AI-BASED TABLET DISPENSING AND PACKAGING SYSTEM AND WORKING METHOD THEREOF

Abstract

The present invention discloses an AI-based tablet dispensing and packaging system, integrating intelligent hardware and software components to automate and enhance precision in pharmaceutical tablet handling. The system features an AI processing unit with machine learning algorithms, sensors, a high-precision dispensing module, robotic arms, and a packaging station. It dynamically adjusts dispensing rates, monitors tablet characteristics, and controls packaging operations, using real-time data to minimize errors and improve accuracy. The user interface (UI) allows operators to monitor metrics, configure settings, and receive diagnostics. Quality control sensors provide multi-stage verification, while adaptive learning capabilities optimize performance over time, predicting inventory needs and reducing manual intervention. This invention significantly advances automated pharmaceutical dispensing and packaging with high reliability, efficiency, and regulatory compliance.

Specification

Description:[014] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[015] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[016] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[017] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[018] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[019] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[020] In an embodiment of the invention and referring to Figures 1, the present invention relates to an advanced, AI-based tablet dispensing and packaging system that utilizes a combination of intelligent hardware and software components to achieve high precision, adaptability, and reliability in the dispensing and packaging of pharmaceutical tablets. The invention integrates machine learning algorithms, sensors, actuators, robotic mechanisms, and user interfaces into a cohesive system, which allows for the automated control of tablet handling and packaging processes. The invention is structured to minimize manual intervention, enhance accuracy, and maintain compliance with regulatory standards in pharmaceutical settings.
[021] The hardware components of the system comprise an AI processing unit, a robotic arm assembly, a high-precision dispensing module, tablet feeders, a packaging station, various sensors, and a user interface. The AI processing unit is the core component that hosts the system's software and machine learning algorithms. The AI processing unit is connected to a database, where historical data on tablet dispensing, packaging, inventory levels, and error occurrences are stored. This data serves as the training set for the machine learning algorithms, enabling the system to adapt to operational demands and optimize its performance over time.
[022] The tablet feeders are specially designed to ensure uniform, stable tablet flow from the storage bins to the dispensing module. Each tablet feeder is equipped with sensors to monitor the tablet count, shape, size, and orientation, which are critical parameters for maintaining dispensing accuracy. These sensors are linked to the AI processing unit, allowing real-time data processing and enabling the AI to adjust the feed rate based on tablet characteristics and dispensing requirements. The feeder sensors are complemented by image recognition cameras, which verify that only defect-free tablets proceed to the dispensing module.
[023] The dispensing module incorporates high-precision actuators that control the release of tablets from the feeders. These actuators are programmed to dispense a predetermined number of tablets per cycle, as specified by the AI system based on historical data and the current demand. The AI processing unit dynamically adjusts the dispensing rate by communicating with the actuator controllers, ensuring that each dose is dispensed with accuracy. The actuators interact with load sensors to confirm the exact weight of each dose, adding a secondary layer of verification to maintain dispensing accuracy.
[024] Following dispensing, the tablets are transferred to the robotic arm assembly, which is designed for flexible, multi-axis movement to handle tablets with care and place them into the packaging station. The robotic arm operates under the control of the AI processing unit, which sends instructions based on the real-time analysis of the tablet position and orientation data from the sensors and cameras. The robotic arm is equipped with a soft-touch gripper mechanism to prevent damage to the tablets, which is crucial when handling fragile or coated tablets.
[025] The packaging station is another key component in the system, where tablets are organized and sealed in individual or grouped packs based on preset configurations. The packaging station comprises a conveyor belt, sealing unit, and labeling unit, all of which are controlled by the AI processing unit. The AI system manages the conveyor belt speed and synchronizes the tablet placement to ensure alignment with packaging slots, thus minimizing errors and maintaining consistency. The sealing unit then hermetically seals the packs to ensure the tablets remain uncontaminated. The labeling unit uses real-time data from the AI system to print accurate batch information, expiration dates, and other necessary details on each package.
[026] To further enhance accuracy, the system integrates quality control sensors at various stages. These sensors monitor parameters such as tablet count, weight, and packaging integrity. For instance, load sensors at the packaging station measure the final product's weight, which the AI processing unit cross-references with the expected weight to detect discrepancies. Similarly, optical sensors and high-speed cameras verify that tablets are correctly positioned within the packaging slots and that the packaging is intact. Any anomalies are instantly flagged by the AI system, which either corrects the error automatically or alerts the operator if manual intervention is needed.
[027] The AI-based system incorporates a deep learning algorithm that processes the data collected from the sensors and cameras at each stage. The algorithm is trained to recognize patterns in dispensing and packaging errors, allowing it to predict potential issues before they occur. For example, if the system detects a higher frequency of tablet misplacement, it may adjust the robotic arm's speed or position, thereby reducing the occurrence of errors. Additionally, the AI system can anticipate demand for specific tablet types based on historical trends, ensuring optimal inventory levels and preventing stockouts or overstocking.
[028] A user interface (UI) forms an essential part of the invention, providing real-time monitoring and control capabilities to operators. The UI is connected to the AI processing unit, displaying metrics such as tablet count, packaging status, machine health, and system alerts. The UI also enables operators to configure dispensing parameters, initiate packaging cycles, and monitor inventory. Furthermore, the UI provides diagnostic tools and logs error reports, which assist in troubleshooting and facilitate preventive maintenance.
[029] The following table demonstrates the efficacy of the AI-based system in comparison to conventional tablet dispensing and packaging systems, showing improved accuracy, speed, and reduced human error.

