Description:FIELD OF THE INVENTION
This invention relates to Medicine vending machine using Machine learning.
BACKGROUND OF THE INVENTION
The healthcare industry is facing several challenges in ensuring the quality healthcare services. One of the key issues is the efficient and accurate dispensing of medications, which can often be a complex and time-consuming process. With increasing number of patients, the process become time consuming and complex leading to inaccurate dispensing and medication stockouts of medication can lead to health consequences. A promising solution is the use of a medicine vending machine powered by machine learning algorithms. By leveraging image recognition and deep learning, the machine can accurately identify and dispense the correct medications by scanning the doctor’s prescription. This help to prevent medication errors and ensure that patients receive quality healthcare services swiftly.
WO2009007766A1 Vending machine comprising a product dispensing unit located in the safety-lock protected housing, where the product dispensing unit has multiple, independently controlled product dispensers, and where the product dispensing cell of the product dispensing unit has a hinged door opening to the front panel of the machine. The machine also comprises a coin identifying unit having a coin insertion slot disposed on the front panel, a coin return unit, and a banknote unit with a banknote insertion slot and a bank card reader unit having a bank card insertion slot, the machine further comprising a receipt printer, operating means and a display, which are all connected either directly or indirectly to the input and output ports of a control unit characterized by that the control unit is a computer, with1 the product dispensing unit, the bank -card reader unit, a wireless internet adapter, and a peripheral interface unit being connected to the control input-output ports of the computer.
RESEARCH GAP:
1. The device presented in this patent uses Machine learning and deep learning algorithms
2. The device presented in the patent dispense medicine on doctor’s prescription only.
3. The medical history for the patient is stored on a cloud server for future use.
US20020133505A1 The present invention relates to the idea of enabling an individual to conveniently purchase herbal medications and prescription medicines from specialized vending machines. The system provides for the individual to be processed through a central database to be certain that the item being purchased has been legally authorized by an appropriate medical authority such as a licensed physician and has provided appropriate verification to confirm that the individual who is receiving the medication is the correct individual. The present invention enables the individual to conveniently purchase the medication from a vending machine
RESEARCH GAP:
1. The device presented in this patent uses Machine learning and deep learning algorithms.
2. The device presented in the patent provides the dosage details of the medicine provided along with the medicine.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed. This invention relates to Medicine vending machine using Machine learning.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
A medicine vending machine based on machine learning is a type of automated dispenser that uses artificial intelligence and machine learning algorithms to optimize its operations and improve the user experience. The machine works by taking a certain input from the user in form of written prescription or his/her personal health card. In case of the written prescription the machine scans and processes the written prescription and extract necessary information using image processing. In case of health card, the machine communicates with a server to obtain the information of patient and medicine prescribed along with the dosage details specifically that is stored in the server or updated by the doctor. Edge based system is responsible for computing the written prescription details using Machine Learning Algorithms and connecting the Machine with cloud server using LoRa communication. Medicine vending machine consist of a large cabinet with multiple compartments or shelves, each of which holds a different medication or product. The machine then uses the extracted data on medicine to collect medicine from the different compartment into a single container. An inspection of medicine is done to prevent any errors. After collecting the medicines, a message regarding medicine details with their dosage details is generated and is send to the patient mobile number simultaneously when the patient is provided with the medicines. The patient will be able to access his purchase details and medical history from the cloud server. The machine learning algorithms used in the vending machine can help prevent medication errors by accurately dispensing the correct medication and dosage, reducing the potential for human error. The purchase details of the patient are stored on the server with their provided information for maintain medical history for the patient. The machine is able to self-diagnose on regular basis to prevent any fault in the system and is able to request for restock in case of a medicine running out low. The machine uses ML models to analyze data and make decisions based on that analysis, which can lead to more accurate inventory management, improved customer satisfaction, and increased efficiency.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: FLOWCHART SHOWING STEPS OF ANALYSIS PROGRAM
FIGURE 2: OVERVIEW OF THE WORKING OF THE CONNECTION
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein 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 scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
A medicine vending machine based on machine learning is a type of automated dispenser that uses artificial intelligence and machine learning algorithms to optimize its operations and improve the user experience. The machine works by taking a certain input from the user in form of written prescription or his/her personal health card. In case of the written prescription the machine scans and processes the written prescription and extract necessary information using image processing. In case of health card, the machine communicates with a server to obtain the information of patient and medicine prescribed along with the dosage details specifically that is stored in the server or updated by the doctor. Edge based system is responsible for computing the written prescription details using Machine Learning Algorithms and connecting the Machine with cloud server using LoRa communication. Medicine vending machine consist of a large cabinet with multiple compartments or shelves, each of which holds a different medication or product. The machine then uses the extracted data on medicine to collect medicine from the different compartment into a single container. An inspection of medicine is done to prevent any errors. After collecting the medicines, a message regarding medicine details with their dosage details is generated and is send to the patient mobile number simultaneously when the patient is provided with the medicines. The patient will be able to access his purchase details and medical history from the cloud server. The machine learning algorithms used in the vending machine can help prevent medication errors by accurately dispensing the correct medication and dosage, reducing the potential for human error. The purchase details of the patient are stored on the server with their provided information for maintain medical history for the patient. The machine is able to self-diagnose on regular basis to prevent any fault in the system and is able to request for restock in case of a medicine running out low. The machine uses ML models to analyze data and make decisions based on that analysis, which can lead to more accurate inventory management, improved customer satisfaction, and increased efficiency.
