAPSULE FILLING TECHNIQUE AND METHOD THEREOF

Abstract

An AI-powered capsule filling system (101) designed for precise pharmaceutical tablet filling. System includes a tablet dispensing unit (102) with adaptive rate control, an AI-powered weight detection module (103) using multi-sensor fusion for accuracy, and a precision filling mechanism (104) with micro-adjustable pistons. A machine learning module (105) drives real-time process adjustments through a continuous feedback loop (106), compensating for environmental changes and tablet variability. A user interface (107) provides system monitoring, and the system supports multi-product processing (126) with real-time quality assurance. It also integrates blockchain (127) for traceable batch records. The associated method involves adaptive tablet dispensing, real-time weight detection, AI-driven analysis, and continuous optimization, ensuring accurate, efficient, and compliant pharmaceutical manufacturing. Batch-specific learning further enhances system adaptability, while blockchain integration guarantees traceability and regulatory compliance.

Specification

Description:The following is a step-by-step description of the invention, detailing the components, and their functionalities mentioned below:

The present invention, a capsule filling technique an AI for detecting underweight and overweight tablets for effective and uniform tablet filling, is an advanced system designed to revolutionize the pharmaceutical manufacturing process. This detailed description elucidates the various components and functionalities of the invention, providing a comprehensive understanding of its operation and benefits.


System Overview:

The capsule filling system (101) integrates multiple components to ensure accurate and efficient tablet filling operations. At the core is the tablet dispensing unit (102), which precisely delivers individual tablets to the filling chamber. This is supported by the AI-powered weight detection module (103), which continuously measures tablet weight in real time, ensuring each capsule meets precise weight specifications. The precision filling mechanism (104) carefully places the tablets into capsules, guided by machine learning modules (105) that optimize the filling process based on historical data and real-time performance. A continuous feedback loop (106) monitors the entire process, adjusting parameters dynamically to maintain optimal performance and prevent errors. The user interface (107) allows operators to control, monitor, and adjust settings with ease, providing real-time visualizations of the system's performance. Together, these components work harmoniously to provide a reliable and efficient tablet filling system.

Tablet Dispensing Unit (102) is engineered for versatility, capable of managing a wide range of tablet sizes, shapes, and formulations. It features a high-capacity hopper (108) that employs a vibration mechanism to prevent tablet clumping and ensure smooth flow. The unit includes multiple dispensing channels (109), each equipped with sensors that monitor the tablet flow in real time, preventing blockages or overfeeding. The dispensing speed (110) is adjustable and precisely controlled by servo motors, allowing for tailored tablet release based on the production requirements. This adjustability and the ability to handle various tablet types without extensive retooling make the unit ideal for diverse pharmaceutical manufacturing environments, where precision and adaptability are crucial.

AI-Powered Weight Detection Module (103): The AI-powered weight detection module (103) forms the core of the system's advanced quality control mechanisms. It features high-precision load cells (111) capable of measuring tablet weights with an accuracy of ±0.1 mg, ensuring that every tablet meets strict quality standards. In conjunction with these load cells, optical sensors (112) monitor tablet count and positioning, making sure that the right number of tablets is processed accurately. Additionally, the module integrates an AI-driven image processing system (113) that scans each tablet's shape and integrity, quickly identifying defects such as chips, cracks, or irregularities. This combination of weight measurement, optical monitoring, and AI analysis provides a robust, multi-layered approach to ensuring consistency and quality in tablet production, with all data being fed into machine learning modules to enhance future detection accuracy.

Precision Filling Mechanism (104) is engineered for optimal accuracy and flexibility in tablet filling. It incorporates micro-adjustable filling pistons (114) controlled by high-precision stepper motors, allowing for fine-tuned control over the amount of material dispensed into each capsule. The mechanism also includes a capsule positioning system (115), ensuring that every capsule is perfectly aligned during the filling process to prevent spillage or misfilling. Integrated within the unit is a capsule sealing system (116), which seals the capsules immediately after filling, creating a seamless, streamlined process. Additionally, the mechanism is responsive to real-time feedback from the AI system, making automatic adjustments to maintain consistency and ensure that each capsule contains the precise weight of tablets required for high-quality pharmaceutical production.