[030] The hardware and software components of this invention work in tandem to create an efficient, adaptable tablet dispensing and packaging system. The interaction between the hardware sensors, robotic arm, and AI algorithms ensures a high degree of precision and reliability in handling tablets. For instance, the load sensors work closely with the AI module to verify the weight of dispensed doses, while the cameras provide real-time imaging data that guides the robotic arm for precise tablet placement. The continuous data feedback loop between the hardware and software components enables the system to self-adjust based on real-time data, ensuring consistent performance even under varying conditions.
[031] In Conclusion, the AI-based tablet dispensing and packaging system presented here represents a significant advancement in pharmaceutical automation. The integration of sophisticated hardware components with a robust AI-driven software infrastructure enables this invention to achieve unprecedented levels of efficiency, accuracy, and adaptability in dispensing and packaging applications. Through real-time monitoring, dynamic adjustments, and predictive capabilities, this system addresses the limitations of prior art while setting a new standard in automated pharmaceutical handling. , Claims:1. An AI-based tablet dispensing and packaging system comprising:
a) an AI processing unit configured to control the dispensing and packaging operations, said processing unit including machine learning algorithms for analyzing historical data and adjusting system parameters dynamically;
b) a high-precision dispensing module operatively connected to said AI processing unit, comprising actuators and load sensors for controlled tablet release;
c) tablet feeders with integrated sensors and image recognition cameras to monitor tablet characteristics, ensuring uniform and accurate tablet flow;
d) a robotic arm assembly with multi-axis movement and a soft-touch gripper mechanism configured to handle tablets and transfer them to a packaging station;
e) a packaging station comprising a conveyor belt, sealing unit, and labeling unit, operatively controlled by the AI processing unit for synchronized packaging, sealing, and labeling of tablets;
f) a user interface (UI) connected to the AI processing unit for real-time monitoring, parameter adjustment, and error diagnostics;
g) quality control sensors located at various stages for monitoring parameters such as tablet count, weight, and packaging integrity;
wherein the AI processing unit integrates real-time data from the sensors and actuators to dynamically adjust the dispensing, robotic handling, and packaging operations, minimizing manual intervention and enhancing accuracy.
2. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the machine learning algorithms are configured to predict inventory levels and demand for specific tablet types based on historical trends and real-time data, enabling optimized inventory management and reduced stockouts or overstocking.
3. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the sensors in the tablet feeders monitor tablet count, shape, size, and orientation, and communicate with the AI processing unit to adjust feed rates dynamically, ensuring accurate tablet dispensing.
4. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the robotic arm assembly includes position sensors and operates under AI processing unit control to adjust speed and movement based on real-time tablet orientation data from image recognition cameras, thereby minimizing tablet misplacement.
5. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the quality control sensors in the packaging station include optical sensors and high-speed cameras that verify tablet placement and packaging integrity, with anomalies flagged for correction or operator intervention.
6. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the packaging station further includes:
i. a sealing unit that hermetically seals each package under control of the AI processing unit;
ii. a labeling unit that prints accurate batch information, expiration dates, and regulatory details based on real-time data from the AI processing unit, ensuring compliance.
7. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the user interface (UI) provides an operator with real-time metrics, including tablet count, packaging status, and system alerts, and enables manual adjustment of dispensing parameters and packaging configurations as required.
8. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the AI processing unit detects patterns in dispensing and packaging errors, adjusting the system's parameters to reduce such errors over time through adaptive learning.
9. The AI-based tablet dispensing and packaging system as claimed in claim 1, wherein the load sensors at the dispensing module communicate with the AI processing unit to verify tablet weight per dose, adding a secondary verification layer for dispensing accuracy.
10. A working method for an AI-based tablet dispensing and packaging system as claimed in claim 1, comprising the steps of:
I. receiving data from the sensors and actuators at each stage of dispensing and packaging;
II. processing real-time data through machine learning algorithms in the AI processing unit to dynamically adjust dispensing rates, robotic handling, and packaging speed;
III. analyzing historical data to optimize inventory levels, predict demand, and adjust system performance;
IV. continuously monitoring quality control parameters and making corrections or alerting operators as needed, thereby maintaining high accuracy and operational efficiency in the dispensing and packaging of tablets.

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