Fig.1 is a flowchart showing steps of analysis program for use with a computational device (Edge based system). This assists the patient to get medicine accurately and swiftly. The input is taken from user in form of written prescription or health card. The data extracted from the Machine learning algorithm or cloud server is used to collect medicine from different section of vending machine. A hard copy of recommended dosage information for each method is generated and provided to the user along with medicine. The details of the user and medicine given are stored in server for future use.
The Fig.2 is a block diagram to presents the overview of the working of the connection of the devices and the cloud server. The edge-based node (10) is responsible for matching and providing the necessary details from the cloud server (30) and machine learning algorithms. The communication with cloud server is bidirectional using an IoT based gateway (20) which is combination of LoRa and Wi-Fi. The IoT based gateway is used to send the purchase details and dosage details as a message (40) to the patient or user in form of a message. The patient can also obtain their medical history on a mobile dashboard (40) using cloud server from its database (50).
In another embodiment the machine learning algorithm is used to optimize the operations and improve the user experience.
In another embodiment The medical history for the patient is stored on a cloud server for future use.
In another embodiment the Edge based system is responsible for computing the written prescription details using Machine Learning Algorithms and connecting the Machine with cloud server using LoRa communication.
In another embodiment the communication with cloud server is bidirectional using an IoT based gateway which is a combination of LoRa and Wi-Fi.
In another embodiment The patient can also obtain their medical history on a mobile dashboard using cloud server from its database.
ADVANTAGES OF THE INVENTION
1. This model facilitates the medicine dispensing procedure.
2. The model can be used at any moment as per required by the user.
3. This model can self-diagnose itself on regular basis for maintaining efficiency.
4. This model used to help with monitoring of medicine stock.
5. This model stores the user and medicine data on a cloud server for maintaining medical history or for future use.
, Claims:1. A medicine vending machine using Machine learning comprises Edge based node (10), IoT based gateway (LoRa + Wifi) (20), Cloud Server (30), Mobile Dashboard (40), Database (50) wherein camera sensor is used to detect the real time of the data; and the machine learning algorithm is used to optimize the operations and improve the user experience.
2. The machine as claimed in claim 1, wherein the medical history for the patient is stored on a cloud server for future use.
3. The machine as claimed in claim 1, wherein the Edge based system is responsible for computing the written prescription details using Machine Learning Algorithms and connecting the Machine with cloud server using LoRa communication.
4. The machine as claimed in claim 1, wherein the communication with cloud server is bidirectional using an IoT based gateway which is a combination of LoRa and Wi-Fi.
5. The machine as claimed in claim 1, wherein the patient can also obtain their medical history on a mobile dashboard using cloud server from its database.
6. The machine as claimed in claim 1, wherein the input is taken from user in form of written prescription or health card; and the data is extracted from the Machine learning algorithm or cloud server is used to collect medicine from different section of vending machine.
7. The machine as claimed in claim 1, wherein a hard copy of recommended dosage information for each method is generated and provided to the user along with medicine; and the details of the user and medicine given are stored in server for future use.
8. The machine as claimed in claim 1, wherein the edge-based node (10) is responsible for matching and providing the necessary details from the cloud server (30) and machine learning algorithms.
9. The machine as claimed in claim 1, wherein the communication with cloud server is bidirectional using an IoT based gateway (20) which is combination of LoRa and Wi-Fi.
10. The machine as claimed in claim 1, wherein the IoT based gateway is used to send the purchase details and dosage details as a message (40) to the patient or user in form of a message; and the patient also obtains their medical history on a mobile dashboard (40) using cloud server from its database (50).