Machine Learning module (105) is the intelligence backbone of the AI-powered capsule filling system, optimizing its performance in real time. These advanced module analyse data from both the weight detection module and the precision filling mechanism, identifying patterns and trends in tablet weights and filling accuracy. They can predict deviations from target weights or filling inefficiencies and make on-the-fly adjustments to the dispensing speed and filling precision, ensuring optimal operation. Over time, the modules refine their performance using neural network architecture, learning from new data to improve accuracy and consistency. The system's computing unit (117) processes vast amounts of data at high speed, enabling rapid decision-making to maintain efficiency and quality control during the entire manufacturing process.

Continuous Feedback Loop (106) plays a critical role in maintaining optimal performance and precision within the capsule filling system. It operates by constantly collecting real-time data from all major components, including the weight detection module, filling mechanism, and dispensing unit. This data is immediately analysed by the AI, which assesses system performance for accuracy and efficiency. Based on this analysis, the system can implement adjustments in real time to correct any inconsistencies, such as changes in tablet weight or alignment. Additionally, the feedback loop incorporates long-term learning, enabling the system to adapt to varying tablet formulations and environmental factors, such as humidity and temperature fluctuations. This dynamic adjustment process ensures that the system consistently operates at peak efficiency, delivering high-precision results across diverse production conditions.

User Interface (107) is designed to provide operators with an intuitive and efficient control center for managing and monitoring the capsule filling system. It features a responsive touchscreen display (118) that presents real-time system status updates, performance metrics, and operational data in a clear and accessible format. Customizable alerts and notifications inform operators of any deviations from pre-set parameters, allowing for swift interventions. In addition, the interface offers powerful data visualization tools (119) that enable operators to analyse trends and review historical performance, helping to optimize the filling process over time. For greater flexibility, the system includes manual override options, enabling direct control when necessary. The interface is highly user-friendly, simplifying complex operations and ensuring seamless system management, troubleshooting, and overall efficiency.

The operational process of the capsule filling system involves a series of precisely controlled steps to ensure accuracy and consistency in tablet filling. First, tablets are loaded into the high-capacity hopper (108) of the tablet dispensing unit, where they are released at a controlled rate into the weight detection module (103). In this module, each tablet is individually weighed and analysed by the AI-powered system, ensuring that every tablet meets the required specifications. The machine learning modules (105) process the real-time data, detecting any deviations or patterns, and adjust the dispensing or filling mechanisms as necessary. Next, the precision filling mechanism (104) accurately fills capsules with the exact tablet weight, as directed by the AI system. Throughout the process, the continuous feedback loop (106) ensures ongoing monitoring and optimization. Operators have full visibility and control through the user interface (107), ensuring consistent output across large production runs.

The method for AI-powered capsule filling in pharmaceutical manufacturing (200) involves several key steps to ensure precision and quality.

Dispensing tablets at an adaptive rate based on real-time weight analysis (201): Tablets are dispensed at an adaptive rate based on real-time feedback from the system. The tablet dispensing unit releases tablets into the system at a controlled pace, with the flow rate automatically adjusted according to real-time weight analysis. This adaptive dispensing ensures consistent tablet flow and minimizes the risk of clogging or misalignment, optimizing the entire capsule filling process.

Detecting tablet weights using multi-sensor fusion (202): The system employs multi-sensor fusion technology to detect the weight of each tablet with high precision. Load cells and optical sensors work together to measure and verify tablet weights. This ensures accurate data collection, allowing the system to assess each tablet's suitability for filling, detect any anomalies, and maintain uniformity across the batch.

Analysing tablet data using machine learning modules (203): Once weight data is collected, the machine learning modules process the data to detect trends, predict deviations, and identify patterns in tablet characteristics. The system analyses the data in real-time, making it possible to adjust operations based on the unique properties of each batch of tablets. This analysis ensures that only tablets meeting the specified weight criteria are passed along for filling.

Adjusting a precision filling mechanism based on AI feedback (204): The precision filling mechanism is adjusted based on insights from the AI-driven analysis. If discrepancies in tablet weight or size are identified, the system recalibrates the filling process. This ensures that the capsules receive the correct amount of tablets, maintaining strict adherence to quality standards and reducing wastage.

Continuously optimizing the filling process through a feedback loop (205): The system operates within a continuous feedback loop, meaning it constantly receives data from various components and adapts its processes accordingly. Real-time optimization ensures that changes in tablet properties, equipment performance, or environmental conditions are accounted for, leading to greater efficiency and accuracy over time.

Compensating for environmental factors affecting tablet properties (206): Environmental factors such as humidity, temperature, and air pressure can influence tablet properties. The system compensates for these variations by monitoring environmental data and adjusting its internal processes. This ensures that the system performs consistently, even in fluctuating environmental conditions, maintaining the desired quality standards.

Applying batch-specific learning for different tablet formulations (207): For each batch, the system applies machine learning modules that tailor the capsule filling process based on the specific properties of the tablets being processed. This batch-specific learning enables the system to adapt its operations to different formulations, tablet shapes, or sizes, improving efficiency and minimizing the need for manual adjustments.

Generating real-time quality assurance reports (208): As the system processes the tablets, it generates real-time quality assurance reports. These reports offer immediate insight into production quality, highlighting any deviations from specified parameters. This helps operators ensure that quality standards are met without the need for additional post-production inspections, significantly reducing time and cost.

Simultaneously processing multiple product types (209): The system is designed to handle multiple product types simultaneously. With the ability to switch between different tablet formulations or capsule sizes on the fly, the system increases versatility in pharmaceutical manufacturing. This reduces downtime between product changeovers and maximizes production efficiency.

Recording batch data on a blockchain for traceability (210): For enhanced traceability, batch data is recorded on a blockchain, ensuring secure and tamper-proof tracking of production records. This provides an immutable record of each batch, from raw materials to finished capsules, enabling better regulatory compliance and improving transparency throughout the supply chain.

Method of Performing an Invention:

The optimal method for implementing and operating the capsule filling technique and AI for detecting underweight and overweight tablets for effective and uniform tablet filling invention involves a systematic approach that maximizes the capabilities of each component. The following steps outline the best practice for utilizing this advanced system:

1. System Initialization and Calibration:
- Perform a comprehensive system diagnostic to ensure all components are functioning correctly.
- Calibrate the weight detection module (103) using certified reference weights.
- Initialize the machine learning modules (105) with pre-trained models specific to the tablet formulation being processed.

2. Environmental Control:
- Maintain a controlled environment with stable temperature and humidity to minimize external factors affecting tablet weights.
- Utilize the environmental compensation system (123) to make real-time adjustments based on any fluctuations.

3. Tablet Loading and Dispensing:
- Load the dispensing unit hopper (108) with a sufficient quantity of tablets, ensuring even distribution.
- Activate the adaptive dispensing rate feature (120) to optimize tablet flow based on real-time weight analysis.

4. AI-Powered Weight Detection:
- Engage the multi-sensor fusion system (121) to gather comprehensive data on each tablet.
- Allow the AI to analyze tablet weights, shapes, and integrity in real-time.

5. Precision Filling Process:
- Activate the precision filling mechanism (104), ensuring it's synchronized with the AI system.
- Monitor the micro-adjustments made by the filling pistons (114) in response to AI feedback.

6. Continuous Optimization:
- Utilize the continuous feedback loop (106) to constantly refine the filling process.
- Implement batch-specific learning (124) to create optimized parameters for each product type.

7. Quality Assurance and Reporting:
- Enable real-time quality assurance reporting (125) to maintain a comprehensive record of production metrics.
- Integrate blockchain technology (127) for enhanced traceability and regulatory compliance.

8. Operator Monitoring and Control:
- Train operators to effectively use the user interface (107) for monitoring system performance.
- Establish protocols for manual intervention when necessary, utilizing the override features.

9. Predictive Maintenance:
- Regularly review the predictive maintenance AI (122) reports and schedule preventive maintenance accordingly.

10. Multi-Product Processing:
- When applicable, utilize the multi-product simultaneous processing capability (126) to maximize production efficiency.

11. Data Analysis and System Improvement:
- Periodically analyse long-term performance data to identify trends and potential areas for system enhancement.
- Update machine learning models and modules based on accumulated production data.

By following this method, pharmaceutical manufacturers can fully leverage the capabilities of this innovative capsule filling system, ensuring maximum efficiency, accuracy, and product quality.

Embodiments of Invention:

• Adaptive Dispensing Rate (120): The system can dynamically adjust the rate at which tablets are dispensed based on real-time weight analysis, ensuring a consistent flow of correctly weighted tablets.

• Multi-Sensor Fusion (121): By combining data from load cells, optical sensors, and image processing, the system achieves unprecedented accuracy in tablet weight and integrity assessment.

• Predictive Maintenance AI (122): The machine learning modules not only optimize the filling process but also predict potential mechanical issues before they occur, scheduling maintenance to prevent downtime.

• Environmental Compensation System (123): Sensors monitor environmental factors like temperature and humidity, allowing the AI to make preemptive adjustments to maintain filling accuracy.

• Batch-Specific Learning (124): The system can create and store optimized parameters for different tablet formulations, quickly adapting to product changes without manual reconfiguration.

• Real-Time Quality Assurance Reporting (125): The system generates comprehensive reports on filling accuracy and consistency, integrating seamlessly with broader quality management systems.

• Multi-Product Simultaneous Processing (126): Advanced scheduling modules allow the system to handle multiple product types concurrently, maximizing production efficiency.

• Blockchain Integration for Traceability (127): Each batch processed by the system can be recorded on a blockchain, ensuring complete traceability and compliance with regulatory requirements.

By incorporating these novel embodiments, the invention not only solves the immediate challenges of accurate capsule filling but also sets a new standard for intelligent, adaptive manufacturing in the pharmaceutical industry.
, Claims:1. An AI-powered capsule filling system (101) for pharmaceutical tablet filling, comprising:
a) a tablet dispensing unit (102) with an adaptive dispensing rate (120);
b) an AI-powered weight detection module (103) utilizing multi-sensor fusion (121);
c) a precision filling mechanism (104) with micro-adjustable filling pistons (114);
d) a central processing unit running machine learning module (105);
e) a continuous feedback loop (106) for real-time process optimization;
f) a user interface (107) for system monitoring and control;
g) an environmental compensation system (123);
h) a batch-specific learning module (124);
i) a real-time quality assurance reporting system (125);
j) a multi-product simultaneous processing capability (126); and
k) a blockchain integration module (127) for traceability;
wherein, the system is configured to detect and adjust for underweight and overweight tablets in real-time, ensuring uniform and accurate capsule filling.

2. A method for AI-powered capsule filling in pharmaceutical manufacturing (200), comprising the steps of:
a) dispensing tablets at an adaptive rate based on real-time weight analysis (201);
b) detecting tablet weights using multi-sensor fusion (202);
c) analysing tablet data using machine learning modules (203);
d) adjusting a precision filling mechanism based on AI feedback (204);
e) continuously optimizing the filling process through a feedback loop (205);
f) compensating for environmental factors affecting tablet properties (206);
g) applying batch-specific learning for different tablet formulations (207);
h) generating real-time quality assurance reports (208);
i) simultaneously processing multiple product types (209); and
j) recording batch data on a blockchain for traceability (210).

3. The AI-powered capsule filling system as claimed in claim 1, wherein the tablet dispensing unit (102) comprises:
a) a high-capacity hopper (108) with a vibration mechanism;
b) multiple dispensing channels (109) with flow sensors; and
c) servo motors controlling dispensing speed (110).

4. The AI-powered capsule filling system as claimed in claim 1, wherein the AI-powered weight detection module (103) comprises:
a) high-precision load cells (111) with an accuracy of ±0.1mg;
b) optical sensors (112) for tablet counting and positioning; and
c) an AI-driven image processing system (113) for detecting tablet shape and integrity.

5. The AI-powered capsule filling system as claimed in claim 1, wherein the precision filling mechanism (104) comprises:
a) micro-adjustable filling pistons (114) controlled by stepper motors;
b) a capsule positioning system (115); and
c) an integrated capsule sealing unit (116).

6. The AI-powered capsule filling system as claimed in claim 1, wherein the machine learning module (105) are implemented on a high-performance computing unit (117) integrated into the system.

7. The AI-powered capsule filling system as claimed in claim 1, wherein the user interface (107) comprises:
a) a touchscreen display (118) showing real-time system status and performance metrics; and
b) data visualization tools (119) for analysing trends and system performance.

8. The AI-powered capsule filling system as claimed in claim 1, wherein, the blockchain integration module (127) records each processed batch on a blockchain, ensuring complete traceability and regulatory compliance.

9. The AI-powered capsule filling system as claimed in claim 1, wherein the environmental compensation system (123) monitors and adjusts for variations in temperature and humidity to maintain filling accuracy.

10. The method for AI-powered capsule filling in pharmaceutical manufacturing as claimed in claim 2, wherein the batch-specific learning (124) involves creating and storing optimized parameters for different tablet formulations, and further comprising the step of predicting and scheduling maintenance operations based on AI analysis of system performance data.

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